remote sensing and permanent plot technioues for ...

REMOTE SENSING AND PERMANENT PLOT TECHNIOUES FOR WORLD FOREST MONITORING

Proceedings of the luFRo s4.o2.05 Wacharakitti lnternational Workshop 13 - 17 January 1992 Pattaya, Thailand

Organized hy

Faculty of Forestry, Kasetsart University National Research Council of Thailand Royal Forest Department University of Helsinki University of Joensuu

tuFRo s4.02.05 FINNIDA

Chíef Editors

H. Gyde Lund Risto Päivinen Songkram Thammincha

Associate Editors

Jerry Vanclay Raymond Czaplewski Thomas Stone Ladawan Puangchit Wanchai Arunpraparut

CONTENTS Page

Preface.

Opening Report Niwat Ruangpanit Opening Address H.E. Eero Salovaara

I II

III

Opening Remarks

Suvít Vibulsresth..... Opening Remarks Yanyong Thanompichai Vote of Thanks Risto Päivinen Keynote Address: A primer on permanent plots for monitoring natural resources H. Gyde Lund

IV V

VI

I

Remote Sensing Techniques for Forest Monitoring lnternational efforts at global forest monitoring using remote sensing

Sipi Jaakkola...... Forest change on a landscape scale using LANDSAT imagery: temperate and tropical forest examples Steven A. Sader. Application of remote sensing in tropical rain forest and mangrove forest monitoring in Thailand Suvit Víbulsresth and Surachai Ratanasermpong... Forest health monitoring : A new program of the USDA forest service and environmental protection agency Charles G. Shaw //11...... Monitoring changing of forest land-use by means of aerial photographs lshak Sumantri and Wesman Endom.....

13

25

3V

43

49

Permanent Sample Plot Techniques United States environmental monitoring and assesment program : An overview Craig J. Palmer and K. Bruce Jones Methodology and training in monitoring deforestation using satellite data for sustainable environmental management Hervé Jeanjean and André Husson Using 1 km resolution satellite data to classify the vegetation of South America Thomas A. Stone and Peter Schlesinger Calibrating AVHRR data w¡th LANDSAT TM data for tropical closed forest assessment in Ghana Risto Päívinen and Juho Pitkänen

59

7I 85 95

Forest inventory in Nepal Eka Raj Sharma

Unite States environmental monitoring and assessment program : landscape characterization and remote sensing Raymond L. Czaplewski, Douglas J. Norton and Denis White. Resource potential : Policies for scaling up to global significance K.D. Singh. Establishment and analysis of permanent sample plots Símo Poso

Data registries for growth and yield plots in ASEAN : A network to review current permanent sample plots in the tropics

Hashim bin Saad and Chung Kueh Shin.. Forest area est¡mates : Sampling error and classification problems Christoph Kleínn and Dieter R. Pe12...... The use of permanent plots for monitoring vegetation change in Somalia : Problems opportunities, recommendations R.M. Watson and J.M. Nímmo...... Permanent plots for multiple objectives : Defining goals and resolving conflicts Jerome K. Vanclay. The importance of permanent plots for growth and yield studies of forest plantation species in Nigeria John O. Abayomi... The ITC/RFD Huai Kha Kheng permanent sample plot (1987) Sydney G. Banyard. Permanent research sample plots in Peninsular Malaysia : Establishment, maintenance and problems lsmail Harun, Azman Hassan and Khali Azzíz Hamzah

io3 r09 119

125 129 143

149 157

165

t73

I8l

Geographic lnformation Systems GIS and tropical forest monitoring

Boonchana Klankamsorn Estimating forest biomass for Continental South and Southeast Asia using GIS Louís lverson, Sandra Brown and Anantha Prasad., RGC : A new method for dynamic monitoring of forest resources

Xu Maosong and Kou Wenzheng.. Techniques and establishment of forest resource dynamic

195

199

203

monitoring system in China Kou Wenzheng, Zhou Changxíang and Xu Maosong. Application of GIS ¡n protected areas management Youngyut Trîsurat......... lnventory of natural and forest resources of Kalimantan Timur/ lndonesia using remote sensing methods Frithjof Voss......*..

2Ls

Working Group heports.

23L

Poster Abstracts

245

List of Participants

249

Additional Papers...

257

lnworkshop Act¡v¡t¡es

273

209

227

PREFACE

ln IUFRO World Congress in Montreal, a resolution was given that the working party 4.02.05 - called 'Remote Sensing and World Forest Monitoriñg' - should promote establishment of permanent sample plot networks for global forest assessments. As the first step towards worldwide forest monitoring network, the international workshop 'REMOTE SENSING AND PERMANENT SAMPLE PLOT TECHNIOUES FOR WORLD FOREST MONITORING'was held in Pattaya, Thailand. The main objective of the workshop was to develop guidelines for establishing permanent monitoring sites throughout the world. The workshop was dedicated to the late IUFRO 4.O2.05 co-chairman, Dr. Sathit Wacharakitti, who intended to organize this meeting. The workshop was sponsored by Thai and Finnish institutions; Kasetsart University,

Faculty of Forestry, Royal Forest Department, National Research Council of Thailand, University of Joensuu, University of Helsinki and Finnish lnternational Development Agency FinnlDA. Altogether 82 participants from 1 4 countries and 7 internat¡onal organizations spent

three days having fruitful discussions in Pattaya, During two field trip days questions of tropical forestry were seen at the spot, The proceedings of this workshop include the papers, poster abstracts and the recommendat¡ons for further development of the guidelines based on the group works. lt is intended that the guidelines drafted here will be refined during the following IUFRO 4,02. and 4.02,05 meet¡ngs in Finland in August 1992.

We would like to devote our sincere thanks to the sponsors, organizers and part¡cipants of this workshop for their valuable contribution to the attempts to monitor thè forests of the world.

Risto Päivinen IUFRO 4.O2.O5 Chairman University of Joensuu Finland

Songkram Thammincha IUFRO 4.O2.05 Co-chairman

Kasetsart University Thailand

OPENING REPORT Dr. Niwat Ruangpanít Dean, Faculty of Forestry Kasetsart University Bangkok, Thailand

Your Excellency Ambassador of Finland, distinguished guests, participants, ladies and gentlemen, On behalf of the Organizing Committee, I would like to extend my sincere thank to Your Excellency the Ambassador of Finland who kindly agreed to preside over the opening ceremony of the IUFRO Wacharakitti lnternational Workshop on Remote Sensing and World Forest Monitoring. Also, lwould like to thank every one here for coming to part¡cipate in this special occasion,

This workshop is a distinguished one, being dedicated to Dr.Sathit Wacharakitti, former Dean of the Faculty of Forestry, Kasetsart University snd the late CoChairman of IUFRO 54,02.05. Dr. Wacharakitti planned to organize this meeting , but he never fulfilled his intention because of his untimely death in February 1990, The workshop is jointly organized by the Faculty of Forestry, Kasetsart University, the Royal Forest Department, the National Research Council of Thailand, University of Joensuu, University of Helsinki, and, of course, the IUFRO 54.02.05. The Finnish lnternational Development Agency (FINNIDA) kindly provides financial support through the University of Joensuu, The sessions will take place today until Friday 1 7th January. During this five-day period distinguished participants will present papers and posters on ;3mote sensing and permanent plot techn¡ques for world forest monitoring. One day will be devoted to work group intended to prepare the draft guidelines for permanent monitoring of forest resources. The other day will be spent for the field trip to National. Park, mangrove forest, and Coastal Development Project in Chantaburi Province. The participants will spend their last day of the workshop visiting the satell¡te data receiving statio;ì and the laboratory at Asian lnstitute of Technology, The output, conclusions and recommendation from this workshop will be brought to discussion during the next IUFRO meeting in Helsinki, Finland in August this year.

The Organizing Committee truly believes that this workshop will prove to be a venue where the exchange of knowledge and experience among distinguished participants will lead to the development of global network of cooperation. Now, I would like to take this opportunity to ask His Excellency Eero Salovaara, the Ambassador of Finland, to declare the official opening of this workshop.

II

OPENING ADDRESS H.E. Eero Salovaara The Ambassador of Finland

Esteemed Participants, Ladies and Gentlemen,

I feel very honoured to have the opportunity to address this very important international Workshop on Remote Sensing and World Forest Monitoring which is partly organized through my goverhment. One can justifiably ask shy such a remote country as Finland is interested in the forestry sector in South East Asia, For one thing, without much exaggeration our present property in Finland is largely based on our forests. lt is natural that over the decades Finland has gathered fairly vast knowledge and experience on different fields related to forestry, Much of this knowledge has proved to be universally applicable, We have also been very keen

to share and develop our expertise with other partners and researcher in the field, ln our modern world no one can escape the fact that apart from being vital natural resource of great economic importance, forests are crucial element in the ecological system of the Earth, ln this respect the whole mankind has a common interest in preserving sufficient and healthy forest cover, For our part, we have over the last two decades financed through our development assistance programmes dozens of forestry related project in over ten tropical countries. one example is the Thai Forestry Master plan which is under preparation, I referred to the universal responsibility in preserving and maintaining of forests for

us, I believe that this workshop is one good example of concerted efforts towards our common goal. Our workshop here may deal with limited number of issues, but in my opinion they are all of crucial importance. Since requirement for rational and comprehensive planning is the basic data which can be achieved only by thorough and reliable forest assessment and continuous monitoring system. the benefit of all of

Ladies and Gentlemen,

With these short remarks, I would like to wish you a very fruitful and efficient workshop.

III

OPENING REMARKS

Dr. Suvit Wbulsresth u.ty Sec r eta r y-g en eral National Research Council of Thailand D ep

Your Excellency, Ambassador of Finland to Thailand Dean, Faculty of Forestry, Kasetsart University Deputy Director General, Royal Forestry Department IUFRO

54.02.05 Chairman

Distinguished participants, Ladies and Gentlemen It is a great pleasure and honour for me to speak on behalf of the National Research Council of Thailand on this special occasion. First of all, I wish to convey on behalf of the NRCT grateful thanks to the Ambassador of Finland, IUFRO, Kasetsart University, and Royal Forestry Department for kindly hosting this lnternational Workshop which would certainly contribute to the research on the development of natural resources specifically for the forest resources in the tropical region,

Your Excellency, ladies and gentlemen, may I draw your attent¡on to the great contribution of Asdociate Professor Dr.Sathit Wacharakitti in the development of remote sensing in Thailand. Associate Professor Dr.Sathit Wacharakitti was the first person in Thailand to engage in remote sensing since 1 971 . His pioneering and various efforts in remote sensing activities during a period of 1 5 years brought about the position of Thailand as of today as the regional remote sensing centre in Southeast Asia. To dedicate to the memory of Dr.Sathit, the National Research Council of Thailand initiated the annual Dr.Sathit Wacharakitti Seminar on Remote Sensing and GIS for National Resources and Environmental Management soon after his untimely and sad departure from us. Two such seminars had been organized in 1990 at NRCT auditorium and in 1991 at Khon Kaen Province which is his home town. These two seminars had been successful in achieving its goals for strengthening the remote sensing technology for the benefits of our country. This year we feel very honoured to organize the Third Seminar in conjunction with FAO, Finnish government, Kasetsart University and Royal Forestry Department, lt reaffirms our belief that Dr. Sathit Wacharakitti had been contributing not only to Thailand but to the world in this important field. I remember his enthusiasm to go to Papua New Guinea to help lecture on remote sensing applied to forestry under the UN/ESCAP Regional Remote Sensing program some 5 years ago, Therefore, I hope that his spirits of international cooperation will guide us towards even closer cooperation in the future. Finally, I would like to extend my gratitude to the Finnish Government for the support and cooperation in this workshop. Let me once again welcome all overseas participants and hope all of you enjoy your stay in Thailand, and I wish the workshop all the best and great success. Thank you.

Iv

OPENING REMARKS

Mr. Yanyong Thanompichai Deputy Director General Royal Forest Department Bangkok, Thailand Your Excellency Ambassador of Finland, distinguished guests, ladies and gentlemen, It is my great honour indeed to be among distinguished and knowledgeable people like you all here today, This kind of international gathering has long been desired by the Royal Forest Department. May I take this opportunity to express my deepest appreciation to the Finnish lnternational Development Agency for the financial support to th¡s meeting. My special thanks are also extended to the Organizing Committee and the people behind the scene who have devoted their tireless efforts to make this workshop come true. Forests in developing countries have declined by nearly half in this century, and the rate of deforestation is still increasing. Recent studies using remote-sensing data and extensive ground surveys have found that between 17 million to 20 million hectares of forest - mainly tropical moist forest - are being lost each year. The loss of tropical moist forest is especially worrying because these forests have a much greater influence on the global climate than do the other main types of forest - tropical dry forests and temperate forests - and because they are a major repository of biological

diversity, Moreover, they are the most fragile forests in that their soils are easily degraded once deforested, and experience to date indicates that even if reforestation or selective felling is attempted, the original ecosystems cannot be fully renewed or sustained,

Thailand is among the countries in developing world having faced a very serious problem of deforestation. Half of its forest land has been transformed to other types of land use during the last three decades, Remote sensing techniques becomes one of the most important tools in Thailand's f orest resources management, and permanent monitoring of forest resources will play an important role in forestry sector planning.

It has long been realized that deforestation yield the impacts on both local and global ecosystems, Therefore, forest resources monitoring system in each country must be standardized for we can determine the global change more efficiently, More efforts are needed, and I can only see that the right path to the success has to be made through a closed collaboration between the rich and the poor countries. Thus, this is a very special occasion for you all to discuss and share among each other the knowledge and experience. I am absolutely sure that more substantial cooperat¡on among scientists will be established, so do I believe that this workshop will be fruitful, Ladies and gentlemen, may I take this opportunity to wish you all the success during five days of this important meeting. For our foreign guests, I also wish you a pleasant stay in Thailand and have a save and sound journey back to your home country,

Thank you,

VOTE OF THANKS Dr. Risto Päivinen Chairman of IUFRO 4.O2.05

University of Joensuu, Finland

Your Excellency, Ambassador of Finland, ladies and gentlemen. During the previous luFRo world congress in Montreal almost two years ago, it was proposed that working party 4.o2.o5, which was called 'Remote Sensing and World Forest Monitoring' should devote the efforts to the global forest assessments by promoting the establishment of permanent sample plot network, Now, as the first step towards worldwide forest monitoring network, we are here to discuss about the guidelines for that network. The output from this meeting will be brought to d¡scussion during the llvessalo Symposium held in Finland in August this year

As an

example of earlier luFRo guidelines, I would like to mention the 'Standardization of symbols in forest mensuration', which has been successfully utilized over three decades as a basis of common language between foresters all over the world. Now, we might face a more complicated pr,9blem concerning standardization of the methods of establishing permanent plots all over the globe,

on behalf of the luFRo working party 4.o2.ob, I would like to extend our sincere thank's to Finnish Ministry of Foreign Affairs, who is behind the main financial support of th¡s meeting. I am absolutely sure everybody here share my gratitude to the organization committee, and its members from Kasetsart University, National Research council of rhailand and Royal Forest Department, who made this workshop possible. Special thanks I would like to give the IUFRO 4.02 officers, whose encouraging spirit gave strength to all organizers of this workshop.

But most of all, I would like to thank you all for gathering here around this important issue, Ladies and gentlemen, your contribut¡on is now requested. Thank you.

YI

A PRIMER ON PERMANENT PLOTS FOR MONITORING NATURAL RESOURCES H. Gyde Lund USDA Forest Service P.O. Box 96090 Washington, DC 20090-6090, USA

ABSTRACT This paper lists basic considerations for developing a world-wide, permanent plot system for monitoring natural resources. Considerations include information needs, constraints, sample design, plot confíguration, and infrastructure. The paper also gives recommendations on ihe content for guidelines or direction for setting up a permanent plot system for monitoring natural resources. Lastly, the paper outl¡nes proposed activities of l|JFßO for activating the instructions on a global basis.

that histor¡c meet¡ng, the Food and Agricultural Organization of the United

INTRODUCTION Sawaddi klap - good morningl Decrease of forest areas is inevitable because of mounting pressures caused by increasing

human populations. lntegrat¡on

of

agricultural and f orestry products with each other and other sectors, linked to environment and nature protect¡on, is a need. Unfortunately, there ¡s too little information upon which to build reliable alternatives to get rid of uncontrolled and

probably dangerous development. We know too little about the dynamics and

changes in natural forests and the environmental impacts of managed forests.

A network of a permanent plots offers a good aid in finding out how resources behave under alternative land use regimes. Monitoring provides the data for

building plans we need f or sustained development. Building of production

models for alternative land use to get land use

combinations may help under control.

The lnternational Union of Forestry Research Organizations (IUFRO) celebrated its XIX World Forestry Congress in Montreal, Canada in 1990. At

Nations (FAOI asked Working Party

S

4.O2.O5 to help develop requ¡rements for

such a world-wide permanent

plot

network.

FAO seeks guidance on how to link sampling for land cover through remote sensing with sampling for biomass. lt seeks help on how to sample for biomass and how to set up a network of biomass plots. This network of plots would add to the remote sensing part of FAO's Global Continuous Forest Resources Monitoring (GCFM) project. Other international organizations have similar needs. The theme of this international workshop

is Permanent olots f or world forest monitorino. We are here to develop a set of guidelines that cooperating nations may follow. The final result of these guides will be a multi-nation network of databases that will provide consistent

information to the international community. Ouestions we need to address are: what information do we need

for effective global monitoring,

how

should we measure the data, and how should we organize for the work. lt is appropriate that we try to answer these

questions using this workshop as

an

international platf orm.

will need. As a minimum, guides should contain direction thal, ¡f you are go¡ng to establish permanent plots in your country,

DEFINITIONS Monitorino is the periodic measurement or physical, chemical, and biological parameters for establishing baselines and for detecting and quantifying changes over time.

observation of selected

we recommend you collect these data to these standards and make the resulting information available to the international community. ldeally, any guidelines should contain the following.

1

.

A olot is as a known location on the Earth's surface having defined boundaries or point of origin. A oermanent olot is:

1.

2. 3.

monitoring

and

Goals - The reasons for setting up the permanent plot sy.stem. Develop goals through an information needs assessment (lNA). ldentify lands you

wish

plan for remeasurement.

to

mon¡tor and variables you

wish to measure. Clarify the various

broad classes of

Note that you may ,not have to locate a plot physically on the ground. One could

identify an area on imagery

One uses permanent plots for a variety of purposes. We often use plots to est¡mate land cover changes between ¡nventories, set up a basis for long term study of the

ects of climate change,

monitor response'to treatments, and to construct

coded manuals according to these issues.

4.

Statements of who will do what, where, and when. Who sets up the plots, who mainta¡ns data bases, has

author¡ty to abandon a plot system.

Who maintains the databases

how can others access inf

5.

and

the

ormation.

Definitions of Terms, References, and Sources of lnf ormation Common standards and inexpensive,

micro-compilation procedures provide regional estimates of key

agencies may use networks of permanent plots to model biological, socio-economic,

variables for global aggregation of comparable results.

affect iion

Responsibilities and lnfrastructure -

who remeasures, and who

growth and yield predictions (USDA Forest Service 1992). lnternational

ref oresta

information

potentially involved and

and

repeatedly observe the same area over time. ln such cases, the plot may be quite large. lt may range in size from 0.1 ha to a pixel to a Landsat scene to a whole continent depending on what you wish to monitor.

def orestation and eventually and afforestation.

on

establishment of permanent plots.

2. f or which there is an intent and

and political factors that

legal mandates or

Policy - The general guidance to the

organization

(Lund and Thomas 1 989) and

eff

- The

charters under which the organization will carry out the monitoring program.

established, monumented, and documented in such a manner so

one can remeasure the exact area and same oblects at a later t¡me

Authority

6.

- lnclude sample intensity, plot design and Sample Design

conf iguration, and statistical review.

Consider a robust, f lexible design that can handle various mixtures and densities of linked plots from space imagery to ground. As a minimum,

PERMANENT PLOT GUIDELINES To be compatible with current efforts, we need to determine what information we

consider using an survey where there

need and what guidelines are already

available - both nationally and internationally. From here, we can determine what additional guidance we

image-based is no local

support. 7

.

Variables

to Measure or Observe -

lnclude standards,

def

initions,

and

coding.

8, Field

lnstructions lnclude procedures for referencing and

the plots and plot maintenance, measurement techniques, data recording processes, and completing f ield monument¡ng

forms.

9.

Data Ed¡ting and Analysis Procedures - How one will use the data to carry

out the study goals and the statistical procedures one is to use.

10.

for Plot Establishrìrent,

Schedule

Remeasurement, and Reporting. 11

.

Budget Requirements and Funding

Sources

f

or

Establishment

and

measure

There is a major shift from timber-oriented

to environmentally-oriented forest monitoring. We need to consult with other international organizations including FAO, WMO, UNEP, UNCED, IPCC, IUCN,

and some NGO's as well f or their information needs. From these we can agree upon a minimum set of common parameters for global synthesis. Once we have identified the needs then we can determine the minimum information that we will need to measure or observe on

our plots.

Use

def initions and classif ication systems that are compatible

promote data sharing.

12. Signed Plan Approval.

to

onê would observe species and measure dbh and tree length.

as possible with other institutions to

Remeasurement.

Deciding what

biodiversity, one would have to monitor changes in composition of plant and animal species and the condition and population dynamics of each. For trees,

or

observe

requires an information needs assessment (lNA). Available funds and time constra¡n

the lNA. The lNA, funds, and time, in turn, dictate the sample design and plot conf iguratiöh. Lastly, an established intrastructure or inst¡tutional base is a need f or a remeasurement program. These topics ate the focus of the remainder of this paper.

INFORMATION NEEDS ASSESSMENT

Minimum Data

The minimum required information for nearly any type of natural resource monitoring includes: plot identif ication number, datum source

(f

ield observations,

remote sensing), location coordinates (Universal Transverse Mercator or Latitude and Longitude), measurement date, vegetation type, and land cover. Note, we can often interpret the last two items f rom remote sensing with minimum field checking. This data corresponds to the inf ormation FAO and others are gathering through remote sensing efforts.

The most important step in a monitoring

ort, global or otherwise, is the ormation needs assessment (lNA). Start the INA by examining current and eff inf

Vegetation Production

applicable laws, regulations, organization charters, memorandums of understanding, and cooperative agreements (Lund 19861. Ref ine the requirements in these

To monitor and model vegetation

documents

principles

by applying the laws

of

and

nature. For example, a national environmental law may require that a given agency maintain susta¡ned biodiversity in the nation's forest lands. We know that biodiversity includes plant and animal species. We also know that to sustain plant and animal species, we have

to know about the¡r production and growth rates. Thus, to monitor

production, such as forest growth

and

yield, we traditionally measure and record (USDA Forest Service 19921: aspect,

slope percent, elevation, plant species, tree identification number, tree history, tree length, diameter at breast he¡ght (DBH) or diameter at root collar, crown

ratio, and area history.

Standard

initions f or these variables are available from the U.S.D.A. Forest Service (19891. Note that we need most of these def

variables

for biomass estimation.

We

often gather this information through f ield

Regardless of f unding, monitoring projects take time to carry out and to measure

samples.

Environmental Health

lf we were

monitoring environmental

health, we would add some measure of plant vigor and observations on possible

damage causes

to the above list. ln

change. Some variables (such as snow depth) are seasonal. One cannot speed up seasonal changes with additional funding. Time spent in planning is time well spent. Often, we never have time to do jobs right the first time, but always have time to do the task again.

addition, we may gather information on

water quality and soil depletion, We usually need both remote sensing and field plots to gather environmental data.

Standards and Classification Systems Data standards and classification systems may provide additional constraints upon

CONSTRAINTS

of how well one determines ormation needs, there are always constraints with whìch we will have to deal. Limitations in funding and time are the two major constraints. Lack of people and equipment may also be constraints, but often we can overcome these if f unds are available. Existing standards and classif ication systems impose a third group bf constraints.

the monitoring system. Standards often establ¡sh minimum data sets, accuracy

Regardless

requirements, and stratification schemes

inf

that may or may not meet our needs at the local level. Having to meet these standards may dictate oqr sample or monitoring design.

Yet adherence to standards is important

or

most

ensuring compatibility. Potential users of monitoring data are f

more conf¡dent in the results if they know prescribed standards.

the data follow

One would certainly require international Funding

Available f unding is the f oremost constraint. lf funds are not available, obviously we cannot carry out the mon¡tor¡ng program. lf funds are less than what we need, then we must cut back the monitoring program. Change the design, reduce the sampling intensity, or drop some of thê data we would collect. Keep in mind that remeasurements should

cost less than setting up a base. One does not have to spend the time in plot referencing and monumentation. Also you can use the old plot data to help locate the trees that you need to remeasure more quickly.

A crew may not have to remeasure all variables that are measured at time of installation, Some variables may change more slowly than others. For example,

crews may record soil characteristics when they set up the baseline, but may

standards

f

or global monitoring.

For

example, if we all were to provide data to the FAO for global forest assessments, we would have to use the classification system that the FAO uses. lf this were

not possible, we would have to crosswalk the data with the FAO system. lf the cross-walk were not possible, then we would have to change the data collection system to meet the standards. lf that were not possible, we would have to settle on providing less than desired information. FAO and other international organizations

(lOsl are try¡ng to develop a system that can ¡nterest individual nations by its success and one which nat¡ons can change and intensify for their own needs.

lOs are seeking designs that fosters a natural evolution f rom an ¡nternational system to many national systems. These will have a certain degree of compatibility because of their shared origins.

not need to measure the soil again for the

20 or 30 years. SAMPLE DESIGN Time

Sample design is very important in most

monitoring efforts. However, it is not as important as information needs assessment and use of standards in sett¡ng up a global plot network. As long

as you can provide the

required

information to the specified standards, those to whom you provide the data will be satisfied. Based upon the papers presented at this meeting, we will review current remote sensing and field plot networks. We will recommend characteristics we think lOs

and

cooperators should

f

ollow

in

developing the permanent plot networks. When practical, adapt successful designs of other agencies, nations, or international

programs. This will show others that your design is practical. lt will also exhibit the potential utility by describing the usefulness in other parts of the continent or world. Vary the sample design to use available f unds and inf

ormation.

set up a baseline retroactively. Start a monitoring effort today, but use remote sensing from the past to set up a base. Remote sensing platforms and sensors vary from AVHRR satellite imagery with f our km resolution to hand-held video cameras in a fixed-winged aircraft. The former may be suitable for setting up a base and for monitoring major changes in land cover. The latter is more suitable for monitoring more subtle changes in land cover and changes in visible characteris-

tics of individual trees. Bias will be greater for the more rare cover types. lf such types are important, then design the

monitoring effort to include some way to calibrate remotely-sensed area estimates,

if not initially, then in the future.

or monitoring usually requires less field effort than setting up the base. lnterpretefs can see some afea Remeasurement

on remote sensing media (satellite imagery, aerial photos,

changes

lf developing a new program, keep the sample design simple. Start vlith a design that you can easily implement. Use a design and system that will yield instant results. Then act to get results out. Build upon it as interest, support, and funding increase. Explore use of remote sensing and plot conf iguration to match the technology you will be using.

videography) thus reducing field efforts. ln addition, one may be able to develop relations between variables measured on the ground and those observed on aerial photographs or other forms of remote sensing. Use these models to predict f ield conditions in f uture monitoring

Role of Remote Sensing

Purposive Versus Random Sampling

The most costly and time consum¡ng part of a monitoring system is the field work. Use remote sensing to the maximum extent to keep field costs to a m¡nimum and decrease time one needs to produce

There are essentially two kinds of sampling designs we may use f or

initial estimates. A design where one uses both remote sensing and f ield surveys is very effective for studying large areas. The more general the data sought, the more one may use remote

sens¡ng. The more detailed data needed, the more one must rely on field sampling. You may use remote sens¡ng both as a basis for creating a sampling f rame and as

a

means for expanding plot data to produce thematic maps (Lund and

Thomas 1989).

Use remote sensing f or sett¡ng up baseline inf ormation and monitoring changes in that baseline. One may also

effort.

monitoring - purposive and random. Purposive sampling requires someform of prestrat¡fication. One decides in advance

what kinds of conditions one wants to monitor, seeks those conditions, and sets up plots only in those areas. You do not

leave

the location of the plot uP to Research scientists use

chance.

purposive sampling for special monitoring this method for setting up a global network of plots for forest health monitoring using a small number of intensively-studied, longterm ecological research sites.

studies. Bailey (1 991 ) suppsrts

ln random sampling, you do not have a choice where you will locate the plot. One leaves the location up to chance.

Like purposive sampling, the designer may

systematic sample and reduce the overall

use some form of stratification before or

sampling errors (Lund and Thomas 1989).

after drawing the sample, but sample plots within strata are randomly or systematically selected. ln either case, one should sample all strata. However,

you may sample some strata intensely than others.

more

Resource

specialists often use random sampling for

natural resource inventories. Lund and Thomas (1989) discuss a wide variety of designs suitable for setting up a network

of

lf information is available, consider using a prestratif¡ed sample. This is less costly

than a systematic sample

with

poststrat¡f¡cation. One may also consider a double sample with the first sample being a systematic remote sensing sample ollowed a prestratif ied sub sample of

f

larger scale photo plots or field plots.

randomly-located plots including

systematic sampling with a random start.

Use of Existing Networks

Purposive sampling works well when one has extremely limited funding and where the location of the plots ate true

Often there will be an existing network of permanent plots w¡thin a given area. The

indicators

of

similar areas elsewhere.

This sampling technique also works well when there is a very specific target one is

trying to monitor. The

primary

disadvantage ¡s that you have to use caution in how you use and interpret the

resulting statistics because purposive sampling often leads to inferences that are not valid for the entire population. Random sampling may not be as efficient as purposive sampling. However, because

this method avoids bias in the location of plots, the data have more general utility and acceptance.

Systematic Sampling

lf

there is little information about the extent of a particular resource or the resource is extensive with small inclusions

of other cover types,

consider a randomly-located, systemat¡c sample design across all lands. ln forestry, we often want to monitor changes in the land base including area or extent, condition,

composition. lf we have no

prior

knowledge about the resource situation, then we need to establish a base (survey all lands). lf we just want to see if the forest land base is decreasing, then in the future we just need to check the forested lands. lf, however, we wish to know if

forest lands are increasing

and

decreasing, then we have to monitor all lands. The changes we could see would

include forest to forest, forest to nonforest, and non-forest to forest. lf we did not monitor all lands, then we could miss the latter group. One may poststratify a

will arise if one can use that network to accomplish the goals of the new monitoring effort. lntuitively, one would want to take advantage of work question

already done. This is 'good advise providing that the new needs do not somehow compromise the existing system. For example, in the United States, we have an excellent net work of permanent plots set up by our Forest lnventory and Analysis (FlA) units of the USDA Forest Service. FIA uses these plots for forest inventory. Recently, the USDA Forest Service embarked upon a Forest Health Monitoring program (FHM). The FHM program also calls for the USDA Forest Service to set up a network of permanent plots across the United States (Shaw 1992). The initial proposal was to use a sub sample of the FIA plots to meet the needs of FHM. However, because there

would be considerable activity around the FHM plots, it could damage the plots for FIA work. ln this instance, the agency decided to use a separate sample for the Forest Health Monitoring. For our Forest Health Monitoring program

in the USA, we are using a systematic

grid set up by the

Environmental

Protection Agency (EPA) under its

Environmental Monitoring

and

Assessment Program (EMAP) (Palmer 1 992). The grid is very coarse, but covers all lands and ecosystems. Each plot is 27 km from one another at 60 degree angles. The EMAP grid is available

on a world-wide basis. Several other countries are start¡ng to use it to monitor

changes in their resource base.

observations.

For world-wide monitoring, we will probably need to use both existing networks and set up new ones linked by common information gathered. Similarly, nations may use both purpos¡ve and random sampling to set up this network. Plot Gonfiguration

Recently, there has been considerable discussion about plot conf iguration. Should one use a variable-radius plot or fixed areaT lf fixed-area, should the plot be circular or rectangular? What size should the plot be?

For most monitoring efforts we should used fixed-area plots. Crews and the public understand such plots, There is no question if a tree is ingrowth, ongrowth, in or out. Therefore, fixed-area plots are less subject to error. The nature of the vegetation and how one

Hidden Versus Visible Plots

A

question

will arise if a crew should

monument a plot in such a manner that only the crews know where the plots are and which trees were measured. These are hidden plots as opposed to plots where the crews clearly marks the boundaries or plot center and sample trees.

We use hidden plots when we do not want the plot treated differently f rom the surrounding area. The monitoring effort is to measure human-induced eff ects as well as natural effects. One may also use hidden plots on land not belonging to the organization conducting the surveys for aesthetic reasons. The disadvantage of

the hidden plot is that crews on successive surveys may difficulty finding systems (GPS) may reduce field time in the f uture.

the plots. Global positioning

will use the data dictate plot size and configuration. lf you wish to l¡nk the f¡eld plots to remote sensing imagery, then consider using:

We use visible plots in research studies where we own the land and where we must control the applied treatments. ln

1. Plots that are equal in area to a pixel size or larger,

this case, one applies special treatments to the plot area and measures the effects. Crews may also use visible plots in areas

2. A cluster of small field plots spread out over an area that is at least equal to the area of a pixel, or

3.

lgnore trying

to

correlate

ield data with a given pixel. lnstead, treat the field plots as

f

samples

of a

remote sensing

classified stratum. Be sure the

sampling intensity

is

large

.enough to describe the stratum adequately. For our world-wide network, we probably will need a network of multi-layered plots.

The f irst layer being a network

of

satellite-based remote sensing plots for monitoring land cover change. We would sub-sample these plots using larger-scale

imagery including satellite,

aerial photography and videography. We may

sub-sample these

plots with

f

ield

where recovery would be extremely diff icult such as in extremely brushy areas, dense forests, swamps, etc.

Visible plots are less costly to relocate. A disadvantage of visible plots is that people may treat the area diff erently

when they see the tags and other markers. This biases the intent of the

monitoring program. ln addition, people may find tree tags tempt¡ng souvenirs or targets. The tags may also attract

wildlife which, in turn, may damage the tree.

During the past few years, crews have started using hand-held global positioning systems (GPS) (Lund et al. 1991). Currently, these are useful for directing a crew to within 25 meters of a known

location. ln the future, when

coupled

w¡th real-time differential GPS, crews will be able to locate themselves to within a few centimeters. Eventually, this may

make v¡s¡ble plots unnecessary.

about. lf you do not, then you have less likelihood of carrying out see ¡t come

the project. Sampling lntensity Sampling intensity will vary with the goals and standards of the monitoring project. It will also vary funds and time available as discussed above. lf funds or time are limiting, then goals may have to change and standards and sampling intensity reduced. ldeally, one designs a project to

lf you believe in what you want to do, then lay out a strategy to get there. Specify what it will take to do the job and negotiate from there. Don't start by making concessions first. Lay out what needs to be done. Look at options if so ¡nstructed. The options and approaches for working with the constraints are:

meet certain accuracy requirements. ln reality, however, it is the funding or time

1,

available

that will dictate the

sample

it will take to get that information. Specify how much the monitoring effort will cost and how much time it will take to meet specif ied

intensity. Sampling intensity for global monitoring at

the national level need not be very high. Some 200

to 1,000 plots may be enough

for most countries. The critical task is to find good experts and organizations to do the work.

Take the direct approach. Specify what information you need and what

standards and needs.

2.

Accept reduced funding or time then

cut back on or change

the

monitoring program accordingly. Monitoring Rate

Different variables change at d¡fferent rates. Soils change slowly and annual

vegetation changes rapidly. Hence monitoring rates for different attributes will vary. Adjust the period of remeasurement to meet the expected rates of change of the most important variables of interest. The determination of

the cause of change may require special

research. One could use

an

interpenetrating design. One measures

some plots on the f irst occasion

3.

Change the standards, if that is within your power. Given this information and this amount of time and f unding, we will have to change

these standards.

4.

lmplement all constraints. We have this amount of funding, this amount of time, and these standards to

which we have to adhere. What inf ormation can we gather with these restrictionsT

and

different plots on successive occasions.

Be persistent. Seek public support and publicity for the monitoring effort. Form

INFRASTRUCTURE

Our last task is to develop recommendations on ¡nfrastructure

support. How do you see the role of national and international organizationsT

How can we arrange funding for those that need it7 How can we ensure data availability and sharing? How can we maintain support f or the monitoring effortT

partnerships or alliances with environmental groups and other ministries. Such groups may be willing to

give time or funds to the project. Also they may be less reluctant to criticize the effort at a latter date if they are part of the effort. Find common ground and build upon it.

Be willing to give to gain support. Support others needs. Look ahead for problem areas or areas of disagreement.

The best-designed monitoring system will

not be

implemented

if there is

no

infrastructure to support it. The first step to building an infrastructure is you. You must believe in the system and want to

Try to f¡nd a way to resolve the problem before it occurs. Alert others to potential

problem and seek their solution. Be honest in what you say about what the system will do once it is in place.

Tie the task to national laws or international agreements. ldentify monitoring needs and publish in local

lournals or other outlets. Write the article with environmentalists or scientists as coauthors. This publication will serve as a

handout and reference later. People accept published documents more readily than verbal approaches.

Seek long range comm¡tments. Get of

funding through memoranda

understanding or cooperative agreements.

remedial actions (Schmid-Haas 1 981 ). lmplied is the assumpt¡on there is some plan in place to deal with the problems that we may reveal from monitoring. To

a global basis. Hopef ully, one will emerge from the UNCED Congress this coming June. date, no such plan exists on

Successful global monitoring will require the voluntary participation of all nations. It will begin, however, with your active participation in the meeting and in the work groups to develop these guides for

Permanent plots for World Forest Monitoring. Thank you - kob khum!

Seek a permanent staff. lt is better to have a small continuous permanent s'taff rather than having to create a new staff

every monitoring period. Build up a contiiruous staff at proper time and cut back when workload is over. The staff can do updates, techniques research, training, and respond to information needs

at off periods.

When the project

is

approved, share

funding, responsibilities, and most of all, information and credit.

ACKNOWLEDGEMENTS

My thanks to: Dr. Yong-Chi

Yang

National Taiwan University; Dr. Sipi Jaakkola -UNEP/GRlD, Geneva; Dr' K.D. Singh - FAO FBA 9O, Rome; Mr. Joe J. Lowe - Forestry Canada; Drs. Simo Poso University of Helsinki and Risto Päivinen

-

University of Joensuu, Finland; Dr. A.B'

WORK GROUPS AND FOLLOW-UP

Temu - Sokoine University of Agriculture, Tanzania; and Drs. Ray Czaplewski and Mark Hansen - USDA Forest Service for their helpful suggest¡ons for this paper.

During this meeting we will form work groups to develop guidelines for a global

REFERENCES

pefmanent plot network as described above. Once drafted, we will publish these rough guides in our IUFRO Forest

Resource lnventory

and

Monitoring

Newsletter for other members to review. We will also send review copies to FAO and other international organizations for

their input. We will assemble comments and revise the guides

all as

necessary. Lastly, we will send the flnal guides to the President of IUFRO, Director

of FAO, and other organizations to publicize and use. IUFRO members should be proactive in implementing the

guides

at the local and international

levels.

Sett¡ng up a network of permanent plots is but the beginning of the resolving some

of the

environmental, social,

and

economic problems we, the citizens of Earth, face, Monitoring is not done for the simple sake of collecting data. lt is done to reveal discrepancies between f

orecast and achievement

in time f or

Bailey, Robert G., 1 991 : Design of ecological networks for monitoring

Global Change. Environmental Conservation l8(2):173-175'

Lund, H. Gyde, 1986:

A

Primer on

integrating resource inventories.

Gen. Tech. RePort

WO-49.

Washington, DC: U.S. Department of Agriculture; Forest Service. 64 p'

Lund, H. Gyde; Thomas, Charles E., 1989: A primer on stand and forest inventory designs. Gen' Tech. Report WO-54. Washington, DC: U.S. Department of Agriculture; Forest Service. 96 P. Lund, H. Gyde; Jasumback, TonY; Allison, Ray; Falconer, Allan, 1991: Taking back the desert. GPS World 2ßl:24- 31.

Palmer, Craig. 1992: United States

Environmental Monitoring

Shaw, Charles G. 1992: Forest health monitoring -- a new program of the

and

Assessment Program: an overview. Paper presented at the IUFRO

USDA Forest Servíce and the

S4.02.05 lnternational Conference on Permanent plots for World Forest Monitoring. 1992 January 13-17; pattaya,

Environmental Protection Agency. Paper presented at the IUFRO S 4.02.05 lnternational Conference on Permanent Plots for World Forest

Thailand.

Monitoring.

Schmid-Haas, Paul. 1gB1: Monitoring change with combined sampling on

Proceedings

- Arid Land Resource

:

Developing Cost

Effective Methods. 1980 November 3O-December 6. La paz, Mexico. G

992

Janua

ry l3-17;

USDA Forest Service, 1989: lnter¡m resource inventory glossary. U.S.Department of Agriculture; Forest Service. 96 p.

aerial photographs and on the ground. ln Lund, H. Gyde; et al. lnventories

1

Pattaya, Thailand.

USDA Forest Service, 1992: Timber permanent plot handbook. FSH 2409.13a. Washington, DC: U.S. Department of Agriculture; Forest

en. Tech. Report WO-2 g.

Washington, DC: U.S. Department of Agriculture; Forest Service: 3g3388.

Service. Misc. Pagination.

10

REMOTE SENSING TEGHNIOUES FOR FOREST MONITORING

INTERNATIONAL EFFORTS AT GLOBAL FOREST MONITORING USING REMOTE SENSING Sipi Jaakkola United Nations Environment Programme, Global Resource lnformation Database, CH-1 227 Carouge-Geneva

ABSTRACT The world's decreasing forest resources cover a total of about 3.4 billion ha. This equals to 27 per cent of the global land area. Deforestation by shifting cultivation, cattle rànching, fuel wood collection, and logging, has during historical times reduced the global forest area by almost 5O %. Deforestation is the second largest contributor to the global warming. lt threatens biological diversity and global ecosystems fundamental to life, and it is prompting soil erosion and habitat destruction. The world community increasingly requires timely and reliable information on the s¿afus of worlds forest resources and the changes therein. A global data base for forest resources monitoring can best be established when relevant information is available at natíonal level. The new awareness on global forest monitoring needs is leading to increased institutional responses.

ln

temperate forests the monitoring techniques are rather well estabtished, although the as.sessrnent practices lack harmonisation. Concerning the tropical forests, the methods and practices tend to be poorly establíshed. The choice of methodology is largely that of appropriate sampling, measurement and estimation methods. Remote sensing from space can provide global measurements for resource monitoring. Today's methodology for global forest cover assess/?enÍs is largely based upon NOAA/AVHRR data analysis. Two interrelated branches of methodology can be differentiated: those of assessrng the status of forest and those of change monitoring. Further, world community is witnessing a sh¡ft from timberoriented towards environmentally oriented needs of forest monitoring. lnternational organizations have a role in providing methodology and guidelines for global forest monitoring. A more direct role is to implement global forest assessments and distribute the results. FAO and UNEP made the first tropical forest inventory in 198O, and FAO is now carrying out the Forest Resources 4ssessrnenf I 99O project. UNEP helps the pro¡ect by providing certaín satellite image processing and GIS services. UNEP can contribute in the design of future global forest monitoring systems, in particular their environmental dimensions.

number

BACKGROUND

of f orest

classif

ication schemes

ex¡sts at global level, such as the UNESCO lnternational Classification and Mapping of Vegetation. They distinguish between

tropical and sub-tropical forests

The diversity of forest is enormous. A large

13

and

woodlands, temperate, sub-temperate and Mediterranean zones, as well as the boreal forest zone. The diversity of forest types results also from the interference by man, such as deforestation, logging, grazing and fires. Finally, air-, soil- and water pollution, pests and diseases are foundations of the

diversity, Together with technical

community modelling geosphere-biosphere interactions at global scale, needs both a

baseline map of global f orests and the changes therein. IGBP will generate a predictive understanding of the effects of climate change on terrestrial ecosystems. Governments, various NGOs, development aid agencies, and a number of international

and

political constraints involved, the diversity

of forests is a fundamental

organizations, such as FAO, ITTO, UNEp, IUCN or World Bank, need timely information both on world forest status and

difficulty to

cope with when designing forest monitoring systems at a global scale.

The latest survey of world's

condition. WhyT To assess

the

environmental impact of deforestation, i.e. f

its impact on climate change,

orest

resources was made by FAO and UNEp in 1 980. The results indicate that the forests then covered a total of 3.6 billion hectaès (ha). This equals To 27.7 per cent of the global land area. Forests of the tropical countr¡es and territories covered about 1.94 billion ha, representing 53 per cent of the global forest area. About one half of it was tropical moist forest.

land

degradation, resource depletion and loss of biological diversity; to have baseline facts f or sustainable development of f orest

resources; and to plan afforestat¡on and orestation programmes.

ref

SCIENTIFIC CHALLENGE

At the moment, FAO is carrying out the Forest Resources Assessment

1

990 project;

the tropical forests are assessed by the headquarters in Rome and the temperate forests by the FAO/ECE office in Geneva. The latter, compiled on the basis of an enquiry, will be completed bef ore the UNCED conference, June 1 992. The

How does one implement global forest monitoring: a mission impossible or just difficult? Key issues to consider at the outset include: 'l ) What parameters to monitor: timber, fuel, carbon, biodiversity,

soils, protected areas, agroforests, or what? 2) How to interlink the monitoring activities

preliminary results of the tropical part of the project showed that during 1 981 -1 990 the annual deforestation rate was 16.9 million ha. Although the preliminary estimate needs to be verified, it seems that deforestation has accelerated in the tropical zone (FAO, 1990).

from national to global level? Should one choose bottom-up or top-down approach? 3) What monitoring techniques and practices exist f or the provision of cont¡nuous, and compatible forest resource inf ormation?

ln certain parts of the world, notably in

Obviously, in temperate f orests the monitoring techniques are rather well established at national level, although the

northern Europe, forest area and volume of growing stock are increasing. Examples can even be found in the tropical zone where afforestation and reforestation programs are becoming an increasingly important activity. Tree plantation - "Greening of the World" seems to become a major political, technical and funding issue in the near future. Who needs

inf

practices tend to favour timber inventories.

Concerning the tropical forests, the methods and practices tend to be poorly established. Remote sensing oriented systems are often suggested for these conditions as a quick way out.

ormation on forests at global

The primary outputs of any global forest monitoring system designed to serve

level? The lnternational GeosphereBiosphere Programme (IGBP) and, more

generally,

decision makers at national, regional and global levels should include

the international scientif ic

l4

- a periodically updated digital database, implemented in a geographic information system, comprising summary

Today's remote sensing methodology for global forest cover assessments is largely based upon NOAA/AVHRR data analysìs These techniques were first adopted to

statistics and maps over the current status of worlds forested ecosystems,

vegetat¡on mapping by NASA's scientists in early 1 980's. Since then, the f urther

- a grid of permanent sample plots

periodically measured with replacement for provision

development for forestry applications has succeeded in a number of North American and European universities and research

impact analysis.

centres. Two interrelated branches

partial

of changes and trends needed in detail in environmental

of

methodology can be differentiated: those for mapping of forest status and those for monitoring of changes. Mapping methods

cover both visual interpretation or digital

METHODOLOGY

analysis of satellite data.

Dig¡tal

interpretation can now be performed using

clustering techniques, single band or box classif ication, or by

The choice of methodology is largely that of

thresholding

appropriate sampling, measurement and estimation methods' The assessment of current status and detection of changes in

forest resources readily suggest

applying discriminant f unctions such

minimum distance

or, in

as

particular, maximum likelihood. A new development among classification algorithms is so called

a

multistage sampling approach using permanent sample plots with partial

mixture modelling

by which the

class

replacement. What is more, if one applies

proportions, rather than class labels, can be estimated for individual Pixels.

NOAA/AVHRR satellite imagery,

forests can be done by two methods;

preliminary strat¡fication, e.g. forest/non-forest classification from of

sample size

a

Monitoring

reduction

by 30 % at global level

seems possible. The recent development of

ul.

def

orestation

in

tropical by

classifying the imagery at a single point of

time or, which is most often applied,

forest assessment and monitoring methods involving remote sens¡ng components has

been active and successf

of

by

multitemporal analysis for change detection. The latter requires image data from at least two po¡nts of time and may, in a successf ul case, demonstrate well the best properties of satellite remote sensing. Besides f or clear cutting, the monitoring methods apply for

Although

further research and validation are needed, any serious global forest monitoring plan now has to incorporate remote sensing methodology.

the detection of other disturbances

in

forest, such as new roads, fires, etc. ln the

Remote sensing f rom space - typically aided by surface-based measurements - provides global and long-term measurements f or

same way, afforestation

the changes caused bY or reforestation can be

monitored.

resource monitoring. The polar orbiting satell¡tes are particularly usef ul for data acquisition about the status and changes in the forests. Computer-aided processing of

l'hese rapid advances of inf ormation technology can, hopefully, soon be welcomed as new means for contributing to and, ultimately, achieving a goal advocated by the United Nations and various other international organizations: the sustainable development of earth resources.

satell¡te imagery is rapidly advancing in the

industrialised world. NASA estimated, in

1 990, that the international space programme f or global environment monitoring will cost 30 billion dollars over

Since 1970's, UNEP/GEMS has contributed to the development of capabilities for global tropical forest resource assessment. The work is now linked to UNEP's concentration

the next ten years. lt seems safe to predict that the supply of satellite data w¡ll not be a limiting factor in forest monitoring by the end of the century.

15

of land resources by combating deforestat¡on and area "Protection

it was recently expanded to cover temperate forest conservation issues as well. An important, though heavily criticised, initiative is the Tropical Forestry Action Plan coordinated by FAO since 1987. Major changes in TFAP are expected after an independent progress review made forests, but

desertif ication". UNEP has stressed that the

world community should urgently

be

provided with access to timely information on the condition and change of tropical forest cover. UNEP has argued that the new earth satell¡te observations, together with the recent advances in the fields of GIS and digital image analysis have made the task technically possible. UNEP has implemented

this work in

cooperation

with

last year. IUFRO is actively participating in the debate natural

on needs and ways of global

resource monitoring. IUFRO-meetings in Venice, 1989, and Montreal, 1 990, helped specifying the goals and problem areas, as summarized by Lund (1990): "The need for global assessments is becoming more and

the

specialized UN agencies (notably FAO), other international organizations, the relevant research organizations, and the

major bilateral and mult¡lateral aid authoritles.

more evident. Better information about the environment is necessary to understand and

Clearly, the international organisations have

solve emerging problems such as global warming, deforestation and acid deposition.

a role in

collecting and disseminating information on the world's forests. Since 1989,!n ad hoc group of experts from ¡nter alia FAO, UNEP, JRC and NASA has cooperated in the development of global

For any actions to change these processes,

data are needed at the national international levels.

satellite remote sensing. Today, the strengths and limitations of such

level? "

methodology are relatively well known.

As a methodology solution IUFRO suggests adopting various combinations of existing inventory data and new data through

remote sensing.

FOREST

ASSESSMENT EFFORTS

UNCED: World public concern has led to the

convening of the UN Conference on Environment and Development (Earth Summit) in Rio de Janeiro, June 1992,

lncreæed inten¡ationd i¡¡stitrfímd actiyity

which may result in Global Convent¡ons on Climate and Biodiversity. Steps are being taken for a Global Convention on Forests as well, i.e. an international legal instrument for forest management. During UNCED's recent preparatory meetings, reforestation

The institutional activity related to global forestry issues is expanding. ln 1985, the UN Conference on Tropical Timber ratified

the lnternational Tropical

Timber Agreement, which regulates the tropical timber trade and encourages co-operation

has become a major f unding and technology

between producing and consuming nations. The secretariat, ITTO, is actively pursuing studies on tropical forests. lt has recently

published

a set of

guidelines

and is:

basic question

how can we link inventories and monitoring systems developed at the national level with inf ormation needs at the international

forest assessment methodology based upon

REVIEW OF TROPICAL

A

for

transfer issue. Summarizing, the new awareness on global

the

f

sustainable management of natural tropical

orest monitoring needs, as described to increased institutional

above, is leading

forests.

responses. Some init¡at¡ves focus attention on the need for policy reform considering economic, political and institutional factors. Others focus on the need to provide data concerning the status of forests and the

IUCN has recently revised the¡r Forest

lt includes the establishment of protected areas in tropical Conservation Programme,

16

rate of deforestation.

WCMC,

in

conjunction

with IUCN, is

implementing the "Tropical Rain Forests - an Atlas for Conservation", a project started in 1 989 and sponsored by British Petroleum Co. lt aims to demonstrate graphically the present knowledge of rain forest extent and distribution. The work covering South-East Asia has been completed in 1 990, and they are now doing Africa. The data are stored in ARC/INFO ¡n vector form. After completing the Atlas they intend to update it, so the work is likely to lead into a mon¡tor¡ng type

ONGOING TROPICAL FOREST ASSESSMENT PROJECTS Food and Agriculture Organisation IFAO) Forest resources appraisals on a global basis of FAO's mandate. The latest

are a part

of activity.

world-wide f orest resources assessment was carried out in 1980 by FAO in conjunction with UNEP. The current

The Woods Hole Research Center

assessment, " Forest Resources Assessment 1 990', began in 1989, and its f inal report is

expected by the end of 1 992. The main goals of the project are the inventory of present state and the assessment of changes in 1 980-1 990. FAO headquarters

The Woods Hole Research Center, based in Massachussetts, USA, carries out a "Global

are responsible for the work in tropical and sub-tropical countries (109 in number). The

recent inaugurat¡on of the lnternational Boreal Forest Research Association the Center offered to map the boreal forest

methods involve remote sensing

Forest lnventory Prolect" which

and

statistical sampling surveys. Even if the project has problems with funding, it is

zone using AVHRR data.

to serve as a benchmark of international efforts, as was the case in

expected 1

lnternational Space Year 1992 (ISY)

980 assessment.

The Global lnformation System Test in the context of the international space year includes a project called " Rate of

NASA

Deforestation" which aims at lmprovements in the reliability

NASA Goddard Space Flight

Center's Biospheric Sciences Branch has, during the 1 980's, performed a number of studies where methodologies for Amazonian forest assessment with AVHRR data were developed. They have recently developed a

satellite-based methodology

f

or

forests of Central Africa. A goal has been to establish the current forest areal extent and land use practices in the context of regional and global climate and ecology. NASA is currently engaged in a global tropical forest mapping project using visual ¡nterpretat¡on

is

forest cover and monitoring maps with appropriate spatial and temporal resolution

in

which are organised in geographic information systems. An atlas of deforestation case studies is one of the

progress in South America.

intended results. Presentation of the global forest assessment results is planned at ISY

The World Conservat¡on Unìon (IUCN) and

World Conservatíon Monìtorìng (wcuc)

establ¡shing forest

assessments, 2) Definition of procedures for monitoring forest changes, 3l Generation of case studies in major forested areas both in tropical and temperate/boreal forest zones. The approach to meet the objectives involves: 1 ) Generation of a set of calibrated data and optimised products combining optical and microwave radar data for forest analysis, and 2l Production of

mapping

Landsat MSS data. The work

1)

and

procedures for the use of spaceborne data

as a means to

and continuous monitoring of the tropical

of

is

attempting to measure Jhe global forest area and rates of deforestation. ln the

Cent¡e

World Forest Watch mid-1992.

l7

Conf

erence

in

of remote sensing oriented projects dedicated to the f orest monitoring and

TREES

Tropical EcÒsystem

Environment

assessment at a global scale. lt is expected

that the next

Observations by Satellite (TREES) is a joint project of the Commission of the European

Communities and

the

European

gerreration of baseline ormation on tropical f orest cover will become available for major parts of trop¡cs by mid-1992. The usefulness of this inf

Space

Agency concerned with the development of space observation techniques to the service of a better monitoring of the tropical forest of the world. The project has been defined for 1991-1993, but its ambitions seem to

information 'for, e.g., quantifying environmental impacts remains

reach beyond 1993, TREES is addressing deforestation issues at a global scale and

satellite remote sensing. The project is financed by the European Parliament and the European Space Agency (ESA) first for a period of two years. Later it may become a regular activity of Joint Research Centre

ERS-1

Assessments with weather satell¡te data

Although NOAA weather satellite's AVHRR sensors provide only 1 to 4 km spatial resolution they offer a possibility for daily

are: 1 ) A global tropical

as the main sources of

coverage. NASA Goddard Space Flight Center has, during the 1 980's, performed a number of studies where methodologies for Amazonian forest assessment with AVHRR

and

data supplemented by higher resolution data (SPOT and TM)when required; 2) Detection

and intensive rqonitoring of the active def orestation areas; measurement of def orestation rates in critical areas; 3)

data were developed (e.9. Justice, et

ln 1987, UNEP/GRID

launched a tropical

forest cover assessment study in order to develop methodology to apply at global scale. The study which was based on the analysis of NOAA/AVHRR data was conducted in two major regions of tropical moist forest: West Africa and Amazonia.

tropical

forest inventories: it is intended to be linked

to other projects, but us¡ng new approaches.

The Amazonian study was made digital map showing France and the South-East Asian Ministers

Education Organisation

in

cooperation with the University of Reading and the European Community. A large area

SEAMEO

of

al,

1985).

Modelling of tropical deforestation dynamics. TREES project has not declared an ambition to replace existing

orestation

fuutEAme¡*n

and ESA.

(baseline) inventory using AVHRR

def

STATUS BY REGIONS IN 1991

developing new monitoring techniques using

TREES goals

of

to be evaluated.

def

orestation

in

southern Amazonia was compiled from a number of 1988 NOAAiAVHRR scenes at

(SEAMEO)

undertake since 1989 a joint project to assess and map the extent of deforestation in SE Asia and to train national experts in

one-kilometer resolution.

After

some

preliminary methodological experiments, a maximum likelihood classifier was selected

trop¡cal f orest monitoring. The countries involved are Brunei, lndonesia, Malaysia, the Philippines and Thailand. The aim is to ana,lyze the change f rom 1 982 to 1 988 in

f

or the

discrimination

of f orest

and

non-forest classes. The map was checked

¡n

detail against secondary sources including Landsat TM and Brazilian forestry maps.

forest and land under cultivation on an area of three million sq.km. The project is geared

The f inal product represented the first large

to the ISY Global lnformation System Test.

area digital map

Summarisínq: There is currently a

region. The map was combined with various otlîer carto€raphic dataplanes within a GlS.

nurmber

1E

of

deforestation for this

Amazon was 29.8 million ha. Only 1,382,000 ha of the Amazon rain f orest were cleared in 1990. This means a 27 o/o reduction in the rate of deforestation compared with 1 989, or a 36 7o reduction compared with 1988. This estimation was based on the analysis of 226 Landsat TM

Some simple GIS operations were carried

out to analyze the incidence

of deforestation with respect to political units, floristic zones, protected areas and roads. The integrated data set can be used to focus f urther monitoring work. The result is

a f irst digital, large area map

on

deforestation compiled over Amazonia.

scenes.

The accuracy of the map was evaluated by

comparison

Despite the work done during the last ten or so there still are gaps in the coverage of satellite image analysis of the

with

secondary sources, particularly those produced by the former Brazilian Forestry Department. The tests

years

tropical f orests of South America. The North Western parts of the continent as well as parts of Central America need to be

showed that forest/non-forest results from AVHRR data analysis are well correlated with those from higher resolution satellite and ground data, but that a tendency exists

mapped soon. Some international activities are now being introduced in those areas in order to fill the gaps.

to overestimate the deforestation area. This

is however perf

ormance

countered by the poor of the AVHRR sensor in

West Africa

distinguishing areas of secondary regrowth from undisturbed forest. Dependency on the geometry of deforestation patterns coupled with an inability to distinguish undisturbed

The West African part of GRID's study was implemented in conlunction with the Finnish

lnternational Development Agency

orest f rom secondary regrowth are suspected as sourcqs of errors. Field checking of the classìf ication results is

(FINNIDA) and the University

f

of

Joensuu,

Finland. The main purpose of the project was to test the utility of AVHRR data for

indispensable (Cross, 1990).

mapping forested and non-forested regions,

.4ssessments using Landsat TM data

and other broad vegetation categor¡es, within the humid tropical forest zone of West Africa. A variety of digital image processing techniques were tested and compared using single date scenes. The

Along with the advent of

optical

high-resolution satellite sensors, notably Landsat TM, the opportunities for forest cover assessment were d rastically improved. Perhaps the best estimates on

the extent of

deforestation

in

results were compared with high-resolution

satellite data f rom Landsat and SPOT, which in turn had been classif ied using information obtained during field visits.

"Legal

The best results for mapping the closed

Amazonia", an administrative zone of some

forest (>40 Yo canopy coverl versus surrounding vegetation classes were

500 million hectares, have been derived by (INPE). ïhey used visual interpretation of TM-imagery f rom 1988 in mapping the

obtained using a supervised classification and a segmented approach to mapping across the AVHRR scenes used. A total of three scenes was used to produce a map of closed forest of West Africa. An effort to update this map using additional cloud-free AVHRR imagery and more recent field data

deforested areas, and produced a 200-page

atlas

to

document the results. The total

damaged area added up to about

hectares, i.e., less than

!5

million the

a half of

corresponding estimate by the World Bank

based on a projection from Landsat MSS imagery of 1978 and that by World Resources lnstitute 1990/9 1 report.

is now underway (Päivinen and

Witt, 1 991

).

Central Africa

The Brazilian Science and

Technology Secretary announced recently that, in the 1 98O's, the total deforestation of the

NASA/GSFC Biospheric Sciences Branch has recently developed a satellite-based

19

methodology for mapping and continuous monitoring of the tropical f orests of Central Africa (i.e., Zaire, CAR, Congo, Cameroun,

test AVHRR approach for global forest assessment and, in particular,

Gabon and Equatorial Guinea). According to FAO (1 988) the total forest area concerned

deforestation fronts, and discrimination of

f

seasonal effects. The approach has involved

calculation of maximum

is about 280 million hectares. A goal has been to establish the current forest areal extent and land use practices in the context

NDVI during dry seasons was performed in order to discriminate between seasonal and evergreen forests. Finally, a non-supervised classif ication was made using the most

To accomplish the objectives AVHRR data was used as the primary mapping tool for

the region. A coarse vegetation map which

promising combinations of transf ormed AVHRR channels (MeanCh2, MeanCh3,

delineates primary forest, degraded forest, mixed forest/savanna, and primary savanna areas was then produced from this data. ln addition, the fire distribution (from biomass burning) was established for the region by month and vegetation type.

MeanNDVl, MaxNDVl).

The preliminary results obtained indicate that 1 ) the thermal channels and NDVI show promise in forest/non-forest

The cooperation with FAO's FRA 1990 involves the provision of f orest area

discrimination, 2)the MaxNDVl - MeanNDVl transf ormation helps in discriminating between seasonal and evergreen forests, 3)

statistics and land use information for the Af

normalized

difference vegetat¡on indices (NDVI) of scenes masked for clouds and water. As analysis of the seasonal evolution of the

of regional and global climate and ecology.

Central

orest/non-forest classif ication, detection of

rica component. FAO has

the dense primary f orest and degraded secondary forest are separable in the classif ication perf ormed. The area

selected Zaire as a pilot study for showing the usefulness of rempte sensing for the tropical forest assessment. Data from the AVHRR are being used in the primary Ievel sampling for change detection.

estimation

of

South-East Asian f orests

seems plausible using AVHRR methodology.

Further research is needed for refining the discrimination procedure (e.9,, GAC time

series). The evaluation

South-East Asia

of results is

underway.

The IUCN/WCMC project "Tropical Rain Forests - an Atlas for Conservation" introduced above has demonstrated graphically the present knowledge of rain forest extent and distribution in Asia.

UNEP/GRID-Bangkok in con.junction with TREES project is now participating ¡rr the forest cover mapping of South-East Asia.

The

original and remaining tropical forest cover has been estimated, and the existing and proposed conservation areas outlined by countr.ies. The total forest area is reported

EXPERIENCE GAINED

to be 280 million ha. The project has published the Atlas

of

Asian and Pacific

Following is an effort to summarize the experience so far gained in forest cover assessment projects in the tropical zone using digital AVHRR data analysis.

Rim.

The lnstitute of Remote

Sensing

Applications at the Joint Research Centre, lspra, is currently making assessments of

tropical f orest cover in the region of South-East Asia comprising Kampuchea,

Data'. The NOAA/AVHRR LAC imagery seems feasible as a data source for global forest cover assessments. However, there are st¡ll gaps in the global coverage of the

Laos, Myanmar, Thailand, and Viet Nam. According to FAO (1988) the total forest area conceàed is about 84 million hectares. The objectives of the work have been to

imagery, and the clouds are problem.

20

a

serious

Preprocessing of LAC data.. The minimum geometric and rad¡ometr¡c corrections needed for forest mapping are available. A minimum set of preprocessing steps for forestry work should be defined.

compilation of such a map is dependent on

the contributions of those investigators working on regional AVHRR mapping

projects. The map will (or will not) be born as a result of international cooperation. The

result may not be perfect but better than

Classification scheme/definitions: lt, is of crucial importance to agree upon what is meant by forest; a carbon sink, a timber resource, or what. For a first global map, evergreen tropical forest may be a better

any corresponding product available today.

POTENTIAL AND PROBLEMS

concept than rain f orest. No generally applicable classification scheme for global applications has been defined; a set of regional schemes may be the solution. Rough stratif ication into low density (savannah, open woodlands) and high density forests seems desirable as a first step. Rubber-, oil palm and other plantations should be discriminated using

The potent¡al of the remote

the advent of satellite imagery. And it is true that new satellite data in combination with the advances in computer and data management techniques, as well as new experience in image processing and GIS analysis provide usef ul methodology f or

ancillary information.

G/S-aspecfs; Adding a number of to the AVHRR output map increases its value. The data layers for

GIS-layers

global applications could comprise, e.g.,

forest monitoring at global scales.

state and province bouhdaries, roads, rivers, population by districts, and ecof loristic zones. Analysis of environmental impact of deforestation, long term cause/effect

At the same t¡me a number of

technical problems remain to be solved. The difficult

and variable definition of forest

analysis and global carbon modelling are

discrimination of the two. Diff erences between published f igures on tropical deforestation arise largely because these problems are ignored or are tackled in

Mapping and/or monitoring'. The results so far obtained emphasize the need of a global baseline forest cover map made by using

different ways.

AVHRR/LAC data as a necessary f ¡rst stage. Once completed, it should be updated

Other problem areas are data availability; the global LAC data coverage is incomplete. The clouds disturb the data acquisition, and

(monitored) using high resolution data in an

appropriate sampling f rame (Jaakkola, 990).

the accuracy of classification results is variable and dependent on f actors like

Compilation of a global tropical forest cover

map'. The world community should

seasonality, land-use patterns, access to field data, etc. Finally, there are various political problems, and, of course, a constant shortage of f unding. A partial list

be

provided with access to timely and usable data on the condition and change of tropical forest cover. A digital global tropical forest cover map should be compiled, archived and made available to all. The NOAA/AVHRR data acquisition system together with the

of problem areas where immediate progress is needed could read as follows:

- Definition of forest - Agro-forestry: how to assess or monitor it - Growing criticism of the

recent advances in the fields of GIS and digital image analysis have made the task technically possible, although there still are

problems

to solve. Success in

and

non-forest is of crucial importance for the

examples of potential GIS-applications.

1

sensing

methodology discussed here is now being recognized in the international debate on tropical forest issues. Many have realized that an objective record of forest cover at a single moment of time was impossible until

the

2L

issues. They also are potential items for intergovernmental agreements to come.

ongoing assessments - Linking national and global monitoring - Problems in data collection, sharing and

Timely and reliable global statistics on deforestat¡on are requested, at the same time as f resh f unding and technology transfer for reforestation are required. Environmental impacts of def orestation have to be assessed. The best way to respond to this increasing demand is

handling

- Lack of guidelines, coordination, leadership, funds. - Harmonization through

through improved International cooperation, e.g. through integration of allwork, f inished

various levels. - Accuracy required and

and ongoing, in the various parts

of

our

globe.

obtained.

coNcLUsroNs REFERENCES

The global forest informat¡on ¡s high in demand but short in supply. The international organisations have a role in acquiring, processing, managing and

Cross,

deforestation and remote sensing:

The use of NOAA/AVHRR

distributing such information, as limited as they may be with their mandates, resources or methods. But first of all, they need to

help in the methodology

4., 1990: Tropical data

over Amazonia. Final Report, 43 pp

+ App.

UNEP/GRlD, Geneva.

development,

FAO, 1988: An interim report on the state

harmonisation and drawing up guidelines for forest monitoring.

of forest resources in

the

developing countries.

FO:

Misc/88/7.

The world community is witnessing a shift

from timber-oriented towards

environmentally oriented needs of forest monitoring. This change should be ref lected by any new set of guidelines for forest

Rome.

FAO, 1990: lnterim Report on Forest

Resources Assessment 1 990 Project, p. 6. Committee on Forestry, 1Oth Session, 24-28

monitoring.

September. FAO, UNEP can contribute to f uture global f orest monitoring designs assisting in

by

Rome.

FAO/UNEP, 1982: The Global Assessment

methodology development (remote sensing

of Tropical Forest Resources.

and GIS) and, in particular, by incorporating

GEMS PAC lnformation Series No.

environmental dimensions in such

3.

UNEP, Nairobi.

procedures.

Glück, P., 1989: Summary of session E-4

: policy

Satellite remote sensing certainly is a useful tool f or environment data acquisition.

Together

aspects

of

monitoring

systems. Proc. Global Natural Resource Monitoring and Assessments: Preparing f or the 21 st Century, Venice, 24-30

with GIS technology, and

incorporated in existing procedures, it can eff ectively help in monitoring the f orest resources at global, regional and national

September, pp. 845-846. ASPRS, Bethesda, MD.

levels.

Deforestation and reforestat¡on have become hot environmental and political

Jaakkola, S., 1990: Managing data for the monitoring of tropical forest cover:

22

The Global Resource lnformation

Forestry Research Organizations (IUFRO), Montreal, 5-1 1 August, Session 54.02 : " Research in

Database approach. Photog.

Engineering & Remote Sensing, Special lssue on Remote Sensing of Tropical Moist Forest. Vol. 56, No. 10, pp. 1355-1357.

Forest lnventory,Monitoring, Growth and Yield" [ed's] H.E. Burkhart, G.M. Bonnor & J.J.

Lowe, Publ. No. FWS-3-90, Virginia State University, pp.

Jaakkola, S., 1990: Global tropical forest cover assessment at UNEP/GEMS/

134-1 41. Blacksburg, Virginia.

GRID. /n Burkhart, G.M. ef al Proc. XIX World Congress of

lnternational Union

of

Myers, N., 1989:The Greenhouse Effect: A Tropical Forestry Response. Biomass 18:73-78.

Forestry

Research Organizations (IUFRO),

Montreal,

54.02 :

5-1 "

1 August,

Research

in

Session Forest

Myers, N., 1990: Tropical forests. /n J.

Leggett, Global Warming; The Greenpeace Report, Oxford

lnventory, Monitoring, Growth and

Yield" Publ. No.

FWS-3-90,

Virginia State University,

University Press, pp. 372-399.

pp.

95-101 . Blacksburg, Virginia.

Options, 1990: The price of pollution. Option. Septembe.r 1990, pp.4-8,

Jagannathan, N., 1989: Poverty, Public

The lnternational lnstitute of Systems Analysis (llASA),

the Environment. World Bank, Environment Policies and

Department Working

Laxemburg, Austria.

PaPer

No.24.34 p. Washington, D.C.

Päivinen, R. and Witt R., 1991: The methodology development project

for tropical forest assessment in West

Justice, C.; Townshend; J.R.G., Holben,

B.N; and Tucker, C.J., 1985: Analysis of the phenology of global vegetation us¡ng meteorological

cover Af rica.

UNEP/GEMS/GRlD, Geneva. Manuscript 51 pp. + 4 app.

satellite data. lnt Journal Remote Sens., 8, pp. 127 1-1318.

UNCED, 1991 : Combatting Deforestat¡on

:

Options for Agenda 21. Report of

Secretary General of the Conference (DrafT, 21 pp.). United

the Lund, H.G., 1990: Linking national and

Nations Conference on Environment and Development

global ¡nventories. ln Burkhart, G.M. et al. Proc. XIX World

Secretariat, Geneva-Conches.

Congress of lnternational Union of

23

FOREST CHANGE ON A LANDSCAPE SCALE USING LANDSAT IMAGERY: TEMPERATE AND TROPICAL FOREST EXAMPLES Steven

A. Sader

College of Forestry University of Maine 260 Nutting Hall Orono, Maine, U.S.A

ABSTRACT

Change detection techniques were applied to visualize and quantify major forest changes across a landscape. Two-date change detection is suitable for assessing short term change such as in updating a forest data base for new harvest areas, roads and establishing age of clearcuts. Three-date change detect¡on provides a method to interpret longer term timeseries changes (forest harvest and regeneration) on a landscape scale. Both change detection techniques incorporated the computation of the normalized difference vegetat¡on index (NDVI). A simple and logical technique (RGB-NDVI) to visualize changes in a 3 date tíme sezes rs presented. Landsat-MSS data was chosen as the f¡rst date (two-date method) and first-two dates (three-date method) in the temporal sequence to reduce data acquisition costs and increase the chance of finding suitable satellite coverage of any region (especially tropical). Change detection examples from temperate forests (Maine - USA) and Central America tropical forests are presented. Special considerations in image acquisition and data processing are described.

that breed in the northeastern U.S. but spend the winter in southern Mexico and Central America (Robbins et al. 1989; Powell et al. in press). Deforestation in the wintering grounds is believed to be a major contributing f actor to eastern songbird declines but only limited information exists on deforestation and

INTRODUCTION

Satellite remote sensing techniques have

great utility for extensive land surveys where current forest resource inf ormation is either scarce, too expensive or infeasible to acquire by alternative methods. ln tropical regions, satellite remote

sensing may be the only feasible

habitat relationships (Terborgh 1 990).

The capabilities of satellite data

and

'tor

detecting forest change has great promise f or updating geographic inf ormation systems (GlS) maintained by large industrial forest landowners and state agencies in Maine. The forests are Maine's most important resource in terms of major economic indicators (Land and Water

reliable technique to monitor def orestation rates and land use conversion trends (Sader ef a/. 1990). Knowledge of deforestation and reforestation rates can improve the global climate database (e.9., carbon modeling of terrestrial environments) and provide quantifiable data to

Resource Center, 1987), therefore timely

support international programs f or biodiversity conservation and critical

information about forest condition and trend is essential f or making inf ormed f orest management decisions. Results of

habitat protection. For example, wildlife

biologists have reported population of many migratory bird species

decline

f

26

orest change monitoring studies in Costa

Rica C.A. and Maine U.S.A. indicate that

digital analysis

of

OBJECTIVES

multi-date satellite

imagery can provide reliable estimates of major forest canopy changes (Sader e¿ a/. 1991, Sader and Winne, in press).

The primary objective of this paper is to present simple techniques for visualizing

and Previous Work

ying major f orest change

niques rely on routine image processing

Most of the digital image

and GIS editing functions that are avail-

able in most commercial and public domain computer software packages.

change

detection investigations reported in the literature have ut¡l¡zed two dates of imagery. A f ew investigators have attempted to analyze 3 or more dates of

The use of the older, Landsat MSS data is advocated as a valuable, low cost archive

to support global change research and landscape level forest moni-

available

imagery (Sapitula and Killip 1985, Chaudhury 1985, Parada et al. 1981, Woodwell et al. 1987) using two date

toring for time periods dating back two decades. Forest change monitoring techniques presented in this paper accommodate measures of both forest clearing and regrowth area.

or est¡mates in sequence rather than a composite analysis of 3

comparisons

dates as a multitemporal data set. Other investigators have applied data transfor-

mation techniques (e.9.,

principal

components) to analyze multiple dates of AVHRR imagery (Tucker ef a/. 1985, Townsend and Justice 1986), however,

the transf ormed image is not

to

quantif

events on a landscape scale. These tech-

METHODS

easily

determine the original

Twc and three date change detection

For

techniques utilizing multitemporal Landsat

forestry applications, the dates of change may be important to: 1) update a forest

imagery were applied to temperate and tropical forest study sites. Both techniques can incorporate the use of the

deconvolved

dates

of

reflectance changes.

resource inf ormation system f or new harvest activities,2) to develop forest

more economical Landsat MSS data. A Landsat Thematic Mapper (TM) image is

atea stat¡stics at each date, 3) to establish age classes of natural regene-

used as the second date in a two date change detection sequence and as the third acquisition in the three-date t¡me series change detection method. Land cover maps were derived through image

ration sites, and 4) to utilize these data to support forest supply analysis for regional planning (Sader and Winne, in press). lmage differencing techniques have been to detect abrupt changes in canopy

classif

used

ication procedures to aid

inement

of the change

in

detection images by minimizing commission errors (e.9. identificat¡on of a change event that ref

reflectance resulting from forest harvest and road construction (Woodwell et a/. 1983, Tucker et al. 1984, Pelletier and Sader 1 985, Singh 1 986). ln image diff erencing, one or more wavebands from two coregistered images are subtracted to produce a residual image indicating the relative change in reflectance between

is not associated with forest clearing or regrowth). Land cover strata can be

derived f rom

a GIS f ile and

logical

operations applied to the change detection images to reduce these errors.

the two dates (Singh 1986). Landsat MSS data can be preprocessed, resampled and co-registered with TM data to depict f orest change boundaries with adequate resolution for extensive forest surveys. The spatial and temporal data base that results allows harvest and regeneration zones to be monitored and

The TM image was registered

to UTM

coordinates. Matching control points

(pixels) were located in each MSS scene and these images are resampled to 30m and registered to the TM base map. A

destriping program was applied to the MSS images prior to rectif ication and resampling. Haze correct¡ons were applied and the Normalized Diff erence Vegetation lndex (NDVI) was computed

their area quantified using a sequence of image processing steps (Sader 1988).

26

',

r \

Jf1 OT

l.'

s{ t

\.

l.

Great Pond, Maine, 1:24,OOO scale quadrangle. a) 1987 Landsat-TM band 3 (0.63-0.69 um), NDVIimages derived from b) 1978 MSS, cl 1981 MSS, and dl 1987 TM' High biomass is,indicated by light tones and low biomass by dark tones ¡n the NDVI images' Forest change in a time ser¡es can be visualized by creating color composites of NDVI images (not shown herel.

Fìgure

27

Table

l.

Maine).

lnterpretation of Additive Colors in Three Date RGB-NDVI lmage (Great pond,

NDVI DATE (1)

Additive Color

1

978

1

981

1

987

I

nte

rpretation

o/o

Area Black

L

L

L

Low Biomass - no change

Blue

H

L

L

Decrease

978 to 1 981

19

Red

L

H

L

lncrease, then decrease

33

Green

L

L

H

lncrease 1 981 to 1 987

27

Magenta

H

H

L

Decrease 1981

to 1987

4.4

Cyan

H

L

H

Decrease, then increase

26

Yellow

L

H

H

lncrease 1 978 to 1 98.l

6.8

Dark Grey (2t

H

H

H

High biomass - nochange (SW)

22.5

Light Grey (2)

H

H

H

High biomass - nochange (HW)

44.5

-

-

1

(1)

Greenness index by year: Blue greeness, H : High greeness.

(2t

Grey replaces white (in additive color scheme)

1978, Red

1981, Green

differentiate hardwood and softwood.

I

to

-

11.3

1987;

L

=

Low

increase contrast and to

007

75t

25Í

1978

lXo Mature Forest

1981

V-

Regeneratlon

1987

l-_-l Non-forest

Figure 2' Forest change dynamics in the G¡eat Pond quadrangle as indicated by the percentage area of forest, regenerat¡on, and non-forest types at three dates spanning a 9-year time frame' The trend for the past decade has been a reduction in older age classes through industrial forest harvest and an increase in young forest age classes (regenerationl by 1 987 following harvest at earlier dates (late 1970s and early 19g0sl.

28

f

or each data set using the f ollowing

f

orm ula:

change events (harvest, regeneration, and road building) including the time period

when change occurred. Unfortunately, it is not possible in the proceedings to reproduce the color composite image that

near infrared - red near infrared + red

results f rom this technique.

A non-linear contrast stretch (scaling) was

performed on each NDVI image (Figure 1b,c,d).

f

ull

image and interpretation of change events is reported in Sadef and Winne (in press). A generic interpretation of RGB-NDVI

additive colors after application of a modified parallelepiped classifier (e.9. RGBCLUS in ERDAS or CLPIPE in NASAELAS software) is presented in Table 1

ADDITIVE COLOR THEORY FOR VISUALIZING CHANGE ¡N A TIME SERIES

.

The classifier converts the analog images into discrete classes that can be tabulated

A

simple and logical technique was developed to visualize forest change in a 3-date time series using the NDVI calculation and RGB image display functions (Sader and Winne, in press). The principles of additive color theory were the basis for image interpretat¡on whereby there are 3 primary colors (red, green and blue) and any combination of two primarily colors in nearly equal brightness produces a complementary color (yellow, magenta or cyan). For example, yellow is

created when

red and green

into forest change area statistics for

CHANGE DETECTION EXAMPLES FOR TROPICAL FORESTS

The annual rate of deforestat¡on in Central

to 1990, was approximately 1.8% which was the second highest rate of any region (f ollowing Af rica) in the world (Forest Resource America f or 1 980

are

or green and blue f orm magenta and

Assessment Project 1 991 ). lnf ormation on habitat loss in Latin America is among the poorest of all major regions (World Resource lnstitute 1990). Forests in southern Mexico, Belize and Guatemala are the subject of on-going investigations by U.S. Fish and Wildlife Service where

cyan. respectively. The absence of red, green and blue result in black (no color). High brightness of all 3 primary colors create white. The 3 primary color guns in an image display device generate a color

the additive color process when individual channels of digital brightness are displayed on a composite through

population assessments of migratory birds

and their habitats are beîng conducted.

computer color monitor screen.

Researchers

at the University of

Maine

are supporting the effort by developing the habitat maps and assess¡ng changes

Figure 1 b, c, and d are the NDVI images that correspond to the Great Pond U.S. Geological Survey 1:24OOO scale map quadrangle in east central Maine. The landscape is a mosaic of conifer, mixed and broadleaf f orests primarily under private industrial ownership with many

in f orest and land use using

satellite

digital analysis techniques.

Sierra de los Tuxtlas, Veracruz, Mexico

lakes, bogs and regenerating stands that occur in former clearcuts (Figure 1a).

the

3

time periods (Figure 2).

combined. Combinations of red and blue

Following

A

description of the RGB-NDVI technique w¡th the corresponding color composite

pre-processing steps

This study site is a rugged mountainous region of volcanic origin, situated on the

summarized on the previous page, the three NDVI images were displayed as a

Gulf of Mexico approximately 100 km southeast of La Ciuded de Veracruz.

color compos¡te. Using additive color ¡nterpretation and knowing what NDVI dates are coupled with red, green, and blue, it is possible to visualize f orest

According to Rappole and Warner (1980),

about one-third of the 42,OOO km2 mountain range was forested in 1975. 29

Winker et al. 11990) estimated that less than 15% was forested in 1986. Most of the former forest land was converted to

edit the change image (Figure 3c). Forest change was falsely indicated in the high elevation forest apparently due to slight misregistration and resampling effects of MSS pixels to 30 meters (see area A,

grasslands, citrus groves and weedy fields. The remaining forest now resides primarily in the less accessible, higher elevation mountain slopes. A 1O24 x 1O24 pixel subsection representing a 1049 sq. km area was selected f or analysis of forest change between 1986

stratified forest) to 443 ha (poststratified). The difference between the two estimates (632 ha) was added back

and 1 990.

into the "no-change" class (Table 2). The

Two date 'diff erence' images

ximately 1.7 percent of the study area. Only .2 percent of the area was categorized as biomass increase or regrowth zones. Figure 3d is the f inal change

Figure

1

were derived using the formula (ND1-ND2), where ND1 is the NDVI for the f irst date, while ND2 is the NDVI for the second date. Forest canopy alteration or biomass decrease can be inferred with high NDVI diff

erence values and an increase

eliminated most single pixels and edge effects.

The first attempt to calculate area and location of major change zones (forest

indicated that most of the sites were by pasture, scrub vegetation and low broadleaf vegetation which may include citrus; however further work is in occup¡ed

clearing and biomass increase) resulted in

of change.

986-90deforestation represented appro-

application of the "f orest mask. " Air photos and ground truth data were not available (at the time of this writing) to evaluate the areas indicated as biomass increase zones. These were likely areas cleared of forest shortly before 1986 and significant vegetation recovery occurred by 1 990. Comparison of these biomass increase zones with the land cover image

in

yield a category of canopy biomass increase, one of biomass decrease, and one of no change. A majority f ilter

estimate

forest

image after the false forest change areas in the mountains were eliminated through

biomass (regrowth) with low values. The difference image was density sliced to

a liberal

3b). The estimate of

clearing was reduced from 1075 ha (pre-

For

to refine and quantify these results. Change locations from the progress

example, commission errors were noticed in some mountain areas where f orest clearing apparently did not occur between the two dates (compare Figure 3a, 3b). A binary image "mask" of the medium tall broadleaf forest type classified from the 1990 TM image was used to stratif y and

difference calculations can be converted

into vectors f or plotting on maps

or

overlaying on a 3 band color composite image for visualization.

Table 2. Pre-stratified and post-stratified forest change estimates for Los Tuxtlas study site based on 1 986 and 1 990 Landsat imagery.

Class Prp- ctr¡+if ia

r'l

rea lha I

3

1

oo?

Rinm¡cc

444)

g6Â

Nn nhanne

1075

.o42

25,57 4

1.OO

67

oo)

)

?F.î7 3

ñ

Pêraêntânê

67

1

?

Pnct-cfrelifiod

A

innrorco

lronrnrrrl-hl

B omass

decrease (forest

Rinm¡cc

innrorco

fronrnr¡rihì

Ã

.981

No chanoe

443

.o17

Biomass decrease (forest

2557 4

1.OO

30

¡lorrinn\

1o24x1024pixel (30m)subsetof al pre-stratified 1986-90 NDVI difference image 1990 Landsat TM, band 3 0.69 uml, bl - 3 class density sliced, cl 1990 medium tall broadleaf forest binary mask derived from GIS

Fìgure3. SoutheastSierradelosTuxtlas,Mexico. 10.63 -

land cover and

dl post-strat¡fied 1986-90 change detection image indicating

increase (blackl, no change (medium grayl and forest clearing (white).

31

biomass

Northern Peten, Guatemala

by displaying them on the background of the 1986 color composite image to reveal the spatial proximity between old and new clearings; however, this is not possible for these proceedings. ef f ectively

diff erence in land use along the Mexico (state of Chiappas) and

The

Guatemala (Peten district) border is very

distinct on a satellite image (Figure 4a). Land use on the Mexico side is mostly

CONCLUSIONS

pasture and crops wit'h only remnants of

disturbed forest remaining.

The

Guatemala side of the border was almost

totally forested in 1986 although some

Two and three date change detection

orestation has occurred within the last

techniques have been briefly discussed with examples applied to a north temperate and two tropical study sites. Landsat MSS data are available at several dates and at low cost for most of the world. These data can be merged with higher

def

few years.

According

to

Guatemala government sources, illegal t¡mber cutting has increased substantially in the northern

Peten Region (Bangor Daily News 1991).

Most of the illegal f orest

removal of the The peten

occurred within the boundaries

Maya Biosphere Reserve. f

resolution Thematic Mapper or Spot multi-

spectral data for time-series change detection. By acquiring L¿ndsat-TM at the third date in the time series, the high

orest shelters a wealth of wildlife species

and conta¡ns Central America's largest f resh waterwetlands. The archaeological heritage and the integrity of one of the largest tropical wet forests in Central

resolution satel¡ite data can be classified separately using a classification algorithm

to generate a current land cover map. The land cover classes help to strat¡fy seasonally high biomass, non-f orest

America may be threatened by increased deforestation in this region (Garrett and Garrett 1989),

lmage

dif

f

erencing techniques

surfaces (e.9., bogs or agricultural areas)

that might be confused with forest areas where change has occurred.

were

applied to a section of the border region.

The imagery was TM data acquired

April 14, 1986, and April 17,

All imagery should be collected in the same season if possible, to reduce large

on

1990. Further east from the border region, near the small town of Carmelita, shifting

NDVI difference that can be attributed to seasonal changes in vegetation phenology

cultivation is practiced by local people. Recent forest clearing can be detected

rather than NDVI differences caused by

forest harvest, regeneration or

near Carmelita but the size and appearance of the recent clearings are different than those of the border region. Figure 4c and d are NDVI difference images of the two sites. The average size of the

fields atta¡n high amounts of green biomass. ln Central America, the best

orest clearings along the

Mexico/ Guatemala border (within Guatemala) was f

other

forest canopy alterations. ln Maine, the opt¡mal season to collect imagery is spring (late May to June) before herbaceous growth in bogs and agricultural

chance of obtaining imagery is in the dry season (usually February to April). Atmospheric correction and scaling (histogram stretch) are recommended techniques to

11.25 ha with a standard deviation of 14.25 ha. For comparison the average forest clearing near Carmelita was 2.77 ha with a standard deviation of 3.36 ha.

reduce haze effects, gain and offset differences between sensors. The NDVI computation (band ratioing) reduces sun angle effects and enhances the stratification of forest/nonforest areas. Forest

The change detection results suggest that

traditional shifting cultivators in this region clear less land for subsistance agricultural purposes. The forest clearing

activities along the border may be motivated by profits made from selling

road locations are also high-lighted by the NDVI calculation which can be useful to update old maps with new road locations.

logs (perhaps illegally) and cut boundaries are larger and more angular in appearance

A

(Figure 4c). The new clearings (1990) around Carmelita can be visualized most

post-change analysis majority f ¡lter

reduced isolated pixels and edges that

may result f rom resampling MSS data to

32

Figure 4. lal 1996 Landsat TM image of the Mexico-Guatemala border, (b) enlarged subsection 11O24x 1024 pixel) of the border where deforestation was detected between 1986 and 1990, and (c) the 3 class density sliced NDVI difference image. Forest clearings in a shifting cultivation area around Carmelita, Guatemala (dl are smaller and less angular

than forest clearings near the border zone, possibly indicating the differences between traditional agricultural practices and timber market extract¡ons in the two locations'

33

30 m pixels. Boundaries of change areas are often better defined by application of lnvestigations that focus on forest change

1985: Landsat: applicatr0ns to mangrove ecosystems studies. Remote Sensing in Vege tation Studies, Report of the ESCAPBIOTROP Training Course, UNDP-

detection usually emphasis only f orest

ESCAP, Bangkok, Thailand.

Chaudhury, M.U.,

the majority filter.

clearing; however, f orest change is dynamic and data about regrowth and

Forest Resources Assessment Project, 1991: Second interim report on the

land use conversion trends are processes

that would improve the inf ormation content of change detection investi-

state of tropical forests, FAO

gations. The techniques presented herein allow locations and area estimates of

Sept. 1 991.

regrowth zones to be determined in addition to areas where forest clearing has occurred. These techniques are not intended to represent a stand alone, fully

Garrett, W.E. and Garrett, K., 1989:

ruta Maya. National

La

Geographic

Society. Washington, D.C. Pp. 424478.

automated digital approach. lnstead, they rely on interactive steps and integration with GIS procedures that are required for refining change estimates and the visual display of major change events (afforestation and deforestation). The practice of ¡ntegrating change detection procedures with land cover information will undoubtably lead to significa¡t improvements in

Land and Water Resources Center, 1987:

The forests of Maine, University of Maine Press, Orono,

ME. 12 pp.

Parada, N. de Jesus; Tardin, A.T.; dos

Santos,

A.

P.; Filho, P. H.

a

nd

Shimabukuro, Y.E., 1 981 : Remote sensing in forestry: applications to the

quantifying forest change events. The

visualization

Rome.

1Oth World Forestry Congress, Paris.

Amazon region. INPE-2035-RPEl292, lnstituto de Pesquisas Espaciais, S.J. dos Campos, Sao Paulo, Brazil.

of changes, although

presented in color at the workshop, could not be reproduced in these proceedings published in black and white.

Pelletier, R.E. and Sader, S.A., 1985:The

relationship between soils data and forest clearing and forest regrowth trends in Costa Rica. ln: Proceedings of Pecora 10, August 20-22, 1985,

ACKNOWLEDGEMENTS

Colorado State University, Colorado. Pp. 276-285.

This work was supported by Mclntire-

Powell, G.V.N.; Rappole, J.H. and Sader, S.A., (in press): Nearctic migrant use of low land Atlant¡c hab¡tats in Costa

Stennis Project ME 09608. Other support provided by FinnlDA, the Maine Research

Fund, NASA-Stennis Space

Center

Rica: A test of remote sensing for

Science and Technology Laboratory and U.S. Fish and Wildlife Service-Patuxent Wildlif

e

Research Center

is

acknowledged. Maine

identification of habitat.

ln: Symposium on Ecology and Conservation of Neotropical Migrant

gratef ully

Agricultural

Landbirds, Woods Hole Marine Biology

Experiment Station External Publication

Laboratory,

#1617.

7

-9

December

1

(Woods Hole: Manoment

989. Bird

Observatory). Rappole, J.H. and Warner, P.W., 1980:

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'

. Ecological aspects

of

migrant

bird

behavior in Veracruz, Mexico, p.353-

393. ln Keast, A. and Morton, E.S.

Bangor Daily News. 1 991 . Guatemala f orests at risk. June 2, 1 991 , Bangor,

(Eds). Migrant birds in the neotropics,

Smithsonian lnstitution Washington, D.C.

Maine.

34

Press,

Robbins, C.S.; Sauer, J.R.; Greenberg, R.S. and Droege, S.,

1989:

Sensing and Tropical Land Management. John Wiley and Sons. 365 pp.

popula-

tion declines North American birds that migrate to the tropics. þ Proc. of National Academy

of

J. 1990. Where have all the birds gone? Princeton University

Terborgh,

Sciences.

86(1 9): 7658-62.

Press, Princeton, New Jersey.

Sader, S.4., 1988: Remote sensing inves-

tigations

of f orest

biomass

Townsend, J.B.G. and Justice, C.O.,

1986: Analysis of the dynamics of African vegetat¡on using the norma-

and

change detect¡on in tropical regions.

ln Satellite

lmageries of Forest lnventory and Monitoring: Experiences, Methods, Perspectives.

lized diff erence vegetation index. lnternational Journal of Remote Sensing, 7, 1435-1445.

l.U.F.R.O. 4.O2.05, Hyyriata, Fintand.

29 August - 2 September. Departrnent of Forest Mensuration and

Tucker, C.J.; Townsend, J.R.G. and Goff, T.E., 1985: African land cover classification using satellite data. Science,

Management Research Notes lrlo. 21,

(Finland: University of Helsinki), pp.

227, 369-375.

31-42.

Tucker, C.J.; Holben, B.N, and Goff , T.E.,

Sader, S.A.; Stone, T.A. and Joyce, A.T. 1990. Remote sensing of tropical

forests: An overview

of

1 984: lntensive forest clearing in Rondonia, Brazil, as detected by

satellite remote sensing. Remote

research

applications using non-photographic

Sensing of Environment.

1

5:255-261

.

sensors. Photogrammetric Engineering

and Remote Sensing. 56(10): 13431351.

Winker, K., Rappole, J.H. and Ramos, M.4., 1990: Population dynamics of the wood thrush in Southern Veracruz, Mexico. The Condor, 92:444-

Sader, S.A.; Powell, G.V.N. and Rappole, J.H., 1 991 : Migratory bird hab¡tat

460.

monitoring through remote sensing.

lnternational Journal of Remote Sensing 1 2l3l: 363-37 2. Sader, S.A. and Winne, J.C., 1991:RGBNDVI color composites for visualizing f orest change dynamics. lnternational Journal of Remote Sensing (ln Press).

Woodwell, G.M.; Hobbie, J.E.; Houghton, R.A.; Mellilo, J.M.; Peterson, B.J.; Shaver, G.R.; Stone, T.A.; Moore, B. and Park, 4.8,, 'l 983: Deforestation measure by Landsat: steps toward a method, DOE/EV 10468-1 . NTIS, Springfield, VA,62 pp.

Sapitula, B.P. and Killip, T.4., 1985: Forest vegetation mapping in the Philippines. ln: Remote Sensing in Vegetation Studies. Report of the

Woodwell, G.M.; Houghton, R.A.; Stone, T.A.; Nelson, R.F. and Kovelick, W., 1987: Deforestation in the trop¡cs: new measurements in the Amazon

ESCAP-BIOTROP

training

Basin Using Landsat and NOAA Advanced Very High Resolution Radiometer inragery. Journal of

course,

UNDP/ESCAP Regional Remote Sensing Program, Bangkok, Thailand (New York: United Nations Development Program). Singh,

4., 1986:

Geophysical Research.

92,

2157-

21 63.

Change detection in the

World Resource lnstitute, 1990: World Resources 1990-91. Oxford University Press, New York. 383 pp.

tropical forest environment of northeastern lndia using Landsat. ln Eden,

M.J. and Parry, J.T. (eds),

Remote

35

APPLICATION OF REMOTE SENSING IN TROPICAL RAIN FOREST AND MANGROVE FOREST MONITORING IN THAILAND Suvít Vibulsresth Su ra c h ai Rata nasermpo

ng

National Research Council of Thailand (NRCT) Bangkok 1O9OO, Thailand

ABSTRACT Satellite remote sensing data have proven useful in assessrng the forest land resources and

in monitoring the changes. Results that were obtained from the interpretation of the high

resolution satellite data and dígital analysis have been used by policy makers to plan more effectively in areas of forest preservat¡on, reforestation scheme, rehabilitation of watershed on major catchment basin areas and the forest land use zoning. Ihe assessrnent of forqsted area of Thailand by usíng LANDSAT data was conducted by the Royal Forest Department in 1973, 1976, 1978, 1985 and r.989. lt is evident from the results of the study that the forested area of the whole country was depleted to approximately 27.95 percent compared to 43.21 percent sfat/stics of 1973. The rate of forest depletion since l973was greatest in the Northeast regíon and lowest ¡n the South region

at 3.35 and l.3O percent per annum respectívely. Furthermore, the Nationat Research council of Thailand iointly with the Royal Forest Department has conducted a study on man-

groves ¡nventory from satellite data for period 1975, 1979, 1986 and 'l 991 . lt revealed that the decrease in mangroves area of 1,390 sq.km. within l6 years period. From most recent LANDSAT-TM data of lggo and 1991, there are only 1,736 sq.km. or l,o75,o5o rais of mangroves which is about L2o percent of the forested area of the whole country.

LANDSAT-4/5 MSS data available to

INTRODUCTION

users.

Satell¡te remote sensing activities

ln 1986, NRCT received the grant from Canadian lnternational Development Agency (CIDA) to upgrade the existing facilities to receive and process the high

in

Thailand started as early as 1971 when a National Remote Sensing Coordinating Committee was appointed by the Cabinet to coordinate expeimental efforts of various government agencies and to disseminate remote sensing informat¡on. ln 1972, Thailand jointed other countries

in the LANDSAT-1 program, and

resolution data from Thematic Mapper of

Landsat-S and HRV of SPOT-1. The upgraded facilities started to receive TM and SPOT data from December 'l 987.

TRSC also cooperated with Japan's NASDA to construct a new ground station for MOS-1 which started to receive MOS-1 data from May 1988.

since

then the national remote sensing activities have evolved and reached the operational phase in 1981 with the establishment of the Thailand Landsat Station (TLSl. Since

With the new facilities, TRSC became the regional ground receiving stat¡on in South-

1981, Thailand has become a regional data center. To maintain her role, TLS was upgraded in 1984 to make

east

Asia. The total value of

provided 37

data

to users in 1 989 reached one

Thailand are applying TM and SPOT data

- The Central

on an operational basis. Thailand also cooperated with several countries in Asia and else where in the utilization of satell¡te data. A plan to upgrade the existing facilities to receive and process ERS-1 data of ESA is being worked out.

-

The Eastern part consists largely of a broad coastal zone with a hilly hinterland.

Thailand Remote Sensing Center (TRSC)

-

The Southern region is a long narrow

region comprises the Chao Phraya Plain and its tributaries connecting to the Gulf of Thailand.

of the National Research Council has been the agency responsible for the operat¡on of TLS and for distribution of the satellite data to both domestic and foreign uses. The National Remote Sensing Coordina-

Malaysia.

Being under the influence of monsoon climate conditions, the vegetation of

ting Committee, having the mandates given by the Cabinet to devise policies

Thailand is a humid tropical one with vast areas covered with forest. Basically, the forest of Thailand can be classified into two broad categories of Evergreen and Deciduous.

and plans for remote sensing applications in Thailand and to coordinate and promote

such activities. These are 8 subcommittees under the Committee, for example, Planning and Monitoring, Agriculture, Forestry and Land Use

The Evergreen Forest is composed of

Applications, etc. ln addition, TRSC funcRemote Sensing Committee to coordinate research activities, and to provide training

-

provide

research funds and application support to

the users. TRSC has provided

-

the

services of the image analysis systems to government agencies applying satellite

remote sensing technique to natural resources survey and environmental monitoring. W¡th the expansion of the

-

disciplines

including agriculture, forestry, land use, geology, oceanography, hydrology and environmental monitoring. The multidisciplinary nature of Landsat data has brought resource managers, decisionmakers and technicians together in the joint-effort for common goal.

The Forest

of

Rain Forest Coniferous Forest

Mangrove Forest

Mixed Deciduous Forest Dry Deciduous Dipterocarp Forest Savonna Forest

The f orests in the Northern region of Thailand are of leak lTectona grandis Linn.) bearing type. The forests are rich in timbers of commercial value such as bamboo, rattan, wood oil, gums and incense wood. Most of the forests are Tropical Mixed Forest with many valuable species. This type is very dense with considerable understoried plans and climbers making ground inventory very difficult, if not all inaccessible.

Thaíland

Thailand is divided by physical characteristics into five regions as follows:

-

Tropical Evergreen Forest or Tropical

The Deciduous Forest can be subdivided into the following three types :

systems, Landsat data have increasingly

been applied in various

a

great proportion of the non leaf-shedding species and covers about 60% of the total forested area. lt can be subdivided into the following three types :

t¡ons as the secretariat of the National

for new users as well as to

peninsula extending towards

The Northern part is made up mainly

of

highlands and steep mountains

Applîcation of Satellíte Data to Forestry

surrounding rich alluvial valleys.

-

Almost fifty years ago, a large portion of the country was covered with dense forest distributed all over the country

The Northeastern part is a generally

low-lying zone with Korat Plateau having a height of a few hundred

except the great central plain, where orest had long been replaced with

meters above mean sea level.

f

38

agricultural land. The increasing population in the past few decades at a high rate of 3.0 per cent annum led to the

equipped with necessary instruments and

adequate budget including expenses for ground survey. ln fact, the greatest contribution of our data from Thailand

exploitation of forest land f or agricultural purposes. However, in order to sustain economic growth, a rat¡onal utilization of natural resources including forestry must be properly maintained. Such realization

that forest is an integral part of

Landsat Station up to now has been in the field of forest applications. lf we were to conduct a natìonwide survey of existing forest by air reconnaissance, the effort would take three years and cost

the

about four million U.S. dollars, because

ecosystem which must be preserved led to the government policies to maintain the forested area at a suitable percentage of the total land area and to manage the watershed area more wisely. Satellite remote sensing data, especially Landsat, is considered to be useful in assessing the forest land resources and in monitoring the change. Results of the Landsat data derived interpretation have been used by policy makers to plan more effectively for

the preservation of forest area, for

only three months a year are suitable due to haze and cloud cover, and Thailand has a total land area of about 514,000 square kilometers. The last nationwide air survey

was conducted in 1 961 f or

Rate of Forest Depletion

The assessment of forestèd area of Thailand by using LANDSAT data was conducted by the Royal Forest Department in 1973, 1976, 1 978, 1985 and

the

reforestation scheme and rehabilitation of watershed on the major catchment basin areas.

1989. lt is evident from the results of the study that the forested area of the whole country was depleted to approximately 27.95 percent compared to 43.21 percent'statistics of 1 973. The rate of forest depletion since 1973 was greatest in the Northeast reg¡on and lowest in the South region a 3.35 and 1.30 percent

Remote Sensing tech¡ology has been ¡ntroduced in the study of forestry since 1955 by Dr.Fritz Loetzch, a German expert on forest inventory of FAO who used aerial photos in the forest inventory. Since then aerial photos have been used as a tool for forest survey and inventory.

per annum respectively (Table 1). Furthermore, the National-'Research council

It was in 1965 that air survey was first used in the study of mangrove ecology by Sukwong et al. Aerial photos acquired in 1961 at a scale of 1 : 50,000 were used to delineate mangrove forest area. Since

Thailand parricipated

forest

inventory.

of Thailand jointly with the Royal Forest Department has conducted a study on mangroves inventory from satellite data for period 1975, 1979, 1986 and 1991

in NASA ERTS

.

It revealed that the decrease in

program in 1972, satellite data have been widely applied in many fields. The earlier

of 1,390 sq. km. within 16 years period. From most recent

mangroves area

results from the use of LANDSAT data were first applied by S. Vroulsresth e¿ a/. to study mangrove ecosystem by visual-

LANDSAT-TM data of 1990 and 1991, there are only 1,736 sq.km. or 1,085,050 rais of mangroves which is about 1.20 percent of the forested area of the whole country (Table 2).

¡nterpretation.

Most of the interpretations of Landsat data were done visually, using false color

composite

at 1 : 250,000 scale

and

composite transparency at 1 : 1 million scale. Digital analysis started with the

CONCLUSION

establishment of Thailand Landsat station and became quite popular since 1 983

with the

acquisition

of

The forest is one of the few renewable natural resources. With careful planning

DIPIX lmage

Analysis System. The majority of work has been accomplished by the staff of the

Royal Forestry Department,

and management and efficient reforestat¡on programs, we can harvest timber and derive economic benef its indef initely. The

Forest

Management Division with manpower of about 30 who had been well trained and

f

39

orest land use should be planned

in

Table

Total Region

Forosted aroa

afea

I

1.

Forested arec

973'

1

Forested area by region of Thailand.

Forestod 6roa

976"

I

(km'?l km2

È

o

North Northeast East Contral South

169,644 1 68,854 36,503 67,399 70,715

TOTAL

513.1 15

* *

* **

*

**** r+ltr +iilt* rrfl**t

oÂ

13,595 50.671 15,036 23,970 18,435

67.0 30.o 41.2 35.6

221.707

1

km2

oÁ

102,327

60.3 24.6

34.6 32.4

26'1

,494 12.631 21,326 20,139

43.2

189,417

38.7

41

km2

1

km2

%

87.856 25,886 8,OOO 8.51 6 16,442

175,224

156.600

Source: Landsat-1 imagery, taken in 1973 Source: Landsat-2 imagery, taken in 1976 Source: Landsat-3 imagery, taken in 1978 Source: Landsat-3 imagery, taken in 1982 Source: Landsat-4-S imagery, taken in 1985 Sourcg: Landsat-4-S MSS Source: Landsat TM

34.2

Forested aÍea

982""

94.937 56.0 31,221 18.5 11,037 æ.2 20,426 30.3 17,603 24.9

28.5

Source: Royal Forest Department lggÙ

Forested area

978"'

1

1

km2

%

51.7 15.3 21.9 27.5 23.3 30.5

1

Foreste area

985""'

1

oÁ

9aA"""

km2

1

oÁ

A4j26 24,224 7,990 17,22A 15,485

49.6

8,o,402

14.4

23,693 7,A34 17,244 14,630

47.4 14.O 21 .5 25.6 20.7

49,053

29.1

43,803

2A.O

21 .9

25.6 21.9 1

Forested aroa

989"""'

km2

%

AO,222

47.3

23,586 7,7A6 17,223 14,600

14.O

143.417

21.3

25.6 20.7 2A.O

Table

2.

The areas of mangrove forest in Thailand.

Manqrove forest area (km2)

Province

NO

1 1

2 3

4 5 6 7

8

I

10

È

11

12 13

14 15 16 17 18 19

20 21

))

Trad Chanthaburi Rayong Chonburi Chachoengsao Samut Prakan Samut Sakhon Samut Songkhram Phetchaburi Prachuapkhirikhan Chumphon Surat Thani

Nakhon Si Thammarat Phatthalung Songkhla Pattani Ranong

Phang Nga Phuket Krabi Trang Sat¡ ¡n

TOTAL

9611

19752

129

106 261 55 38

154 17

1

9793

88.1 I 145.O7

14.98

46.O8

390

330 340

/,-A?

3.679

3.127

2.873.O8

6

2i 11

81

256

185 82 88

4 74

612

37 155

14

19

13

59

56

11

306

574

45 537

L^2

242 511 31

9864

98.40 240.64

33.12 23.20 10.40 144.16 76.48 77.92 3.36 69.28 48.08 128.32 16.32 51.84 13.92 225.92 487.16 28.48 317.60 328.64 ?q? 7A

30

1

24.'t8 7.40

1

989s

86.38 86.96 17.58 10.48 5.69

77.50 24.50

4.89

3.36 o.70 1 8.18

1.03 1.42

0.49 5.77 1.45

19916

1.O7

1.54 1.50

3.67

36.26 42.84 88.35

22.65 37.67

1.05

335.1 0

262.76 312.39

0.84 6.88 17.59 211.82 356.26 17.86 296.43 250.40 288.93

1.964.28

1.805.59

1.736.08

9.65 't8.28 216.06 364.20

19.35 303.1 2

85.21

22.O4

80.25 0.60 2.29 16.44

't94.70 15.54

31 9.1 5

308.49

?1ô q?

conjunction with the master plan for land

multilateral agreement should be strengthened. The commercialization of remote

decrease of forest land and at present the total f orest area of Thailand is much lower than the desired target. This

the cost-effectiveness and the economic

use. The inf ormation obtained f rom remote sensing data showed a sharp

sensing systems shÒuld take into account

and technological condition of

indicates that Thailand's forest has now reached a critical stage. The government has initiated plans and act¡ons to correct the situation. Monitoring of such efforts by remote sensing is an effective and economic way. With the increasing use of digital image processing, the different types of forest can be classified, which would provide information for the forest management. Better resolution data from

current satellite sensprs and

REFERENCES

of Thailand, Remote Sensing and Mangroves Project (Thailand).

National Research Council 1

991

:

Bangkok, Thailand. 189 p.

future

satellites would enhance the benefits of

remote sensing

the

developing countries.

Royal Forest Department, 1990: Forestry Statistics of Thailand 1989. Bangkok.

to forestry. Regional

79

cooperation, either through UN bodies or

42

p.

FOREST HEALTH MONITORING .- A NEW PROGRAM OF THE USDA FOREST SERVICE AND ENVIRONMENTAL PROTECTION AGENCY Charles G. Shaw

lll

Research Plant Pathologist USDA Forest Servíce

Fort Collins, Colorado, USA

ABSTNACT

The USDA Forest Seryice and Environmental Protection Agency have designed a national system to annually monitor the health of forests ¡n the United Sfafes. This system has three

tiers-+ach with a different intensity of activity and with specific, complementary goals. Detection Monitoring will collect field data on thousands of sifes coupled with remote sensing surueys throughout the nation's forests to document ecosystem condition and to detect changes in condition over tíme. Evaluation Monitoring will begin when Detection Monitoring uncovers unexpected changes in forest condition; it will obtain more detailed information through spec¡al studies designed to establish specific causes of changes in condition. lntensíve Sife Ecosysfem Monitoring will be conducted on about 2O research sites representing major forest types. lts purpose is to provide long-term data that will advance our understanding of ecosystem processes so that changes can be understood and predicted.

Agriculture, Forest Service, was handed a legislative mandate via the Forest Ecosystems and Atmospheric Pollution Research Act (Public Law 100-521) to initiate an ecosystem monitoring program. PL 100-

INTRODUCTION

The goal of the Wacharakitti Workshop is

to develop establishment

recommendations tor global network of

521 directed that "The Secretary (of Agriculture), act¡ng through the United

of a

permanent plots for world forest mon¡toring. The United States has recently in¡tiated an ambitious inter-agency program to document status and trends in the ecological health of its forests. The

Stated Forest Service shall --(a) increase

the frequency of forest inventories

in

matters that relate to atmospheric pollu-

tion and conduct such surveys as are necessary to monltor long-term trends in the health and productivity of domestic forest ecosystems-." The intent is to

purpose of this paper is to provide a review of that program. lt ¡s hoped that some of the lessons we have learned in

design

the establishment of this Forest Health Monitoring (FHM) program may be of benef it to the participants of this

a

program relatively uniform in

measurement protocols so that data can be examined, compared, and reported on a national or regional basis. The monito-

Workshop.

ring will provide data on current forest conditions, so that changes can be documented and meaningf ul inf erences drawn about causes of change.

BACKGROUND

We define monitoring as the repeated recording of pertinent data over time for comparison with a reference system or

ln 1988 the United States Department of 43

identified baseline. Monitoring is always concernèì - _With the determination of changes over time. We define Forest

Health as

the state of the forest

processes can be understood only by repeated, long-term observations. Even the development of researchable hypotheses is often possible only after many years of observing the state of the ecosystem. Finally, a better understanding of the processes that

as

measured by its functioning. and with reference to its normality at any given time.

control the functioning of forests will make ¡t poss¡ble to better def ine

The Forest Health Monitoring program has been developed in close coordination with and assistance of the Environmental

baseline conditions.

Protection Agency, Environmental Monitoring and Assessment Program

(EMAP) (Palmer et at. 1991). The EMAp program was designed to monitor ecological resources of the United States. The

THE THREE TIERS OF FOREST HEALTH MONITORING

described here

The program has three tiers of interrelated

Forest Health Monitoring Progràm is only one of seven

resource categories monitored

in

monitoring activit¡es: Detection Moni-

a

coordinated national program (Palmer and

toring, Evaluation Monitoring, and

Jones 'l 9921.

lntensive-site Ecosystem Monitoring.

From

a f orest resource

management pfactice. For example,

A f ourth component of the program,

To detect changes and to establ¡sh a baseline to determ¡ne if, when, and where changes are occurring and to quantify those changes.

2. To evaluate possible

causes of change. lf a change is undesirable, unexpected,

or

unexplained,

it

is

to document the cause/effect relationship to evaluate the

management practices could be related to control of air pollution or forest harvest practices. Natural and

which applies to all three tiers of monitoring activity, is research on monitoring techniques (Lund 1992). This activity necessarily involves research on topics

desirable changes should be evaluated enhance under-standing of the resource for future management.

to 3.

the

¡mportant to evaluate the cause of the change to decide if remedial action is indicated. lf a change results from a planned manipulation, it is important

perspective, the goals of Monitoring are threefold:

1.

Detection Monitoring records

condition of forest ecosystems, estimates baseline conditions, and detects changes from those baselines over time. Evaluation Monitoring determines the causes of detected changes, if possible, or hypothesizes causes that can be tested experimentally or with information from lntensive-site Ecosystem Monitoring. lntensive-site Ecosystem Monitoring provides high quality, detailed inf ormation that can be used (1) for a rigorous assessment of cause/effect relationships, and l2l to support experimental research on a small set of representative ecosystems. All three tiers are needed to fully understand the state of health of forest ecosystems.

management Forest Health

such

as:

sampling methods, sampling

This is achieved by understanding the

design, stat¡st¡cal analysis, assessment, remote sens¡ng and other measurement procedures, and indicators of forest health. Research on monitoring lechniques ensures that Forest Health Monito-

processes that are involved in regulating the function and controlling the

eff icient.

To increase our ability to anticipate or

predict changes in forest resources.

ring will continue

to be eff ect¡ve

structure of forest ecosystems. Forest ecosystems change rather

Detection Monitoring consists

gradually because they are dominated

network

by long-lived individuals (trees). Therefore, many forest ecosystem

of

and

of

a

permanent plots distributed

throughout the forests of the United States, coupled with remote sensing

44

observations and pest surveys. The sampling frame for the permanent plots

connects

an existing Forest

attr¡butes must be described

that

evaluated to determine if changes in them, individually or collectively, represent ¡mpacts on f orest health.

Service

network of inventory sample locations to a hexagonal grid of some 12,600 points

acfoss the contiguous united States (Palmer and Jones 1992). This design allows for augmentation with additional sample locations as needed to represent

Growth eff iciency currently is determined primarily through tree ring analyses.

Nutrients are currently evaluated f rom foliage collected in the upper portion of tree crowns, and soils are evaluated for

all forest lands in the United States. From

this augmented network, a subset

so

repeated observations over time can be

physical, chemical, and

of

biological

some 4,000 "sentinel plots" in forested lands, which comprise roughly one-third of the land base in the contiguous United States, will be visited annually. Sentinel plots are selected to closely conform with the EMAP network of 40 km2 hexagons

These "core indicators" may be augmented by several potential indicators

(Palmer and Jones 1992). The amount of

Lichens (characterization and chemi-

components.

currently under

--

information collected on sent¡nel plots will during

--

A component critical to this phase of the

---

be greater than that collected regular forest inventories.

program is selecting attr¡butes to measure

on the plots (Hunsaker and Carpenter 1 990). EPA has developed a process for testing and selecting these attributes, called indicators (Knapp ef a/. 1 990; Riitters et al. 1992],, and the FHM program f ollows these procedures.

These include:

)

Photosynthetic Active Radiation (PAR) - may emulate leaf area and is a part of the growth efficiency indicator Tree Height Growth

Understory Vegetation and Wildlif

e

Habitat

Tree Core Dendrochronology

and

Chemistry

-- Bioindicator Plants (i.e., ---

Desirable features to look for in indicators

include: an early and observable response perturbation events; a high precision (low variation) for measurement recording; capable of being successively remeasured; known inferences can be drawn from changes that occur over time; and reiative inexpensive to implement. Some general indicators that have been evaluated in pilot tests in major regions of the

to

U.S. over the past two years

stry

test.

ozone

impacts, etc.) Root Diseases Soil Microbiology (mycorrhizae)

For whatever indicators are measured, several subsamples are taken on each sentinel plot for each indicator to account f

or within-plot variation.

lnf

ormation

derived from indicators on the sentinel

plots will be coupled with information collected during routine forest pest surveys and other specif ically f ocused moni-

toring activities. All information will be spatially linked (Czaplewski 1992) to

are:

landscape attributes; visual symptoms of or other vegetat¡on; growth efficiency; foliar nutrient status; and soil characteristics.

damage on trees

provide a more complete, annual estimate

of forest condition. Over 700 sentinel plots have been estab-

lished in 1 2 states during the f irst two years of the FHM Program. Each plot established represents 640 km2 of forest land. Preliminary results indicate that the EMAP grid network provides for a repre-

Landscape characterization of vegetation a¡d land use patterns likely will be initi-

ated through thematic mapping f rom satellite imagery or aerial photography, and then ground-checked on the plots (Czaplewski 1992). Evaluation of visual symptoms involves checking for biotic

sentative sample

of f orest types

and

stand/age class distr¡bution. Furthermore,

except for the American beech situation noted below, no unexpected phenomena have been detected during this baseline establ¡shment period. As indicated earlier, we antic¡pate the need to collect data

and abiotic effects on vegetation, primarily foliage. Specific measurements include

crown ratio, crown diameter, crown density, crown dieback, and f oliage transparency and retention. These

45

over several years before any inferences concerning change can be drawn.

ant¡cipate changes in forest health; and (4) provide the understanding necessary to develop management responses to unexpected changes. The sites differ from Detection Monitoring sites in the frequency of measurement (i.e., daily vs. annual) and the number of parameters measured (many vs. few).

Evaluation Monitoring will usually be activated by the results of Detection

Monitoring. When Detection Monitoring reveals changes that represent areas or problems of concern, a spec¡f ic evaluation

will be

made

to

determine necessary

ln some cases, the availability of this detailed information might resolve questions that were ra¡sed but not answered by Detection or Evaluation Monitoring.

follow-up activities. The details of Evaluation Monitoring can not be specified in advance and thus have

not yet been fully developed. By definition, Evaluation Monitoring will be

ormation f rom lntensive-site Ecosystem Monitoring will contribute to better understanding of intra-site variability as well as better understanding of relationships between Detection Monitoring indicators and other ecosystem characteristics. lnf

implemented where unexplained changes have been detected, and it will be tailcìred

to the

specific nature of the problem.

Activities could include additional targeted surveys, site-specific evaluation visits, more detailed temporary monitoring, and specific research studies.

More importantly, lntensive-site Ecosys-

tem Monitoring will provide

long-term data and the sampling infrastructure that will support research on mechanisms and processes that shape forest ecosystems. ln this sense, lntensive-site Ecosystem Monitoring s¡tes are similar to the United States National Science Foundation's

Detection Monitoring plots identified an unusual amount of dieback in American beech during the 1990 f ield season in the New England States. An Evaluation Monitoring program has subsequently been initiated to survey this problem.

Long-Term Ecological Research (LTER) sites (Franklin et ai. 1990). lnformation from Ecosystem Mon¡toring sites can be

lntensive-site Ecosystem Monitoring will be designed to provrde a more complete understanding of the mechanisms of change in f orest ecosystems. Monitoring

with additional short-term measurements or ¡t can be expanded by measuring new variables during specific

augmented

studies. Such studies, along with ecosystem process modeling efforts, will be

at th¡s level provides data f rom a group of precisely measured parameters to better understand causal relationships and pre-

dict direction and rates of changes

needed to enhance the value of the lntensive-site Ecosystem Monitoring information in context with the more extensive aspects of the Detection and Evaluation Monitoring data and analyses.

in

forest condition. Ecosystem Monitoring sites will represent

key forest ecosystems throughout the United States. Ten to 50 primary sites likely will be established to represent

Together, data from all three tiers of monitoring should provide a statistlcally

major forest ecosystems. These sites will be centers f or collecting detailed inf ormation on all components of the forest ecosystem. The purpose of these detailed monitoring sites is to supplement Detec-

reliable basis f rom which to determine the

nature and direction of change in forest condition and if any such changes have resulted in a deterioration of the system's health. lf so, then the Forest Service can use the data to determine if certain land

tion and Evaluation Monitoring and to support mechanistic research to identify, describe, or model tree and forest processes in ways that: (1) increase basic

management practices need to be changed. Similarly, the data also may allow the Environmental Protection Agency to evaluate if further regulations may be necessary to control possible off-

understanding of causal relationships; (2)

provide explanations

or

projections of

observations in the other levels of the Forest Health Monitoring system; (3) help

site pollutants.

48

ment Program. EPA 600/3-90/060. U.S. EPA, Office of Research and

SUMMARY

Development, Research Triangle Park, North Carolina. 416

The U.S. experience in the establishment of a f orest health monitoring program provides several concepts for this Workshop

to

consìder.

Dif

f

permanent plots can

PP.

Knapp, C.M.; Marmorek, D.R.; Baker, J.P.; Thorton, K.W.; Klopatek, J.M.;

erent types of be established,

Charles, D.P., 1990: The indicator

development strategy f or the environmental monitorìng and assessment program. U.S. EPA, Environmental

based upon the objectives of the monitor-

ing program and associated spatial and temporal resolutions. A sampling design

Research LaboratorY, Corvallts, Oregon. Contract No. 60-CO-0021,

that provides unbiased regional estimates

is critical to the success of forest assessments. A continuous indicator

84

evaluation process is needed to improve

pp.

Lund, G., 1992: A Primer on Permanent plots f or monitoring natural resources'

the effectiveness of the

monitorìng program. A sound information management program f acilitates the evaluation of

IUFRO Remote Sensing and World Forest Monitoring lnternational Work-

the field data and its rapid dissemination to clients. Cooperation between agencies will extend resources and improve the overall utility of the information derived.

shop (PattaYa, Thailand,

13-17

January, 1 992). Palmer, Craig; Jones, K. Bruce, 1992: United States environmental monitoring and assessment Program - an Overview. IUFRO Remote Sensing and World Forest Monitoring lnternational WorkshoP (PattaYa, Thailand, 13-17

REFERENCES

January, 1 992).

Czaplewski, R., 1992: United States Environmental Monitoring and Assessment Program - LandscaPe charac-

Palmer, C.J.; Riitters, K.H.;Strickland, T'; Cassell, D.L.; BYers, G.E.; PaPP, M'L ; Liff , C.1., 1 991 : Monitoring and

terization and remote sensing' IUFRO Remote Sensing and World Forest

research strategy for forests - Environmental Monitoring and Assessment Program. U.S. EPA, Environmental

Monitoring lnternational Workshop (Pattaya, Thaìland, 13-17 JanuarY,

Monitoring Systems Laboratory, Las Vesas, NV. EPA 600/4-91 /01 2' 1 90

19921.

pp.

Franklin, J.F.; Bledsoe, C.S.; Callahan,

J.T., 1 990: Contributions of the Long-

Riitters, K.H.; Law, B.E.; Kucera, R'C'; Gallant, A.L.; DeVelice, R.L.; Palmer, C.J.: A selection of forest condition

term Ecological Research Program' BioScience, vol. 40, PP. 509-523.

indicators f or monitoring' Environmen-

CarP,çnter, D.E., (eds), 1990: EcologicaI indicators for the

Hunsaker,

C;

tal Monitorìng and Assessment: pre ss).

Environmental MÓn¡toring and Assess

47

(in

MONITORING CHANGING OF FOREST LAND.USE BY MEANS OF AERIAL PHOTOGRAPHS lshak Sumantrí Wesman Endom Forest Products Research and Development Center Agency of Forest Research and Development Department of Forestry, lndonesia

ABSTRACT This study is carried out to evaluate the trend of forest land-use change as a result of the communitY's activ¡ties in pursuing its need for settlement and land cultÍvation in forest land in the vicìnity of Kampar river, Riau. To understand the trend of forest land utilization changq; 4 data resource references including aerial photo I g64, general view of forcst map, 1982'land-use map, and ground check in 1984, have been used. Duríng two periods i.e., in the years from 1964 to 1974, and from 1974 to 1984, it is known that an invasion occurred into both limited production forest and conversion forest in the Sub of Kampar Kiri and sengingi. An analysis of data using "populat¡on prcssure ', and ,, location- quotíent formulation " technique shows that populatíon pressure and low income causes ínvasion into the forest areas.

INTRODUCTION

such as an easy road and or by the rivers, very often made problems. The problems are related to the illegality of land utili-

zation. Many people encroached to a piece of land that never used before. They used for agriculture, rubber plantation, mix garden, spontaneous transmigration, etc. The piece of land belonged are the forest area.

To

accelerate National Development Programme, many projects since 1969 t¡ll now has been working. Those projects worked through exploration and exploi-

tation of natural resources, such

as

mining, gas, oil, and also the forest.

ln

For long period with minimum tillage and initially low fertile, with number of people

lndonesia, National forest resources

covered r. 7O% of entirely land. Therefore ¡t is wise and important that

growing continuously due to fertilization and or migration, those land then would

forest should be managed professionally not only for living environmental condition such as water supply, soil conservation, healthiness and recreation, but it should

be more and more deteriorate. For example eroded land.

wood

Therefore to overcome of encroachment that gradually or fastly will influence more seriously problems. Problems solving

be produce especially f or

productíon and its industries. lt was the facts that since 1 970, forestry sector has been giving large contribution to

wisely are required. Referring to th¡s reality, monitoring of management forest land-use are important. ln this paper forest landuse changes was studied. The location was selected in Kampar River area, Province of Riau.

government ¡ncome, regional development and social livelihood.

However, in other side local and regional acceleration due to availability of facilities 4S

Table

l.

Percentage of people's livelihood in the research area in 1985.

Profession

D¡srriburion (%) Sub Dist.Kampar Kiri

Sub Dist.Sengingi

52.90 2.36 2.O4

75.75 5.98 1.60 1.94

Others

41.00

14.7 3

Total

100.00

100.00

Agriculture Commerce Government employee

1.70

Fishermen

GENERAL INFORMATION

6.932. ln

OF

general the livelihood are

STUDY AREA

agriculture f ield as seen in Table

Administratively part of study area under the management of Sub District Kampar

RESEARCH METHODOLOGY

Kiri District of Kampar, and the other part under the Sub District Sengingi D¡strict of lndragiri Hulu. The forest area partly

A. LocatÍon

Selection of the area was taken considering the facts that location has to be easily to achieved weather by road and or by the rivers and also relatively close to the villages. Hence analysis of forest land-use changes to the other type can be monitor and worked out.

belong to Forest D¡strict of Bangkinang, and the other part belong to Forest District of Rengat. ln this area there were 5 concession, i.e. PT.Siak, PT. Pertisa, PT.Brajatama I and ll and PT.lndowood. The terrain are flat to mountainous, with a swampy area specially in the right side of Sebayang and part of River Teso area

with an alt¡tude + 143 m above

1.

B. Data collection

sea

level. The soil are reddish and yellowish podsolic and glei humic with sedimentation stone. The climate based on Schmidt and Fergusson classification was Type A with rainfall between 256 272 mm per month.

Primary data are taken during field check

and f rom previous landuse map i.e. general situation map of 1 974, Landuse map of 1982, Agreement of Forest Landuse map of 1984 and aerial photographs of 1 964 as an initial land-use observed. Secondary data consist of

Landuse type are forest, village, public rubber estate, shrubs, swamp areas, dry

social economic and taken f rom Munificial

Forest District, Local

field and others. The forest vegetat¡on

Government,

Cadãster off ice, Planning Board

type dominantly distributed over

the whole area are Meranti lShorea sp), Kapur

and

interview.

(Dryobalanops sp), Keruing (Dipterocarpus sp), Mentangur lCallophyllum sp), Medang

(Litsea sp) Kempas (Kempasia sp) and Rengas lGluta renghasl.

C. Data analysis

About population, registration of 1 985 mentioned that in Sub District of Kampar Kiri number of population were 16.982 and in Sub District of Sengingi were

analyzing data of forest land-use changes are used. To know the tendency of

Descriptive and comparative data land-use changes, some f ormulas used as given below.

óo

in

are

Table 2. Value of carrying capacity factor based on the level of social welfare.

Z value

.20

1

Items

sustenance life, equaled as an income of 240 kg rice per capita per year

2.0o

Better life condition, equaled as an income of 320 kg rice per capita per year

3.0O Better life condition, denoted with possibifity to plant a small rubber estate ¡n the

future

5.00 Better life condition,

1

more properly life

I Regional conception (lsard 1 968 after

material

Warpani 1984)

3) Population Pressure (Myrda, 1968)

M = (P,*n-P,) -N,

Po

PP=Z'tl.L

where:

where

M

PP = Z =

=

P, *

The value of changed area The specific landuse area of the year t+n = The specific land-use area of the year t = The specific area on natural growing population condition without any imgration

n =

Pr Nr

(1 +

r)t

:

Population pressure/ car The value related to an area that required to support of sufficient

living condition. The val of Z as

f = Po = Í -

2) Location quotient/carrying capacity

| =

(Warpani 19841

xlOO% ,^ _ - (NtñftJoo-% '" (Si2/S)

L=

seen in the Table 2. Number of the farmers (%)

Number

of

population

specific period Population rate

at

a

of growth per

yeaf

lnterval rate of calculated (20 years)

Crop area (ha)

where:

LO

Regional carrying capacity to specif

Si = $ = Ni = N

ic

material supply

in

RESULTS AND DISCUSSION

a

region study area

Number

of a

A.

specific mater¡al

Forest landuse

observed

Based on aerial photo 1964, it is known that land cover of the study area can be

number of total material specific observed

classif

number of specific material in a wider region number of total specific material in a wider region

The criteria to decide a valuation was LO

> 1:

LO


the white squares are eliminated

. pixel coded in 10 bits . pixel size at nadir 4.4 km x 3.3 km . number of pixels on one line : 409 pixels I

4.4

r

r3.3

The purpose of the

B Washìngton format including one main

geometric correct¡on is to get an image in UTM projection (Universal. Transverse

header and one header for every line is essential : this gives the necessary data

for geometric and radiometr¡c correct¡ons (resampling of the GAC pixels at 4 x 4

Mercator). The bicubic method was

km). PACKED DATA (Packed image data) have pixels coded on 10 bits and are considered as original data and used for the

geometrically corrected with control

used here. Every NOAA image

is

points. The correction accuracy must be below one pixel in order to get an eff ective juxtaposition between

project.

rmages.

LAC format LAC (Local Area Coveragel data are recorded onboard for a ground

The radiometric calibrations are consisting in correcting responses

transmission. The onboard recorder capacity is lim¡ted to 10 minutes data recorded per orbit. One scanning line is constituted of 2048 pixels, and the pixel

into ref lectance (Visible and Near lnf rared bands) and temperature (lnfrared bands).

resolution is 1.1 km. This imagery is more difficult to acquire; the data archive is not systematic because of the heavy quantity of data to be stocked. ln order to have

A sixth band is created by combining bands 4 and 5 by the formula :

LAC format images, planned recording is

TBand 6 = C4xTB4 + C5 xTB5 + Cox 10 (Spl¡t w¡ndowl

needed.

. .

w¡th TB4 = Calculatod tomperaturo Band 4 TB5 = Calculated tamperature Band 5 C4 = 3,60 C5 = -2,60

pixel coded on 10 bits pixel size at nadir 0.8 km x 1.1 km

Co =

1.1

. .

r----l o.e

i

number of pixels on one scanning line : 2048 pixels IFOV (lnstantaneous field of viewl :

ooerat¡on

ref

erence image

is chosen

and

superimposed with the other images to check the accuracy of the geome-

Preorocessino ooerations 1

2nd ooeration:improvement of image correction

A

2580 km

. st'

2,2O

:

tric corrections. The tolerable shift between two images of the same

:

area should not exceed 1 pixel. lf the

correction

is greater than expected, the geometric correction ¡s performed again with this new value estimated shift

and

calibration

78

of shift. The operation must

pixels coming from corrected monodate

be

images, and having the highest vegetat¡on

repeated until an acceptable result is

index value. This composition minimizes monodate image but does not eliminate completely clouds and haze. Selected monodate images have to be recorded in a period as short as possible. lf not, radiometric values of the composite image would not mean anything.

obtained.

the cloud cover from each

3rd ooeration : making of the composite image with the best monodate images of the same area. The composite image is the result of a combination of several monodate images which need to have all the same number of lines and columns, the same number of bands and the same pixel size. The algorithm used is based upon the maximum of the vegetation index : each pixel (and its values in all bands) in the composite image is coming from the monodate image where the NDVI was found to be the greatest for this particular pixel, in comparison with the other monodate images.

lmages interpretation :

After geometric and radiometric corrections, monodate and composite images are available for each geographical area. lmage enhancement on the composite image are made f or a better display (masking procedure, bands combination,

contrast stretching).

2 and 3 are found to be the most discriminating for the interpretation of the vegetation cover. Band 6 may bring also some useful information but ¡t needs to be corrected by the air temperature to get the ground temperature. But the use Bands 1,

Output data

t Monodate images, when corrected, conslst of 7 bands (when raw images

of thermal bands is rather complex

have only 5).

. the 5 concerned AVHRR bands, . the sixth band is obtained by the combination of temperatures of

lnterpretat¡on of NOAA - AVHRR images is greatly facilitated by using other

sources of information for a "thematic calibration" such as cartographic documents (date of production being similar to the date of acquisition of NOAA images)

NOAA bands 4 and 5, with the Spl¡t Window algorithm. The coefficients

.

in the f ormula ate taking into consideration water vapour in bands 4 and 5. the seventh band is the vegetation index calculated with the standard f

or high resolution satellite

=

band2-band

1

bandl +band2

data.

Furthermore, it has been shown that in the ¡nterpretation process based on a barycentrlc classification algorithm, the selection and the delineation of training

ormula:

NDVI

and

hazardous in a composite image.

areas for NOAA imaoes

is the most important step

f

or

carrying out reliable results. Thus, the knowledge of the main types of vegetation in the Region is a prerequisite f or achieving results with a suff icient accuracy. This required ground knowledge is provided by the training participants

.

The composite ìmage lcomposed of I bands): . the 7 first bands of the monodate images which have the maximum value in NDVI, . band I gives f or every pixel the

who have a strong experience of their country.

number of the monodate image where it comes from (1 to 5 if 5

At last, the results are

evaluated

on

sample areas which were not used for the

monodate images have been involved in the composition).

interpretation

of

NOAA images. Those

sample areas may be provided by the high

resolution satellite data set which can be

The composite image is a patchwork of

7S

considered as reference data.

the land-cover classes

-

The link between the low and the high resolution satellite data is presented in Figure 4. The global study is airning at

comparison with existing reliable staof 1980 - 82 lf or instance: FAO 1 982 Forest Resources Assessment) on a country by country basis.

tistics

stratifying the main types of forest cover and at monitoring the global changes. The detailed studies using SPOT or Landsat data are carried out for the calibration and the validation of the global study. The location of the scenes presented in Figure 4 was made on random for the purpose of the f igure. The exact location of the

The deta¡led studies

:

Two sets of high resolution satellite data ate being purchased for the deta¡led studies. One is still in progress for the

scenes is not yet fully decided. lt can be noticed that nine sites have been already

calibration and the validation of the global

selected (Figure 5), but those detailed

study (par.2.1).

studies have other purposes (see par. 2.2) and are more training oriented.

lnteroretation leqend

overall statistics of deforestation by

The second one is directly pertaining to the training of national experts (Figure 5). Nine sites have been already selected and

are corresponding to two sites

:

To allow a proper

interconnection

data were acquired and visually interpreted by the tra¡ning participants. The interpretation legend, based on the FAO classification, is leading to the following

between the detailed studies and the global study, the interpretation legend of NOAA - AVHRR data is hased upon the FAO classification used for the "Forest Resources Assessment 1 990" Project.

classes:

-

The discrimination of the classes are based on the physiognomical characteristics of the forest cover.

-

The main classes will be:

-

- continuous forest - mosaics of forest and other land cover - cultural areas - other land covers (water, clouds...)

The interpretation of the eight geographical areas will be merged in a single document. This draft of the 1 990 forest state map will be evaluated by the Asian partners of the project on a country by

Scrubs

Water

Other land covers

which occurred on the nine selected sites,

additional high resolution satellite data (SPOT, TM or MSS) acquired at different dates with a significant time lag will be purchased. The national experts will be directly involved ¡n the interpretation of this new set of data. Statistical tables of the land covers will be performed for both sets of data and a change matrix will be derived from the crossing of the two sets

The final output products shall be:

statistical tables on the total areas of

cover Fallows

To assess the changes of forest covers

be performed.

-

Fragmented f orest (mosaics forest/other land coverl Man-made woody vegetation

mechanism of def orestation.

country basis. Then statistical figures will

global map

Closed forest (cc > 40 %l Open forest (cc 1O - 40 %l

The detailed studies help in the understanding of the deforestation process. Based on specific examples, they show what are the causes, consequences and

Final results :

-

per

country (except three for Malaysia). SPOT

of the forest cover at 1:2,000,00 carried out from the interpretation of NOAA-LAC 1990 data.

of results.

80

REFERENCES

CONCLUSION

The spectral bands of

Ducros-Gambart D. and GastelluEtchegorry J.P., 1984: Automat¡c analysis of bi-temporal Landsat data: an application to the studY of the

NOAA-AVHRR

radiometers were originally planned for

meteorology. Therefore,

they do

not

provide the optimum inf ormation f or vegetation monitoring because of their bandwidth. Their low spatial resolution has to be added to this shortage in spectral resolution. NOAA data will ¡ot

allow any

identif

ication of

evolution of vegetation-covered areas

in a tropical region, Proc. FAO/UNEP, 1 981

detailed

: Forest Resources

Assessment Pro.iect.

phenomena, such as transition areas or encroachments areas within the forests. But at this point, there is no similar tool for global monitoring of the vegetation. Seasonal or irreversible changes occurring on large extent can be detected by NOAA satellites. lts repetitivity is a powerful mean to solve, at least partially, the problem of cloud cover. And it has been

Gohin, F.; Delmas, R. and Legentil, S., 1988: Logiciel de Traitement d'images NOAA, unpublished report, IFREMER/

crsr. Laumonier Y., 1983: lnternational map of

the Vegetation, scale

shown that with NOAA-LAC data a relevant classification can be achieved

:1

,000,000;

sheet southern Sumatra;

BIOTROP,

1

Bogor, lClV, Toulouse.

regarding the deforestation process. This

is

IGARSS,

187-192.

Laumonier, Y.; PurnadjaYa, P.; and Setiabudi, S.; 1986-1987: lnternational map of the Vegetation, scale 1:1,000,000; sheet central Sumatra (1986); sheet north Sumatra (1987);

especially true when external data

(topographic, climatic...) are integrated in the interpretation process through Geographic lnformation System facilities.

BIOTROP, Bogor, lClV, Toulouse. For the next decade, NOAA-AVHRR data can be considered as a reliable tool for a regular monitoring of the vegetation on a two to five years basis (Figure 6). The detection of changes will always be facilitated by the use of high resolution satellite data for the calibration of NOAA data and for the validation of results.

SCOT CONSEIL, 1990: Les Prétraite-

ments d'images NOAA Pesket) version

1

.1

(Logiciel

.

SCOT CONSEIL, 1991, The SEAMEOFrance Project : the Sumatra Pilot Project, Methodological Test.

So Thirong Patrick, Juillet The be'st wish we could have for the future Ñhe development of new tools with a better spectral resolution. The methodology could be still improved and

the

SEAMEO-France

Project

1

991

:

Potentialités des données NOAA-LAC pour une reconnaissance des végéta-

tions de I'Asie du Sud-Est'

Essai Mémoire sur Sumatra' méthodologique de 3ème année, ENITEF.

mainlY

Whitmore, T.C.; 1985: TroPical

oriented to the transfer of know-how as well as its trained national experts would take very much benefit of it.

forest of the far East, University Press, New York.

E1

rain

Oxford

SEAMEO-FRANCE PROJECT

no

\._-_-.-.-.-

\ù2

$Çèe)ñ

! ! I

i

rl

DETAILED STUDIES

GLOBAL STUDY

-STRATIFICATION OF THE MAIN FOREST COVER TYPES -GLOBAL CHANGES MONITORING : DEFORESTATION, CATASTROPHY, FIRES. FIGURE 4

82

I I

DETAILED STUDIES USING SPOT DATA CARRIED OUT BY NATIONAL EXPERTS les

Ø

Malaysia

\r D

-o.A

\

¡

'o\

\

C-

\

Èó

Oñ {^ J\

lndonesia

_.^=\: :ccgr*r_

t

rvr

=O_7fr,J

SEAMEO-FRANCE PROJECT FIGURE 5

SEAMEO-FRANCE PROJECT

LOW RESOIUTION SATELLITE DATA (NOAA-AVHRR I-A,C) LOW RESOLUTION SATELLITE DATA (NOAA-AVHRR LAC) LOGICAL CROSSING CÀTIBRåTION

CHANGES DETECTION AND STATISTICS

EIGE RESOLIITION SÀTEI,T,ITE DÀIÀ

t ¡tss, sPoT)

84

USING 1 KM RESOLUTION SATELLITE DATA TO CLASSIFY THE VEGETATION OF SOUTH AMERICA Thomas A. Stone Peter Schlesinger PO Box 296 The Woods Hole Research Center Woods Hole, MA 02543, USA

ABSTRACT

Until the completion of this project there have been no other continental-scale land cover classification maps created from I km resolution satellite data. The objective of this work was to build such a map in a digital format by relying primarily on National Oceanic and Atmospheric Admin¡stration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) sateilite data. The advantages of such a map over conventional 'maps are the ease of continuously updating the map as new data are acquired (daily if requiredl, its convenience for use in modelling, and its ready compatibility with other digital continental-scale data bases such as soils, potential vegetation, climate anomalies, cloud cover percentage. The digital map's primary intended use will be to stratÌfy South America into zones of intensity of recent deforestation to allow the construction of a sampling scheme for the purchase of hígher resolution data from the Landsat and SPOT satellites. The created land cover digital map of South America described here is a unique product that has specific advantages and disadvantages compared with other continental-scale land cover maps of South America. lt has been created solely from satellite data and therefore is

relatively consistent from country to country. The individual classifications were completed objectively; all are based on the digital data from the NOAA AVHRR satellites. The labellìng or naming of the c/asses has been done in a manner that relied upon numerical data on vegetation vigor which was related to leaf cover and rate of growth, on elevation, on vegetation seasonality determined from the satellite data, on visual pattern analysis and by interactive examination of continental and national-scale vegetation maps and atlases. Estimates of the area of 39 land cover vegetation types are presented for the continent and estimates of more generalized land cover are presented for all countries of South America.

is due to uncertainties in the rate of land clearing and conversion and in the amount

INTRODUCTION AND OB.JECTIVES

of

carbon

or

biomass

in the

original

vegetat¡on. Understanding the terrestrial components

of the global carbon cycle knowledge about the location

There have been many studies using remotely sensed data to def ine the locations and the rates of tropical

requires large-

of

scale current land use change. For ¡nstance, it is estimated that tropical deforestation accounts for 1.1 to 3.6 X 1015 g of carbon released to the atmos-

deforestation in many regions aroundJhe world (Nelson et al. 1987, Malingreau and Tucker 1987, Woodwell et al. 1987, Green and Sussman 1990, Stone e¿ a/. 1 991 ). A f undamental drawback to these

phere yearly in the form of carbon dioxide

(Houghton 1991 ). The range of estimates

8ó

studies was that they were regionally specific and, to date, have not allowed scaling to continental or global estimates. The objective of this work was to build a continental land cover map in a digital f ormat by relying primarily on NOAA AVHRR LAC satell¡te data which would provide a basis for stratification of the continent for later more detailed analysis with higher resolution satellite data.

km2 at the equator. A digital potential vegetation map based on the theoretical concepts of L. R. Holdridge was also available. About 50 other reg¡onal maps, national-level atlases, and maps of vegetation f or various locations and dates

were available. A list of maps and atlases are available from the authors. For this project we assembled NOAA 9, 10 and 11 AVHRR LAC satellite imagery data of South America. These satellites

Previous continental-scale estimates of

the amount and rates of

acquire digital reflectivity and emissivity data from the surface of the earth from

deforestation (FAO 1 981 a,b,c, WRI 1 990, Myers 1 980,

the visible red (0.58 - 0.68 microns), near-infrared (O.725 - 1.1 microns), mid-

1 989) have not been presented at a subnational level and have not allowed assignment of carbon values to the land clearing that has occurred. ln short, they have not been spatially specific. More recent United Nations' FAO national and continental estimates of land cover (FAO, 1987) include only 5 categories; land area, arable land, permanent cropland, permanent pasture, and forest and wood-

infrared (3.5 - 3.93 microns) and thermal ( 10.3 - 11.3 and 11.5 - 12.5 microns)

portions of the spectrum (Kidwell 1988).

The LAC data is approximately 1 .1 km resolution at nadir. We purchased 34 tapes of which the majority were from 1988, the year of primary focus for this project. Because we did not have cloud-

lands. New estimates of the rates of tropical deforestation for 1 990 are due this year from the FAO's Forest Assessment 1990 Project.

free 1 km resolution LAC data of all South America, a three year weekly data base of 15 km resolution Global Vegetation lndex

(GVl) data from NOAA (Kidwell 19881 was utilized in the place of the 1 km data. All the AVHRR LAC data for this project

Developing the 1 km data base of South American land cover allows us not only to define areas of recent forest clearing but also to more closely define the probable

was purchased from the US Geological Survey's (USGS) EROS Data Center (EDC)

in Sioux Falls, South Dakota.

vegetation covers being affected by man's increasing role in the management

and alterat¡on of natural systems.

Because the map produced f rom this work was based primarily on the classifi-

METHODOLOGY

cation of satellite data, it is f undamentally different from typical vegetation maps.

We first divided South America into 75 classes of vegetation based on their seasonal patterns of vegetation activity using 15 km resolution NOAA GVI data.

AVAI1ABLE MAP AND SATETLITE DATA

GVI data is the only current

The number of continental-scale maps of the vegetation of South America are very limited. We had available for this effort only two at a reasonable map scale. These were the UNESCO Vegetation Map of South America at 1:5,000,000 scale (1 980) and Hueck's Vegetationskarte Von Sudamerika at 1 :8,000,000 scale 11 97 2l,.

A digital land cover map of

AVHRR

product with global coverage. The primary advantage of using this data set was that it enabled us to analyze t¡me series of vegetation indexes and to look at vegeta-

tion phenology globally. Again, th¡s data were to be used if there were no 1 km resolution (LAC) data available and also to aid in the interpretation of the available 1 km resolution data. The phenology information was not available from the 1 km AVHRR data used in creating the mosaic as each region classified with LAC data were based on imagery from one or two points in time.

South

America by Matthews (1983) was available but has a resolution of about 100O

86

Classification of the NOAA GVI Data

the resulting 75 classes had similar vegetation phenology and vegetation index values. The majority of the classes were then labelled interactively using the UNESCO vegetat¡on map (UNESCO 1981) as a guide and by examining the seasonality of the classes. Some 42 of these 75 classes based on the 1 5 km GVI seasonality data were satisf actorily labelled. These 42 classes covered 947o

The satellite data used were weekly NDVI of the globe covering the years 'l 986, 1987, and 1988 purchased f rom NOAA. The vegetation index was created by subtracting Ch. 1 or visible red data from Ch. 2 or near-infrared (NlR) data and then

normalized

by dividing by their sum.

Because healthy vegetation reflects light strongly in the NIR and absorbs strongly in the visible red portion of the spectrum the NDVI is a reliable indicator of vegetation vigor or rate of growth (Holben et a/. 1981, Tucker et a|.1980). The data are produced by NOAA by selecting thê max¡mum daily vegetation index for the week and retaining it to represent the weekly NDVI. NOAA produces as a standard product weekly NDVI composites at the

of the continent. The estimates of forest area based on the GVI data showed good agreement with other continental estimates and will be described in more detail in a forthcoming paper.

Classification of the NOAA AVHRR 1 km LAC Data

global scale. The data was at 15-2b km

in a

Plate Carre projection,

extending from

75' North to 55" South

resolut¡on

At EDC, the AVHRR data were converted rom the original 10 bit format to an 8 bit ormat, corrected f or radiometric and atmospheric effects and provided to us rectif¡ed to a latitude-longitude grid. The AVHRR data were rectified to match the f

f

latitude.

The NOAA values for the NDVI were

re-

World Data Bank ll national and coastal boundaries. ln addition, all the boundaries of the Brazilian slates were digitized. This

calculated back to a more typical range of values -0.1 to .63 by taking the formula f rom Kidwell (1 988) and inverting it. Unity or one was added to the value and

then multiplied by 100 to yield values

the range of 80 to 1 80. Then,

allowed us to select and analyze one country or state at a time. The advantages of this technique are that the data-

in

the

maximum value of the NDVI from each

sets being analyzed are a manageable size fit the regions

week was used to represent the month for each of the three years. Each of the three values for the three years for each of the'l,2,months were then averaged to produce a global image consisting of one band for eách of the 12 months with an average value for each pixel for each

month. A mask of values of zero

and the map resources

being analyzed. The disadvantage of this method are the creation of artificial discrepancies at the borders of states and national ent¡t¡es. Bands 1 , 2, 3 and 4 of the LAC data were read into the ERDASTM system and the upper left hand corner of the image was assigned a specific latitude and longitude. All pixels were specif ied to a dimension of

was

used to remove oceans and major bodies of water from the data set.

From this data set the South American continent was extracted and classified. We performed a principal components (PC) transformat¡on on the 12 bands

0.0089932 decimal degrees which is 'l 000 m at the equator. The region of interest was then extracted from the full

8

image using an appropriate polygon file. To create an image suitable for a classification clouds were removed.

bands of the principal components image which contained about 960/o of the variance of the full 1 2 bands of data. The

We used the CLUSTR program to do an unsupervised classification and create 50

(months) of data. An unsupervised classi-

fication was then done on the first

15 km resolution GVI data set of South America was sorted into 75 classes with

to

10O classes. We used the smaller value (50) if the region was small and contained few remnant clouds, smoke or shadows or the larger value (100) ¡f the region was large or complex. This was done for as

an unsupervised clustering algorithm called CLUSTR from ERDASTM (ERDAS, 1990). Consequently, the elements within

87

many different images of a region that

Any remaining holes in the

were ava¡lable. We prioritized the satellite images used by choosing first, the image most cloud and smoke-f

ree and closest to nadir

classif ied

region due to unavailable cloud-free 1 km LAC data were patched or filled with the classif ication based on the 1 5 km resolution GVI data. Again this operation was order dependent so the h¡gh resolution

and

classification (1 km scale),

second, how recent the image was. This

if

available,

process, generally resulted in several classifications of a region from different dates. These classifications were then

was added last and overwrote any classification based entirely on the GVI data.

superimposed or stitched together in the order worst to best so that the best pixels would be the last included in the final LAC classification and would overwrite any other pixels in the same geographic location.

Direct comparison our 39 land cover classes to other continental-scale estimates of land cover was not useful because of the variety of classif ications schemes and the f undamental differences between classif ications based on remotely sensed data and more typical land cover classif ications. To f acilitate intercomparisons, we combined the 39 land cover classes into 1 3 broader groups described in Table 2.

For each class def ined f rom the LAC data labelled them according to the following five steps. First, we determined

we

the mean NDVI of the class. Second, we determined where the NDVI stands in relation to the year-long phenology curves

as defined from the 15 km GVI data. Third, the class was compared with any map, atlas or ancillary information. Our primary reliance was on the UNESCO vegetat¡on map (1 980), but if a more detailed national vegetation map was available, then that was used as the

RESULTS Both country level and continental-scale values for the 39 land cover types were generated, Because of the inherent distortion in the original project¡on, particularly at higher latitudes, the projection of the output was converted to Lambert Conformal Conic.

primary source. Fourth, we determined if

there were recognizable spatial patterns which might indicate agriculture, pasture, or cultural features (roads etc.). And fifth, the data was reclassif ied to assign the 50 to 100 classes into a f ew meaningf ul

ïable 1 shows all 39 land cover classes for South America determined from this

ones. This last step was the most subjec-

study ranked by size. lt was important to note that many of the categories contain possible overlaps with other categories particularly if the land cover categories

tive and was largely a function of the analyst's interpretation guided by the information generated f rom the preceding steps and the relationship to data across

are the result of anthropogenic inf luences.

borders.

Shown in Table 2 are country level estimates of the generalized land cover types or groups described above. Brazil, not unexpectedly as it is the largest country in South America, has lost the greatest area of both closed and tropical moist forest. But, not only has it lost the largest amount in an absolute terms but also as a percentage of its total forest. The amount of tropical moist forest recently cleared in Brazil was 12o/o wiTh the majority of the

Thirty-nine f inal land cover classes, def ined in Table 1 , were created as the classification proceeded. Generally, the countries w¡th montane environments were the last to be examined so the montane classes were the last to be defined. Although the primary reliance was on the UNESCO map (UNESCO, 1 98O) classif ication scheme, national maps took precedence in creating some of the labelled classes. Many classes contain obvious overlaps. Within a particular region only a f ew classes,

change in Amazonia and in the Atlantic Coastal Forest. Large losses of closed

forests are also seen in Chile, Bolivia,

generally less than six, could be defined.

Peru, Colombia and Surinam. We estimate

88

Table 1. Gontinental-Scale Estimales of Land Cover, Soñed by Size.

't7,680,200 1,7æ,sn

89

Table

2. Results by country of generalized landcover typ€s for south America from th¡s work.

Becently Closed Trop. Degraded Moist Forest TMF

1.2

Argentina Bolivia

3

Brazil

Degraded

Degraded

519.7

179.4

Chile Colombia

Ecuador

Snow,

Desert,

Closed Closed Degraded Savannah, Savannah, Scrublands, Bare Rock Forest Forest Woodlands Woodlands Grasslands Grasslands Shrublands Soil Water lce Other 0.0 96.8 0.6 755.4 232.4 894.0 37 e 34.0 31.4 35,7

115.5

1.7

12

126.0 1.4 3/s.l

2.5

1285

o.o

o.o

o.o

0.0

Total 2,779.8 5

Fr. Guyana @

Guyana Paraguay Peru

Surinam Uruguay Venezuela

--

TOTAL

0.0

o.2

415.5

9.9

5,909.9 563.4 6,530.7 1,803.7

Unclassified

N.B. AII Values in

3.0

0.0

5.9

33.9 3,109.8

666.9 2,642.0 808.5

1,080.6 331.7

163.2

¿t8.9

2'17.2

17,403.1

313.0 1,0O0s of Sq. Km.

'TMF'includes Tropical Moist, Semi-deciduouE and Gallery Forests "Grasslands' include those seasonally flooded -Closed forest' include TMF, Montane forests, Cool and Temperate Deciduous Foresls and Tropical Seasonal Forests 'Degraded grasslands' lnclude Agricuhure 'Desert, Bare Soil'include inland Salt Marsh communities 'Othe/ includes wet vegetat¡on and mangroves Although not presented here, Brazil can.be subdivided to the slate level

that Brazil has lost or degraded at least 31V" of its total original closed forest while Chile appears to have lost or

sure exists.

We developed reliability rat¡ngs f or all country level classifications and for the large states and regions within Brazil. Three categories of data, LAC data quality, 1 5 km data quality, and ancillary map data quality, were rated by assigning values from 0 to 'l 0 (worst to best). For LAC data, distance from the nadir of the

degraded about 18%.

Major losses or degradations in woodlands are seen in Brazil (18% of national woodlands lost or degraded), Bolivia (23% lost or degraded), Peru (47% lost or degraded), Paraguay (2Oo/o lost or degraded), Venezuela, 154% lost or degraded) and Chile (28o/o lost or

image(s), smoke and haze content, age of

the data, presence of shadows, data noise were rated with the 0 to 10 scale,

deg raded).

and then multiplied by the percentage of the region covered by LAC data. For the

From the work shown here we have estimated that about 9o/o ol the closed tropical moist forests of South America has been cleared recently, perhaps within the last 10 years. Also about 22o/o of The

15 km GVI data ratings were assigned to whether or not the region was classif iable with the 15 km GVI data and whether the classification was a major or a minor one. For the ancillary map data, the quality of the relevant national map or atlas, the similarity of the national map's vegetation classification scheme to the scheme developed in this work, and the age of the nat¡onal map or atlas were all rated. Extremes in each of the three categories result in ratings of 0.0 and 1 .0. Each of the three categories was weighted so that the quality of the LAC data was responsible for 50% of the score, the quality of the 1 5 km classification was responsible lor 1Oo/o of the score and the quality of

closed f orests of South America have been cleared or degraded as have about

19Vo of the woodlands and about 25% o'Í

the grasslands. The

largest losses in absolute terms have occurred in Brazil except in the grasslands category where

the largest amount of degradation was seen in Argent¡na. Comparison of the,results shown here with the work of others will the focus of future work.

the relevant national map or atlas was responsible for 4Oo/o of the score. The mean reliability rating for the entire map was .69 out of a possible 1 .0.

ACCURACY AND RELIABILITY OF RESULTS

It is clear that a more rigorous accuracy

To determine th¡s map's accuracy, one might consider comparing it with other maps; however unless the reference or "ground truth" map is known a priori to be of higher accuracy than the new map,

assessment scheme is needed as we have made numerous assumptions about data quality, geometric accuracies, projections and other map qualities.

the accuracy of the new map cannot be determined. We know of no continental-

coNclusroNs

scale map that was necessarily more accurate than the one that we have produced. Undoubtedly some of the national-scale maps (eg. Venezuela by Huber and Alarcon, 'l 988) are more

The land cover digital map of South America described here is a unique product that has specific advantages and disadvantages compared with other continental-scale land cover maps of South America. lt allows rapid updating as new satell¡te data becomes available and its d¡gital format allows ease of use in modelling. lt has been created solely f rom

accurate than the map produced f rom this

work and could be used to assess accuracy at the national-scale for those countries where such maps exist. Combining these national-scale accuracy

estimates into a

cont¡nental-scale

estimate assume some form of intercomparability between maps that we are not

satellite data and therefore is relatively

91

consistent from country to country. The individual classifications are done objectively based on the digital data from the NOAA AVHRR satellites. The labelling or naming of the classes has been done in a manner that relied on numerical data on vegetation vigor which was related to leaf cover and rate of growth, on elevation, on vegetation seasonality determined from

REFERENCES ERDAS, 'l 990: lmage Processing Module,

ERDAS

lnc., Atlanta, Georgia. 218

pp.

FAO/UNEP, 1981a: Los Recursos Fores-

tales de la America Tropical, United

Nations 3216.1301-78-04, lnforme

the satell¡te data, on visual pattern

Tecnico 1, FAO, Rome.

analysis and by interactive examination of cont¡nental and national-scale vegetation

FAO/UNEP, 1981b: Forest Resources of

maps.

Tropical Asia, United Nations 3216.

Estimates of the area of 39 land cover vegetation types are presented for the continent and estimates of more generalized land cover are presented for all South Amer¡can countries. We estimate

FAO, Rome.

13O1-78-O4, Technical Report 2, FAO/UNEP, 1981c: Forest Resources of Tropical Africa, Parts 1 and 2, United Nations 3216.1301 -78-04, Technical Report 3, FAO, Rome.

that 9% of the closed tropical moist f

orests

of

South America have

been

cleared recently, ThaI 22o/" of the closed

orests

FAO, 1987: Magnetic Tape from FAO,

of

South America have been cleared or degraded as have 18% of the woodlands and 25o/o of the grasslands.

f

Untitled, Rome. FAO, 1985: 1984 Production Yearbook, FAO, Rome.

The methodology described in this paper represents a first attempt at producing a continental-scale digital map based on 1

Green, G. M. and Sussman, R.W., 1990:

Deforestation History of the Eastern Rain Forests of Madagascar f rom Satellite lmages, Science 248:212-

km resolution satellite data and represents

an order of magnitude improvement in available continental-scale land cover mapping f or South America. Similar

215.

products could be produced for all regions of the world with defined accuracies if funds were available to use higher resolution satellite data such as that from Landsat or SPOT to produce a continental-scale sample of imagery data for use in an accuracy assessment. A preli-

minary version

of this digital map

Holben, B. N.; Tucker, C. J.; and Fan, CJ; 1981: Spectral assessment of soybean leaf area and leaf biomass.

Photogrammetric Engineering

and

Remote Sensing, 46(5):651 -656.

Houghton, R. 4., 1991: Tropical defores-

tation and atmospheric

is

available by contacting the authors.

carbon 1 8.

dioxide. Climatic Change 19:99-1

Kidwell, K., 1988: NOAA Polar Orbiter Data Users Guide. NOAA/NESDIS,

National Climatic Data

ACKNOWLEDGEMENTS

Center,

Washington, DC. Malingreau, J.P. and Tucker, C.J., 1987: Large Scale Def orestation in the Amazon Basin. Ambio 17(1):49-65.

Major funding for this work was through a grant from the W. Alton Jones Found-

ation. We would also like to thank Norman Bliss and Jeff Eidenshenk of the

Matthews, E., 1 983: Global Vegetation and Land Use: New High Resolution

EROS Data Center, The Organization of American States, Dept. of Regional Development and Environment, and Dr. Claudia Sobrevilla, The Nature Conservancy.

Databases f or Climate Studies. Journal of Climate and Applied Meteorology

s2

,

22:47 4-486.

Management, 38(3,41:291 -304.

Myers, N., 1980: Conversion of Tropical

Moist Forests, National Research

Tucker, C, J.; Holben, B. N.; Elgin, J. H. and McMurtrey, J.E,, 1980: Relationship of spectral data to grain yield variation. Photogrammetric Engineering and Remote Sensing 46(5): 657-666.

Council, Washington, D.C. 205 pp.

Myers, N., 1989: Deforestation Rates in Tropical Forests and \eir Climatic lmplications, Friends of the Earth, London,116pp.

Woodwell, G. M.; Houghton, R' A.; Stone, T. A.'; Nelson, R. F. and Kovalick, W., 1987: Deforestation in

Nelson, R. F.; Horning, N. and Stone, T.A., 1987: Determining the Rate of Forest Conversion in Mato Grosso, Brazil Using Landsat and AVHRR

the tropics: new measurements in the

Amazon Basin using Landsat and NOAA AVHRR imagery. Journal of

Data. lnternational Journal of Remote Sensing 8(1 2): 1 7 67 -17 84.

Geophysical Research 2157-2163.

Stone, T. A.; Brown, l. F.; and Woodwell,

92

lD2l:

World Resources lnst¡tute, 1990: World Resources 1990-1991. Oxford University Press, New York, 383 PP.

G.M. 1991: Eslirnatas, By Remote Sensing, of Deforestation in Central Rondonia, Brazil. Forest Ecology and

93

CALIBRATING AVHRR DATA WITH LANDSAT TM DATA FOR TROPICAL CLOSED FOREST ASSESSMENT IN GHANA Risto Päívínen Juho Pitkänen University of Joensuu, Faculty of Forestry P.O. Box 1'l l, 8OlOl Joensuu, Finland

ABSTRACT

The calibration method for tropical closed forest cover a.ssessrnenf using Advanced Very High Resolution Radiometer (AVHRR) and Landsat Thematic Mapper (TM) data was tested. High-resolution satellite data is used to calibrate the coarse resolution AVHRR data for estimating class proportions within a single AVHRR pixel. The study area was one guarter of TM image in western Ghana. The TM data were ctassified into forest and non-forest classes using ground information and a supervised maximum tikelihood classifier. lroportions

of forest on the TM classification

were calcutated for each coregistered AVHRR pixel corresponding to the area of 33 bV 33 TM pixels. Relationship was determined between forest cover and AVHRR spectral signatures using multipte regression. Regression model was applied and tested for a quarter of other TM image.

pixels have mixed spectral

INTRODUCTION

response

which may lead to biased estimates of forest cover.

One possibility to overcome

Advanced Very High Resolution Radiometer (AVHBR) data of NOAA satellite have been used for large scale assess-

these problems is to estimate class proportions rather than a class label for a single pixel. Linear mixture modelling has been tested for tropical forest cover assessment using AHVRR data by Cross et a/. (1991). ln this method, 'pure' sites of each class of interest are identified, and signatures for these are defined. These 'endmember' signatures are ends of the continuum in spectral feature space. lt is then assumed that the spectral signature of a mixed pixel is a linear combination of the signatures of 'pure' sites, and it varies

ment of tropical f orest extent 1990, Päivinen et

(Cross â1. 1991) and defores-

tation (Malingreau et al. 1989, Malingreau and Tucker 1987, Woodwell et al. 1987, see also Sader ef a/. 1990) with encouraging results. The low data volume, due to the coarse resolution, and low cost of AVHRR data are advantages over Landsat and SPOT data in large scale mapping. ln tropical areas the daily coverage of the AVHRR data is also useful in order to

increase

the probability of

with the relative proportions of

obtaining

cloud-free data. The coarse resolution

(1

.1 km at nadir) of

the AVHRR data causes also

problems:

details of vegetation patterns are lost and

some misclassifications are obvious

ground

classes. Thus, the class proportions for a mixed pixel can be defined according to the position of the signature of the mixed pixel along the continuum in spectral

feature space.

in

heterogeneous areas. Forest or non-forest patches less than a pixel size and edge

ln another variation of mixture modelling remotely sensed data are calibrated using

95

ground or other measurements to provide est¡mates of vegetation cover with¡n the pixel. lverson et al. (1989 a,b) used this method for forest cover assessment in

Closed forests are typically dense, tall (up to 40 m) and have several canopy layers. Cocoa plantations are cleared f rom closed forest and have usually an upper storey of tall forest trees with crown coverage from

the central and southeastern USA with AVHRR and Landsat Thematic Mapper (TM) data. The classified TM image was used to determine the relationship between forest cover and the spectral signature of AVHRR pixels covering the same location. The relationship was

5 to 30 % to shadow the

cultivation.

Cocoa trees are 5-8 m tall, and the crown cover of cocoa is from 70 to 10O %.

Areas cleared f or diff erent kinds of agriculture are usually burnt, cultivated for some years and then left as fallow for some t¡me. Forest fallow refers to the area which is cleared so heavily that it

determined using multiple regression, and

the regression equat¡on was then applied

to the ent¡re AVHRR scene.

cannot be considered as forest anymore.

ln suitable conditions and without any urther disturbance, it can develop to

ln this study, the calibration method ¡s tested for tropical closed forest cover assessment using AVHRR and TM data. The TM data are classified to forest and

f

secondary forest.

non-forest classes, and regression analysis is used to derive an empirical

The ground truth information for the TM classif ication of the study area was

classification, from the calibrated AVHRR

collected during a field trip to Ghana 1 9.1 .-1 3.2.1 990. The ground truth sites were first located on the Landsat TM color composites and then in the field. More than one hundred field sites were

data and from the trad¡t¡onal AVHRR

assessed and assigned

relationship between AVHRR spectral signatures and the percentage forest

cover on the classif ied TM image. Estimates of forest cover from the TM

to vegetat¡on or land use classes with the assistance of

classification are compared.

experts from local forestry

and

environmental agencies. The classes and

the number of field sites in each class were as follows:

THE DATA

The study area was one quarter of TM image (path/row 199/55, quad 4, 20 Dec

-

1986) in central Ghana, west of the town

of

Kumasi. The

test area was f rom

adjacent image (194155, quad 3, 29 Dec 1986). The TM images were cloud-free,

but channels 1 , 2 and 3 were of poor quality because of str¡ping and atmospheric effects. lne RvHnn data

closed forest

(>40

7o crown

coverage)

39

forest fallow cocoa plantation fallow

34

agriculture

16 27

I

METHODS

used were from a local area coverage (LAC) scene of the NOAA-g satellite

The field sites were located on the TM image for tra¡ning sites and verification areas. Some additional training sites were also selected to represent urban areas (towns, villages and roads). Before the TM image was classified, separability of the spectral signatures of the land cover classes was studied.

collected 13 January 1987.

ln this area the remaining closed forests are in managed forest reserves, which form clear blocks. Outside the reserves most of the land is used for agriculture and agro-forestry, especially for cocoa plantation. ln Figure 1 , a subset of closed f

orest map derived using

supervised The TM scene was classified for selected land cover classes using a supervised maximum likelihood algorithm. Only

classification of AVHRR data (Päivinen et

al. 1991) is

presented, including the coverage of two Landsat TM quads used in this study.

channels

96

4, 5 and 7 were used. About

Fígure 7. TheTMscenesof thestudy(195/551 andtest(194/55) areasaredepictedinthe closed forest map derived using supervised classificat¡on of the AVHRR data (black = closed forest, dark grey=agriculture and agro-forestry and light grey=savannal.

one half of the field sites were used for

the

ication, and

calculated

f

or each AVHRR pixel

in

accuracy was tested against the other

corresponding area of TM pixels. ln other words, the TM classif ications were

half

converted to forest proportions maps.

classif

classif ication

.

The TM classification of the test area was obtained from an earlier study (Päivinen e¿ al. 1991) and it was based on few field

Every fourth line and fourth column of the AVHRR pixels of the study area were used to derive an empirical relationship between channel values of AVHRR pixels and percentage forest cover on the TM

plots and maps.

ln the next stage, the AVHRR data were coregistered to the classified TM images,

classif

ication. Correlations

between

AVHRR channel values and percentage

so that a single AVHRR pixel covered the area of 33 by 33 TM pixels. The study site was now covered with 86 by 89 AVHRR pixels, and the test s¡te with 91 by 89 AVHRR pixels. ln the final adjustment of the coregistrations, correlations betwèen AVHRR channel 2 and TM channel 4, and AVHRR channel 3 and TM

f

orest cover were analyzed, Multiple

regression was then used to derive a model to predict forest cover from the spectral response of the AVHRR pixels.

The regression model was applied to all AVHRR pixels of the study and test sites, and estimates of forest cover iq both areas were calculated as an average of the estimates of sìngle pixels. Also, correlation coefficients were determined

channel 5 were employed. The percentage of forest cover was then

s7

on a pixel-by-pixel basis between

The signature analysis of the TM data of the study site indicated that there was spectral overlap between the land cover

the

estimates of AVHRR forest cover model and TM proportions maps.

in the field (Figure 2). Class pairs cocoa plantation/ forest fallow classes defined

For comparison, total f orest

cover estimates were calculated for the study

and test areas from the

and agriculture/ fallow were almost totally

supervised

overlapping, and both of these pairs were to one class, when the TM

maximum likelihood classification of the AVHRR data. This was done by coregistering an earlier made closed forest map (see Päivinen et al. 1991) to the TM

combined

classif ications.

cocoa/f orest f allow and f allow/agriculture)

image was classif ied. However, clear still existed between three

overlapping

main cover classes (closed

f

orest,

after combination.

RESULTS

c)

c H

5

70

cll4

2. The signatures of the land cover classes in TM channels 4 and 5, presented as 90 isoprobability curves derived from covariance matrices. Class means are depicted by the locations of the abbreviat¡ons (closed forest CF, forest fallow FF, cocoa plantation CO, fallow FA and agriculture AGl. Figure o/o

The classified TM image was smoothed by 3 by 3 pixels modal filter algorithm in order to meet the minimum area unit of 0.5 hectares. The classification accuracy

Table 'l , because it was of minor importance (cover < 1o/ol. Approximately 85% of the reference pixels were found to be classified correctly. The area of closed forest was overestimated 2 lo, and the area of forest fallow/cocoa was similarly

was tested against the f¡eld sites, which were not used for classif ication (Table i ). Land cover class 'urban' was classified totally right, but it is not presented in

underestimated.

98

Table 1. The classification error matr¡x (per centl of the TM classification tested against the

classified pixels of 59 field sites.

clæsification CF

reference

FFlCO

27.9

4.4

6,2

31.3

34.1

2.4 38.1

All channels of AVHRR were significantly correlated with the percentage f orest cover on the TM classification. AVHRR channels 2 and 3 had the highest corre-

lation

coeff

icients

(-O.7

=

ao

roo.o

Stepwise regression method was used when selecting a model to predict forest cover over an AVHRR pixel. The selected regression model was of the form

+ ar(255-ch3-ch2)/(25b-ch3+ch2) + ar(2bb-ch3)/ch2 r2

of 0.7b and

forest percentages from the TM classifications and the AVHRR f orest cover model are presented in Figures 4 and 5.

root mean square error of 18.7. The model was then applied to all AVHRR

pixels in the study and test areas. The correlation coeff icient between the est¡mates of AVHRR forest cover model and the TM proportions was 0.86 in the study area, and 0.67 in the test

Compared

to the

AVHRR classification

(Figure 1), regression model produces smoother forest map than class labeling method, but the TM forest percentage map lies somewhere in the middle of these two approaches. The overestimation of the forest cover in the test area is

area.

Estimates of total forest extent in the study and test areas obta¡ned from

due to high coverage of non-forest area, where the model is producing some forest cover for almost all pixels.

different methods are presented in Table 2.The estimates differed from each other less than 3 %. Pixelwise estimates of

2.

27.7

centages associated with the values of 2 and 3 are presented.

4 and -0.69,

The model had an adjusted

Table

40.o

t 25.3 -------¿-27.8 |

AVHRR channels

respectively) and AVHRR channel b the lowest (-0.55). ln Figure 3, the forest per-

CFo/o

32.3

25

Estimates of closed forest cover in study and test areas

forest cover,

study a¡ea

%

test

area

TM supervised classification

32.1

14.5

AVH RR supervised classif ication

29.7

13.6

AVHRR regression model for forest cover

31.O

16.1

99

CF 100

90

fl) 70

60

50

() g)

n 10

0

CF

ïl ;J

"."':

]'r'i"

ooo

o'3¡8.

"."ss":3;":

s:::::;s: ".:.t;.;fl1"ö

tto "o

go

."i;;iåiii! 100

¡lså,¡: 1æ 1Æ 1æ 135 cH3

3. Closed forest percentage from the TM classification ly-axisl for every 16th AVHRR pixel in the study area plotted agaínst the pixel values of AVHRR channels 2 and 3.

Fígure

100

Figure 4. Forest proportion maps in the study area derived from the TM classification (leftl and from the AVHRR forest cover model (black = lOOo/o forest cover, lighter = decreasing forest cover and white = no forests).

Figure 5. Forest proportion maps in the test area derived from the TM classification (left) and from the AVHRR forest cover model.

101

DrscussroN

lverson, L.R.; Cook, E.A. and Graham, R.L., 1989 b: Estimating forest cover

over Southeastern United States using TM-calibrated AVHRR data.

The classificat¡on of AVHRR data has a disadvantage of simplif ication of the

Global Natural Resource Monitoring and Assessments: Preparing for the 21st Century. Proceedings of the

forest/non-forest map of tropical forest. The employed variation of mixture model-

international conference

ling has not this problem, but it gives even too smooth forest cover map. This phenomenon is visible in other similar trials as well (Cross et al. 1991). lt is necessary to study if the smoothing

and

workshop, September 24-30, 1989, Venice, ltaly, Vol.

3, 1252-1262.

Malingreau, J. P. and Tucker, C.J., 1987: The contribution of AVHRR data for measuring and understanding global processes: Large scale deforestation in the Amazon basin. lgarss'87 Proceedings. Ann Arbor, Michigan. pp. 484-489.

effect can be eliminated by improving the applied regression model. The encoura-

ging result was, that the model was working in a similar way when applied to

the neighboring test area.

The use of AVHRR data w¡th mixture modelling techniques has obtained promising results for large scale forest cover assessment in earlier studies (lverson et al. 1989 a and b, Cross et a/. 1 99 1 ). The results of this study also

J.P.; Tucker, C.J. and N., 1989: AVHRR for monitoring global tropical

Malingreau,

Laporre,

deforestation. lnt. J. Sensing, 4-5, 355-867.

validate f urther work with these methodologies. ln tropical areas one

of

Remote

Päivinen, R.; Pitkänen, J. and Witt, R., 1991: Mapping closed tropical forest

important task is to test the robustness of these models when applied to much larger

cover in West Africa using NOAA/ AVHRR-LAC data. Silva Carelica 21, 27-52, in press.

areas.

Sader, S.A.; Stone, T.A. and Joyce, A.T.,

REFERENCES

1990: Remote Sensing of Tropical Forests: An overview of Research

Cross, A.M.; Drake, N.A.; Päivinen, R.T.M. and Settle, J.J., 1990: Subpixel measurement of tropical

Applications Using Non-photographic Sensors. Photogrammetric Engineering and Remote Sensing, Vol. 56, No. 1 0, October 1 990. pp. 1 343-

forest cover using AVHRR data. lnt. J. of Remote Sensing, Vol. 12, No. 5,

1351.

1119-1129.

Woodwell, G.M.; Houghton, R.A.; Stone, T.A.; Nelson, R.F. and Kovalic, W., 1987: Deforestation in the Tropics: New Measurements in the Amazon

lverson, L.R.; Cook, E.A. and Graham,

R.L:, 1989 a: A technique

for

Basin Using Landsat and NOAA Advanced Very High Resolution Radiometer lmagery. Journal of

extrapolating and validating f orest cover across large regions-calibrating AVHRR data with TM data. lnt. J. of

Remote Sensing, Vol. 10, No. 11, 1805-1812.

Geophysical Research, 92, D2, 2157

2163.

to2

-

FOREST INVENTORY IN NEPAL Eka Raj Sharma Forest Survey and Statistics Division Ministry of Forest and Environment P.O.Box 3339, Kathmandu, Nepal

ABSTRACT

National and district levels are the two types of forest inventories taken in Nepal. Due to absence of statistical data the first inventory plots were systematically distríbuted with only the total number of sampling units in mind. Subsequent ¡nventories were designed to get the required precision when statistical information was available. The first national and district level inventories used a cluster of five rectangular plots as a sampling unit, the central plot being permanent and four other plots being temporary.

Since 1967 aerial photo based stratified random sampling was applied. A cluster of three círcular plots was used as a sampling unit. The radii of f¡ve concentríc plots ¡ncreased wíth the diameter of trees tallied. ln later surveys the total number of plots were so based as to est¡mate the volume within five percent accuracy with 95 percent probability. The plots were distr¡buted to strata on proportional allocation with a minimum of four plots in a stratum. Every fifth field plot was assigned to be a permanent plot for remeasurement. Problems of inventory were inaccessibility, stiff terrain, loss of trees due to theft and encroachment and relocation of plots. The present system of forest inventory uses a cluster of three circular plots with four concentric circles. Metric units of measurement have been adapted, and the sampling design criteria is the same as before except a limit of 25 plots as a maximum number per stratum. For national inventory satellite imagery based stratificat¡on w¡th auxiliary data is under development. However, in districts where intensive forest management is intended, inventories coupled with aerial photography will be continued. The planned Forest Resource lnformation Sysfern will combine different data sources and produce national forest information as well as some information for the purpose of districtwise forcst management.

only the total number of plots. When

INTRODUCTION

statistical

inf

ormation is

available,

subsequent inventories are designed to

achieve the required reliability

Two types of forest inventories are in vogue in Nepal. One is national forest inventory designed

to achieve

of

estimates.

data for

national planning and policy purposes and the other is the district inventories taken

PREVIOUS FOREST ¡NVENTORIES

for the purpose of preparing the forest management plans for implementation. Some statistical parameters such as mean volume per unit area and their variation are required to design the inventory. As such information is not available ¡n the first survey, the f¡eld plots are arranged in a systemat¡c pattern with a view to fix

lnventory design

First national inventory in Nepal was taken during 1964-1965 in cooperation

with USAID. The f ield plots

103

were

arranged in a systemat¡c gr¡d of 2x1O miles. A cluster of 5 rectangular plots, each of size 2 chains by 1 chain (66 ft x 132 fTl was used as a sampling unit. The afrangement of the plots in a sampling unit is shown in Figure 1 . The central plot was established as a permanent plot for

table was f irst prepared f rom the old data and the size of the radii of concentric plots was fixed aiming to tally 2 trees in 1 1 " dbh and 1 tree in 20" dbh (20" dbh being the exploitable size). The layout of these plots in a sampling unit is given in Figure 2.

remeasurement, and four other plots were ln such plots pole timbers (5.0" to 10.9" dbh) were tallied in the central strip of 1 32 ft x 1 3.2 ft and saw timber trees (11.0" dbh and large) in the whole plot. To determine the timber drain in the past five years stumps inside the plots were tallied. Five 1/500 acre (2 milli acres) plots located along the cçntral line

temporary.

For other district surveys the total number of sampling units was based on precision criteria. The aim of survey design was to

estimate the total volume within f ive

percent accuracy at 95 percent probability level. The plots were

distributed to various strata (stratif ied by forest type, stand size and density class)

and placed at an interval of 33 ft each were taken in central plot A to determine the proportion of 2 milli acres plots having

1,2,3,4,5-7

us¡ng proportional allocation

or 8 and more sapling/

a

systematic grids was laid over an aerial photograph and each dot lying in the

seedling, number of sapling/seedling by dbh, and proportion of commercial forest

eff ective area of the classif ied according

photograph was

to the stratum

by controlling cover or surface conditions.

lmportant individual tree species

with

minimum of four plots in each stratum. A transparent sheet printed with 25

in

and

which it felled. After classifying the dot

miscellaneous group of trees were tallied of bamboo were also enumerated.

they were added together according to

grids in all the photographs of the d¡strict,

and clumps and culms per clump

the stratum. The systematic selection of dot grid with a random start was done to fix the f ield plots in a stratum, and all the fixed plots in the strata were

The first intensive district inventories for making forest management plans were also done using the same 5 plot cluster design arranged in 2x2 miles grid in the

systematically numbered. These plots

were then

forest reserves of Timber Corporation of Nepal and extended additional plots in

transf

erred

to the aerial

photographs. Plot numbers ending in 0 or

5 were taken as permanent plots f or

Banke and Bardia districts. However, the classical ten percent strip enumeration was carried out in three other distr¡cts.

remeasurement.

The plot center marked on the photo was transferred on the ground with the help of two identifiable objects both on the aerial

The areas which had a recent complete coverage of aerial photographs were measured from the maps prepared with

photograph as well as on

the ground. From the center po¡nt three sets of circles were established and enumerated. The

the help of interpreted aerial photographs.

For areas having older photographs

layout of the sampling unit was similar as in figure 2, only the radii of the circles were adlusted f rom one district to

broader classification was done. The old and new photographs of the sample strips

were then compared and the changes

another.

were noted. The ratlo of means est¡mator was used to find out the changes and thus the new area under different strata was worked out.

Measurements in the field plot Field plots were laid out in the forest and the general ref erence of the f ield plot was noted. Two witness trees with species, dbh, azimuth and distance from the plot center were recorded for relocation. The trees were then tallied. For permanent plots the distance of individual trees f rom

Since Bheri-Karnali survey in 1967 a cluster of 3 circular plots was used as a sampling unit in aerial photo based stratified random sampling. Each plot had 5 concentric circles. The radii of these increased progressively with the increase in diameter of trees in the plot. A stand

the reference central line (in case of

104

rectangular plot) or distance and bearing from central point (in case of circular plot) were also recorded. Random checking of field work by the supervisor or the crew who did not establish that plot was done to reduce the location, measurement and

hill survey there was no complete cove-

rage

at ten mile intervals were photographed for comparative study of changes in the land use. As the area was determined by using the ratio of means estimator, it was bound to have sampling errors and the volume estimate was, therefore, subject to both area and volume sampling errors.

recording errors. The tally sheet inf ormation was processed in the office to determine volume per unit area in a stratum. Total volume of the stratum and then volume by area units such as blocks, ranges, districts, regions, etc were obtained.

For

conif

of recent photographs. One mile

wide strips running east-west and placed

PRESENT METHOD

ers growth ring measurement

The number of sampling units required to

was carried out by taking

increment boring. For hardwoods remeasurement of permanent plots was done whenever and

est¡mate

the total volume within 5

percent accuracy at 95 percent probability level is calculated, and the sampling units are distributed to each stratum

wherever possible.

to its proportion'in the total orest area to be inventoried w¡th a restriction of minimum of 4 plots and maximum of 25 plots per stratum.

according f

Accuracy of the survey The forest estimates were subject to two

types of errors, sampling errors

At least 20

and

nonsampling errors. Nonsampling errors such as use of inappropriate tree volume tables/equations, incorrect identification of tree species, errors in measurement,

of a stratum, determination of stratum area and compirecording, classif ication

inf

are

cluster (Figure 2) is used except that there are

only 4 concentric circles with

radii

5,10,1 5 and 20 meters and the minimum

dbh tallied being 1,12,25,4O

cm

respectively.

ln the earlier surveys the plot radius was changed from one district to another according to the per hectare figures of stand d¡str¡but¡on but in general, the radii did not vary considerably. The plot radii have, therefore, been fixed for the Terai

ormation.

Two variables were involved in volume estimate: area and volume per unit area,

and both of them were subject

of the plots

The same three circular plot

lation, were kept to a minimum with repeated checks in the field work as well as in the office compilation. Sampling errors are kept to the specified limit by doing proper design of the forest inventory, that is based on the available stat¡stical

percent

established as permanent. The location of the trees is measured, and the UTM-coordinates for the plots are found out by using a dígital mapping program.

to

sampling errors. However, for the Terai inventory the whole area had a complete

districts.

coverage of recent aerial photographs of 1:12,0OO scale. So, after interpretation of these in the off ice repeated field checking was carried out and three measurements of the same area were taken to get consistency in determining the exact area of a stratum from the map. No area error was therefore assumed for the Terai. The volume e¡rors for the total volume to 4" and 8" top diameters were 2.61 and 3.1 8 percent respectively at 66 percent probability level. Without stratification they were found to be 3.68 and 4.61 . For the

The metric system of measurement have been adapted and volume equations have been developed to f acilitate computer processing of field data. As biomass is

more important, biomass estimates are provided along with volume estimates. More appropr¡ate b¡omass and growth equations are being developed. The following are the estimates and their precision obtained during the inventory of Dang district in 1990.

10õ

Variable

Unit

Mean

Std. Error

Total stem volume

m3/ha

69.4

3.03 2.36

Vol.to 1Ocm top Vol.to 20cm top Total dry mass

50.2 32.5 T/ha

Stem mass Branch mass

3.62 2.56

57.9

Foliage mass Trees/ha

* at 95 percent

2.O2

82.1

no

20.6 3.5 229.01

0.91 0.1 6

16.71

Confidence limitsr

63.43 45.59 28.52 74.95 52.91

- 75.30 - 54.86

- 36.46 - 89.15 - 62.95 18.80 - 22.35 3.23 - 3.86 196.30 -261.78

probability level

PROBTEMS IN DATA COLLECTION

This method does not seem suitable for

Nepal has only 20 percent of her land with flat terrain, and these areas are almost all under agriculture. Forests are located in moderate to steep hill slopes except a narrow stretch on ¡ts south. Roads are limited due to which many

tion is required for national planning and policy purposes. lts role in limited areas is, however, well justified. One of the best ways of doing national ¡nventor¡es may be by using satellite ìmagery and

national inventories where quick informa-

geographical information system.

orest areas become inaccessible. Terrains with 100 and more percentage of slopes are categorized as noncommercial, though

f

Nepal is planning to have its national inventory data by 1993 implementing two-phase sampling through the use of

some limited collection of forest produce

for local use is

satellite imagery, available auxiliary data and field sample. The work has recently started with a pilot area of f ive districts in the Western Development Region. These areas have been photographed last winter to the scale of 1:25,000 and the Landsat TM-satell¡te imageries have been

done even from such

forest areas.

The f ield plots or trees marked f or remeasurement are lost due to encroachment or theft which creates problems for the determination of growth hardwood species. Exact location of field plots becomes a problem specially in a dense hardwood tract. Problem of

of

procured. Now field sample plots are

of f ield plots f or growth measurement renders growth determi-

nuing. The results of this study will be out by the end of 1 992, and the technique thus developed will be utilized to assess the forest resources of the country. The

being laid in the aÍea with f ield measurement, and the study is conti-

relocation

nation

diff

icult.

Sometimes diameter

measurement point specially in young trees are healed due to faster growth.

Forest Resource lnformation System is being developed in the Ministry of Forest and Environment w¡th the assistance of FINNIDA. A four year program is being launched to establish the system and

FUTURE PI.ANS

Forest lnventory system with

Nepalese staff will be trained to maintain and run this system.

aerial

photography is usef ul for a relatively small areas where intensive management operations are to be applied. When th¡s system is used for national forest inventories, it is going to involve lots of aerial photographs which are to be manually interpreted. This involves lot of works besides the huge

expenditure

to take

REFERENCES

aerial photography

District forest lnventory Manual, Forest Survey and Research Office, Department of Forests, HMG, Nepal.

and by the time the inventory report is out it w¡ll be already four to six years old.

106

lnstruct¡ons for Field Data collection in the Forest lnventory of Nepal, 1 963:

Field lnstructions for Forest lnventory, 1 991: Kapilbastu and Rupandehi District, HMG, Ministry of Forest and

Forest Resources Survey, Department

Envlronment, Forest Survey

of Forests, HMG.

Statistics Division, Babar

and

Mahal,

lnventory Report of Dang District, 1990: Forest Survey and Statistics Division, Ministry of Forest and Soil Conservation.

Kathmandu, Nepal.

Forest Statist¡cs for the Hill Region, 1 973: Forest Resources Survey, Department of Forests, HMG, Nepal.

Timber Resources and DeveloPment

Forest Statistics for the Tarai and Adjoining Regions, 1967: Forest

Opportunities in the Lower Bheri and

Resources Survey, Department of Forests, HMG, Nepal in cboperation

Karnali Watershed, 1969: Forest Resources SurveY, DePartment of Forests, HMG in cooperati'on with

with USAID/N.

USAID/N.

lo7

I õi

A

N

Fígure

l.

Figure

The national forest inventory cluster.

2.

District forest inventory cluster. I Otl

UNITED STATES ENVIRONMENTAL MONITORING AND ASSESSMENT PROGRAM: LANDSCAPE CHARACTERIZATION AND REMOTE SENSING Raymond L. Czaplewski Rocky Mountain Forest and Range Experiment Station 240 West Prospect Road Fort Collins, Colorado 80526-2098 USA

Douglas J. Norton U. S. Environmental Protection Agency Vìnt Hill Station Warrenton, VA 22186 USA

Denis Whíte ManTech Environmental Technology, lnc. 200 S.W. 35th Street Corvallis, OR 97333 USA

ABSTRACT The United Sfa¿es Environmental Protection Agency has started the Environntental Monitoring and Assessment Program to monitor status and trends in the condition of all of ecological resources in the llnited SÍa¿es. This program wilt be integrated related prograrns, such as the Forest Health Monitoring system being developed by the lJnited States Forest Service. Remote sensing and the landscape perspective are important to the success ancJ effectiveness of this and other environmental monitoring systerns. Five distinct roles of remote sensing and landscape characterization are given. Past, present, ancl future strateg¡es are briefly discussed.

inf rastructure of its large Forest lnventory and Analysis system. EMAP will monitor and report on the condition of the nat¡on's ecological resources, evaluate the success of cumulative environmental policies and regulations, and identify

¡NTRODUCTION

The United States has started a long-term program to monitor the status and trends of all its ecological resources (Palmer and

emerging problems before they become widespread or irreversible (Messer et a/. 19911. Palmer and Jones (1992)give the

Jones 1992). This program, known

the

Environmental Monitoring

as and

Assessment Program (EMAP), is a multi-

three objectives of EMAP. Remotely sensed and map data are required to

institutional program, which has been developed or evaluated by over 400

achieve these objectives.

scientists, ln cooperation with EMAP, the United States Department of Agriculture (USDAI Forest Service is developing its Forest Health Monitoring program (Shaw

EMAP uses

a

probability-based sample

survey that includes a systemat¡c grid of points covering the entire United States

1992), which builds upon the existing 109

(Palmer and Jones 1992). Each sample unit at the f irst level is a 40-km2 hexagon; sampling intensity is 6 percent by area. Center points of adjacent hexagons are separated by 27-km at 60'angles. There

are 12,600 such cells in the

comprehensive environmental monitoring system for the condition and use of renewable natural resources at the national scale.

United

States. which totals 8 million km2. Small field plots for making indicator measurements on specif

ic

are

Landscape-scale lndicators

Landscape-scale indicators

sub-populations

established within each 40-km2 hexagon. Each major type of ecosystem can have a different sampling frame for f

measurements

of

are

environmentally

meaningf ul features at scales bigger than

field plots, which often require remote sensing and, cartographic methods.

ield measurements, but all sample f rames

are confined to the 40-km2 hexagons. Sampling intensity of 40-km2 hexagons

Examples include distribution

of forest patch sizes, perimeter-to-area ratio,

can change in increments of 1l7tþ, 1l4th,

fractal dimension, contagion index, and

1l3rd, 3, 4, 7, and multiples of these f actors, while maintaining a unif orm spatial distribution. This grid geometry

habitat proportions. These indicators are used to study the possible cause-effect relationships associated with broad-scale environmental phenomena. Objectives

facilitates uniform

interpenetrating subsampling (Palmer and Jones 1992). For example, measures can be made during the first year within one of the 40-

include monitoring condition, extent, change and trends in broad-scale features such as different landscape pattern types that have environmental and social importance.

km2 hexagons in each cluster of 4 hexagons; each subsequent year, measurements are made on a different hexagon in each cluster, i.e., a 4-year

Low-altitude aerial photography can image

remeasurement cycle.

0.25- to 4-km2 plots, and resolve features 0.1- to 1 .0-m2 in size. High-altitude aerial photography can image 20- to 4O-km2 plots, and resolve features 1- to 25-m2 in

ROLES OF REMOTE SENSING IN ENVIRONMENTAT MONITORING

size. Earth resources satellites can image 4,000- to 16,000-km2 plots, and resolve features 0.5- to 2-ha in size. Weather

satellites can image sub-continental

Most indicators of

regions, and resolve f eatures 5- to 4O-km2 in size. Cost per unit area is lowest with

environmental

condition are measured on small f ield plots. However, remote sensing may serve the f ollowing f unctions:

(1 )

weather satell¡te data, and highest with

low-altitude aerial photography.

measure

However, cost of aerial photography can be decreased by sampling rather than complete coverage of large regions, and sample aerial photography can cost less

indicators to study associations between

landscape structure and environmental condition at the regional scale; (21 measure diagnostic indicators that

than satellite data. Landsat satellite pixels are too big to monitor most field

describe the vicinity near each field plot, which are used to study associations between local landscape features (e.9., degree of forest f ragmentationl and forest condition (e.9., defoliation from insects); (3) cost-eff ectively measure ecological condition of f ield plots (e.9., covariates of field measurements in a double sampling framework); (4) develop frames for field sampling of certain ecosystems types

plots, and AVHRR weather satell¡te pixels are too big to accurately register to 40 km2 EMAP hexagons. Landsat film output classif ied with manual photointerpretation techniques at less cost than classification of digital Landsat data; however, classification and spatial detail of manual photo-interpretation is less.

can be

(e.9., wetlands); and (5) estimate area occupied by major ecosystem types, and changes in area of different types. The

There are 4 general technical alternatives to measuring landscape-scale indicators for the 8 million km2 in the United States. (1 ) Each year, aerial photography could be

remaining sections discuss these 5 components, which relate to any

110

level processes are poorly understood,

acquired for a subsample of large plots with low-altitude aerial photography. This might be an ¡nterpenetrating (e.9., 1/4th) subsample each year (e.9., 1 800 4O-km'z EMAP hexagons imaged each year), with a 4-yeil rotation; the expected cost is US$1 million to $10 million per year. (2)

and landscape diagnostic indicators will be used most frequently ¡n exploratory analyses. The scales and mechanisms of

the

of remotely sensed imagery, classification detail, and interpretat¡on techniques. Since our understand¡ng of landscape-level

Landsat digital data could be acquired for

a rotating subsample of current satellite

processes is limited, landscape diagnostic

scenes. This might be an interpenetrating subsample of Landsat scenes, e.9., 1 Landsat scene in each contiguous block of 7 Landsat scenes classified each year with a 7-year rotation. This would sum to

indicators will likely be built using variety of remotely sensed data.

detect within- and between-season spectral patterns that indicate regional

a cost of $5 million to $50 million per year. (3) Photo-interpretation of Landsat film products could be used for a rotat¡ng

env¡ronmental stress (e.g., unusually early senescence of vegetation in a subcontinental region). This might explain responses of field plots in that region. However, seasonal phenology varies among vegetation types, and some kind of prestratif ication by vegetation or

subsample of current satellite scenes. For example, a 1l4th interpenetrating

of Landsat scenes could

be

interpreted each year in a 4-year rotat¡on,

costing $0.1 million to $1 million per year. (4) Annual classif ication of weather

landscape patterns would be required. ln addition, Landsat data could also be used

satellrte data (e.g., 1 .1-km2 pixel AVHRR data) for the entire United States could be

digitally classified, which could cost million

measure

harvesting, defoliation, or moisture stress close to field plots. Landsat could detect changes in local forest fragmentation or

urbanization,

or might produce coarse

characterizations of horizontal vegetation structure near f ield plots f or wildlif e

habitat analyses. Periodic Landsat ications would help study the interactions among forest, agro-

classif

ecosystems, and arid lands f or integrated

stand- and tree-level processes (Saunders et al. 1991 ). Compared to the landscape

assessments. For examPle, Landsat classif ications could provide coarse measures of forest conditions in entire watersheds to analyze effects of land management on the quality of surface waters. Aerial photography obtained for

indicators discussed above, landscapelevel diagnostic indicators are more local km2).

Diagnostic indicators seek associations between responses of ecological resources at the scale of field plots that

other ob¡ect¡ves could provide more detail or certain landscape-level diagnostic indicators.

surrounding

landscape. Land use practices

to

could detect changes in biomass associated with disturbances such as

be

plot can be stressed by disturbances in the surrounding landscape, such as patches of tree defoliation or tree mortality, catastrophic events (such as fire), timber harvest and timber management, and changes in land use (e.9., clearing for agriculture or urban usesl. A fragmented forest landscape can affect

are affected by the

ormation

subsample of satellite scenes. Multispectral leaf area or greenness indices

associated with diff erent types of stress on small field plots. For example, a field

in scale (e.g., 25 ha to 1000

inf

certain sources of environmental stress on field plots. Change detection techniques could be done periodically, such as once every 4-years, for an Interpenetrating

to $10 million per year.

erent landscape f eatures might

or diagnostic

f

$'l

Landscape Data for Diagnostic Purposes Diff

a

AVHRR weather satellite data might

72 scenes per year in the United States at

subsample

landscape-level processes should

determine the type

f

and

interactions among different land cover types may cause some of these changes. Remote sensing might provide data that describes land cover and land uses close to each f¡eld plot. However, landscape-

Remotely Sensed Measurement of Field lndicators

111

is a cost-effective measurement tool for certain indicators of Remote sens¡ng

forest condition on f ield plots.

laser altimeters can accurately measure

heights of tall trees (>15 m). Crown diameters, and crown competition indices for dominant and codominant trees might be more accurately measured with such photography. Another example is the

The

following are examples of indicators that could be measured by, or correlated with

interpretations

of

low-altitude

aerial

photography and videography: rates of individual tree mortality, defoliation, and regeneration; coarse indices

of

area

vertical

and horizontal vegetation structure at diff erent scales, which are related to

Some remotely sensed variables will be

highly correlated with f ield

wildlife and bird abundance and diversity; indicators of tree stocking, gap-dynamics,

calibration estimators will be needed to adjust f or measurement error. These

intercepted photosynthetically active radiation or leaf

statistical approaches require

of

area ¡nd¡ces; prevalence and e{fects of disturbances, such as pest and disease

sensed and field

of

will not eliminate need for field work, but it m¡ght allow longer intervals between some field remeasurements.

individual wetlands, lakes, or riparian s¡tes. Satell¡te imagery could detect major disturbances near f ield plots, today or in the past (with

historical imagery).

Low-altitude, high-resolution aerial photography will be needed to measure most indicators that relate to individual field plots. Cost of low-altitude high-

Compared to f ield sampling, remote sensing might provide less expensive measurements, and perm¡t larger sample size or more frequent remeasurements at the same cost. Cost depends upon the number of different objectives served by the same remote sensing activities. For example, cost of aerial photography for indicator measurements on field plots will be less if the same imagery is also shared for assessing accuracy and calibration of ¡nterpreted satellite data. Some indicators

resolution aerial photography does not permit "wall-to-wall" imagery f or the entire United States. Rather, aerial photography for sample plots is required (e.9., 0.1 to 1.0 percent sample by area of a nation). Locations for sample aerial photography must be established a priori.

Field of view from low-altitude, high-

resolution aerial photography is limited to

1 to 4 km2 for 230 x 230-cm image format. There are numerous potent¡al

be

measured during a narrow phenological window during the year. Compared to

sampling designs and image acquisition

alternatives. The benef its of any alternative cannot be objectively determined until requirements for lowaltitude aerial photography are better

ield work, remote sensing provides ability

to

image and later measure plots at approximately the same season. Also, frequent remeasurements by field crews

defined. Optimal timing of imagery might differ among ecosystems.

unusual

fashion, such as trampling damage field crews.

measurements.

However, remote sensing

and changes in area of

can affect field plots in an

subsample

to develop a statistical model between remotely

disturbance risks, such as fuel loading;

of ecosystem conditions must

a

accurately registered calibration (or

second-phasel plots

outbreaks, land use changes, harvesting,

windthrow, and f ire; magnitude

measure-

ments. However, double sampling or

and crown competition;

f

of certain wetlands, ponds, and

lakes.

by

Some field variables can be measured

rom past imagery. This will permit retrospective analyses, where past

f

Certain indicators might be

better measured with remote sensing techniques

conditions are measured using historical imagery. However, this requires saving suitable imagery. Since low-altitude aerial

than with field protocol. For example,

leaf area index might be more accurately measured with a Normalized Difference

photography is needed f or monitoring most indicators of environmental conditions, this imagery must be acquired and stored now, so that it will be available for historical analyses. This would maintain

Vegetation lndex f rom multispectral satellite data or an airborne spectrophotometer. Photogrammetric analysis of low-altitude aerial photography or airborne

rt2

the option to intensify sample size in the f uture, with minimal initial investment relative to the cost of field plots. Systematic sampling

with

eliminate or reduce redundancy or incompleteness. The selected f rames constrain

the sampling, results, and

low-altitude

aerial photography is not routinely done in

the United States; therefore, cooperating institutions would have to pay directly for

a new, large program. However, there

diff

are opportunities to use the same imagery

or f ield indicators, landscape ecology indicators, landscape-level diagnostic

indicators, f rame development, extent estimation, and aids f or classif ying data.

calibration

of classifications of

f

rames can be applied

bef

ore. these

materials are available. Grid and list f rames can be used together, such as lists of tree plantations within a sample of Landsat scenes. List frames need not

Cost-effectiv eness

increases with the degree of shared benefits. For example, the same imagery used to measure indicators could also be used for accuracy assessment and

identif

y all detailed sub-categories of

resources, but

Landsat data, or prestrat¡f ication of field sampling,

f

both of which provide opportunities for increases in efficiency. To attract cost-

this restricts

prestrati-

ication on these attr¡butes.

There are many different ways to develop frames. (1) The common practice among existing monitoring programs is separate frames that can be optimized for each

sharing by different institutions, the same imagery would have to effectively serve many different objectives; unfortunately, multiple programs and objectives lead to

complex institutional challenges

erent f rames will depend on the

degree of correspondence among f rames. List frames require identifying or mapping of the entire statist¡cal population before samples are selected, whereas grid

f

satellite

inf erences.

Missing resources cannot be estimated, and statistical efficiency suffers if sample units are misclassified. Frames can be improved or replaced in the future, but estimates of changes or trends across

resource or sub-population according to objectives of different inst¡tutions. This reduces costs for any single institution,

and

conflicts, which are rarely solved.

but total costs can be higher, and integration among

diff

erent institutions

Frame Development

requires close cooperation. (2) A single shared frame can be constructed for all

For each population of resources sampled

resources, which was the first EMAP strategy. Such a frame reduces missing resources, but very accurate remotely sensed classifications are required, which can be costly and t¡me-consuming f or

with a

probability design, a tangible representation of the population is

necessary to select a sample. This representation is called a frame. A ,s¿ frame might consist of a list of members

a region is arbitrarily divided into areas, which are analogous to elements of a list. A complete grid of Landsat scenes is an

large regions, and which can quickly become out-of -date if land uses ate changing. This frame also requires classif ications systems and population definitions that are shared by all participat¡ng institutions, and bureaucratic consensus is difficult. (3) A very dense grid would avoid cost and delays from remote sensing, but would preclude any deta¡led prestratification, thus compromising eff iciency. (41 Two separate frames could be used: a grid frame for extensive resources such as forests and agricultural lands, and a list

example of an area frame.

f

of the stat¡stical

population, such as stream locations on a map. A grid f rame is a method to guarantee that any point, line, or area in the geographic area being

covered by the design can be selected

with known

probability

f

or

sample f rame

selection. For example, a grid

could be defined by the center point of each 0.S-degree map in a region. An area rame is a variant of a list f rame in which

f

rame f or discrete resources such as lakes or isolated plantations. This would avoid problems with a shared frame as in (2), and reduce eff ects of misclassif ying similar resources such as small lakes and wetlands. (51 Existing or convenient

For each major resource, the sampling frame must be representat¡ve, complete, and available when needed. lf possible, different frames should be combined to

113

frames could be used in the beginning,

There are several alternative approaches

and eventually modified into an improved and more integrated frame. This current

to

estimate extent. (1 ) Extent for different resources can be estimated with

EMAP strategy allows more time for

independent sampling f rames, either within EMAP or from different institutions. This solves certa¡n pragmat¡c problems, but can lead to contradictory

institutional negotiat¡ons, but may require future adjustments to est¡mates that use multiple frames. (61 A hierarchical frame

could use classif ications f or 10 or f ewer categories of landscape patterns at its f

irst level (e.9.,

interpretat¡on

of

AVHRR

estimates. For example, the USDA Forest Service and Soil Conservation Service use

or

different tree densities to define forests, and estimates of forest extent made by

visual Landsat images), plus

overlays of more detailed wetland and surface water map_s from maps or other remote sensing sources. Within these classif ications, separate f rames would then be developed f or f ield sampling

each institution can differ greatly

in

certain regions. (2) A single integrated sampling frame could be used; estimates would sum to 100 percent, but might be inconsistent w¡th est¡mates from other

within landscape patterns. However, problems would remain in integrating sampling f rames f or extensive and

institutions. (3) Estimates could be made from EMAP field plots alone, but these might have high sampling error. (41

discrete resources. Also, classifications at the first level could change through

Separate frames could be developed for extensive resources such as forests and

landscape-level changes in land use or by

arid lands, and discrete resources such as small lakes and streams. (5) Extent could

registration errors, which could make

with past data, and

statist¡cal estimation diff icult.

be

Extent Estimation

improved methods adopted slowly over time, although this will make estimates of changes and trends in extent difficult.

estimated

The extent of a resource is a measure of its size. Extent is described in units of

area

for extensive

hectares

resources, such as

of forests, arid lands,

EMAP I.ANDSCAPE CHARACTERIZATION STRATEGIES

and

agricultural lands; units of length for linear resources, such as kilometers of streams or riparian zones; or number of discrete resources, such as lakes of certain sizes,

ln May, 1990, the EMAP Landscape team presented an implementat¡on strategy to classif y Characterization

or some types of wetlands. EMAP estimates should be consistent with estimates

resources, develop sampling frames, and estimate changes and trends over time in

of extent from other U.S.

the extent of different ecological

institutions and agencies. Also, est¡mates should sum to 1 00 percent of region, although this could make EMAP est¡mates disagree with those of other institutions because classification systems may be

resources. The strategy called for photo-

interpretation

diff erent.

Extent and condition estimates

are

can be obtained indirectly through ratios of plots sampled weighted by extent of the entire population. For example, the extent of the pine sub-population can be estimated by the proportion of forested

For

lnventory system (Cowardin et al., 1979],, land use concepts were influenced by the U.S. Geological Survey system (Anderson et al., 1 976), arid lands classif ication concepts were affected by the work of

ied in the f ield as pine, by the extent of f orestland

classif

mult¡plied

:40,000-scale aerial

example, wetlands and deepwater habitats were classif ied in a manner compatible with the National Wetlands

frame

development. Extent of sub-populations

plots

1

systems as much as possible.

constrained by success at defining the

statistical populations and

of

photography to classify land use, and digital classification of land cover using Landsat data, with over 500 scenes for the United States. The EMAP classification system used existing institutional

Brown et al. (1979), and forest classes

used

obtained through independent techniques.

LL4

def

initions of the Society

of

19801. A single, comprehensive land classification system {Norton et 31., 1990} was

American Foresters (Eyre,

being developed, and

photo-interpretation

will be applied

by

f

ilm

of Landsat

products. A large experimental study has been started by EMAP, using the highly urbanized Chesapeake Bay watershed

compiled by the EMAP Landscape Characterization team and a subset of 83 priority classes was identified from this system. It was proposed to enter these and other

(160,000 km2) in the eastern United States to test alternatives. Landscapelevel indicators are being researched.

spatial data into a large geographic information system. A plan was evaluated to image all remotely sensed

Practical compromises are being made to

develop f ield sampling f rames more quickly. Efforts are being made to involve other Federal institutions, and remove inStitutional barriers that have stopped development of common national

data between 1988 and 1992, and again between 1 998 and 2OO2 for change detection. lmage interpretation would have cost $56 million for the 1988 to 1992 data, and require 10 years to

standards for land cover classif ication and mapping. Existing land cover classif i-

process.

cations This plan was abandoned as the short-

while

a

will be used where available,

consistent national system is

developed. AVHRR data is being evaluated for environmental monitoring

term EMAP strategy. Field work could not wait until remote sensing produced sampling frames for EMAP field work. Rather, list f rames for surface water sampling were constructed from existing maps, although these maps contain

and ecological indicators.

errors, and some lakes and streams are

missing. Grid frames for monitoring forest health were based on the existing grid used by the USDA Forest Service's Forest lnventory and Analysis projects.

An area f rame was used for

ROLES OF 1ANDSCAPE CHARACTERIZATION TEAM The Landscape Characterization team has 3 distinctly different roles in EMAP. (1)

agro-

ecosystems based on an existing frame maintained by the National Agricultural

They serve as landscape ecologists to study landscapes as distinct resources, which are broader in scale than natural resource as they are commonly defined; (21 they provide operational support to other EMAP teams in the areas of geo-

Statistical Service for annual crop reporting. The National Wetlands lnventory of the F¡sh and Wildlife Service cooperate in development of the wetlands sampling frame, ln addition, the first EMAP strategy was inadequate for est¡mating extent of various resources. Area estimates would be 3 to 10 years out of date when image interpretation was completed, and est¡mates of changes in extent could not be produced until 2003 to 201 0. Rather, existing estimates

will

of extent from other

graphic information systems, remote sensing, and classification systems; and

(31 they provide coordination

and

oversight of remote sensing activities of other EMAP teams for development of landscape-level diagnostic and remotely

sensed indicators. This

includes

identification of innovative opportunities,

government

inst¡tutions in the United States will be

where the same remotely sensed data can effectively serve different objectives, thus providing better data while minimizing

used in the short-term, Also, new emphasis was placed on monitoring landscapes as natural resources, using

additional costs. These roles must be separate to maintain accountability for

concepts from landscape ecology.

different responsibilities. For example, the responsibility for frame development and extent estimation has shifted away

The landscaþe characterization strategy will be revised during the coming year. Existing spatial data for the nation is being entered into the EMAP geographic inf ormation system. A classif ication system for landscape pattern types is

from the Landscape Characterization team and towards other EMAP Ecosystem Resource teams as technical strategies have changed.

11ó

coNclustoNs

Department

of

lnterior, Geological

Survey. 28 pp. Brown, D.E.; Lowe, C.H. and Pase, C.P.,

Remote sensing is important to the success of environmental monitoring, as discussed by Lund (1992). But remote

1979: A d¡gitized

North America, with

sensing and landscape characterization for a

continental-scale monitoring system can

the Southwest. Journal of

the Arizona-Nevada Academy of Science

Probability of success depends on image analysis methods and statistical sampling designs. But a larger factor in success is

14:1-16. Cowardin, L.M.; Carter, V.; Golet, F.C. and LaRoe, E.T., 1979: Classification of wetlands and deepwater habitats of the United States. FWS/OBS-79/ 31, U. S. Dept. of lnterior, Fish and Wildlife Service. 131 pp.

as

existing infrastructure and the success of

institutional strategic planning. Success requires sound goals, precise objectives, and a clear identification of the various roles of remotely sensed data and those who produce these data. lt may not be

Eyre, F.H. (ed.),

possible to implement the ideal monitoring system ¡n the short-term, but a general

natural resources. Paper presented at the IUFRO S 4.02.05 lnternational Conference on Permanent Plots for

World Forest Monitoring. 1992 January 13-17; Pattaya, Thailand.

The authors thank Drs. Dan McKenzie,

Norton, D.J.; Slonecker, E.T. and Mace,

T.H., 1990: EMAP Landscape

Dave Mouat, Eric Hyatt, and Craig Palmer with the U.S. Environmental Protection

Characterization Research and lmplementation Plan. Review Draft,

Agency f or help with the conceptual development of this paper, and Gyde Lund and Susan Franson and Kristen Stout for reviewing this paper. The

EPA-600/X-90/1 099A, U. S. Environ-

mental Protection Agency, Washington, DC. 224 pp.

information in this paper has been f unded in part by the United States

Norton, D.J. and Slonecker, E.T., 1990:

Landscape Characterization: the Ecological Geography of EMAP.

Environmental Protection Agency. lt has

for

Forest cover

Lund, H. G., 1992: A primer on permanent plots f or monitoring

ACK]ìIOWLEDGEMENTS

approved

1980:

types of the United States and Canada. Society of American Foresters, Bethesda, MD. 148 pp.

long-term vision of an ideal system will maximize the probability that short-term decisions will help today's compromises evolve into a better future svstems.

been subjected

community

(series) and association examples for

be large and complex endeavors.

the ¡nst¡tutional environment, such

classif ication

system for the biotic communities of

to

Agency review and publication. Mention of

GeolnfoSystems

trade names or commercial products does not const¡tute endorsement or recommendation for use.

1

:33-43.

Messer, J.J.; Linthurst, R. A. and Overton, W.S., 1991: An EPA program f or monitoring ecological status and trends. Environmental Monitoring and Assessment 17:. 67-

78.

REFERENCES

Palmer,

for use with

C. and Jones, K. 8.,

United States

Anderson, J.R.; Hardy, E.E.; Roach, J.T. and Witmer. R.E., 1976: A land use and land cover classification system

1992:

Environmental

remote sensor data.

Monitoring and Assessment Program: an overview. Paper presented at the IUFRO S 4.02.05 lnternational

U.S.

Conference on Permanent Plots for

Professional Paper

964,

116

World Forest Monitoring. January'l

l gg2

Shaw,

3-17 ; Pattaya, Thailand.

C. G., 1992: Forest

health

monitoring -- a new program of the USDA Forest Service and the Environmental Protection Agency.

A.; Hobbs, R. J. and C. R., 1991: Biological consequences of ecosystem fragmentation, a review.

Saunders, D. Margules,

Paper presented at the IUFRO S 4.02.05 lnternational Conference on Permanent Plots for World Forest Monitoring. 1992 January 13-17;

Conservation Biology 5:18-27.

Pattaya, Thailand.

Lt7

RESOURCE POTENTIAL: POLICIES FOR SCALING UP TO GLOBAL SIGNIFICANCE K.D. SÍngh Food and Agrículture Organization of the United Nations Via delle Terme di Caracalla 00100 Rome, ltalY

!NTRODUCTION

It is possible, as will be demonstrated

As a result of interest in studies of global C-budget, a demand has arisen for informat¡on on the status of global biomass and its f lux over time. This would not be a difficult problem, had every country of the world a continuous forest inventory which could be compiled to provide a

this ad hoc status information cannot substitute the estimates obtained from statistically designed global biomass inventories. Such inventories are not

later, to have a reasonably good picture of status of the global biomass but not its rates of changes based on the use of existing data. lt must be remarked that

too expensive as will be shown later, based on the studies of Forest Resources Assessment 1 990 necessarily

global synthesis of

inf ormation on rate of change. biomass status and

Project.

However, the state of national surveys by end 199O does not provide a very optimistic picture to accomplish this task, as only less than half the planet has been

ANALYTICAL MODEL FOR ESTIMATION OF GLOBAT BIOMASS STATUS AI{D BATE OF CHANGE

statistically studied. There are major gaps in the knowledge of

changes in the tropical zone. Regarding the surveys in the temperate zone, these cover the woody vegetat¡on only and

The total biomass (w) could be expressed as a function of three macro variables: vegetation area (xl, quantity of vegetation per unit area (y) and biomass conversion factor (z): the last one is needed to convert vegetal quantity per un¡t area into biomass units. Expressed statistically:

even here the survey data, due to def iciency in design, do not Permit

calculation of biomass changes over time. There are only very few countries which have operational continuous forest inventories with permanent plots wh¡ch are a basic tool for reliable estimations of

w=x.y.z.

forest biomass dynamics.

The associated uncertaintY ¡n estimation of biomass status could be expressed in terms of relative variance (RV) as:

ln spite of very unsat¡sfactory state of the

current situation, the best possible stat¡stical estimate is necessarY to be made to serve the needs of global

RV(w) =RV(x) +nV(Y) +RV(z)

C-modellers. The term 'statistical' here is

intended

to

Where relative variance of a variable is obtained by dividing the square of the

imply the use of objective

technique(sl which could provide not only the mean value but also some measure of its reliability. This is in contrast to the term "guesstimate', which gives a rough estimate of the mean and no idea about ef ror.

standard error of the variable by the square of the mean of the variable' The advantage of using relative variance lies in

the f act that terms aÍe additive

variables are independent (Cunia 1985,

119

if

USDA

1

986). The above model for

categories on the basis of mean value of biomass per unit area. Many studies on

propagation of error shows that all the three components have equal importance so far as the total biomass is concerned.

primary productivity of the world follow this approach. For example Lieth (197g) stratif¡es the world vegetal cover into

ln the above expression the relative variance of the each term inclüdes random errors arising due to sampling, errors of observation and systematic errors (or bias) arising due to use of

orests, woodland, grassland, deserts, cultivated land, etc. The FAO Forest Resources Assessment 1 990 project, in

f

cooperat¡on with lnternat¡onal lnstitute f or

Vegetation Mapping, Toulouse, has prepared a broad vegetat¡on and eco-floristic zone maps of the tropical zone which off er new opportunity f or biomass stratif ication and biomass

survey model or measurement procedure.

With particular ref erence to biomass estimation it is well known that the location of sample plots, if not done using

an objective technique but made on

sampling.

a

subjective basis, could contribute to bias. For example, location of plots in well stocked or undisturbed forest areas, could become a major source of bias and give rise to higher biomass estimates per unit

Remote sensing has opened up new opportunities for this purpose. A number of studies have been done to estimate

actually is. ln case of permanent plots, it is important that they do not have any conspicuous markings. Otherwise, they may be treated in a

mean biomass over an area, based on high resolution and coarse resolution satellite data (Cook et al. 1g9g,t. The results obtained so far indicate that it is possible to def ine uncertainty in the estimation of biomass mean per ha.

One common way

For the study of biomass change, let us assume that survey has been done on two occasions with the following results:

area than

it

different manner leading to biased results (Sinsh 1985).

to reduce variance of biomass estimation is to strat¡fy the vegetal formations of the world into broad

x1, y1, z1 x2, y2, z2

The relative change in total biomass (w1 / w 2l and the relative variance:

RV (wl lw2l

= Addition

=

RVt(x'l /x2) +(v1

is

RV(x'l /x2)

Where VAR (x1) and VAR (xZl

relative variance terms

and

lV

1

x2.

\

This equation shows that reliab¡lity of is

change estimation can be improved, if the

possible as the three compound variables lx1 lx2l ,

are

variance of x1 and x2 respectively and COV (x1, x2) is covariance between,xl

lV2l+þ1 lz2ll

RV(x1/x2) + RV(y1 ly2l +Rv(21 lz2l

of

= VAR(x1) + VAR(x2)-2COV(x1 ,x2)

two surveys for estimating x1 and x2 could be correlated with one another.

lV2l , þ.1 lz2l are ¡ndependent.

This suggests use of sampling techniques based on continuous forest inventory concepts, where a part or all of the plots are remeasured and hence data observed are correlated (Cunia 1974l,.

Considdr now the relative variance of change in any of the three terms, for example, RV (x1lx2). This expression could be written as:

t20

The above considerations hold for all the

three components (or

OPTIONS FOR ASSESSMENT OF GLOBAL BIOMASS

macro-variables)

involved in biomass estimation. lt would be a very cost-effective approach to plan and implement a comprehensive survey design, in which area, volume/ha and

Three approaches to global biomass monitoring, diff ering in the level of

biomass conversion factor are observed in

certainty will be presented here.

an ¡ntegrated and continuous manner.

Option

Relative Degree of Certainty

(1) Estimat¡ons based on existing data. (2) Remote Sensing-based area estimation (3)

combined with existing biomass data. Remote Sensing based area est¡mation combined with statistically-designed field sampling for biomass.

The costs of assessment and complexity

involved in implementation of

Low Medium High

specific statistics on main parameters. The new estimates are expected to be

the

programme increase with the increasing demand on 'certainty'.

available by mid 1992. For the temperate forests, a major effort prepare a status report for the reference year 1980 was made by joint FAO/ECE Agriculture and Timber Division, Geneva, who published a synthesis of information in 1985. An assessment for the reference year 1 990 is currently on-going which is expected to be completed in early 1992.

to

Estimation þased on exìsting data The f irst effort on a global tropical bas¡s was made by FAO in Forest Resources Assessment 1 980 Project, to compile the

existing reliable stat¡stics on area and volume and adjust these to a common concept, classif ication and ref erence date,

viz. 1 980. The data were compiled at the country level. Based on these figures,

FAO Forest Resources Assessment 1990

Project is expected to produce a global synthesis of the results of the two

area and biomass by end 1980 were calculated (FAo 1988).

assessments towards mid 1992.

This approach has been improved by FAO

Forest Resources Assessment 1 990

The remote sensing based estimatíons

Project. Existing forest area information is used in conjunction with a model to describe the state and rate of change of forest area at various points of time. The

Forest Resources Assessment 1 990 Project has demonstrated that high resolution satellite images (LANDSAT

figures are reported by country as well as by ecofloristic zones. Another distinctive feature of the 1 990 approach is use of

TM/MSS, SPOT and others) can provide a reliable est¡mate of global vegetation cover area and its changes in time on a

erenced vegetat¡on,

continuous bas¡s. The f irst round of survey for the tropical zone is presently

geographically

ref

ecological and demo-graphic information

in a

cartographic f orm which

are

being implemented. The current state of

combined with statistical data on forest area and biomass. These provide location

forest cover is derived from the interpretation of a recent satellite image; the

t21

orest cover change

ormation

is

Remote sensíng based estimation com-

the recent and a historical image for the same

bined wíth statÍstícally desígned field

f

inf

estimated from the comparison

of

sampling of bíomass

location (FAO 1990).

This option is considered as the most

The selection of the monitoring sites, which is being done using statistical sampling techniques, permits the estimation of global values of forest cover and its rate of change together with the associated error. The forest change

comprehensive approach. The procedure

involves use

including area and volume/ha.,

data allows study of the

underlying causes of tropical deforestation and forest degradation (FAO 1 991 ).

reliability at

(1

regional and global levels, but not at the

) Survey with help of

hoped that the

area into biomass classes and in selection of sampling units for the next phase.

study of the trends of tropical deforestation at local and national levels.

(21 Detailed study of the selected sampling units with help of High

The interpretation and validation of the satellite images is being carried out in cooperat¡on with regional and national

Resolution Satellite Data: This phase

serves many purposes, viz. estimation of forest cover area close to 1 990; and estimation of the forest

remote sensing institutions. The Project is providing methodological guidance with

cover area changes during 1981-90. The recent high resolution data could also be used to calibrate results of the coarse resolution satellite data.

the objective of strengthening capacity of these institutions, thus enabling them to continue monitoring of forest resources in the future.

(3) permit delineation of biomass strata but not their complete mapping. The Project satellite

images with 1 km resolution to make a map of tropical moist f orests of the world observe 'patterns biomass distribution.

of forest

Use of bio-climatic data and

sampling units) is laid. Their distribution over various strata is determined after an analysis of the

and

first and second phase results. maps

Ultimately all the data collected in the three phases are statist¡cally analyzed to produce results on area and biomass/ha. and changes on a global basis.

available on a global basis combined with

existing ¡nventory data

expected

to

pro-vide

of

Field sampling: This phase should not

be seen as pure validation, but a source of statistics for correlation with the second phase. ln areas sampled with high resolut¡on satellite data, permanent filed plots (tertiary

The remote sensing sample ciata would

and

Coarse

Resolution Satellite Data: This step would enable stratification of survey

continuat¡on of sampling over a period of time will provide a complete cover of all the tropical forests. This will also allow

is also using NOAA AVHRR

a

present purposes the following three-step approach is outlined in Singh (1990):

actual tropical forest cover area, adequate

level. lt is

are

There are many options to combine remote sensing and f ield sampling as discussed in Singh (1986). For the

The sampling intensity chosen by the Prolect is currently about 10% of the

country

as

logically integrated in the framework of continuous monitoring design.

information combined w¡th other ancillary

to give a high degree of

of remote sensing

described earlier and field sampling for biomass. All components of the survey,

countries

the basis

is

for

estimating biomass mean value by strata under this option. The area and mean biomass per ha could be combined to produce an estimate of the total biomass.

Two measurements on permanent sample plots are necessary to estimate changes

t22

in biomass/ha. However, one time survey

data combined with modelling

non-tropical zones of the world. The precision of estimates for the biomass change in Option 1 are unknown, the other f¡gures are based on guesst¡mates only. The specification of precision for the area and area changes in Option 2 are based on sample survey design investigations described in FAO 1991; those for

could

provide a provisional estimate of rate of change of biomass in the first round of survey.

EVATUATIOIT OF OPTIONS FOR

biomass and biomass changes are based precision

GLOBAT BIOMASS ESTIMATION

on guesstimates only. The

A very approximate comparison will be

Option 3 are the same as those for Option 2. The estimated precision for biomass

estimates

area and area change in

and biomass change in Option

made in the following table of the three options f or global biomass estimation

3

are

extrapolated from a study of Schmid-Hass (1983). The cost est¡mates are based on experience of Forest Resources Assessment 1990 Project. The numbers quoted should be taken as indicative of the broad other of precision and cost.

presented earlier.

Some qualifying remarks are necessary on The

the figures quoted in Table 1 .

coverage is global including tropical and

Table

of

1. Comparison of cost-effectiveness of three alternative methods for estimating total

biomass and its rate of change

Option State

Estimated precision at 9S% confidence Change Assessment

Assessment

Area

Biomass

Total

Area

Biomass

Total

Estim.

Cost

MiI US$

1

1Oo/o

lOo/o

1

4Vo

20lo

Unknown

Unknown

2

2

5o/o

1Oo/o

11o/o

1Oo/"

3OV"

31o/"

4

3

5Vo

5o/o

lOo/o

1Oo/o

14%

þ

7

o/o

From the figures in Table 1, it would be

Chart. lt is hoped that this programme

obvious that the f ield sampling of biomass on continuing basis is an essential requirement f or an accurate modelling of

will be realized and a global monitoring of forest and natural resources realized in the near future.

C-budget and, in particular, its changes. This should not be a difficult task as the

The global continuous biomass inventories

Project experience suggests. A prerequisite for implementation of such a design is establishment of standards for field measurements and their acceptance by countries. Further, a minimum of funding must be assured by the internat¡onal community to undertake regular remote sensing and field measurements on selected sites and dates. Training of staff in the countr¡es on the common methodology must be provided. These ideas are contained in the Expanded Scope 1

of

would serve an invaluable purpose to

wide range of global scientists

a

and

modellers. As the human impact on the planet increases, so the need increases for a global continuous monitoring. Now

is the right time to start such a programme.

ACKNOWLEDGEMENT The author wishes to acknowledge the assistance of Mr. Klaus Janz, Senior

Forest Resources Assessment

990 Project, illustrated in the attached

t23

Forestry Officer (Resources Appraisal and

Monitoring) Forestry Department,

FAO, 1991 : The Sample Survey Design". Forest Resources Assessment 1 990 Project, Rome, ltaly.

FAO

and of Mr. Walter Marzoli, Consultant to the Forest Resources Assessment 1 990 Project in reviewing the paper and to

Lieth, H.F.H., 1978: Patterns of Primary

thank Ms. Pauline Simonetti for preparing

Production in the Biosphere. Dowden, Hutchinson & Ross, lnc. Pennsylvania

the manuscript.

USA.

P., 1983: Swiss Continuous Forest lnventory twenty years experience". Proceedings of the Renewable Resources Monitoring Changes and Trends. College of Forestry, Oregon State, University of Corvallis, USA.

Schmid-Haas,

REFERENCES Cook 4.E., lverson R.L. and Graham R.1., 1 989: Estimat¡ng Forest Productivity

with Thematic Mapper and

Biogeo-

graphical Data. Remote Sensing and

Environment 28:131-141

.

Elsevier

Science Publishing Co. lnc.

Singh, K.D., 1985: Non-Statistical

Cunia T., 1974: Monitoring Forest Environment Through Successive

Report of Tenth UN/FAO lnternational

Training Course on Application of Remote Sensing to Monitoring Forest

Sampling, Conference Proceedings.

State University of New

York

Lands. FAO, Rome ltaly.

College of Environmental Sciences and Forestry, Syracuse USA.

Singh, K.D., 1986: Conceptual Framework for the Selection of Appropriate

Cunia T., 1985: On the Error of Biomass Estimates in Forest lnventories, Part 1: lts Major Components. SUNY

College

of

and

Statistical Aspects of Monitoring. ln

Remote Sensing Technologies. ln

Practical Applications

of

Remote

Sensing in Forestry. Martinus Nijhoff Publishers for the United Nations.

Environmental Sciences

and Forestry, Syracuse USA. Faculty of Forestry Miscellaneous Publication Number 8 (ESF 85-0041.

of a Global Resources Assessment. Photogrammetric Engineering and Remote Sensing

Singh, K.D., 1990: Design

Tropical Forest

FAO, 1988: An lnterim Report on the State of Forest Resources ¡n the

56(

Developing Countrles. Forestry Department, Rome, ltaly.

1

0):1 353-4.

United States Department of Agriculture,

1986: Estimating Tree Reg ressio

FAO, 1990: Problems Associated with Estimations of Def orestation and

ns and

Th

Biomass

eir

Error.

Workshop Proceedings. State University of New York College of

Proposed Methodologv for the Project. Forest Resources Assess-

Environmental Sciences & Forestry, Syracuse USA.

ment 1990 Project, Rome, ltaly.

t24

ESTABLISHMENT AND ANALYSIS OF PERMANENT SAMPLE PLOTS Simo Poso Department of Forest Mensuratíon and Management University of Helsínki, Uníoninkatu 4O B 0Ol 70 Helsinki, Fínland

principles. Still, separate experimental plots are needed f or improving the

WHY PERMANENT SAMPLE PLOTS?

efficient use of land. The technical development in the field of data capture by remote sensing and data

The establishment of permanent plots

analysis and storage by electronic computers has been rapid. The development in the f ield of forest

offers a good aid in finding out how the land resources develop under alternative regimes. Thereby, it is possible to get data for building scenarios needed f or

inventory has not been as rapid as could have been expected and needed. lt

sustained development. Building of production models for alternative land use combinations may help to get the land

that the

potentials are not recognized well enough. lt is the duty of

seems

use under control.

international scientific organizat¡ons such as IUFRO to discuss the possibilities and to try to improve the level of knowledge needed for sustained development.

Decrease

LOCATING

of f orest area in

AND MEASURING A

tropical countries seems inevitable because of the pressure caused by the high rate of birth.

PERMANENT SAMPLE PLOT

lntegration of producing agricultural and forestry and other products inked o environment and nature protection is

The term sample plot refers to locating the plot by sampling, i.e. objectively. By this way it represents the population or a part of population according to the rules familiar from the statistics. lt may be

regarded as necessary. ln genera, there is much too little relevant data to build reliable scenarios and alternatives to get rid of uncontrolled and probably

diff

icult to f ollow strictly these

rules.

However, it is important to know the rules and deviate from them only for a

dangerous development.

good reasons.

The problems lie basically in the demand and supply situation. The supply of wood may be improved by making the use of products more efficient, cf . ¡mprovement

Proportional allocation of plots over large areas using equidistant placing can be the

in

first alternative to start with. There are at least two common reasons which cause deviations from this choice.

stronger.

First, the areas of interest may

burning technique. However, the opposite effect due to increase of the human population seems to be much

dif f er

much in physical conditions

There is a big demand for reliable ground

and

importance. This leads to stratification. ln each stratum the permanent sample plots are then established by own rules.

truth data especially in tropical conditions. lnventory and monitoring of forests by remote sensing requires ground truth collected and maintained according to the requirements of GIS and sound statistical

Second, travelling costs from a plot to another may become high. This leads to

t26

It is very important to make the

decision that the plots are to be measured ¡n clusters. ln national forest inventories clustering is a common technique.

measurements in well defined standardized form. Requirements for standardi-

zation have been presented by IUFRO many times earl¡er. The role of sound measurements should be increased over ocular estimations and classif ications. For example, the concept and role of forest land requires new thinking. This is

Whichever choice for stat¡stical sampling is used, each unit should be selected and located in general x- and y- coordinate

to enable the application of geographic information systems principle. ln addition to x- and y- coordinates two other variables are recommended : a for altitude and to for time of measurement. system

because of integral land use and subjec-

tivity of classifications. lt could be better to f avor post-classif ications of sample units based on the measurements.

It is most important to establish the plot

in the field in a way which makes it possible to find the plot easily enough after a period regarded as suitable. Specif ic marks can be made on the

DATA STORAGE AND ANALYSIS

ground and to the trees. Drawing maps

describing the location of the plot in relation to the environment is usually

Very many f ield measurements have been made without good storage and analysis. The inf ormation value of accurate measurements somet¡mes grows along

needed. Satellite based global positioning system may be used for help if the forest is not too dense.

the time, for example, in studying the The form and size of a plot may vary in

long run effects.

erent conditions. However, it is important to map the individual trees in the plot. A good technique is to apply polar coordinate system. ln this, every tree is supplied by data on the bearing diff

To avoid losses, specif ic responsibility system on data storing should be established in official form.

The analysis of measurements should primarily be done by computers. PCmicros are efficient enough. The power of the permanent sample plots is can be recognized only after 2 or 3 remeasure-

and distance from the plot centre.

The recording of location and data for each tree independently offers possibilities for effective analyses. The development of each tree can be followed. This gives a chance to study the dynamics of f orest stand by tree species and diameter classes and the

suitable program packages for the analysis should be intensified rapidly. This calls for international cooperation.

removal of trees and possible degradation by natural mortality and cuttings. Mapping of the individual trees often helps finding of the exact location of the permanent plot.

The original measurements should be stored carefully. Many estimat¡ons are made through models. These models should be always stored. Later, new and

The main task in the measurement of permanent sample plot is to gain

better models can be introduced. T):lese can also be applied to older matêrial in order to find the changes mgst reliably.

ments. The development and application

of

information on forest characteristics and

the changes through increment

The data should be measured and stored in a way that it is possible to get answers to the most important problems. ln many

and

removals. Also the changes in land use are important. Putt¡ng emphasis on the integration in land use one should be

cases

ready to'establish permanent plots on the areas in which it is possible to grow trees. General coordinate system offers a good

inf

it is very ¡mportant to

get

ormation about the effect of treatments

on forest and agricultural production

in

relevant physical measures. This calls for corresponding cooperation.

frame for sampling also in this respect.

r26

APPLICATION

OF

organizations such as FAO and IUFRO and

PERMANENT

hopefully aid organizations as well

PLOTS

After some remeasurements it ¡s possible to f ind out the changes in land use and in tree- and forest stand- variables. This makes

are

interested in promoting these activities.

it

possible

WHY GUIDELINES?

to build models and to

calibrate general models for estimating the production of trees and other vegetation. This, again, is necessary for building

long run scenarios f or practicing

Too little is known about the dynamics and changes in natural forests. The state

an

appropriate land use policy.

of technical and methodological development and the rapid decrease of forests

Satellite imagery is a powerful tool which enables monitoring of changes over large areas. The problem in using this technique is that ¡t requires a reliable field data. Permanent sample plots could offer

call for deliberation and cooperation.

The questions are: what is needed, how the data should be measured and how the

work should be organized. lt

ideal material f or the purpose. lt is pref erable to use satellite imagery in combination of permanent sample plots. Thus, the danger of biased results if only permanent plots are used can be kept under control. ln principle, permanent sample plots should be secret to avoid exceptional treatments. The treatments

seems

appropriate to try to answer the questions

using international platform. A sensible

way f or promotion could be to give common principles to be followed and construct guidelines for establishing and analysing permanent sample plots.

The work with permanent sample plots

shouldn't be dependant on the consciousness of the existence of the plots. ln practice, this is not easy to

must not be necessarily very high; some 1 ,000 plots could be enough for most of the countries. The critical thing is .to know how to work. With good planning of the work overlapping of efforts can be

arrange successfully.

minimized and

Forest inventory and monitoring and forestry planning are important issues on

eff iciency maximized. Establishment of permanent sample plots could turn out to be economical procedure in respect to alternative decisions. An important task is to f ind good experts and good organization to do the work.

local, regional, national and international

levels. These separate efforts can be integrated. lntegration seems very reasonable and explains why international

127

DATA REGISTRIES FOR GROWTH AND YIELD PLOTS IN ASEAN A NETWORK TO REVIEW CURRENT PERMANENT SAMPLE PLOTS IN THE TROPICS Hashim bin Saad Chung Kueh Shin ASEAN lnstítute of Forest Management Kuala Lumpur, 504O0, Malaysía

ABSTRACT

An important component of ASEAN cooperation in addressing foresf assessments and other management pr¡orities is the task of collecting or acquiring data from permanent sample plots (PSP), particularly the data which is essential for defining the productivity of the forest land base and for projecting forest inventories. Such data, wh¡ch is a scarce commodity in many instances in ASEAN, is difficult and costly to acquire. The collaboration of ASEAN through AIFM ìn respect of the provision of information and the sharing of data, can be beneficial. These include extending the value of the existing data base available to individual countries and influencing the method and manner of future data collection to produce mutual benefits, but in keeping with individuat country requirements. The collaboration will also consolidate guidelines for fhe establishment of a network of permanent monitoring site at the AIFM. The registries contain descriptive data only..4ccess to the registries enables usets to search

the files and to identify the nature and jurisdiction of PSP located in ASEAN member countries. Upon finding plots which are potentially relevant to their needs, users may then approach the proprietors and negotiate the sharing of information.

BACKGROUND

knowledge in growth & yield modelling, to

support the establishment of ASEAN standards and to set up a regional

The concept of Data Registry was first mooted by AIFM at the Second Meeting

network.

A

of the Forest Opèlation and Work Studies Technical Working Group held in Thailand in March 1988. The intention was to set up a computerized registry at the AIFM f

paper on "Proposal for Creating and Maintaining A Registry of Growth and

Yield Plots

in ASEAN" was also put

forward at the ASEAN Seminar "lnformation for Forest Management" in Johor Bahru, Malaysia in November 1988.

or all silvicultural, ecological and

plantation sample plots and trials which

have been established in the ASEAN region. The registry would help to avoid

Previous to this proposal, there were some information on growth studies in

duplication (by facilitating the location of existing information before scheduling of growth and yield studies), to promote the

the region stored at the library of the University of Philippines at Los Banos and

of data and research information, to upgrade sharing and exchange

the Yale School of Forestry and Environ-

mental Studies in the U.S.A.

tzg

(Revilla

1988). This information is incomplete and contains little on growth stud¡es. There is also a report on "Register of Current Growth and Yield Besearch Sample plots in lnsular and Peninsular South East Asia" published by FAO in 1976 and contained some growth data for the region (FAO 1976).

METHODOLOGY

Two Registries have been constructed, one for natural forests and the other for plantation forests. Both contain headers (fields) to describe PSP parameters. These

are stored in dBASE lV,

a

database

management software.

INTRODUCTION The basic design of the Registries uses

To date, growth data in the

a

Forest lnventory (CFl) data and'Malaysia has plantation data. ln the State of Sarawak (Malaysia), a comprehensive

two-dimensional spread sheet. The column shows the names of each plot while the row shows the related plot information. By selecting headers of any combination, a selection of datasets can be scanned, viewed and retrieved. The file structure of these Registries is listed

method

in the Appendix.

ASEAN

Region has been accumulated using diffe-

rent formats, approaches and objectives.

As such, the Philippines has Continuous

of data collection for

natural

mixed dipterocarp forests was developed by FAO in the early eighties (Hutchinson 1982).

The Data Registry is a

portable

information system and is designed to

operate on an IBM-compatible

A Data

Registry can categorize and document Growth and Yield plots in a systematic manner because it contains records for each growth and yield plot in

microcomputer equipped with MS-DOS, hard-disk drive, and dBASE lV. Although the user need not be familiar with DOS to learn dBASE lV, some experience w¡th

a manner similar to a library catalogue or

DOS

the entries in an address book.

It's our intention not to maintain a data bank of complete plot records at the lnstitute due to high cost involved. The

concept is similar to that of a catalogue service, i.e. a user draws up a shopping list, goes to the catalogue, finds the items and then orders them. However the Data Registry has several advantages over an

cost of operating a catalogue system only is estimated to be significantly less than maintaining a data bank. Reimer (1977l,

address book, the card system in a library or a catalogue service because ¡t supports the following functions.

-

new entries can be added easily

systematically

as they

will be helpful.

The

estimated

the cost of operating a

catalogue system was only 10% of the later method. The earlier is considered to be much more desirable in term of f inance

and

and practicality.

become

available.

-

manpower

necessary to maintain the data bank were

estimated to exceed US $sO,Ooo/year in 1976 in the Washington and British Columbia Regions. Considering the legal

information can be easily retrieved. Criteria need only to be specified to

-

that the costs in and computer resources

Reimer estimated changes to old data captured in the Data Registry are simple.

pull out the records of interest.

aspects of crossing international

information can be saved and stored.

data which would have

inf

ormation can be printed into

variety

of

useful formats such

boundaries in ASEAN, the large volume of to be updated annually, the large number of changes in data recording formats which agencies are continually making, the high operating costs, and the proprietary nature of the data, this alternative is non feasible.

a

as

customized list, mailing label and letter form.

130

CONTENTS

OF

THE

DATA

cies in the ASEAN countries.

REGISTRIES

*

representatives f rom each ASEAN

Relevant data for 212 permanent sample plots from natural forests and 1 267 plots

from plantation forests have

member country.

*

been

collected. The collection was made available to Malaysia, lndonesia, philippines, and Thailand. Brunei Darussalam and made

Registries f rom lndonesia, Malaysia,

through:

*

members of various AIFM Technical Working Groups.

The following two tables show the outline of data distribution available in the Data

Singapore have yet to submitted their pSp

to AIFM. The collaboration were

the members of the AIFM Growth and Yield Study Group which comprises

Philippines and Thailand. The tables serve

to brief the reader before embarking on search of the Registries.

direct correspondence between the lnstitute and the various forest agen-

Table 1. Growth and yield plots of natural forests in ASEAN

lndonesia

Sumatera Selatan Riau

1

Jambi

Kalimantan Kalimantan Kalimantan Kalimantan

2 1

Tengah

1

Barat

3

Selatan

1

Timur

1

1

Malaysia

Johor Kelantan Pahang Perak Sabah

Sarawak Selangor N. Sembilan Philippines

Mountain Province Zambales Palawan

Camarines Norte Negros Oriental Western Samar Southern Leyte Zamboanga del Sur Misamis Oriental Benguet Province Nueva Vizcaya Ouirino lsabela Cagayan Ouezon

Total Plots

131

1

2 2 1

128 27 1 1

,

a

Table

2.

Forect Plantation

Growth and yield plots of plantation forests in ASEAN.

Cor

Þrnr¡i

rntrv

Nln n{ Plnfc

Sumatera Selatan Sumatera Utara Kalimantan Selatan Sulawesi Selatan Sulawesi Utara

lndonesia

Jawa Barat Jawa Timur Jawa Tengah Flonnl¿r

Malaysia

rlr

17

46 12 10 7

257

265 314 a

r

2

Johor Pahang

1

4 257

Perak Sabah

Sarawak

48

Selangor

18

frl aamhil Tntel

1

Plnfc

1

coNcLUsroNs

)^7

The methodology described in this paper will be adopted by lnternational Tropical timber Organization (ITTO) coordinated by ANUTECH, the consulting arm of the Australian National University in Canberra,

This paper describes a method to facilitate the sharing and exchanging of growth and yield data for the improvement of forest management. lt will have

Australia. The data base will

be

expanded to cover non-ASEAN countries.

the following potent¡al benefits for the ASEAN.

+

ACKNOWLEDGMENTS extending the value of existing data base available to individual country.

*

The authors wish

future data collection to

to thank

those who

gave information, suggestions and advice for the construction of the Reg¡stries. Without their assistance these Data Registries would not have been

influencing the method and manner of produce

mutual benefits. One of the central l¡mitat¡ons of the data registry is the varying sets of parameters each country and agency used ¡n the past. lt would be helpf ul in the f uture for

completed.

- Forest Department lndonesia - Forest Department Peninsular

ASEAN working committees to design and promote a flexible set of standards

Malaysia

- Forest Department Sabah - Forest Department Sarawak - Sabah Foundation - Sabah Softwood Sdn Bhd - Sabah Forest Development Authority - Sabah Forest lndustries - Forest Research lnstitute Malaysia - Forest Management Bureau

coùer¡ng parameters to be measured, data entry and updating procedures and data sharing prerequisites.

The constant improving, updating and control of the data registry is of importance. At present it contains only part of existing PSP data from the ASEAN member countr¡es. ASEAN foresters and researchers should assist AIFM to improve the expansion of a comprehensive registry.

-

Philippines Ecosystem Research and Development Bureau (ERDB) Philippines

Boyal Forest Department Thailand

r32

REFERENCE

8000, Cary, NC, USA.

FAO, 1976: A Register of Current Growth and Yield Research Sample Plots in lnsular and Peninsular South East

Reimer, D.R., 1977:

Asia. Project Working Document N0. 48, Food and Agriculture

the

MacMillan Bloedel Limited, Forestry Operations, Nanaimo, B.C. Canada.

Organization of The United Nations, Regional Office for Asia and the Far East, Bangkok, Mataysia.

A.V. Jr. and Gregorio, M.C., 1988: Proposal for Growth and Yield Studies in the ASEAN

Revilla,

Hutchinson, 1.D., 1982: Field Enumeration of Permanent Sample Plots in the Mixed Dipterocarp Forest of

Sarawak. Forest

Report of

Committee on Standards of Measure and Data Sharing.

countries. ASEAN lnstitute of Forest

Management, Kuala Lumpur, Malaysia.

Department, Smith, S., 1988: Report ASEAN lnstitute of Forest Management Mid Term Assessment. Stephen Smith & Associates, Forestry Consultants

Kuching, Sarawak. Linden, C.4., 1987: SASIFSP Guide for

Personal Computers

Version 6Edition. SA,S lnstitute lnc. Box

lnc. Victoria, Canada.

133

APPENDIX

FILE STRUCTURE

A.

2.

PLOT LOCAL NUMBER: Character.

The name by which the plot is known in the country. Such as AIFMNUMBER 0001 is known as

NATURAL FORESTS

4.1 DATABASE FOR NATURAL

"Gunung Tebu" in Malaysia.

FORESTS DATA REGISTRY 3

The Registry Database consists of two

iles, namely 'AlFMNG35.dbf ' and "AlFMNG35.dbt'. lt is a logical f ile structure similar to a 2-dimènsional

lndicates the origin of data.

f

1 2 3 4 5 6

spread sheet (Table). Every record (row)

on this file describes a specific growth and yield plot. Each record consists of 35

fields as detailed below. The file name

constructed based

on the

is

following

naming convention:

4

dbr

code for Johor.

(6)

21 22 23 24 25 26 27 28 29

number of fields. extension file name for database which holds all the fields except the memo field. extension file name for database which holds only the memo f ield. )

Riau

211 Jawa Barat 212 Jawa Timur 2'l 3 Jawa Tengah

NUMBER, (6} LONGITUDE, (7) LATITUDE, (13) NUMBER OF PLOTS, (14) ESTABLTSHMENT DATE, (15) PLOT AREA, (18)

31 Johor 32 Kelantan 33 Pahang 34 Perak 35 Sabah 36 Sarawak 37 Selangor 38 Sembilan 39 Terengganu 41 Mountain Province 42 Zambales 43 Palawan 44 Camarines Norte 45 Negros Oriental 46 Western Samar

DATE OF FINAL FELLING, and (29) NUMBER OF MEASUREMENTS. In numeric field, -99 will display if data is not available. ln character field, F will display. Plot general information

.

Sumatera Selatan Sumatera Utara

Jambi Kalimantan Tengah Kalimantan Barat Kalimantan Selatan Kalimantan Timur Sulawesi Selatan 210 Sulawesi Utara

All entries ate coded except (1 AIFMNUMBER, (21 PLOT LOCAL

1

PROVINCE: Numeric code. Province

lndicates administrative location of the plot, such as 31: 3 is a country code for Malaysia; 1 is a provincial

memo.

35 dbf

Brunei lndonesia Malaysia Philippines Singapore Thailand

is also interpreted as "state".

AIFM : the lnstitute. N Natural Forests. G General lnf ormation consisting of (1) plot general information; (2) environmental data; (3) tree measurement; (41 silvicultural

treatment; (51 soil type;

COUNTRY: Numeric code.

AIFMNUMBER: Numeric four digits.

A unique running number assigned by AIFM to identify each ASEAN Growth and Yield plot in the database. AIFMNUMBER runs from

0001.

134

2 3 4

47 48 49

Southern Leyte Zamboanga del Sur Misamis Oriental 410 Davao oriental 411 Benguet Province 412 Nueva Vizcaya

Lampang

10.

lndicates average gradient across the plot.

with the

ownership. Such as 1 is for Forest Department.

1 2 3

Address (location) appears in the memo field.

1 Forest Department 2 Sabah Foundation 3 Sabah Softwoods 4 Sabah Forest Development

1

1.

Ecosystem Research

12.

is used. This classif ication is

categorized in the following codes:

1 upper montane 2 lower montane 3 upper monsoon 4 conif erous 5 lower monsoon 6 dipterocarp 7 mixed evergreen 8 dry deciduous 9 mixed deciduous

and

(ERDB}

9

Forest Research Centre Bogos lndonesia (FRDC) 20 Forest Management Division

10 submarginal

LONGITUDE: Numeric in degree.

LATITUDE: Numeric in degree.

decimal.

STATUS: Numeric code.

Relates

1

1 savanna

1

2 beach

13 health 14 fresh water swamp 15 peat swamp 16 mangrove

Records to the nearest one decimal.

Records to the nearest one

FOREST TYPE: Numeric code.

The forest classification proposed by the AIFM for the ASEAN region

Development Bureau Philippines 1

and

address.

Malaysia

8

REPORT: Character code.

such as abstract, author

Benguet Consolidated lnc. Taggat lndustr¡es Zambeles Timber Corp. 10 Western Palawan Timber Corp. 11 Heirs of Leonardo Mendoza 12 Negros Timber Co. lnc. 13 Basey Wood lndustries 14 Silago Timber Co, lnc. 15 Sta. Clara Timber Co. lnc. 16 P.N. Roa Ent. lnc. 17 Pahamutang Logging

1

2O%+

lndicates report availability. lf "yes",

5 Sabah Forest lndustries 6 Forest Research lnstitute 7 I 9

O -5o/o 5o/o - 2Oo/o

detail is shown in the memo field

Authority

8.

SLOPE CLASS: Numeric code.

AGENCY: Numeric code.

Associates

7.

Numeric

1 0-300meters 2 300 - 1000 meters 3 1000 + meters

416 Ouezon

6.

destroyed

code.

41 5 Cagayan

5.

closed

9. ELEVATION CLASS:

413 Ouirini 414 lsabela

61

dormant

the current position or

13.

NUMBER OF PLOTS: Numeric.

14.

ESTABLISHMENT DATE: Numeric. Refers to the establishment of PSP and records year in numeric four

status. 1 active

digit format.

13ã

15.

PLOT AREA:

Numeric.

24.

CROWN - POSIT|ON tN CANOPY: Character code.

The unit is in hectares.

16.

STEM MAP: Character

lndicates that the plot has a

maP. 17.

lndicates f orm measurement f or

code.

crown.

25. CROWN -

stem

VIRGIN OR CUTOVER: Numeric

T for "yes" and F for "no"

code.

1

2

DAMAGE: Character

code.

26. TREE - VIGOUR:

virgin cutover

18. DATE OF FINAL

Character

code.

lndicates examination of

the

as health, cylindrical f orm

and

physical condition of the tree such

FELLING:

Numeric.

stra¡ghtness. Records year ¡n numeric four digit

format. ENVIRONMENTAL

19.

DATA

27.

code. PRECIPITATION: Numeric code.

lf yes, deta¡l is specif ied in the

lndicates annual average rainfall in

memo field.

mm.

28.

1 less than 1000 2 1000 - 2000 3 2000 - 3000 4 3000+ 20.

OTHER MEASUREMENTS: Character

MEASUREMENT PERIODICITY: Numeric code. lndicates the periodic frequency of repetitive occurrence.

1 2 3 4

TEMPERATURE: Numeric code.

lndicates annual average degrees in Celsius.

29.

1 0-20 2 20-30 3 30+

measured measured measured measured

NUMBER

annually biannually triennially irregularly

OF

MEASUREMENTS:

Numeric.

lndicates

the sum of

measurements

repetitive

which have

been

taken. Tree measurement

21.

STEM - DBH: Character code. lndicates diameter measurement stem.

22.

STEM - HEIGHT: Character

Silvicultural treatment

for

30.

TREATMENT: Character code. lndicates the type of treatment(s). lf treatment is applied, detail is shown in the memo field.

code.

lndicates height measurement for

stem. 23.

STEM - DAMAGE: Character

Soil type

code.

31.

lnd¡cates damage measurement for stem.

TEXTURE: Character code.

lndicates the relative proportions of the various soil types separated into

136

- Definition of parameters - Sampling inf ormation such

sand, silt and clay as descr¡bed by the classes of soil texture.

32.

as

intensity, buffer zone and allocation of sample

SOIL DEPTH: Character code.

- Others lndicates the depth of forest floor in Litter, A Horizon, Effective, Total and Rooting.

33.

When memo field contains informa-

tion, its marker is shown in the uppercase letters "MEMO'. When

CHEMICAL ANALYSIS: Character

the memo field is empty, its marker

is shown in

code.

lowercase letters

"memo". lndicates the chemical constitution, properties, and reactions of soils.

34.

A.2

PH: Character code.

lndicates the degree alkalinity of a soil.

ADDITIONAL FILES

Three additional f iles

of acidity or

namely

COUNTRY2.DBF, AGENCY2.DBF and TREE3.DBF that display the coding systems for each of these three f ields were created. These subsidiary files are useful as they

Memo file - general comments

provide easy access when decoding

35.

MEMO:

is required. COUNTRY2.DBF has two f ields: country code and

A memo f ile ref erence

country name. AGENCY2.DBF has two f ields: agency code and agency

containing descriptive material.

This wr¡te-up f ile contains

the

name whereas TREE3.DBF has three fields: tree code, genus and species. TREE3.DBF contains only plantation species. Decoding other relatively small files are done manually.

Measurement of other species such as palm, bamboo and rattan - Previous logging history

AIFMNG35.DBF, COUNTRY2.DBF

f

ollowing

inf

ormation:

- Agency's address - Objective of the study - Report writing

lf one wants to view

- Regeneration measured

-

and AGENCY2.DBF simultaneously,

one needs

137

to link them together.

Diagram

1.

The linkage between

fil.es in nâturat forests

AI FI'ING35.DBF 1

.

AI

F],INUMBER

2. LOCANUMBER 3. COTJNTRY 4. PROV¡NCE 5. AGENCY 6. LONGITUDE 7. LATITUDE 8. STATUS 9. ELEVATCLAS

10.

SLoPECLASS

1 1

REPORT FORESTTYPE NOOFPLOTS ESTABDATE PLOTAREA

.

12. 13, 14. 15. 1ó. 17.

18. 19.

sTEt{l'lAP VIRGINOCUT FFELLDATE

PRECIPITAT

20. TEMPERATUR 21. STEMDBH 22. STE}IHEIGHT 23. STEMDAI,IAGE 24. CROIJNPIC 25. CROI.INDAMAG 2ó. TREEVIGOR 27. OTI'IIEASURE 28. PERIOD 29. NUMBER 30. TREATMENT 31. TEXTURE 32. SOILDEPTH

33. cA 34. PH 35. MEMO

138

B.

PLANTATION FORESTS

Plot general information

B.1 DATABASE FOR PLANTATION 1 .

AIFMNUMBER: Numeric four digits.

FORESTS DATA REGISTRY

A unique running number assigned by AIFM to identify each ASEAN

The Registry Database consists of three

fi!es, namely "AlFMPG34.dbf., "AlFMPG34.dbt" and "A|FMPCS3.dbf

The f ile names "AlFMPG34.dbt"

Growth and Yield plot in

the database. AIFMNUMBER runs from

'.

" and are constructed based "AlFMPG34.dbf

0001.

2.

PLOT LOCAL NUMBER: Character.

on the following naming convention:

The name by which the plot AIFM: the lnstitute. P Plantation Forests. G General lnformation

ting of inf

consis-

) plot

(1

ormation; (21

4.

dbr

ile name

or database which holds all the f ields except the memo f ield. f

PROVINCE: Numeric code.

Province is also interpreted as "state". Similar to the province code of natural forests and refer to page

number of fields.

extension

COUNTRY: Numeric code.

Similar to the country code of natural forests and refer to page 8.

environ-

(6) memo.

:

3.

general

mental data;(3) tree measurement; l4l silvicultural treatment; (5) soil type; 34 dbf

is

known in the country

f

8.

extension file name for database which holds only the memo field.

5.

All entries are coded except (1 AIFMNUMBER, l2l PLOT LOCAL NUMBER, (6) LONGITUDE, (7) LATITUDE, (12) PLANTING AGE, (14) NUMBER OF )

PLOTS, (15) ESTABLISHMENT DATE, (16) PLOT AREA, and (28) NUMBER OF

Similar to the agency code of natural forests and refer to page 9.

6.

LONGITUDE: Numeric in degree. Records to the nearest one decimal

7.

MEASUREMENTS. ln the numeric field, -99 will display if data is not available. ln the character field, F will display.

AGENCY: Numeric code.

LATITUDE: Numeric in degree. Records to the nearest one decimal.

8.

STATUS: Numeric code.

The descriptions of A|FMPCS3.dbf are:

AIFM:

Similar to the status code of natural forests and refer to page 9.

the lnstitute.

P

Plantation Forests.

c

Country where seed

source

9.

ELEVATION CLASS: Numeric code.

is originated. S

specres.

3

number of fields.

dbf

extension

f

ile name

Similar to the elevation class code of natural forests and refer to page f

9.

or

database which holds all the

fields except the memo

f

10. SLOPE CLASS: Numeric

All entries are coded except (1

Similar

The description of the lields are

to the slope class code

of

natural forests and refer to page 1 0.

)

AIFMNUMBER.

f

code.

ield.

1

1.

REPORT: Character code.

as

lndicates report availability. lf yes,

ollows:-

139

detail is shown in the memo field such abstract, author and address.

12.

PLANTING YEAR:

as

23.

Numeric.

CROWN - POSITION Character code.

lN

CANOPY:

lndicates that crown has form measurement.

Refers to the year of planting. 1

3.

code.

PLANTING METHOD: Numeric

24.

14. 1

5.

bare root container

measurement.

25. TBEE - VIGOUR: Character code.

NUMBER OF PLOTS: Numeric. ESTABLISHMENT DATE:

Records year of plot

PLOT AREA:

DAMAGE: Character

lndicates that crown has damage

stock

in numeric four digit

16.

-

code.

1 seeded

2 3

CROWN

lndicates that the physical

Numeric.

conditions of the tree are recorded.

establishment 26. OTHER MEASUREMENTS: format. Character code.

Numeric.

lf yes, detail is specified in the memo field.

Unit are ¡n hectares.

17.

STEM MAP: Character

27.

code.

lndicates that the plot has a

map.

MEASUREMENT PERIODICITY: Numeric code.

stem

Similar to the measurement periodicity code of natural forests and refer to page

Environmental

18.

data

28.

lndicates the sum of measurements taken to date.

Numeric

code.

Silvicultural treatment

Similar to the temperature code of natural forests and refer to page 1 1 .

Tree

29. TREATMENT: Character in the memo field.

STEM - DBH: Character code.

lndicates that stem has

diameter

measurement.

SOIL TYPE

30.

STEM - HEIGHT: Character code.

lndicates that stem has

22.

code.

lndicates the type of treatments. lf treatment is applied, detail is listed

measurement

measurement.

.

MEASUREMENTS:

PRECIPITATION: Numeric code.

19. TEMPERATURE:

21

OF

2.

Numeric.

Similar to the prec¡pitation code of natural forests and refer to page 1 1 .

20.

NUMBER

1

lndicates the relative proportions of the various soil types separated into sand, silt and clay as described by classes of soil texture.

height

STEM - DAMAGE: Character code.

31.

lndicates that stem has damage

measurement.

TEXTURE: Character code.

SOIL DEPTH: Character code.

lndicates the depth of the forest

140

floor in Litter, A Horizon, Effective, Total

planted.

and Rooting.

32.

101 ACACIA AURICULIFORMIS 102 ACACIA LAEFORMIS 201 ACROCARPUS TRAVIFOLIUS 301 AGATHIS MACROPHYLLA 302 AGATHIS ROBUSTA 401 ANGSANA 501 ANTHOCEPHALUS CHINENSIS 601 ARAUCARIA CUNNINGHAMII 602 ARAUCARIA HUNSTEINII 7O1 AZAOERACHTA EXCELSA 801 BALSA 901 BRUGUIERA PARVIFLORA

CHEMICAL ANALYSIS: Character code.

lndicates the chemical constitution, properties, and reactions of soils.

33.

PH: Character code.

lndicates the degree alkalinity of a soil.

of acidity

or

1OO1 CALAMANSAI

Memo file - general comments

34.

MEMO:

A

memo

f

ile

1101 CALAMUS CAESIUS 1

containing descriptive material. Similar

to the memo file of

natural

forests and refer to page 1 2.

8.2 1

.

AIFMPCS3.dbf AIFMNUMBER: Numeric four digits.

It ¡s a

series

of plot

this

NATION: Numeric code.

lndicates the country where the

seed is originated. The number is continued from the country code noted in 4.1.

7 Australia I Bahamas 9 British Honduras 1 0 Cuba 1 Fiji 2 Gambia 1 3 Ghana 1

1

14

5 6 17 18 19 1 1

the name of

CUPPRESSUS

DUABANGA MOLUCCANA

3OO1 MACROPHYLLA 3101 MAESOPSIS EMINII

3201 3301 3302 3303 3304 3305 3306 3307 3308 3309

SPECIES: Numeric code.

lndicates

CEDRELA MEXICANA CEDRELA ODORATA CEIBA PENTANDRA CORDIA ALLIODORA

2701 INTSIA PALEMBANICA 2801 KHAYA 2901 LEUCAENA LEUCOCEPHALA

Guatemala Honduras lndia Nicaragua Nigeria Papua New Guinea Solomon Spanish Honduras

20 21 22 Sri Lanka 23 Taiwan

3.

CECOOPTA

2OO1 2OO2 2OO3 2OO4 2OO5

Registry.

2.

CALAMUS OPTIMUS CALAMUS SCIOPIONUM CALLIANDRA CALOTHYRSUS

DURIO ZIBETHINUS EUCALYPTUS ALBA EUCALYPTUS BRASSIANA EUCALYPTUS CAMALDULENSIS EUCALYPTUS CITRIODORA EUCALYPTUS CLOESIANA 2006 EUCALYPTUS DEGLUPTA 2OO7 EUCALYPTUS GONIOCALYX 2OO8 EUCALYPTUS GRANDIS 2OO9 EUCALYPTUS MICROCORYS 201 O EUCALYPTUS SALIGNA 201 1 EUCALYPTUS TERETICORNIS 21 O1 EUSIDEROXYLON ZWAGERI 2201 FLINDERSIA MAESOPSIS 2301 GMELINA ARBOREA 2401 HEKENYA FILICIFOLIA 2501 HIBISCUS ELATUS 2601 HYMENEA

number

by the AIFM f or

designed

'I02 CALAMUS MANAN

1103 1 104 1201 1301 1401 1402 1501 1601 1701 1801 1901

reference

species

L4l

MAHAGONY MELIA AZADERACH MELIA COMPOSITA MELIA BUKIT MELIA EMINII MELIA MELANTAI MELIA PUNAI MELIA SARANG MELIA SERAYA MELIA TEMBAGA

3401 3501 3601 3602 3603 3604 3605

3606 3607 3608 3609 3610 3701 3801 3802 3901

4103 4104 4105 4201 4301 4401 4501 4601 4701 4801

PAKIA JAVANICA PARASERIENTHES FALCATARIA PINUS BAHAMENSIS PINUS CARIBAEA

PINUS PINUS PINUS PINUS PINUS PINUS PINUS PINUS

ELLIOTII HONDURENSIS

INSULARIS KESIYA

MASSONIANA MERKUSII

OOCARPA PATULA RACOSPERMA MANGIUM RHIZOPHORA APICULATA RHIZOPHORA MUCRONATA ROBUSTA

CHLOROPHORA EXCELSA PODOCARPUS TEYSMANII

One can View

The Iinkage between

between files in plantation forests.

fites ìn plantation

forests

A¡FII1PG34.DBF

AI FMPCS3.DBT TREE3 . DB

1. 2. 3.

TREE

F

CODE

AIFMPG34.DBF,

The following diagram shows the linkages

4OO1 SALIGNA

2.

TAXODIUM TECTONA GRANDIS TERMINALIA IVORENSIS

COUNTRY2.DBF AND AGENCY2.DBF at the same time or AIFMPCS3.DBF, COUNTRY2.DBF and TREE2.DBF.

4101 SHOREA ALBIDA 41 02 SHOREA HEMSLEYANA. Diagram

SHOREA MACROPHYLLA SHOREA PINANGA SHOREA TALURA SWIETENIA MACROPHYLLA TABEBNIA

1. 2.

3.

1. AIFMNUI,IBER 2. LOCANUMBER 3. COUNTRY 4. PROVINCE 5. AGENCY ó, LONGITUDE 7. LATITUDE 8. STATUS 9. ELEVATCLAS

AIFMNUIIIBER NAT¡ON SPPCODE

GENUS

SPECIES

COUNTRY2 . DBF

10.

SLoPECLASS

1 1

REPORT

.

12. 13. 14. 15. 16.

PLANTYEAR PLANTMETHO NOOFPLOTS ESTABDATE PLOTAREA

1

STEMTIAP

7.

18. 19.

20. 21. 22. 23. 24. 25. 2ó.

27. 28. 29. 30. 31

.

32. 33. 34.

PREC¡PITAT TEI.IPERATUR STEI.IDBH

STEI'IHEIGHT STE¡IDAI4AGE

cROtJNPtc CROt.,NDAT,IAG

TREEVIGOR OTMEASURE

PERIOD NUI'iBER TREATI'.IENT TEXTURE SOI LDEPTH

cA PH l,rEMo

L42

AGENCY2 . DBF 1 . AGENCYCOÐE

2.

AGENCYNAME

FOREST AREA ESTIMATES : SAMPLING ERROR AND CLASSIFICATION PROBLEMS Chrístoph Kleinn Díeter R.Pelz Uníversität Freiburg Abteilung für Forstliche Bíometríe Freiburg, Germany

ABSTRACT Area estimates are integral part of most forest inventories. Several problems arise when forest inventories are carried out to assess areas and to monitor area changes. Two problems are especially important, one related to the errcr estimation, the other related to the compatibility of area estimates from different inventories, which is in part a classification problem.

ln area estimation often a systematic dot grid is being used, which more efficient than randome sampling. However, the sampling error cannot be calculated with standard statistical methods. Empirical methods are prcsented for estimating the sampling error from a single dot grid sample. Such an estimnate can be used to assess the precision of dot grid area calculations, both for state and change assess/.renf. The area error, although rarely specified explicity, is a maior contributor to the total error of a forest inventory. Therefore, it should be determined and be recognized in a total error budget.

ln most cases, a sampling approach will

INTRODUCTION

be

pref

erable. Standard

samPling

methods for area estimations are dot grids

and line transects. Both methods are being used in forest inventories and in other areas as well, f or examPle in

Forest areas are being estimated in most

forest inventories, differentiated in two (forest/nonforest) or several classes (forest types). The area can be calculated by several methods. The direct measure-

ecology

or microbiology. ln

forest

inventories these sampling approaches are

being used

in terrestrial surveys and

ment of the area on maps or photographs

remote sensrng.

is only possible when the population is relatively small or when little detail is required. This delineation can provide exact results, however it cannot be

ln the following, problems associated with the calculation of the sampling error are

discussed for the case of forest/nonforest area. This binomial problem can easily be extended to the multinomial case (i.e., several forest classes) by treating each

verified (FAO 1981, p. 79) as subjective influences occur, the results depend on experience, prior knowledge, and possible bias of the interpreter.

stratum separately.

143

The use of the f ormulae f or random sampling will result in an overestimation

SAMPLING ERROR

of the actual error. This often is accepted as this error is on the conservative side,

ln area determination often it is assumed that no error occurs, or inappropriate error

however,

estimat¡on methods are being used. Standard statistical procedures used f requently f or the calculation of the reliability of dot grid estimates assume random distr¡bution of points. For practical reasons, however, systematic

accurate . estimate

is

are to be ¡nvestigated. ln the following, the maps in Figure 1 will be used to exemplify the problems. For each of the maps the true standard error

has been calculated by simulation. For each dot grid density with random

selection is preferred. These dot grids are easier to apply and the results can be

starting point and random direction of the grid, sampling was performed with 1O0 replications. Thus, we had 100 est¡mates for the area. The standard error of these estimates is equal to the sampling error. The absolute area is calculated from the

presented in graphical form as raster maps. However, no estimate of sampling

error can be made easily. ln

an

important, especially when area changes

the

following, an approach based on empirical methods will be presented that allows the est¡mation of the sampling error, under the assumption that quadratic dot grids are used.

number

of points in the f orest.

We

assume that each point in the quadratic grid of w¡dth so represents an area sn'.

FÍgure 7. Example of three forest cover maps as used in a simulation study for the sampling error of dot grid sampling. The results are given in Figure 2.

"\

o-o B{ ô-

Mop J

Mop2

-ô Mop

1

-i:

Fígure 2. True sampling error for the three sample maps in Figure 1 . The standard error percent is given

.Þ be L'-J

Ø

as a function of the number of

points in forest area.

The

uppermost line gives the error fo¡ a random process. o.1 0 2.O

I44

To present the relation of standard error and grìd density, a logarithmic scale is

lection. With increaslng number of points in the forest stratum the diff erence between random and

used, where the standard error in percent number of points in the forest is the independent variable. ln Figure 2 the results of the simulations are presented. For compa-

is the dependent and the

systematic select¡on becomes bigger.

For map 1 (see Figure 1l with a number of 6,000 points inside forest the error for the systemat¡c dot grid is about 1 l1O of the error for random

rison, the standard error for random sampling is given also. The following main points can be inferred

-

sampling.

:

- the sampling error of the dot grid estimate is inf luenced by the

the relation is linear in the log scalethe standard errors of dot grids are

fragmentation of the forest area. The simple map in Figure 1 is estimated with much higher precision than the

always lower than the one for random selection

-

map. These diff erences become more pronounced with more scattered

the difference between random and systematic sampling is lowest for wide grids. This can be explained by the f act that f or wide grids the

increasing number of points.

-

correlation between adjacent points is low and approaching the random se-

random selection

Mapl: Map2: Map3:

:

log SE% logSE% logSE% logSE%

a simple linear model for estimating the standard errors of area estimates for these maps is as follows :

= 2.00000 - 0.50000

= 1.97111-0.72972 = 1.97808 - 0.67812 = 1.98398 - 0.62714

log log log log

n n n n

no-., the number of points in stratum 0 with a direct neighbor in vertical or

The formula for the random selection is a f unctional relationship that can be derived from a Poisson process with density n/F, where F = âr€ô, n = expected value of

horizontal direction ¡n stratum 1 we are

the dot grids become lowers.

obtaining a simple measure for the spatial distribution. lf this number is low we have a relatively simple spatial structure, if the number is high the structure is more complicated. This number is also influenced by the scale, i.e., the ratio of dot grid width and dimension. This measure,

A simulation study was conducted that

however, has the advantage thât it can be calculated from a single sample, prior

the number of points in the area. The

formulae for the three maps are based on

simulation. For small n the relation

is

similar as for the random selection, with increasing n the standard error values for

was based on 60 digitized forest maps, which included 30 to 27,5O0 polygons. Based on this study, an estimation formula has been developed that can be used to estimate the sampling error of systematic dot grids

inf

=

1

on the structure is

not

lf this measure is included in the estimation formula for the sampling error, we obtain the following formula, based on simulations of 60 maps :

:

log SE%

ormation

necessary.

.920397 - 0.688748 log n

log SE%

with a 12 = 09.9475 (in log scale).

= 1.60894-1.1O57 log n +0.55296

log

n*,

where n0.., is the number of transitions from stratum 0 to 1 in the perpendicular raster directions. The estimation of the

This estimation formula does not take the

spatial distribution into account, but it is very simple to use. A higher degree of

sampling error can be improved with the addition of this variable (R2 : 0.9788 in

accuracy can be reached if we consider the spatial pattern as well. lf we define

log scale).

r45

the number of po¡nts in a stratum and the

CLASSIFICATION

Spatial patterns also

inf

luence

number of points in a given stratum having a direct neighbor in the other stratum. This formula takes the spatial distribution into account, but no prior inf ormation on the spatial structure is

the

accuracy of the estimation of forest area. ln many inventories an area is defined to be forest if the crown cover is higher than some given percentage. To investigate the effects of different forest definitions and how the accuracy of area estimates

is influenced by the definition and

needed.

There is a correlation between spatlal distribution and standard error. Simple

patterns are est¡mated more precisely

the

than more complicated patterns. Stratification could be advisable if the patterns are very heterogeneous, the grid width for simple patterns can be larger as the width for complicated patterns. Although the calculations have only be performed for

spat¡al structure, a simulation study was performed on several forest maps (Kleinn 1992).

A point was classif ied f orest if

the

surrounding square with side s, had at least a coverage of c.. The simulation study used several maps. Each map

the binomial case, they easily can be extended to the multinomial case. The calculation of the precision of the area in forest inventories is rather important, as it can be a major contribution to the total error of the inventory. ln calculating area changes, the assessment of the error of

composed of 630 x 630 units. Squares with dimension s, x sr were defined from s, : 25 s, = to 75 units. Cover ranged from c. = 0.1 to c,,, : 0.5 in different spatial structures.

the area estimates is even more important, as it is the basis for interpreting the

The simulation study showed how much

area change correctly.

the forest area estimates are dependent on the definition criteria and the spatial structure of the forest. For a aggregated forest cover distribution the change of forest definition from a cover of 0.1 to 0.2 resulted in a reduction of forest percentage from 38%

to 36%.

For

The variable forest area is very sensitive

to the

def inition used and the spatial pattern of the forest. lf definitions are

changed over time, or if diff erent inventories are being compared, the results may not be very meaningf ul. Area est¡mates over t¡me from the same map

a

highly fragmented forest the reduction was from 78o/o To 63Yo.

or remote sensing image are problematic

if the spatial pattern between the

These results indicate how sensitive the variable forest area reacts to the change in definition of the forest - an important point if Inventories are to be compared over different regions or over time when the definition criteria have been changed, or if the spatial pattern changed and the f orest became more f ragmented over

inventories has changed.

REFERENCES Chevrou, R., 'l 973: lnventaire des haies. Revue Forestiere Francaise, 15, S.4753.

trme.

Chevrou, R., 1976: Précisions das mesures de superf icie est¡mée par grille de points ou intersections de parallèles. Ann. Sci. forest. 33(4): s.257-269.

SUMMARY

Systematic dot grids allow the estimation of areas eff iciently. Based on simulations

of actual f orest maps an est¡mat¡on

FAO, 1981: Manual of Forest lnventory

formula for calculating the sampling error of these systematic samples has been derived. The independent variables are

Tropical Forests. FAO Forestry Paper

with Special 27, Rome.

146

Ref

erence

to

Mixed

lity and Statistics. ln Honour of

Kleinn, Ch., 1990: Estimating the area of irregular-shaped objects by dot

Gunnar Blom. (Hrsg. J. Lanke und G. Lindgren), 5.243-257 , Department of

counts. ln : Forest lnventories in Europe with Special Ref erence to

Mathematical Statistics, Lund,

Statist¡cal Methods. Proceedings of the lnternational IUFRO S.4.02 and

Sweden.

G., 1970: La théorie variables régionalisées et

Matheron,

5.6.04 and S.6.04 Symposium, May Birmensdorf, 14-16, 1990.

des ses

applications. Les cahiers du Centre de

Switzerland.

Morphologie Mathériratique Kleinn, Ch., 1992: On the Compatibility

of Forest lnventory Results. The Problem of compatible Forest

'

initions. ln:

lntegrating Forest Management over Space and Time. IUFRO Conference, 1 3-1 7 January 1 992, Canberra, Australia. Def

991

1

:

lnventory Systems Forest lnventories

National Forest

in Europe. ln

:

in Europe with Special Ref erence to Statistical

lnternational IUFRO Symposium, May

14-16,

Global Natural

Resource Monitoring and Assessments

1

990, Birmensdorf

,

Switzerland.

: Preparing f or the 21th Century. lnternational Conference and

Rhody, 8.,

Waldf

Workshop-Sept. 24-40, 1989,

986: Erfassung von lächenund 1

Vorratsstrukturverhältnissen mittels

Venedig, S. 32-40.

satel liten

Mahrer, F., 1976: Abgrenzungsnormen zur Erf assung der Waldf lächen im

b

ild o rientierte

r

Waldverbreitungskarten. Forstarchiv

57.J9.,Nr.5,

schweizerischen Landesf orstinventar (LFl). Eidg. Anstalt für das Forstliche

Zöhrer, F.,

Versuchswesen S. 29-53, Birmensdorf

Pelz, D.R.,

Methods (M. Köhl und D.R. Pelz, Proceedings of the Hrsg. ).

Lund, H.G., 1989: From Terras lncognitas

to llluminations. ln :

de

Fontainbleau, Fasc. 5.

S.1 74-1 80.

1976: Die Genauigkeit der von Waldf lächen durch

Ermittlung

systematische Punktstichproben

, Berich Nr. 1 67.

(Einzelf

s.294-301.

Matern, 8., 1985: Estimating area by dot counts. ln : Contributions to Probabi-

L47

I

lächen). Forstw. Cb. 95,

THE USE OF PERMANENT PLOTS FOR MONITORING VEGETATION CHANGE lN SOMALIA: PROBLEMS OPPORTUNITIES. RECOMMENDATIONS R.M.Watson J.M.Nimmo Resource Management & Research

l6b West Central Street London WCIA 1JJ, UK

FOREWORD

ln 1977 the World Bank recognized that Somalia, a poor, arid/semi-arid "state" of 650,000 km2 at the North East corner of Africa needed to be able to monitor and

of many modern paradoxes is the growing capacity of scientists to study the earth's surface at global scales, in a One

eventually manage changes in its vegeta-

tion at a national level. They financed

geopolitical world where their capacity to

react, or

ectively warn threatened adverse environmental trends, is at best static and probably receding. To put it a more graphic way: When the last few children of man are huddled under the planet's last tree, eating the world's last mango, the event will be recorded in great detail f rom scores of sophisticated satell¡tes. The ext¡nction of the human race, then, will be the best forecast and recorded event in human history. eff

populations

a

multi million dollar programme to establish a National Range (ie. vegetation) Agency and initiated a series of vegetat¡on

of

surveys.

Resource Management &

Research (RMR) was inv¡ted

to conduct

these surveys and this paper highlights the problems they confronted, and how

they dealt with them. The

authors continue by looking at how they might do the same task today in Utopian socio-

economic conditions, and conclude by offering some general recommendations about the use of permanent plots in world vegetation monitoring.

INTRODUCTION THE SOMATIA SURVEYS - GENERAL

The nation state ¡s likely to remain the

principal unit

f

or social and

Although the National Range Agency (NRA) terms of reference specified the production of vegetation maps (together with other natural resource inventories and mapping such as wildlife and live-

natural

resource management for some time to come. lt is argued therefore that it is essential to concentrate on monitoring vegetat¡on' change at national levels, as in this way the probabilities that such

monitoring can

eff

stock counts, water source assessments,

ect and inf luence

management are maximized.

' "Vegetation" and "Forest" are not taxonomically or physiognomically synonymous. However the observation of communities of plants growing at a fixed location over long periods of time (as is implied by tho term "monitoring") is a process we prefer to term "vegetation monitoring" 1o avoid introducing unnecessaÍy semantic corriplications, L4S

counts of houses, assessments

of

SOMALI,A MONITORING SITES -

land

use and agriculture, etc.) as

its

¡n the vegetation of Somalia could

be

PROBLEMS

requirement from RMR, it quickly became apparent that the puroose of the NRA, at least for the vegetation work, was to establish a base line from which changes

of a national network vegetation monitoring sites posed a number of interesting, challenging, and a few insurmountable, problems. These The establishment

of

monitored.

When the survey commenced in 1977 satellite remote sensing could already

were:

provide a means of looking simultaneously

at all Somalia's vegetation at a

1.

coarse

scale of "information resolution". lt was

at the time f orecast (correctly as it happens)

The use of fixed vegetat¡on plots to describe features of a larger vegetation "universe" inherently implies that

that satell¡te remote sensing

would inevitably become increasingly precise (temporally, spat¡ally and

they are being used as sampling plots. ln point of fact the rigours of

entropically) and it was expected that Somalia would be able to take best advantage of this favourable trend by

formal sampling methods (Cochran 19631 (in terms of site location and numbers of sites) make it entirely impractical to apply formal sampling

laying down a large number of fixed sites

(pôints, locations, plots, quadrats

are

synonymous terms) at which the vegetation and other ecologically relevant data could be precisely recorded. The records taken at these fixed sites were of course useful also in the derivation of vegetation descriptions to be attributed to the 496 land system units into which the Somalia land surface has been divided. (Watson ef

al. 1979, Watson 1982,

Watson

Representativeness

rules in selecting monitoring sites. lt must be appreciated by all involved that vegetation changes recorded at monitoring site(s) (permanent plots) can only be attributed to areas surrounding the site(s) or areas judged f or some reason to resemble the site(s), using common sense rather than statistics. For this reason (see

and

Nimmo 1985.

paragraph

3

below)

it

makes little

sense to assemble large volumes of precise data at monitoring sites. This is an insurmountable problem, not

Between 1977 and 1984, working almost of RMR usually deployed as a three person (including the

continuously, the staff

just for Somalia, but for

second author) team travell¡ng in 2 f our-wheel drive ve-þicles located and described 1 695 monitoring sites.

all

vegetation monitoring by fixed plots. ln Somalia the network of sites was selected using these criteria:

-

The first author carried out the aerial photography of these monitoring sites a few weeks or months after they had been located and described, overflying them in logistically convenient clusters. A few sites (112) in extremely difficult terrain

iciently close to a track (apart f rom those visited by STOL aircraft) to be cheap to suff

visit and conveniently relocated,

but not so close as to be affected by ecological events associated with the track

which would have required long and

arduous camel safaris, were visited by the

first author (alone or with one colleague)

distributed

using a PA 1 8 aircraft and short take-off and landing (STOL) flying techniques. Such techniques require a 50-60 m clear area to be found close to the site.

each land system unit and/or distinct vegetation related facet of the land system unit has not less than two mon¡toring sites

The cost of locating and describing these 1695 monitoring sites to the NRA (which includes the RMR prof it) was about $ 160,000 or $94.4 per site.

distributed so that any particularly dynamic or interesting

so that in

general

vegetat¡on boundary

(e.9.

severely eroding areas, Juniperus

150

including massive two ton trig. points. A simple fixed marker does not enable relocation of a site, but rather guarantees intense pastoral use of the site until all traces of the marker have been removed. Every passing herder will linger with his livestock as he makes his own contribution to eliminating the alien

mist forest relicts in open areas,

large f ire scars, etc.)

has

monitoring sites at the boundary.

It is hoped by the consultants,

RMR, suff iciently extensive (3.4 monitoring sites per land system unit and 383.5 km2 per

that the network is

monitoring site) that the sites will enable the Somali Government to:

- use them to

illustrate

to

element in his landscape.

RMR devised a method of map marking using permanent, or semipermanent, conspicuous features

the

public and/or students and/or

donors in support of educational/ management programmes, most of the types of change which occur in Somali vegetation

termed "primary points", f rom which a route to the site is described by

compass bearings and measured distances, using secondary and tertiary points (i.e. less permanent and less conspicuous features). Sketches and photograpþs were made to clarify the route.

- study the processes of a selection of change types in more detail

-

-

detect most of the economically important vegetation changes in

The site itself is marked at its centre point by a small stone, bone, or other semi-durable object found near the site. ln the case of stones, the

the country,

enabling more intensive networks to be laid down where critical changes seem to be taking place provide "ground

number of the site was marked in paint on the underside.

truth' data for

The latitude and longitude to the nearest minute of arc was recorded from 1 :100,000 scale topographical

the more accurate interpretation of satellite derived data covering the whole of Somalia

-

maps, on which the site was marked.

The details of the

make "common sense" analysis of vegetation change nationally,

and reasonable forecasts of future change

-

develop sustainable management programmes for Somali vegetat¡on f or exploitation, conservation, protection and rehabili-

tat¡on in a harmonious

primary,

secondary, tertiary, etc. po¡nts were recorded in lndian ink on A4 80 gsm card, and a sketch of the route to the centre point via the primary point, showing the nearest mapped feature

were also so marked. Black and white prints, where relevant, were mounted on the same reference card.

This card is called the site location

and

economically strategic balance.

map card. Clearly the relocatability of sites will depend on the length of time elapsing between visits and on the durability

Unfortunately it ¡s still too early, and Somalia is too chaotic at present, to have any feedback on this expectation.

of

primary

points. Within a

few

of the original location, of sites were relocatable

months

lOOo/" according to tests made by RMR. lndeed all the sites of S. Somalia

2. Location and Re-location

Somalia presents special problems here, common to a few other African

were relocated from the site location maps cards by the first author from a slow flvinq aircraft which is how the aerial photographs (see belowl were

countr¡es but rare elsewhere. Somali nomads destroy any foreign and nonnatural body they find on their land,

taken.

151

ln the likely event of centre

transparency

po¡nts

Sixteen photo-

at each site,

namely:

-

three aerial photographs taken semi-vertically f rom 1,000 feet (304.8 m), using 24 mm,50 mm and 1 35 mm lenses to provide three scales of aerial view,

the original vegetation elements are st¡ll in place.

it is not at present possible in Somalia to try to relocate sites. lt was envisaged and planned by RMR to revisit sites after ten years Unfortunately

(i.e. between 1988 and 1994). The intentioî had been to make a new marking of centre points, using any technological advances and/or better

-

one general view of the whole site to best show the vegetation, usually taken from the direct¡on of the primary point towards the centre point

-

three ground photographs taken

ideas, during first and subsequent monitoring revisits. Marking by buried steel pegs and steel wire was already planned for the next round of visits, to enable metal detectors to assist in relocation. lf a revisit programme

was started today it is likely that

film.

graphs were taken

being kicked out of place by wildlife/ livestock the centre point is fairly easily relocated by a triangulation method using the photographs taken at the site, assuming at least some of

from head height (say 1 .5 m) using a 24 mm lens along a magnetic north axis f rom the site

centre point. The first photograph is of the ground including the centre point. The second frame is of the ground from the upper boundary of the first f rame. The last frame is a view to the horizon.

an

advanced global positioning system (GPS) could permanently solve the problem of site location/relocation.

-

However as things stand one must

realistically expect that at the moment 80% of sites could be relocated with reasonable efficiency,

three ground photographs as described above along a magnetic east axis

-

and the other 2Oo/o would simply take

too long to find to make it worth-

three ground photograPhs

as

described above along a magne-

tic south axis

while, unless a critical environmental change were involved. By the year 2000 the percentage of f indable sites may have fallen to 60%.

-

three ground photographs

as

described above along a magne-

tic west axis.

3.

the site environment were collected,

General indications about Scope of data to be recorded

concerning these features:

Altitude,

TopograPhy, Geomorphology, Geology,

Because of representativeness problems referred to in paragraph f . it was judged unsuitable to assemble a large volume of analysed data at each site, and pref erence was given to

making

which

Erosion, Drainage, Termitaria, Grazing/Browsing, Vegetation,

Soil, Cropping, Villages

a

photographic record, on measurements and quanti-

and

Water, Archaeology.

f

At the specific request of the client,

a fixed pattern using a 24 mm lens for the ground based views and 24

was stat¡st¡cally more or less unusable for monitoring, and not particularly usable f or base line

ication could be perf ormed retrospectively should they seem relevant. Photographs were taken according to

despite RMR's pointing out such data

vegetat¡on descriptions, vegetation cover was recorded using a modified

mm, 50 mm and 135 mm lenses for

the aerial views, a 35 mm format

camera, employing 100

point transect method.

ASA

L62

Standardisation

4

of data

scattered among the torn papers and

collecting/recording methods

human faeces which littered the f loor.

ln short the place had been

and wilfully destroyed,

small areas are wooded. True forest such as people in Thailand are familiar with does not exist, and the nearest approach, a Juniperus,/Baxus closed canopy mist forest only covers a f ew hundred km2 of Somalia.

A fire was burning vigorously in the room next to the Monitoring and Documentation Centre, and the only people seen were gun carrying looters, who occasionally fired bursts AK47 f¡re at enemies seen in

of

neighbouring buildings or into the air.

developed f or Somalia could be applied at all sites, but they were f

ound diff icult

With the assistance of

to use in the

sites which were located in very dense and

thorny vegetation. ln woodland/f orest sites the

SOS

Kinderdorf lnternational, an Austrian

based lnternational Children's Charity,

the originals of all monitoring

dense

photobe recording only the

site

photographs and site location map

graphs tend to lower levels of vegetation. Had thorn

cards were removed to the RMR office in Nairobi. Here they were cleaned and re-filed. The lnterim Government of Somalia has now requested that they should be kept there until new facilities are built to house them in Mogadishu, unfortu-

thicket andlor f orest been more widespread vegetation types in Somalia, diff erent data collection methods would have had to be developed, although f or methodological/analytical purposes the data recording format would if possible

nately a rather distant prospect.

These rather desperate

have been retained.

and dangerous measures could have been

avoided had comolete sets

Storage and security of, and access

the consultants' offices.

RMR placed all the originals of monitoring site records in a room devoted to their storage at the National Range Agency. This included the 35 mm photographs as 35 mm slides, the site location map

all

Unfortu-

nately there was insufficient money

available at the time to have copies of

the 35,000 slides made.

All textual and digital data collected at the monitoring sites has been

cards, 1 :100,000 maps marked with site locations, and notes collected at sites. The room was to have been air conditioned, dust free and access to

the originals should have

of

monitoring site records been copied and stored, one in Somalia and one in

to, plot records

stored in a database which is based in

the RMR office in London 1

been

6,

carefully controlled.

(Nimmo

991 ).

lnstitutional support necessary to ensure continuity of monitoring

When the f irst author visited Mogadishu on 27th January 1991

Even before the catastrophic sacking of Mogadishu the Government was so discredited with donors that there

after President Siyaad Barre had fled the ruins of his palace he visited the

Monitoring and

even

desecrated.

The data collecting/recording methods

5.

looted

Most of Somalia is too arid to support very dense low vegetation and only

Documentation

was no budget for properly looking after the invaluable records of the

Centre, as this special room was known. lts doors and window had been smashed and the cabinets holding the original photographs and

monitoring sites. Even the Herbarium specimens were more or less eaten by fungi and arthropods.

slides (held in transparent wallets, in

A4 ring binders) had been thrown to the ground. Many slides were

As f ar as raising f unds to make second visits and put in better

1õ3

this possibility disappeared in about 1 986. The possibility of physically travell¡ng

new costs any

bildits, and now it would require a

to offer attract¡ve possibilities to

helicopter supported tank brigade to

the work cheaper, and to overcome some

relocation methods/devices,

modif

ications would

introduce. At present only the roughest est¡mate can be made of these.

to the sites was by then already f raught with risks of attacks by

However certa¡n new technologies seem make

attempt any field work in Southern

of the

Somalia.

RMR in their first programme. These are:

It is obvious that RMR did not foresee changes of this magnitude, and in any

case could not have

1.

influenced

It is today inconceivable that

the problem of location/relocation would be solved using a map marking

ln the long term, when or if condit¡ons retùße to levels of safety such that finance can be found to pay for the re-visit of all sites, and when it is safe to do so, there will clearly be a need to rapidly take stock of the new vegetat¡on conditions and the

method. There is no doubt that

of

a

moöern GPS would allow monitoring

sites

to be effectively

located

and

relocated with much higher efficiency at appreciably lower cost.

changes which have occurred. ln this event these monitoring site records will prove of exceptional value.

effectiveness

Low cost Ground Positioning Systems IGPS}

events.

L Cost

problems only partly solved by

2.

Low cost aer¡al and ground based photographic, video and photogrammetric methods in data recording

vegetat¡on

monitoring in the context of a very poor th¡rd world state

The methods employed here by RMR were rather original and low cost. ln today's technological environment the

The cost of collecting the monitoring

most likely modifications would be the taking of Hi8 video film records (chiefly for educational, media and

site data for the 1 695 sites Somalia has been

S1

in

60,000 at 1 978-

promot¡onal purposes),

84 values. This is a large sum for Somalia, and it remains to be proved whether this spending was worthwhile.

ln the authors' view, however this level

of

investment

in

inf

ormation

with such high potential value

f

and

the

switch from film to digital cameras. These latter would allow storage of vegetation information which could þe either turned into an image for illustration, or fed into an analysis programme. There is no doubt that technique has immense potential, although ¡t will be some years before it also offers a cost saving.

or

t-his

guiding future exploitation, management and conservation programmes for Somalia's chief known economic resource (i.e. its vegetation) is likely to prove highly cost-effective in the

3.

lonq term.

Ground radar methods of recording rainfall and satellite remote sensing methods of assessing other ¡mportant environmental variables

lf a vegetation monitoring

site programme were to be started today one would hope that its funding and

FOSSIBLE MOD¡FICATIOITS OF THE METHOD TO USE NEW TECHNOLOGICAL DEVELOPMENTS

managing agents

lf

RMR were

would

caref ully

consider how the use of the wide array of real time or near real time satellite derived data, and the use of

to start the same exercise

today, would they work differently? Part of this answer depends clearly upon the

real time ground radar derived data on

L54

rainfall could be linked with the monitoring site records, so that it would be possible to understand the causes and effects of the monitored vegetation changes in terms which

taken in digital form. This would solve

serious problem

affordable by Somalia.

modifications (really additions) would be too expensive to be introduced

RECOMMENDATIONS BASED ON THE SOMALI EXPERIENCE

into Somalia in the foreseeable future.

Geographic lnformation Systems (GlS) and databases

Some general recommendations ate offered here based on the author's

lf

experience over the last 13 years with the Somali national vegetation monitoring site work and on consideration of recent

RMR were

to start the vegetation

monitoring site programme today an

appropriate GIS and/or database programme would be selected/ designed to optimise access/analysis possibilities. Field reports would be formatted for rapid translation into a

global environmental events. These recommendations concern the use of permanent plots (monitoring sites) f or world forest (vegetation) monitoring.

monitoring site database.

The greater

eff

1

iciency this

.

modif i-

cat¡on would provide would result in expenstve.

culturo-historical base lines and records, illustrations of important environmental processes, or indicators of management needs, are not

Hardware, especially data storage and

necessarily compatible.

CD

technologies

When the Somali

data

sources for precise scientific studies, educational and opinion forming aids,

and database software is not very

retrieval systems and linked

The specific use to which permanent plots are to be put should be caref ully considered, as their applications to

different purposes, such as

an overall cost savino, as suitable GIS

5.

a

access, retrieval, analysis and security. lt would probably be a cost effective modification

permit forecasting and management of changes at a national' level. Such

4.

in terms of

vegetation

monitoring site work started in 1978, RMR was working with an Apple lle

computer. Today the additional capacity of a Compaq Desk pro 38612Oe, with 1 10 Mb of srorage

would revolutionise the laborious business of putting field notes into permanent legible form, and immeasurably enhance the accessibility and usef ulness of

2.

Permanent plots should be located by intelligent selection rather than f ormal sampling procedures.

3.

The scope of data recorded should be concentrated on those features of forest/vegetation change which have

(or are likely to have) relevance for

global environmental issues.

4.

The linking of a network of permanent global vegetation plots to other networks at Regional, National and local scales and to "globally" available

monitoring site records. Some 35,000 photographic slides at present occupying a large cabinet and

remotely sensed data should be at the design stage to

considered

needing constant vigilance to keep safe and in good condition could be

ensure maximum

eff

iciency

in

forest/vegetat¡on monitoring work. The role of permanent plots in a

stored as digital data, and indeed (see 2 above) photographs could even be

mult¡-t¡ered methodological strategy is

' At.the global/regional/continental level it is even more important to be able to relate vegetation change to.ch.anges in the global atmospheric and hydrologic systems. Global vegetation moniioring networi,s

which merely say how the world's vegetation is changing are to som-e extont missinglheir main

function.

15ó

of critical importance, and should be dominant feature in tho design of

ACKNOWLEDGEMENT

a a

network of recording permanent

vegetation plots.

The authors would like to thank Finnida and the University of Joensuu for support towards their travel costs to Thailand.

5. Costs of effective

forest/vegetation monitoring by permanent plots should be

minimized by appropriate use of global positioning systems, photo-graphy, video recording, and photogrammetry, low cost

low levol aerial platforms, GIS and Database softwaro, and hardware designed to handle a wide range of

REFERENCES

analogue and digital data. Methods should be constantly roviewed to take advantage of technological advances in this dynamic f

6

Cochran, W.G., 1963: Sampling Techniques, 2nd ed. Wiley, New York.

ield.

Nimmo, J.M., 1991: Development of a database system for the evaluation of

Since monitoring is necessarily a long term process, the design of a perma-nent plot monitoring programme should incorporate high levels of flex¡bil¡ty to accommodate the unfore-seen. For the same reason the

land resources f or planning and development in Somalia. D. Ph¡l

Thesis, University of York.

likely longevity of personalities and all institutions involved should be critically

Watson, R.M., 1982: Northern Rangelands Survey. 4 Volumes.

consid ered.

7.

National Range Agency, Mogadishu

The standardisation of data

recording methods should sorve the purpose(s) of

Watson, R.M.; Tippett, C.l.; Beckett, J.J.

and Scholes, V., 1979: Central Rangelands Survey. 4 Volumes.

the network, and it must be recog-nized that the diversity of plots may dictate a diversity of recording methods. Similarly

National Range Agency, Mogadishu

the d¡stribution of plots should reflect the

notwork's purpose, and a

Watson, R.M. and Nimmo, J.M., 1985:

regular

Southern Rangelands

dist¡ibution of plots must be seen to have

no inherent value, and many inherent drawbacks, as I distribution pattern

Survey.

4

Volumes. National Range Agency, Mogadishu

option.

156

PERMANENT PLOTS FOR MULTIPLE OBJECTIVES: DEFINING GOALS AND RESOLVING CONFLICTS Jerome K. Vanclay Royal Veterinary and Agrículturat lJníversity Thorvaldsensvej 57, DK-lB7l Frederiksberg, Denmark

ABSTRACT Many research and mon of results depends upon of such plots that can b as

possible'

a from permanent plots, and the validity these data. Resources timit the number essential that alt plots sat¡sfy as many needs

This requirement impinges on the placement, design and procedures

for existíng paper examines compatibitities and cánflicti between the demands of growth model development' of ecological monitoring systems, and of ground truth for remote sensing' Whilst conventional procedures provide basic data for ail these applications, and proposed

plots'

Th.e

of the data with negligible extra cost. ment of plots, and for increased utility may enable us do more with less, to inferences from these data.

r

INTRODUCTION

* *

Some of the objectives of this workshop include:

promoting the establishment

"...

of

ln formulating our information needs, we

health involves very

Before we rush ¡nto this task, we need to consider carefully our information needs,

and enables very Optimal sampling

and then to identify the data that are required to provide th¡s information. "What to measure" is an issue which all too often precedes these more important considerations, with the frequent result that data collected may be sub-optimal. We should first resolve our information

requires diff erent

procedures than for monitoring change.

All too often the data available in

an

Our information requirements should

be

information system determine its users, rather than the reversel

d-rt,

identif

reouirements, and then decide onìhã

ied explicitly and stated

clearly,

concisely and completely. ln the planning and design of a monitoring system, it is important to identify the real information needs and not to be constrained by the f easibility and costs of obtaining this information, as needs tend to be durable

sampling . design and measurement procedures with these needs in mind. Only then can we resolve:

r

uses these data; how to measure it, and how much data are required.

must be quite specific in defining what we need know and what we hope to infer from this information. For example, the monitoring of forest area, of forest quality

a

permanent sample plot network ...", and to specify the data to collect ..."

needs, translate these ,t"

who measures, who pays, and who

what, when and where to measure; L67

establish a trend (Vanclay 1991). We need to anticipate information needs ten or more years hence, and thus our plot measurement and management procedures must be flexible and robust. We

wh¡lst technology continually alters the easibility and cost of gathering the necessary data (Vanclay 1990a). Failure

f

to identify the real information needs may lead to important attributes being omitted

should f ocus on basic stand variables which can be quantitatively measured

from measurement procedures, and may diminish the value of the system.

rather than subjectively est¡mated. Those

who design and initiate the system may not be the ones who use it, so the design APPLICATIONS DEMANDING

and rationale should be .

PERMANENT PLOTS

documented. Procedures and standards should be established, documented and maintained, and changes should be few

There is no doubt that permanent sample plots can be very useful, but efficiency may favour other forms of sampling for

and upwardly-compatible,

many applications. To qualify

caref ully

as

PLOTS SERVING MANY PURPOSES

permanent sample plots, there must be both an intention and a plan to remeasure them. This involves considerable additional cost over temporary samples, so why and where should permanent plots be used? ln some cases, there is no

One way we can increase eff iciency is to make the same plots serve several purposes, provided that the objectives are compatible. Table 1 illustrates some f undamental diff erences in the design

alternative, but f or many applications, temporary plots are both f easible and efficient, and should be considered. ln general, permanent plots are necessary

only where detection of change

oblectives of some applications. Wh¡lst all these applications can be satisfied by permanent plots, two can be served most

is

required.

eff

iciently by temporary

Resource inventory diff

samples.

ers f rom

most

Where permanent plots are necessary, it is essential that they be managed efficiently to minimize costs and maximize benefits. This requires careful attention to information needs and to data collec-

other applications as sampling should maximize variance within plots and minimize variance between plots in any

tion procedures. We should not

stratum. ln contrast, most

other

applications require homogeneous plots.

only

This different fundamental requirement

focus on existing needs, but should also consider future needs. ln many applications, measurement errors are relatively

continuous f orest inventory schemes which attempt to provide estimates of

large compared to the rate of change, and

both status and change.

may limit the

eff

icacy of

traditional

five or ten years may be necessary to

Table I . Characteristics of some sampling applications.

Pp rma np

Âra

nnp

Resource inventory

Temp or Perm

Growth estimation Site monitoring Ground truth

Permanent Permanent

Silrri¡r rltr rrel pvntç

Temp or Perm Þa rm ¡ non+

1ó8

Variable Fixed Fixed Bis \/arioc

Plnt \/arianne Heterogeneous Homogeneous Homogeneous Homogeneous l-{nmnncncnr

rç

Some useful information regarding environmental change and degradation may be provided by plots designed to provide data for growth model development (e.9. Vanclay 1990b), but additional variables should be measured to provide compre-

proposed) system deliver the information we needT

How big is the sample? Are the plots the right size, shape and orientation for our needs? How many plots are there: are there enough or too many?

hensive site monitoring. The same

sampling designs may be used in silvicultural experiments, but it is often pref erable to customize the design to specif

ic

experimental

Whilst it somet¡mes is more efficient to

needs.

work within an existing system. we should not blindly join an established

Ground truth for remote sensing can be provided

system just to freeload on another budget, as we may not get the information we want. Sometimes it is more efficient to combine several needs into a single sampling system; sometimes ¡t is better to adopt separate systems, and to

by either permanent or temporary plots, but inaccuracies in image registration indicate larger plot areas than those customarily used in other applications. Since we may extract much useful information from existing plots, we should carefully appraise our information needs before we promote the establishment of

combine the data later (Vanclay 1990a). Simple practical matters may be decisive. For example, timber inventory should be

additional plots.

Some additional attributes may

conducted

measured and recorded in existing permanent sample plot systems with minimal extra cost. Where this is possible, this offers a cost effective way to obtain additional inf ormation, and reduces the signif icant burden of plot

ma¡ntenance. There are f

good

plots may be best conducted at dawn using survey crews with different skills.

SAMPL¡NG SCHEME

several

advantages to such an approach, but we should not assume that we can satisfy

our needs simply by

in broad daylight for

visibility, whilst bird surveys on the same

be

reeloading

When we have resolved our information needs, the required permanence of the plots, and the scope for shared plots, we then have to face the diff icult question of sampling design. The question is: what is optimal placement and management of

on

existing permanent sample plot systems.

There are several issues that should

be

examined. Principal among these is the question: will the existing system efficiently satisfy our needs, or are we just trying to avoid paying for our own requirements? This question has a converse: would a new system for our specif ic requirements deprive other worthwhile sampling efforts of resources,

these plots? There is no single best sampling scheme for all applications: the optimal design depends upon information

needs and resources available, so this

paper can only discuss some general principles.

or lead to the neglect of existing sampling systems? Both questions require that we look into the adequacy of existing systems and the resources devoted to

2 (adapted from H.C. Dawkins, pers. comm. ) identif ies some considerations which inf luence the Table

them. We need to

resolve the same issues that arise when designing a new

selection of a sampling design. lt takes

the form of a binary key which may be used to select an appropriate method. For example, it indicates that if we seek a reliable estimate and require ¡nterpolation to prepare a site quality or forest type map, we should employ systemat¡c

system, and we need to give special attention to two matters:

How were the plots located: 'Subjectively or oblectively, systematically or randomlyT Will placement of plots

sampling. Alternatively, if our estimate ¡s critical, is to have known precision, and is to be obtained using a small sample, we should use stratified random sampling.

introduce bias into our sampleT

ln short, will the existing

(or

1ã9

Table

2.

Some considerations in sampling design.

Crifcria & Cnncenrrpnnps

Âltarnetirrac,Q,

Nature of estimate Forest Characteristics Representative selection Time and resources

Critical Unknown/Diverse Unreliable

Objective r Go to 2 Absent Can be estimated

Bias Precision

Periodicity lnterpolation Estimate of Precision

Possible/Unknown

Not required Required

Random - Go to 3 Correct estimate

Samolino Error Periodic Bias Pattern in population Sampling intens¡ty 3

lnherent risks Pattern in population

4

Calculations

*

expedient, even though each stratum may contain as few as two plots (Schumacher and Chapman 1954); Once stratification has been refined

Probably inflated

Hish

Unrestricted random nlr

rsfprinn

Possiblv comolicated

of interest in truthing of remote sensing, as the classification of these can then be considered explic¡tly. However, the "typical" stands should also be sampled, and the decision to sample deliberately should be documented.

NUMBER OF PLOTS REOUIRED

as

Statistical formulae often dictate more plots than the forest manager can afford, and this raises several questions. Are the appropriate formulae being used, is the specified precision really required, and if so, is there a danger that the system will

as far as practicable, further by

cost more than the resulting data

sampling proportional to the variance observed within the stratum.

are

worth (Hamilton 1979)7

Permanent plots usually provide worthwhile data, even if the precision does not

Where a small sample is proposed, it may be desirable to deliberately sample to include extremes. Extremes are essential in regression analyses for growth models

reach the desired level. So a comprom¡se may be to establish as many plots as you can afford. But don't overestimate your capability, as a few good plots are better than many incomplete or inaccurate plot records. The importance of this cannot be overstated, as too many monitoring efforts lie abandoned, rendered useless by

Vanclay 1 991 ). in environ-

Extremes are also important

as it is at

Necessary

Unimportant Systematic Sampling

Visible or Well known Statistical blocking

*

mental monitoring,

Unlikely or Known

Obscure/Unknown Geometrical blocks

The precision of the f¡nal est¡mate is inf luenced most by the initial stratif ication; Precision is gained by dividing the

,

lnl¿nnr¡rn

Samnle

precision for a fixed outlay:

1991

I

Absent or Unlikely

prior information is available. Three principles offer the greatest possible

et al.

Unavoidable

Belatively low Strat.random - 4 Misiudoe oattern

blocking may be used, depending on what

(Beetson

Very limited Subjective Sampling

Clear or Likely

For many applications, some form of ied random sampling may be optimal. Either statistical or geometric

improvement can be achieved

Unimportant/Personal Familiar or Uniform

Þnccihlo

Simole

population into as many strata

ltÁa+ha¡l

Unlikelv

stratif

+

Qamnlin¡

Reliable

Suff icient

1

2

ônrimal

the

extremes where many changes will be first manifested. Extremes may also be

160

insuff

icient attention to detail

and

inadequate clerical procedures.

Efficiency is further enhanced by making

full use of existing data, and by ensurini that new proposals satisfy all existing anã anticipated needs. When formulating

new

proposal, discuss

it with

a

your colleagues and make sure that it satiéfies

all compatible needs of your own

and

the proposal and have it reviewed

by

other local inst¡tutions. Then document

international experts. A l¡ttle extra tim;

and

eff ort in¡tially can save frustration and waste later!

a lot of

tions is diff icult to determine directly from

the soil chemical composition, but s¡te index provides a suitable surrogate. Where a plantation has not yet Leen established, the presence or absence of certain plant species may provide a

reliable indication of productivity (Vanclay 1 992). These concepts may be extended

to other aspects of assessment. Whilst it rs easy to monitor the turbidity, salinity

and acidity of streams, monitoring oi other pollutants is more diff icult, and there is always the difficulty that the real problem is the chemical you.re not testing

for. An alternative may be to monitor thã

development of amphibians, either those occurring naturally in the stream, or some raised in water samples in the laboratory (Tyler 1983). Amphibian developmeni could indicate the water quality in the same way as site index (tree height at a given reference age) ,rpr"r"nta the integration of many site f actors. Surrogates may provide an efficient and effective way to monitor many aspects of the environment; the challenge is to find suitable surrogates for our needs.

WHAT TO MEASURE lf information needs are expressed clearly and concisely, and then translateO intá corresponding data requirements, the

parameters to be measured will become obv¡ous. Many papers (e.g. Adlard 1990,

Curtis 1983, Vanclay 1991, Whitmore 1989) have considered what and how to measure, and their guidelines will not be repeated here. Their suggestions provide

Pivotal

or

keystone species are those

which are critical to the functioning of an

a start¡ng point, and can serve as a check list for your own requirements.

Some attributes you should consider

include those f rom the f ollowing four

categories: 1

) Plot

establishment details should include descriptive location and

the rainforest. ln turn, these species may be dependent upon a limited number oi plant species, especially during períods when alternative foods are scarce. Other plant species may have a crucial role in nutr¡ent cycling, through nitrogen fixing or retrieval of nutrients from deep in the soil prof ile. The health and abundance of these pivotal species may provide a good indication of the overall f unctioning of the

numeric co-ordínates, plot dimensions

and or¡entation, and f ull documen_ tat¡on.

2l Site variables should include f ull descriptive and numerical characterizatio

forest type and 3) Trees should be

numbered, tag attributes recorded should include co_ . ordinates, species, s¡ze, vigour, etc. 4) Other species present (shrubs, herbs

forest.

and other species) and their abundance should also be

SOME EXAMPTES

documented.

Suppose we want to monitor the area of closed forest worldwide. We could do this remotely using satellite imagery, and would requíre ground truth data in several

D¡rect measurement of some parameters of interest may be diff icult, and it may be

necessary to adopt an indirect alternative.

For example, site productiv¡ty ¡n planta-

locations for calibration. Such ground 161

truth need not comprise permanent plots; temporary plots would be sufficient' lt remains critical that standard definitions (e.g. of forestl are used, that plots are of a reasonable size, and that the date of

potential users can save time and resources by sharing, co-operating and co-ordinating. We should anticipate f uture inf ormation needs and data requirements, express these needs clearly and concisely, document methods, maintain standards, sample extremes, and not be too ambitious, but "do what you do do well".

measurement is known. An unconstrained analysis of such data may

provide diff erent regional and global estimates of f orest atea than those obtained in other studies (e.g. FAO's survey data), and it may be interesting to conduct an analysis constrained so that

the regional totals agree. lf both such analyses aÍe comPleted, it maY be informative to conduct additional ground

REFERENCES

survey in areas where the constrained and

Adlard, P.G., 1990: Procedures for monitoring tree growth and site

unconstrained classifications' differ' Again, such ground surveY need not

change. Tropical Forestry Papers No 23, Oxf ord Forestry lnstitute' 188 p.

comprise permanent plots, and temporary plots will be adequate if the appropriate standards are maintained.

T.; Nester, M. and VanclaY, J.K., 1991: Enhancing a Permanent

Beetson,

Strat¡fication is more important in forest

plot system in natural forests.

type and/or biomass

assessment. Existing satellite sensors do not provide a good correlation between spectral signature and biomass, so multi-stage sampling within a stratification based on prior information may improve estimates'

Proceedings of IUFRO Conference on

"The Optimal Design of

Curtis, R.O., 1983: Procedures for

Suitability of prior inf ormation is determined mainly by its accuracy, and

establishing and maintaining permanent plots for silvicultural and yield research. USDA Forest Service General Technical Report PNW-155.

many kinds of information may be useful'

Existing vegetat¡on type maps may be employed, or strata can be generated

rom more basic data such as digital climatic, topographic and geological

f

Hamilton, D.4.,

databases (e.g. MackeY et al. 1988). However, where such digital data are utilized, it is critical to employ the right variables. ln particular, it may be the climatic extremes (e.9. droughts, floods

precision

and the mensurationist. Journal of Forestry 7 7 11 0l:667 -67 O. Mackey,. B.G.; Nix, H.A'; Hutchinson, M.F.; Macmahon, J.P. and Fleming, P.M., 1988: Assessing representativeness of places for conservation

and frosts) rather than the averages,

and heritage listing. Environmental Management

reservation

changes in

forest type, biomass or biodiversity may

12(4):501-514.

require more sophisticated stratification, since political and socio-economic f actors

may be major determ¡nants of

1979: Setting

for resource inventories: The manager

which shape vegetation Patterns.

ln contrast, monitoring f or

Forest

Experiments and Forest SurveYs", 10-13 September 1991, London.

Schumacher, F.X. and ChaPman, R.4., 1954: SamPling methods in forestrY

such

change. Thus for such monitoring, it may be wise to ¡ncorporate nations as a strata additional to environmental strata.

and range management.

Duke

University School of Forestry, Bulletin

7 (revised). 222

Tyler, M.J.,

P.

1983:

Natural Pollution

monitors. Australian Natural History

SUMMARY

21(11:31-33.

Permanent sample plots can sat¡sfy most

of the needs of most users, so all

Vanclay, J.K., 1990a: lntegrated resource

].62

mon¡tor¡ng: an Austral¡an perspective needs.

Vanclay, J.K.,

of current trends and future

for

ln: Global Natural Resource Monitoring and Assessments: Preparing for the 21 st century, Proceedings of the international

iropical moist forests. Commonwealth Forestry Review 70 (31, in press.

Vanclay, J.K.,

conference and workshop, Sept 24. 30, 1989, Venice, ltaly. American

Society

f

or

Photogrammetry

1991: Data requirements

developing growth models ,for

1992:

Assessing site

productivity in tropical moist forests, Forest Ecology and Management, ia.

and

Remote Sensing, USA. Pp 650-658.

press.

Whitmore, T.C.,

Vanclay, J.K,, 1990b: Effects of selection logging on rainforest productivity. Australian Forestry

1989:

Guidelines to

avoid remeasurement problems in permanent sample'plots in tropical rainf orests. Biotropica 21 (3l':282283.

53 (3):200-214.

163

THE IMPORTANCE OF PERMANENT PLOTS FOR GROWTH AND YIELD STUDIES OF FOREST PLANTATION SPECIES IN NIGERIA John O. Abayomí Forestry Research lnstitute of Nigeria, lbadan

ABSTRACT Eoth permanent sample plot data and, to a greater extent, temporary sample plot data have been utilized for growth and yield studies on forest plantation species in Nigeria. This paper

critically reviews past studies, achievements and permanent plot procedures plantation growth and productivity research, and makes proposals for the future.

in

forest

The need rs sfressed for the establishment of an adequate network of spacing and thinning trial plots, crop plots and continuous forest inventory plots for carrying out future studies on site-productivity relationship and stand dyn,amics as well as devetoping accurate growth and yield models. The requirements for achieving the above goal are hightighted.

INTRODUCTION

thinning

Although f orest plantations presently

provenance/progeny trials may be found in Technical Notes wr¡tten respectively by Horne (1962), Cooper (1962) and Jones (1 967), No specific procedures have yet been laid down for spacing and thinning trials. lt should be noted that species trial plots and provenance/progeny trial plot are after about ten years treated more or

trials. The PSP procedures for the forest plantations, species trials and

constitute only 2.24 percent of the total forest estate of 96,518 km2 in Nigeria, emphasis has been shifting over the last three decades towards forest plantation establishment because of the relatively

poor timber yields from

natural

regeneration methods. Growth and yield

studies

to

f

acilitate the planning

less like crop plots, that is,

and

been carried out

in both permanent

sample plots (PSP'S) and,

PSP's

established subjectively within state forest plantations. No forest plantation inventory has been embarked upon by FRIN, but FDF has inventoried a number of extensive forest plantation areas with the a¡d of temporary sample plots. lt ¡s hoped that these plots can be relocated and reassessed in the future to generate data for Continuous Forest lnventory

management of forest plantations have

to a greater

extent, temporary sample plots (TSP's) by Forestry Research lnstitute of Nigeria (FRlNl, Federal Department of Forestry (FDF) and the Forestry Departments of Nigeria Universities. The forest plantation programmes of the thirty state Forestry Departments are being coordinated by

(cFt).

FDF, which has national

policy, programme development and monitoring f unctions.

PREVIOUS GROWTH AITD YIELD

The growth and yield studies undertaken

STUDIES ON FOREST PI.AIUTATION SPECIES IN NIGERIA

by

FRIN cover PSP's in State f orest plantations, species trials, provenance/ progeny trial as well as spacing and

Growth and yield studies on forest plantation species in Nigeria started about

16ó

the 1950's with the establishment

exot¡c tree species have been studied more than indigenous tree species with

and

periodic measurement of forest plantation

PSP's in

diff

erent f orest

regards to the effect of its conditions, planting space and thinning treatment on

reserves.

Summaries of productivity per hectare at varying ages of the PSP's of species such

growth and

as

Tectona grandis, Gmelina arborea, Nauclea didenichii, Terminalia invorensis, Triplochiton scleroxylon and Meliaceae

yield. Site

productivity

studies have been carried out on Tectona grandis and Gmelina arborea; planting space studies have been done on Tectona grandis, Gmelina arborea. Pinus caribaea, Cedrela odorata and Terminalia species. Thinning studies appear to have received the least attent¡on, only Tectona grandis

species have been prepared and studied.

The PSP data have ben used to draw conclusions from the comparative growth

and productivity data f or different species, localities and silvicultural

having been dealt with. Lowe (1970) used the techniques of pattern analysis and stand curve analysis on PSP data to

treatments.

of TSP data have been used f or computing volume regression Large volumes

equations and preparing volume tables for

determine the effect of tree competition on the productivity of some f orest plantation species in the high forest zone.

different forest plantation, species and different localities. The preparation of

The recent development in the growth

volume tabl¿s

f

or f orest

and yield studies of forest 'plantation species in Nigeria is the mathemat¡cal description and prediction of stem diameter distribution. Okojie (1 981 , 1 983) used the Chi-square test to assess the normality of the stem diameter distributions of some partially thinned plantations of indigenous Meliaceae, and also used the 3-way Weibull distribution f unction to construct whole stand/ diameter distribution models for some

plantation

species dates as far back as the 1 960's. Jone (1964) constructed a local volume

table f or Tectona grandis in Gambari, Olokemeji and Akilla Forest Reserves of South-western Nigeria. Other species for

which volume tables have

been

constructed include Gmelina arborea,

Nauclea diderrichii and

Terminalia

ivorensis,

Meliaceae plantations. Adegbehin

Yield tables have been computed using data from forest plantation sample plots

most of which were temporary. The

earliest yield table in Nigeria is probably that of Horne (1966) who prepared an

abbreviate yield table

f

or Ouality

(1

985)

used some stand attributes and site factors to predict the Weibull parameters of the stem diameter distributions of Eucalyptus and Pine stands of various ages.

1

plantation Teak in Nigeria based on the

Ouality

l

table from "Yield and stand

tables for plantation Teak" of the Forest Research lnstitute, Dehra Dun, lndian Forest Records, Volume 9, No.4 (1957). Other yield tables and models have also been developed for forest plantations of Tectona grandrs and Gmelina arborea. No

yield tables have apparently

PERMANENT PIOT PROCEDURES IN FOREST PIANTAT¡ON GROWTH AND PRODUCTIVITY STUDIES

Crop Plots

been

published yet for any indigenous forest plantation species in the high forest zone

of

Nigeria, although yield tables

Crop plots have been established by FRIN normally in easily accessible locat¡ons ¡n order to reduce costs of plot establishment, maintenance and remeasurement, Very swampy areas and areas with large gaps are usually avoided so as to obtain more or less fully stocked plots. Rather few forest plantation PSP's have so far been established in Nigeria due mainly to

are

available for Savanna forest plantations of

Azadirachta

indica. Yield prediction

models have been computed from PSP data by Adegbenin (1985) for plantations of Eucaly'ptus cloeziana, E. tereticornis and Pinus caribaea in the Northern Guinea and Derived Savanna Zones of Nigeria.

constraints of f unds, manpower and equipment. The most important forest

As in the case of yield table preparation,

166

plantation spec¡es in Nigeria are Gmelina arborea, Tectona grandis, Prnus species, Eucalyptus species, Terminalia ivorensís,

silvicultural treatment if great care is not taken. The numbering of crop plot trees should be done earlier than presently scheduled so as to f acilitate accurate estimation of main crop volumes of very young plots through the sample tree method. The numbering should be done after about 90 percent of the trees are of

Nauclea diderrichii, Triplochiton

scleroxylon and Cedrela odorata. At least 1 0O crop plots each ol Gmelina arborea and Tectona grandis,60 crop plots each of Pinus and Eucalyptus species and 30 crop plots each of the last four species should be established within the next ten years considering the total areas and

7 cm diameter and above, or after the top height of the crop is about 1 2 metres.

Newly established crop plots

ranges of site conditions covered by these

are

species throughout the country. The newly established crop plots should be concentrated within a f ew important

described as fully as possible, information being given on items such as situation, relative elevation, slope, aspect, climate,

soil,

vegetation, depth and nature of soil and

forest reserves of different

topographic and climatic conditiòns so that the results of detailed growth studies

carried out

on given sites can

geologic f ormation. The date and method

of planting, the objective of the sample plot, details of treatments to be applied and the age of the crop at first measure-

be

extrapolated to other areas of similar site conditions.

ment are also given. The condition of the

crop is described f rom time to time,

Crop plots in Nigeria are usually four-sided

including any marked variation within the plot. However, owing to constraint of funds, manpower and equipment, adequate data cannot be collected from

with areas ranging between 0.1 2 and 0.40 hectare and with surrounds of at least two rows of trees. Although small plots are more economical to maintain to assess, large plots are less

individual crop plots

on ground f lora ore and after planting as well as physical and chemical soil conditions. These data are very useful for site

and easier

bef

adversely affected by a given measurement error and more closely represent the stocking conditions of the

productivity studies and for extrapolating

surrounding compartment. The minimum

numbers

the results of growth studies to areas of

of 25O and 30 trees are

similar site conditions.

recommended for newly established crop plots at establishment and the end of the rotat¡on respectively (Abayomi 1973).

crop plots are expected to be thinned by the state Forestry Departments as these plots are supposed to receive the same treatment as the compartments surrounding them. However many state forest plantations are not regularly thinned due to limitations of funds, manpower and equipment. The problem of deciding on

Two maps are usually produced for newly established crop plots. One is a large

scale plan for calculating the plot

from horizontal distances and

area

true bearings, while the other is a small scale location map. Large scale plot charts are also drawn showing the arrangements of trees in different newly established crop plots.

the most suitable intensity, type and periodicity of thinning treatment for a given species, site and object of management atso probably militates against the regular thinning of state forest plantations. To improve on the thinning

These plot charts are continually rectified

as stockings decrease due to

treatment of crop plots, FRIN should intensify its thinning research studies so that objective and standardized thinning treatments can be recommended to the

natural

mortality and thinning treatment.

Standing trees of newly established crop plots are numbered and marked at breast

state f orestry departments.

height with paint to render them conspicuous for easy relocation and remeasurement

of the plots.

crop plots are expected to be remeasured

every

The

disadvantage of conspicuous crop plots is

3 to 5

alternating

that they are likely to receive biassed

years, f ull assessment

with partial assessment.

ln

partial assessments, volume estimation is

167

are applied f or main crop

excluded while height assessment is optional. However, shortage of funds usually leads to long delay in plot re-

volume determination where sample tree volume measurements are not taken.

measurement resulting in poor definition

of growth trends. Plot basal atea

assessment involves the girthing of all trees (main crop and thinning) not below 7.5 cm d.b.h. Average d.b.h. of the 100

Spacing and Thinning Trial Plots FRIN has established spacing trials of different species replicated both in time

largest stems per hectare is calculated in addition to mean d.b.h. The double d.b.h.

and space in experimental areas representative of diff erent ecological zones. Thinning experiments are

the dominance/f orm classification of all standing trees assessment and

to be superimposed later on of these spacing trial plots to

spec¡fied in the Nigerian PSP procedure are usually ignored during plot

expected

assessment because of time and financial constraints.

determine the effect of different thinning treatments on volume production. State f orest services also allow portions of their unthinned forest plantat¡ons to be used f or thinning experiments by FRIN and

some

For the estimation of top height and mean

height, about 25 sample trees

are

University Forestry Departments. Detailed inf ormation on soil, .topogra-

systematically selected from the array of d.b.h. classes, in addition to the largest 10 trees in the plot. These sample trees are measured for height usually with a Haga Altimeter or a Spiegel Relascope. The old method of estimating top height and mean height was to, use graphs of

phical and climatic conditions of the spacing/thinning tr¡al plots is obtained to f acilitate the extrapolation of experimental results to other localities of similar site conditions,

height against basal area. This was replaced later by the development of height/d.b.h. regression models. The usual practice now is to simply calculate the arithmetic means of the heights of sample tree represent¡ng the entire main crop and the largest 100 stems per hectare respectively. Although very simple and time saving, the last method

Spacing/thinning trial plots established by FRIN normally measure 40 metres square including a surround of at least 10 metres width. The surround is meant to minimize the inf luence of root competition between plots of different stock¡ngs on the trees

within the assessment core of a g¡ven treatment plot. Depending on the nature and variation of site and crop conditions, the completely randomized, block or lat¡n Square design is adopted f or a given

usually gives height estimates not much diff

erent f rom those of the f irst two

methods.

Main crop volume is estimated

or thinning trial. Combined spacing/thinning experiments have not yet received serious attention in Nigeria. lt is advisable to restrict the replication of treatments in such experiments so as to minimize the total number of plots required and therefore the size of the spacing

rom sectional diameter/height measurements on about ten trees selected systematically from the height sample trees. Formerly, these measurements were taken by a tree climber, while the volume of the upper inaccessible portion of the stem to a top f

experiments layout. A reasonable minimum total number of plots is 1 8, which is required for two replications of a 3 x 3 factorial experiment comparing a thinning and 3 spacing treatments within

diameter limit of 7.5 cm was estimated with the aid of the taper-line method. The spiegel Relascope or the Wheeler pentaprism Caliper is however now being used f or stem volume est¡mat¡on on

a randomized block layout.

standing trees. The analysis of the sample tree data had been formerly by using the Keen's method of drawing an

The establishment, procedures

of

demarcation, survey, mapping and description for spacing and thinning trial plots are similar to those of crop plots.

average volume/basal area line, and later by computing simple linear regressions of volume on basal area. Suitable volume tables or reliable estimates of from factor

These are usually f our diff erent spacing or

thinning treatments in a given trial, and

168

these tend to cover a wide range with the standard management treatment falling roughly midway.

except where World Bank and African

The inclusion of unthinned plots as

tion projects are involved. Small circular ïSP's of about 0.04 to 0.05 ha are being

control in thinning trials is quite

usef ul

low sampling intensity and on small areas Development Bank assisted forest planta-

a

for

used in the large scale inventories instead

the affect of thinning on growth and productivity. Residual assessing

of PSP's. lt

should be noted that inventory sample plots aÍe located randomly or systematically in order to obtain a representative sample of the population, whereas crop plots are subjectively located to obtain management data f or specif ic site conditions. PSP's in CFI's are expected

number of stems or basal area per hectare is the usual criterion of thinning intensity in the thinning trials. There is however the possibility of stem diameter

distribution and hence residual volume varying between replicates of the same thinning treatment.

to receive the same silvicultural treatment as their compartments, and they are ideal

Spacing and thinning trials are expected

or estimating the periodic charges in volume and other forest stand f

to be long-term, possibly lasting to full rotation age, with remeasurements in

parameters.

thinning trials coinciding with the thinning cycle. Unfortunately, shortage of funds and manpower has not enabled FRIN to establish an adequate national network of regularly treated and assessed spacing and thinning trials. Although d.b.h., basal

For future CFI programmes in Nigeria, the use of only PSP's is being recommended as against the combined use of PSP's and PSP's, that is in sampling with partial replacement (SPR) because the use of

area and height assessment have been carried on various spacing and thinning trials the assessment of taper and volume production has received relatively little

only PSP's involves less enough

attention. Estimates of both current

have been obtained from the assessments

of spacing and thinning trials. Statistical tests of significance are varied out on the treatment means and increments using the Analysis of Variance appropriate to the experimental design used. The comparing

the

ef f

ects

data

to allow many to be laid and

PSP's. The PSP trees should

be

numbered and marked at breast height to

facilitate the relocation of the plots for inspection or assessment. Great care has

average d.b.h. increment of the largest 250 or 100 stems per hectare is very

ul f or

icult

assessed per unit-area, and large enough to have more or less the stocking characteristics of the surrounding compartments. lnitial stocking of about 1 50 trees are recommended for these

treatment means and average treatment increments for diff erent stand parameters

usef

diff

computation and statistical analysis. The PSP's should be f our-sided and small

to be taken to

ensure that the PSP's receive the same silvicultural treatment as the compartments containing them. Both random and systematic sampling methods

of

different thinning treatments. Adequate data are however not presently available for developing accurate growth and yield prediction models from the spacing and thinning trial plot already established.

have been used in the inventory of forest plantations in Nigeria, but systemat¡c sampling appears to be a letter choice for

the future as it is more practicable

and

usually less costly than random sampling.

Contínuous Forest lnventory Ptots Although some State Forestry services establish and assess forest plantation sample plots to obtain management data,

CONCLUSION

no actual programme of continuous inventory of forest plantations has

Plantation forestry in Nigeria still leaves much room f or improvement. The establi-

apparently been established anywhere in

shment and management of f orest plantations as well as growth and productivity studies aÍe faced with

the country. lnventory of state forest plantations is commonly done with

a

very

169

and

Adequate financial support therefore has

éocio-economic problems. The annual

planting targets of the State Forestry services are far from being met, while

to be given for the successful execution of research studies on growth and yield, stand dynamics and site productivity

most of the existing forest plantations are

relationship.

f

inancial, technical, administrative

poorly maintained and

managed. Permanent sample and research plots for

growth and productivity studies are limited in number and susceptible to

REFERENCES

illegal felling or destruct¡on by fire. Abayomi, J.O., 1973: The measurement

of growth of tree crops in Nigeria, with special ref erence to volume tables, permanent sample plot procedures and growth models.

Forest plantation management depends on growth and yield research, and vice versa. Growth and yield research in Nigeria should be well developed to provide sound scientif ic and technical

M.Sc. Thesis, University

basis f or the establishment and

management of forest plantations. On the other hand, State forest plantations need to be properly maintained and

Abayomi, J.O. and Nwaigbo, 1.C., 1985:

Growth and yield studies of forest

plantation species in . Southern Nigeria. Research MonograPh, Forestry Research lnstitute of

managed to provide suitable media for growth and productivity research. The

development of plantation f orestry in Nigeria calls for greater co-operation and

Nigeria, lbadan (ln press).

coordination between the various forestry

in the planning and execution of forest plantation

1985: Growth prediction in some plantations of exotic

Adegbehin, J.O.,

organizations

programmes and research studies growth and yield.

tree species in the Northern Guinea

in

and Derived Savanna Zones

Cooper, L.G., 1962: lnstructions f or species trials Tech. Note No. 19,

scope of study or obtain additional useful

ormation. An adequate national network of crop plots, spacing and thinning trial plots as well as CFI plots should be set up through the joint deliberation of various forestry organizations. Periodic assessment of permanent plots requires adequate provision of suitable equipment and well

Dept. For. Res. lbadan, Nigeria.

inf

Horne, J.E.M., 1962: Nigerian samPle plot procedure. Tech. Note No. 1 6,

Fed. Dept. of Forest

Research

Nigeria.

Jones, N.,

1967:

in the breeding

Procedure

trained personnel, including biometricians and computer programmers. Electronic

Nigeria forest tree

process¡ng facilities should be adequately provided for the computation analysis and storage of data. Basic

Nigeria Tech. Note No. 39.

programme. Dept.

data

individual trees of several plantation in Nigeria. Dissertation

species

f or degree of Ph.D. University of lbadan, lbadan,

submitted Nigeria.

Okojie, J.A. 1981. Testing for normality in populations of some Plantation tree spec¡es in Sapoba, Nigeria. Nig.

lnadequate research back up has been ¡dent¡fied by Umeh (1991) as one of the conf

ronting

f

of For. Res.

Lowe, R.G., 1970: Some eff ects of stand density on the growth of

permanent plot summaries of stocking, diameter, basal area and height data are available (Abayomi and Nwaigbo, 19851 but regression analyses need to be carried out to provide accurate est¡mates of main crop and total crop volumes.

plantation development in

of

Nigeria Ph.D. Thesis, University of lbadan, lbadan, Nigeria.

Growth and yield research is best done with permanent plots although temporary plots can also be used to increase the

major problems

of

Aberdeen.

orest

Journ. of For. 1 1 l1l : 24-32.

Nigeria.

170

Okojie, J.4., 1983: The Weibult distribution function as a management tool. Paper presented at IUFRO Workshop on Applying Results of Forest Research, Edinburgh,

1

Umeh, L.1., 1991: Forest Management in

Nigeria-Problems and the Needed strategies. lnvited paper for the Distinguish seminar series of Forestry Research lnstitute of Nigeria, July 17,1991,21 pp.

5 pp.

t7L

THE ITC/RFD HUAI KHA KHENG PERMANENT SAMPLE PLOT (1987I Sydney G.Banyard ITC Forestry Department The Netherlands

ABSTRACT

A single large sguare permanent sample plot of l6 hectares was created by trainees of ITC (The Netherlands) in Dry Evergreen Forest in Huaí Kha Kheng Wildlife Sanctuary, Thailand in 1978 for inventory training purposes. All living trees of all species with diameters above I O em were included. Tree locations were plotted during fieldwork and later digitized. The plot was accurately located on aerial photographs. Observations and recommendations resulting from this exercise are listed.

INTRODUCTION

f

ieldstation.

As part of their annual tropical forestry f ieldwork training programme for 1987, students of the lnternational lnstitute for Aerospace Survey and Earth Sciences (lTC), with the support of the Royal

The creation of this plot addresses the

Forest Department of Thailand, created a single 1 6 hectare permanent sample plot

increasing importance being given to PSPs in natural tropical forests, and was

(PSP) in Huai Kha Kheng Wildlife Sanctuary in Uthai Thani Province. The forest is classified as Dry Evergreen Forest, the

specif

o&rEcTtvEs

ically created with training and lt was not intended to be part of a network of PSPs. Several aspects of forest inventory - including research in mind.

dominant species is Hopea odorata and in 1987 there was little evidence of either

aerial photointerpretation, land surveying,

past or present human activities (other than fire). The average annual rainfall is

forest mensuration, computer handling are involved. A large squâre PSP was intentionally chosen as this would best

above 1500 mm/yr, the terrain is flat, the plot elevation is 50O metres above sea level and soils are red-yellow podsolic.

satisfy another training objective, namely

the application of computer

aided

sampling exercises (using Lotus 1-2-3,

The location of the plot is shown on the map in Figure 1. The approximate location of the Hopea dominated forest was found by aerial photointerpretat¡on. The actual location of the PSP in the field was done using a random start along a cut access line. Tree stem maps were

BASIC) , on the resulting database. ln the long term, it is intended to carry out growth and change studies after remeasurement in 1992.

prepared.in the field on

a per hectare basis. The plot boundaries have been accurately plotted on 1 :8000 aerial

MEASUREMENTS

photograph enlargements (see Figure 2). The plot is close to a footpath and about 40 minutes walk from the Kapuk Kapieng

All trees of all species were included

dBase

lV,

the inventory. The plot, which

in is

4O0 by 4OO metres, has two components

173

1(r

]

NA

i.:N

1.,'

t74

:.*i

y,t

dtr IJ.J

fr

f,

g lJ-

rr;.ti

isili; il,i ç'#,

t7ó

which are distinguished by the lower diameter limit used. For a 300 by 300 metre t hectare Plot, trees with a diameter of 1O cm or above were included (mean of 480 trees per ha). For the other 7 hectares, all trees with a . diameter of 1 5 cm or above were included (the only reason f or this difference relates to the limited time available for gathering data in the field).

in the field having been inked over after control for errors.

For the location of trees in 1992, the digitized tree maps will be plotted by computer at scale 1:250 and waterproof prints taken to the field. lt is expected

that these will ensure accurate

tree

location. Figure 4 shows one hectare reduced to scale (approx) scale 1 :400

All hectare boundaries were set out using an Ushikata surveying compass with telescope. Other lines were set out using

surveying compass and Jacob staff . Tree heights to crown point were estimated for

all trees with the aid of a 4 metre pole with periodic checks using a clinometer. All trees were plotted in the field on tree stem maps (scale 3.5 mm per 1 metre). ln all, 250 man days were spent in the f ield on all activities related to the

SOME OBSERVATIONS AND RECOMMENDATIONS Based

on the

experience gained in

creation of this PSP. The tree stem maps

creating this PSP, the following observations and recommendations can be made:

database having one record per tree and

-

were d¡gitized in the off ¡ce and the resulting coordinates merged into a

the fields: Tree Number, X coord, Y coord, dbh, Height, Volume, SPecies

Even when well trained and motivated

staff ate involved in

f

ieldwork

activities, numerous mistakes

can

occur (in this case all participants were

code. Tree stem volume was estimated from the products of an assumed form factor of 0.7, tree basal area, and bole

B.Sc graduates specializing in forest survey)

height.

-

As mentioned above, it is the intention of

lt was found essential that plot layout, plot enumeration and the creation of

the tree stem maps were clearlY def ined as 3 distinct activities otherwise there was frequent conf usion and a multitude of errors

the ITC Forestry Department to remeasure this Permanent Sample Plot in May 1992.

Checks in 1987, after fieldwork, already indicated that errors existed in the original database. Many of these - mainly related to location and measurement - were corrected in 1988. However, it is known that some errors st¡ll exist and that these will have to be addressed in 1992. Those relating to species identification will need

were introduced

-

However well trained, the local tree spotters cannot be expected to accurately identify all tree species. Forest botanists are required and material for identification will probably have to be collected in the field.

-

Tally sheet data and tree stem maps

special expert attention before the database can be used for detailed analysis at the species level.

have to be cross checked as earlY as possible in the field. Daily checks and correct¡ons of work already carried out were a feature of setting up this PSP

Table 1 gives a list of the more important features of the ITC/RFD PSP. These were

based on recommendations f ound in Tropical Forestry Paper 14 of the Commonwealth Forestry lnstitute, Oxf ord; FAO Forestry Paper 2212; FRI Bulletin 48 of the New Tealand Forest Service.

-

Figure 3 shows part of an original field tree stem map - the original pencil entr¡es

t76

Even during the creation of this PSP, boundary posts and tree number tags were removed by elephant. lt is clear that without tree stem maPS the accurate identification of individual

Table

1.

Main characteristics of the lTc/RFD HUAI KHA KHENG pêrmanent sample plot in THAILAND : Established in 1987 as part of the rrc rropicar Foreetry Fierdwork program.

MAIN CHARACTERISTICS

AS APPLIED IN THE ITC-RFD PSP:HUAI KHA KHENG WILDLIFE SANCTUARY

1, 2.

Numbor of plots

Only one large pormanent plot was created.

Plot size

The plot-is a 1 6 hectare block (divided into 1 6 single but contiguous one hectare blocks).for all trees larger than or equal to I s cm ãi"-"tur. Fo-r 9 of tne prots ail trees largor than or equal to 10 cm diameter were measured. nãctare

3.

Plot shape

squaro blocks of t hecta¡e were used to form the 4oo by 4oo metre 1 6 hecta¡e plot (and also the 3O0 by 3OO metre 9 hectare floti.

4.

Plot subdivisions Each

5. 6.

Sapling design

Thore is no "sample" as such-just ono large permanent plot.

Plot location

purposively selected on aorial photographs bur tho exact I!u,j91""a,"_1"n{ .wa.s locatlon ot the block was made using a randomly ôelectã¿ i¡"t"^^o frnm ¿¡ exa.ctly located starting point (footpath'"ro""inj siåam). ãn ot the ptot on enlarged aorial photographs was done as coffrrmed tn the ott¡ce by superimposing photographs and he

t ha block was divided into loo quadrats of 1o m by .l o m. These were the field recording units.

same scale.

7.

Permanent

Durab

block.

posts.

:

block

of the

Y n

1gg2.

8.

Plot description

The following information was rocorded : (a) Full dotails of the locat¡on of the permanent plot. (b) A description of stable s¡te foatures suctr a" ioit-iyfr, slope,-geology (c) A description of rhe ¡nitial srate of the flora and 1?,p9"t, (d,

faiãa.

Goneral ¡ntormation on the location such as temperature, rainfall and other

climatic data.

9.

Treo numbering

TAc!, wlt! punched numbers were naited to each tree. The

lags yî!-UylI]y-Y 9 ntacgg exactty.2O cm.ABOVE the point of measurement (pom) of tÈe relerence diametor (rd) and always on the NoRTH side of each stem. Each tree has a uniquo number WITHIN EACH IIECTARE BLOCK.

1O. Tree stem map

Tree positions are indicated by circles and tree numbers on the final tree stem

Taps' The circles,

although nor drawn ro map scare, indiòaté iáråt¡*-iiå"

diameter differences. The t-ree stem maps ¡lave Seen made at scale 1:250. ln were.ptotred on a fiétdsheet having a S.S mm squããsr¡¿ l!: ligldl.locgtions represent¡ng I metre in the field. Measurement and plottíng was to th'e n"ui""t for Hopea odorata are fiiled-in on the tre;-sù'' ,nup.-to uia rn 9r2-5,1. -circles llold locatron11

.

Borderline

lrees

The decision on whether to includo borde¡lino trees was based on the centre of

the.slem at the.Po¡nt of measurement. No half tr""" w"ru tuk"n. decisions were difficult, remeasuroments wore made using necessary.

'i2. 13' i

4.

Marking "pom"

point of measuroment is rocated by measuring holding the number tag of each tree.

The.

"

Whero

nigüài-fi""i"läà i

20 cm BELow tho

nair

Trees measurod ALL LIVING trees of ALL SPEclEs having a diameter at po¡nt of measurement GREATER THAN 10 cm (or 15 cm, see iabove) were unum"ruted and giuuñ u unique number within a one hectare bock. Tree diameter measts

177

Table

1

continued. to crown point wers aided by placing a long (4 or 5 m) bamboo pole against each tree.

15.

Tree hoight est¡mates

16.

Remeasurements lt is planned to remeasure the block every 5 years (1987, 1992, 1997 etc'). (fteq)

17.

Remeasurements ltisplannedtoremeasuretheblockinthesameseason(beforo) therainsinMay(time) June) on every occasion.

Ocular estimatos of the height

-fully located on aerial photographs.

trees at a later date would be almost impossible in this PSP. W¡th them it is

-

expected that such problems'can be overcome.

Careful consideration should be gíven

to

using hand held comPuters f or

record¡ng data in the f¡eld. It was found that recording errors were dramatically reduced when numbers (eg. diameters) were reported in digital form (in English "one, three" rather

than "thirteen" which is

REFERENCES

not

infrequently recorded as "thirty") and repeated aloud by the tally man. This became a rigid rule.

FAO, 1980: Forest volume estimat¡on and

yield prediction. Forestry Paper 2212 FRI New Zealand, 1983: lndigenous forest survey manual,Bulletin No 48. New Zealand Forest Service.

Although these are generally disliked by users, steel diametef tapes have to be recommended. lf linen tapes are

ITC Tropical Forestry Fieldwork Reports, 1 987, 1 988

used, these must be regularly checked for they can stretch considerably (cm not mm) after only a few days use.

Lauprasert, M., 1988: The creation of

permanent sample Evergreen Forest

Remeasurements during checks in 1 988 suggested that some trees had

smaller diameters after one

Plot in

of Thailand,

a

DrY

MSc.

Thesis

year

Synnott, T.J., 1979: Tropical Forestry

(negative growth?!Þ

Papers, When available, plots should be care

No 14.

Commonwealth

Forestry lnstitute, Oxford

178

FIGURE 3

AA

BLOCK

.U

Name Date

Time in Tirrc out Scale 3.5 mm

Sue.Ae{K

r/qlzt

HA

L

F E

D

t0:lc

c

thof

A

B

-1

779

FIGURE 4 }T.IAI K}IA KHENG WLDLIFE SAI{CTURY UTtlAl Ttl,ANl PROVINCE: TtL ILANO xecrmeA 2

PCRTIAT{ENT SAI'PLE PLOT

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Cr-tad òt

ITC

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180

.

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!.7

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'

PERMANENT RESEARCH SAMPLE PLOTS IN PENINSULAR MALAYSIA : ESTABLISHMENT, MAINTENANCE AND PROBLEMS Ismaíl Harun Azman Hassan Khali Aziz Hamzah Forest Research lnstitute of Malaysia Kuala Lumpur, Malaysía

ABSTRACT To better manage the 4l .9% of the totat forested land of Peninsular Malaysia,.several types of permanent sample plots were established to gather the appropriate informations. Examples

of permanent sample plots available in the country are silvicultural ptots, phenotogical ptots, b¡g tree plots and virgin jungle reserves. This paper will highlight several of these plots; their obiectives, establishment procedures, maintenance, data collection and problems encountered.

INTRODUCTION

85.5% are forested area which have been as Permanent Forest Estate (PFE) for the purpose of production and protection. An area of 2.81 mill. ha of production forest is being managed to serve the purpose of sustainable forest

allocated

The unique natural tropical rainforest of Malaysia is rich and varied in flora and fauna. lt is estimated that there are some 14,500 species of flowering plants, of which 8000 flowering plants is found in

management. Pertinent to this, basic informations and knowledgés such as

Peninsular Malaysia (Cranbrook 1988). ln addition to this , there are thousands of fauna species available in the country. ln

forest structure, tree growth etc. need to be gathered to formulate sound management and silvicultural system.

order to conserve and preserve these bio-diversity, a total of 12.55 million hectares have been allocated for Permanent Forest Estate (PFE) which is to be managed under sustained

t¡on

to this,

another

One

yield. ln addi6 million ha

national parks, wildlife sanctuaries and nature reserves. A further 6.20 million hectares are Stateland Forests which is managed to serve the economic develop-

of the country (Othman

1990). All in all the total land area under f orest for Malaysia amounts to 20.10 million ha.

ln

1

99'l

plots located in this country. TYPES AND OB.'ECTIVES OF PSP

, the total forested

estimated

at

collect

Malaysian tropical forests is by establishing Permanent Research Sample Plots (PSP) all over the country. Several types of PSP have been established in the country with specific f unctions and representing each of the forest type. This paper will highlight and discuss several aspects of permanent research sample

1 .1

outside of the PFE have been legislated as

ment needs

of the methods used to

reliable information and data of Peninsular

area was about 5.51 million ha or

Virgin Jungle Reserves (VJRSI

.9% of total mass area of Peninsular Malaysia (Thang 1 991 ). Out of this, 41

The establishment of Virgin 181

Jungle

in Peninsular Malaysia was initiated in 1950 primarily to serve as permanent nature reserves and natural arboreta, control for exploited and Reserves

which are related to f lowering and fruiting habit of some selected indigenous species. The importance of these plots is

of their close connection with seedling recruitment, f orest regeneration, tree

silviculturally treated forests

and undisturbed natural areas for general ecological and botanical studies (Putz

physiology and forest composition. The objectives of having this type of plot are

1978). Subsequent to this, almost all permanent research sample plots in the country were established within Virgin Jungle Reserves.

f

irst, to obtain inf ormation,

f

rom

numbered and identified individual trees on the time taken f rom f lower opening to f

Virgin Jungle Reserves in the country were set up to meet six malor objectives. Firstly is to provide control against which the results of silvicultural

ruit-ripening.

Second,

to obtain

inf

ormation over

a

period of years, on the frequency of flowering and fruiting. Third, to obtain information over a period of years, on the vegetative growth cycle. Fourth, to study

operations may be assessed. The'second

objective is to provide undisturbed area for hydrological water management and

the rate of growth of the numbered trees.

other scientific research. Thirdly, to form

and finally to serve as part of a network of observation areas to give early warning of general f ruiting.

Fifth, to serve as seed collection areas

the nuclei f or a network of Forest Research Areas. Fourthly, to ensure natural supplies of seed for reforestation.

Last but not least is to

conserve

representative samples of all existing types of forest, together with the plants and animals they contain, which might otherwise become extinct and to protect

Big Tree Plots

of giant trees in easily accessible places, near

These are small isolated stands

some of the more scenic forested areas for present and future development into touristic, educational and recreational fo

public roads, preserved as Natural Nature Monument. The management of productive forest on rotations of 80 years or less will result in timber stands of mediumsized trees only. Whereas giant trees

rest.

which take 300 years or more to reach size maturity will be conserved in VJR

Silvicultural Plots

but for demonstration purposes. Big Tree Plots have a special value because of their

Silvicultural plots are all plots previously known as Sample Plots with the exception of phenological plots. The f irst

visibility, as living museum pieces.

silvicultural plots were established in 1915 and by'l 975 it reached a total of 62 plots. All these plots are used for the purpose of monitoring growth perf or-

These plots were established and

mance of trees under various silvicultural conditions. Specif ically some of the researches include study on regeneration

composition of f orests, changes in species composition and the rate of

Ecological Plots

managed by ecologist to study the natural

status, growth of natural and planted

growth of trees under natural conditions.

indigenous trees and to mon¡tor effects of silvicultural treatment given to the forest. The coordination of researches on these plots is being done by researchers at

ESTABLISHMENT AND

FRIM.

MAINTENAI{CE

Phenological Plots

Virgin Jungle Reserues (VJRsl

These plots were established primarily to study the periodic phenomena particularly

Up

to 1974,

hectarage

182

of

of 66 VJR with total 18,706 ha were established

a total

in P. Malaysia (Forestry Department of p. Malaysia 1975). These VJRs represent all forest types lying from coastal forest up to montane forest. However due to land development, 1 1 VJRS have been

prepared annually.

Eight phenological plots have

been

excised. For

established in Peninsular Malaysia from 1936 to 1 973. Location of these plots are as shown in Figure 4.

being proposed for this purpose. Location of these VJRs are as in Figure 1 .

Big Tree Plots

replacement, 18 more suitable sites have been identified and

B¡g tree plots are normally located near roads to ensure maximum visibility and ease of access. Normally, simple plot record are immediately opened for each plot during the establishment and trees should be measured for girth and height annually. Accessibility inside the plots are made possible by providing ,jungle tracks and paths which are continuously

Silvicultural Plots Silvicultural plots are normally located in natural or managed forest but as far as possible be near or inside VJRs. The size

of these plots will depend on their objectÍves. As an example, each of

growth and yield permanent sample plot which was established in early 1960s is a 2OO x 20 meters (0.4 ha) plot consisting of 40 sub- plots of 1O meters quadrats (Wan Razali 1986). Detail of each plot,

maintained

only two big tree plots were established in the country as indicated on Figure 4.

quadrats and diameter classes measured are as in Figure 2. Measurement of each individual trees in this plot are carried out at 3-year intervals.

Ecological plots

Up to 1975,61 silvicultural plots were established in the country. Location of all plots are as in Figure 3. By the year

Ecological plots should as far as possible be located within VJRs. During establ¡sh-

ment, a plot record shall be prepared to record all necessary informations. FRIM,

1985, some of these plots have been excised to other purposes resulting in only 55 plots left to be managed.

staff are responsible in observing measuring these related

the plots.

and

ormation of Only f ive plots have been inf

established between 1947 to 1955 consisted of Lowland and Hill Dipterocarp Forests (Figure 4).

Currently, FRIM is reviewing the status of these plots and computerizing previous data into a database. The data collected shall be assessed after each measurement and as soon as the objectives have been

realized,

by Distr¡ct Forest Off ice.

Tree-labels and a big sign-board are also placed to draw public attent¡on. Currently

A

the results analyzed f or

synthesis

of rules concern¡ng the

governing and maintaining of all these plots are attached as Appendix 1, 2, 3, 4

publication.

and 5. Phenological Plots

Phenological Plots are normally established with¡n 6 km of a meteorological station and pref erably

PROBLEMS AND

within VJRs.

Measurements and observat¡on are being made by FRIM staff. Observations of these plot are being carried out at least once a month while measurement be made ¡nitially for all numbered trees. Girth are being

RECOi,IMENDATIONS It should be noted that a majority of the

remeasured every year. Related to data measured, an assessment and analysis of

the results and a summary are

PSPs in Peninsular Malaysia were established during the 50's and 60's. Since

then several problems that need to

being

183

be

Fìgure

1.

Distribution of virgin jungle reserves in Peninsular Malaysia.

184

Subploc no.

l0

'39

a

,3ó,

3¡

,r4 ,t

Â1J. sub¡lJ.ots: measure

9

; 7

:

nreÍrsure

al.1 È,rees 2O cm dbh

al1 Èrees l0

+

cnr dblt +

z, Øi)

2

J-6.;(

t

Spot

>

multi-

spectrozonal

and

spectral resolution, and especially it is depending on the distribution of the objects on the terraln and as well as on their area. Landsat MSS imageries are suitable for the production of maps of small scale

For special resolution :

RECOMMENDATION

-

Landsat and USSR colour synthesized imageries are both suitable for forest

with scales : 1:250,000 :500,000 with general f eatures on f orest resource base, f or monitoring variations on large extent of lands over mapping

uses and land resources. USSR colour synthesized photographs

1

and Landsat MSS imageries provide

-

good

for this study can

interpretation is directly proportionate to the tone (and/or colour) contrast on

(1/500,000 - 1/1,000,000) and when general features on large extent of lands are required, they are very suitable for follow up variations in land

-

ormation through

summarized as follows: For spectral resolution :

the

-

inf

nterpretation. The assessment on various imageries i

further research on the matter.

- The accuracy

-

Spot imageries have the merits of both scanning and photography in space survey are of high spacial resolution and thus can provide high reliability and

quality

space and satellite imageries.

this kind of

during

tnterpretatton.

more effective results for forestry than black and white photographs.

-

a zone or nation-wide. Spot imageries can be used for forest

USSR multispectral (including spectroz

mapping up to maximum scale of 1:50,000. When combining with aerial photographs, ground surveysr it is

onal) imageries are very suitable for

possible

Landsat MSS imageries bear all the features of a picture map.

forest mapping at scale

1/1

to produce forest maps for individual districts, and forest

00,000,

enterprises with high quality.

their 'Stereoscopic parallax" allows the

286

FOREST RESOURCES ASSESSMENT AND MONITORING IN VIETNAM Tran Van Chat Mai Van Mon Forest lnventory & Planning lnstitute Thanh tri, Hanoi, Vietnam

The forests, apart from supplying wood

the system of management, monitor¡ng the changes of inventories, formulated

as an endless resources, take a very important role in the protect¡on of environment and genetic source, in

forest resources.

Up to three quartors l3l4l of the total territory of Vietnam is covered by hills and mountains, it is why, the f orest resources is of significant importance in its economy as well as preservation and

scientific study, culture and tourism; that is of significance not only within any locality or even a country but the whole world.

Forest is a biological resources and its motion f ollows particular rules in the environment of ecosystem of which it is a intergral component. Each impact can

protection of environment. During the second half of this century there have

been many national f orest inventories. But during those inventories, due to the requirements of economy and limit in knowledge on forests, attent¡on was paid only to forest area and the growing stock

lead to the break down of the ecosystem

balance unless scientif ic implemented thoroughly,

research

otherwise

(wood and bamboos). The alsided assessment of forest resources could only

unforeseen catastrophy will happen and

impossible to overcome towards the human life (destroy or degrade the forest

happen in some of the vital areas aiming at the immediate objectives. As far as

resources, increasing disasters.) lt is why each measure of f orest management should strictly follow the principles of the f orest ecosystem. This requires the alsided and deep assessment of man to eff ectively harvest and utilize f orest

the technical question is concerned, there was only the 1 981 -1 983 f orest inventory which followed the systematic method and error assessment. All aforesaid forest inventories concerned no assessment and monitoring the changes of f orest

resources. The evaluation of forest

resources based on not only its present state but also the variation in relation to

resources.

other factors of the ecosystem to find out the reasons of such variation and possibly help the forecast operations for the future use of the forests reasonably and to protect and develop forest resources.

Forest inventory is the requirement of the

State Management in case

Forest lnventory and Planning lnst¡tute to implement the project : INVENTORY AND

ASSESSMENTOF

Global Assessment of Forest Resources Project 1 990-2000 for the preparation of

THE

RESOURCES VARIATION

FOREST

OF VIETNAM

PERTOD t 99t -1995.

The

The Project is the f irst phase of the proposed Programme taking place over

neighbouring countries as Laos, Thailand, Myanmar, Malaysia, lndonesia, the

Philippines,

forest

Vietnam dated August 12, 1991). lt is why, the Government appointed the

Many developed and developing countries in the world have considered the monitoring and evaluating the variation of the forest resources. FAO develops the

the G lobal Strategy f or XXl.

of

resources and environment (Prov¡sion 8Law of Protection and Development of Forest of the Socialist Republic of

etc. have many times

many 5-year periods with

implemented the national forest resources

development objective that is

267

the

to step

by

the primary units is designed coinciding to the cordinators of the UTM map

step supply information on the alsided forest resources assessment as well as the trend of the¡r changes in the relation to the soc¡o-economic operations of the country to serve the State Management towards the f orest resources and the forestry productions. The concrete objectives in the initial phase (1 9911

facilitating the field work. The primary

units are located permanently not only f or the period 1991-1995 but also the suceeding periods. Based on this

intention, the laying out of the sample plots should follow the principles so that

the suceeding ¡nventory can collect correctly and adequately what had happened in the sample plots of the

995) aim at the assessment of the forest

of

the

resources recently

and

resources alsidedly, analysis

variation

of

previous inventory. The delineation of the

establising the system of monitoring the

forest resources continuously

a

forestry land boundaries in the primary units (100 ha) to supplement the area inventory on the satellite images. Two

To construct the present land-use and

they are in the right angles in each primary unit, each range consists of 20 sample plots with an area of 500 m2. Total area to be sampled is 20,000 m2. The contents of inventory in each promary unit is very plentif ul. Beside the sampling of the quality and quantity of

and

permanently to serve the formulation of

strategy and plan of socio-economic development, planning and utilizing reasonably land and forest resources

ranges of sampling are established so that

nationally and zonally.

forest map (land atea surveY) LANDSAT/TM, SPOT and aerial

photographs and inf ormation from ground survey including those of from forest

woods and non-wood producgs-including living and dead trees and stumps of the main storey, the regenerated storey, bushes...the factors of site conditions,

management of the forestry production units will be used. ln 5 years, land area survey is at least implemented twice.

orest situation, wildlif e survey, soil survey, and socio-economy and population concerning the inventory area... Prof ile is drawn f or each

The satellite images used in this inventory have high resolution comparing to that of the 1 991 -1 983 inventory (LANDSATiMSS having the resolution of 70 X 70 meters, 1 : 500,000 scale aerial photos

f

inventor¡ed units to serve the research of silviculture and ecology. The soil profiles should be taken to analyse the physical and chemical charateristics... Each year it is about 1/5 to be inventoried regarding

comparing to that of LANDSAT/TM having the resolution of 30 x 30 meters and 1 : 250,000 scale aerial photos,

SPOT IMAGES having the resolution 10 x

mentioned content (660 primary units) basing on the even sYstem throughout the country. Thus, new

10 meters and 1 : 100,000 scale aerial photos) However, to increase the quality and productivity of photo interpretation

the

LANDSAT/TM ANd SPOT it iS to complete the photo key for interpretation and coming towards the digital processing on the computer. On

the

inventory data can be obtained annually in each zone, which allows the f ollowing the variation of the forest resources

necessary

continuously. The data and documents will be registered on the computer and processed by FlPl and other based units.

the LANDSAT/TM and SPOT ¡r is possible to deeply research regarding ecology and environment as map construct¡on, wildlife habitat maps...

To assess the quality and present use of

the forest resources, beside the data taken from the PrimarY units, supplemented inventories on some

The inventory of forest production and quality over the system of the sample plots has been improved greatly. The

subjects will be implemented to construct the silviculture, wildlife, pest and iseases maps non-wood products reports, volume

surveyors determined the numbers of the sample plots so that the error of volume/ha of each forest range (of over 70 forest'blocks in an area) is 10% and the common error is less than 10%. Base on this, a system of the primary units 8 x

table construct¡on, mapping the forest ecological vegetations, soil maps.. f or each province and zone. The operations of forest inventory should be up-to-date paralelling to the modern opinions on forest and forest resources which access

8 km in the forestry land throughout the country is established. The location of

268

the systematic point of view.

The f ormer

and forecast on the var¡ation of the forest

ormation on

resources, proposing reasonable strategy

orest resources, socio-economv (based on the system of registering of the government) will be gathered, the variation of forest resources will be analysed in relation to the socio-economical operations to find out the reasons and the principles of such variation to form the function for forecast on resources combining with the socioeconomic paramounts...to serve the working out of the strategies of protect¡on and development f or the future. inf

f

f

or the protecti9on, utilization,

and

development forest resurces of the whole country and each zone, forestry database

and the system of monitoring assessment

the forest

and

resources

continuously... which will contribute to the correct assessment the potential of the Vietnam forest resources.

At present, the work of the initial years of the project is being implemented (1991) 660 primary units are under inventory on the basic of a regulation and good results were obtained... lf the prolect was regularly and timely supplied with fund during the implementation, the project will perform well what required.

The organization of monitor¡ng the foresl résources will be implemented through the system of the permanent primary

units and periodically monitoring, the Many colegues and experts both in and outside of the country highly evaluated af oresaid project. The project was implemented almost coincide with the FAO/UNDP Global FRA-20O0 project and it is probably to be harmony with the f orestry operations of the world over. Comparing to the project VIE 761O14

system of databases by time and space, and the system of up-to-date information

from the forestry product¡on and management units... With such a system

of scientific management, from the later periods, it is possible to f ollow and mon¡tor strictly the f orest resources variation, research the forest increment as well as the process of degradation or increasing of the forest resources and

implemented in 1 981-1 983 (sponsored by UNDP/FAO wlth less than 2 million US$

forest land.

excluding the countyerpart contribution) this project (the tentative f und annually is

All

documents and data of f orest inventory as well as other f indings

less than 250,000 US$) will supply many f

concerned (hydrology, pedology, dlimate,

soil assortment, ecological

indings and would

be

implemented

scientifically. Comparing to some of the

zoning,

pro.jects NFI of the neighbouring countries

population and nationality distribution, the

this project consists of some of technical

system of national, provincial and the forest road system transportat¡on) will be arranged into database on the forest resources for all zones and country as a whole. A database consists of data,

equivelent.

To successfully performed the project, it is better to make closed operation with the scientif ic institutions in and outside of

paper documents, maps, and photos (GlS) are registered by a range of time, will be applied in the registslation and processing data for the project. These database is

the country, particularly the case

of technical transformation and equipments and the modern techniques as digital data processing by computer, the techniques

usually up-to-dated and stored permanently in FlPl and in the base inventory units to serve the forest

of GlS, the supplying of satellite images with high quality... The changes in the

system of the State

Management,

many scientific reports on the tropical

stable system of

production

forest of Vietnam, the svstem of analysis

arrangement).

management of the Forestry Sector as well as the other institutions concerned.

Beside data and maps

Sectoral Management on the f orest resources will be important ensurences for the successful of the project (the system of management ¡nformation, the

published

temporarily each year and 5 year period,

265

Collection of

d¡ta in p¡i¡àry units

Dat¡ B¡se

II

bv llation,

legion,

Pnovince

llide I¿vels P¡inciples of Fo¡est

Ch¡nges

lesults of the Prograne fon

l{e thodo I

ogi cal

Corpletion

fl¡tion, legion, P¡ovinc¡ llide

kvels

Fígure 7. Flowchart of work system in forest resources assessment and monitoring

1991-1995. 270

t

¡ tn

li

It

,i

to

,t

¡t

ill'"

in

TN

lr

¡a

¡t

l0

tn

t!

af

It

an

tl ßo

to

tt ¡t m

tt lm lni I

tt

I

ro

il!

tt!

ltn

Ito

t¡r

ti

lrr

¡n

trt

ttr ll0

I

ln

ltt

I

tr

l¡o

tñil

tin

ttr

lao

ti0

lß!

t6t

Ito

Irr lin

rl

lit ltr ,

Itt

tt

,ly

m

rrlån'

Fígure 2. Primary unit network.

I ! = ! = g= o

'l =

primary unit primary un¡t primary unit primary unit pr¡mary unit primary unit

in 1990 & 1996 in 1991 ¡n 1992 in 1993 ¡n 1994 in 1995

27L

ACTIVITIES