ROY, DUTT & JOSHI
Tropical Ecology 43(1): 21-37, 2002 © International Society for Tropical Ecology
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ISSN 0564-3295
Tropical forest resource assessment and monitoring P.S. ROY, C.B.S. DUTT1 & P.K. JOSHI
Indian Institute of Remote Sensing (NRSA), Dehradun 248 001, India 1National Remote Sensing Agency, Hyderabad 500037, India Abstract: Forest is a major resource and plays a vital role in maintaining the ecological balance and environmental setup. Over utilization of forest resource has resulted in its depletion. The changes in tropical forest cover are matter of global concern due to its ability of promoting role in carbon cycle. This renewable resource continues to decrease at accelerated rate. Accurate and timely information in regular interval on the distribution of natural resources on earth is of top priority for understanding dynamics of the human induced land cover/land use accelerated changes. This information can be further utilized in understanding biophysical processes of the earth. In India and the other developing countries it is mostly been lost for the agricultural practices. Aerospace technology is a potential means of collecting information about natural resources including forests at any desired time. The technology is considered important to revise or update available information. The present paper addresses the status of tropical forest and requirements for its monitoring and assessment. It discusses the potential of the remote sensing technology for managing the forests, in general and sustaining the pace of development in this technology. It focuses the technology trends and techniques for tropical forest assessment at different scale and levels. Resumen: El bosque en un recurso fundamental y juega un papel vital en el mantenimiento del balance ecológico y en la organización del ambiente. La sobreexplotación del recurso forestal ha producido su abatimiento. Los cambios en la cobertura de bosque tropical son motivo de preocupación mundial debido a su habilidad promotora en el reciclaje de carbono. Este recurso renovable sigue decreciendo a una tasa acelerada. La obtención de información exacta y oportuna a intervalos regulares sobre la distribución de los recursos naturales en la tierra es prioritaria para entender la dinámica de los acelerados cambios en la cobertura terrestre y uso del suelo inducidos por los seres humanos. Esta información puede usarse además para entender los procesos biofísicos de la tierra. En la India y otros países en vías de desarrollo la pérdida de bosque se ha debido principalmente a las prácticas agrícolas. La tecnología aeroespacial es un medio potencial para obtener información sobre los recursos naturales incluyendo los bosques en cualquier momento que se desee. Se considera que la tecnología es importante para revisar o actualizar la información disponible. El presente artículo trata de la condición del bosque tropical y los requerimientos para su monitoreo y evaluación; discute el potencial de la tecnología de sensores remotos para el manejo de los bosques en general y para el sostenimiento del ritmo de desarrollo de esta tecnología; y se enfoca en las tendencias tecnológicas y las técnicas para la evaluación del bosque tropical a diferentes escalas y niveles. Resumo: A floresta é um recurso importante e joga um papel vital na manutenção do balanço ecológico e ambiental. A sobreutilização dos recursos florestais levou à sua deplecção. As mudanças na cobertura florestal são uma questões de preocupação global devido à sua capacidade de intervenção no ciclo do carbono. Este recurso renovável continua, contudo, a decrescer a uma taxa acelerada. Uma informação precisa e tempestiva, em intervalos regulares Address for Correspondence: P.S. Roy, Indian Institute of Remote Sensing (NRSA), 4, Kalidas Road, P.O. Box – 135, Dehradun UA 248 001, India; Tel: +91-135-744583, 744518; Fax: +91-135-741987; Email:
[email protected]
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FOREST RESOURCE ASSESSMENT
sobre a distribuição dos recursos naturais na Terra é, assim, uma prioridade de topo para a compreensão da dinâmica acelerada de mudança de cobertura/uso do solo induzida pelo homem. Esta informação pode ser utilizada futuramente na compreensão dos processos biofísicos que ocorrem na terra. Na Índia, e outros países em desenvolvimento, esta mudança é fundamentalmente induzida pelas práticas agrícolas. A tecnologia espacial é um meio potencial de colecta de informação acerca dos recursos naturais, incluindo as florestas, em qualquer altura desejada. A tecnologia é considerada importante para a rever ou actualizar a informação disponível. O presente artigo revê o status da floresta tropical, e a informação disponível para a sua monitorização e avaliação. Discute-se também, em geral, o potencial da tecnologia de detecção remota para gestão das florestas e o ritmo de desenvolvimento para sustentar esta tecnologia. Foca-se, igualmente, as tendências na tecnologia e as técnicas para avaliação da floresta tropical em diferentes escalas e níveis.
Key words:
Forest assessment, Geographic Information System (GIS), monitoring, remote sensing, tropical forest.
Introduction Tropical forests are unique in several respects – structurally complex, genetically rich, diversified into several subtypes and highly productive in terms of biomass. The forest resource are one of the most important renewable resource for timber, raw material for pulp and paper, fuel wood and charcoal as well as genetic resources. Further it is increasingly recognized that the disappearance of forest will create a number of serious environmental problem namely soil erosion, sedimentation, flood, loss of valuable material which help ensure the people’s life. It is matter of concern that our forests are continuously disappearing at an alarming rate. Forest is a true indicator of ecological setup prevailing in any area. For sustained yield from forest, it is essential to manage them scientifically which would require up-to-date statistics of their extent and type etc (Mayers 1991, 1992). There is interest much in the extent of tropical forests and their rates of deforestation for two reasons: greenhouse gas contributions and the impact of profoundly negative biodiversity. Deforestation increases atmospheric CO2 and other trace gases, possibly affecting climate, because the absorption of carbon is higher in forests than in the agricultural lands which replace them (Dixon et al. 1994; Fearnside 1996; Gash & Shuttleworth 1991; Houghton 1991; Houghton & Skole 1990; Houghton et al. 1991; Keller et al. 1991; Woodwell et al. 1983;). Estimation of deforestation rates has to take account of the fact that tropical deforestation
does not occur uniformly across a region or a country. Instead it is usually concentrated in a relatively small fraction of the area of interest (Fearnside 1996; Skole & Tucker 1993; Skole et al. 1994; Stone et al. 1994). Further more the knowledge of such ‘hot spots’ of deforestation is at times poor, especially as new hot spots develop. Tropical forests occupy less than 7% of the terrestrial surface, yet contain more than half of all plant and animal species (Mayers 1992). Tropical deforestation is responsible for massive species extinction and affects biological diversity in three ways viz. habitat destruction, isolation of formerly contiguous forest into forest fragments and adverse physical and biological consequences of edge and ‘buffer’ effects within a boundary zone between forest and deforested areas (Prance 1982; Pimm et al. 1995). Global estimates of tropical deforestation vary widely and ranges from ~ 50,000 to 17000 km2 y-1 (Grainger 1996; Houghton 1991; Mayers 1991, 1992). Recent FAO tropical deforestation estimates for 1990-1995 cite 116756 km2 y-1 globally, with 47000 km2 y-1 attributed to tropical South America – the majority of that in Brazil.
Approaches for tropical forest resource assessment A variety of methods exist for the assessment of the forest types and status. However, many of that have been used to date have been inadequately researched, and their interpretation is dif-
ROY, DUTT & JOSHI
ficult. The standard method of plot assessment is to locate subplots and assess among five or eight trees on each sub-plot. There have been several major developments in the assessment of forest vigor by visual methods over past a decade. Regrettably, most of these improvements have still to be incorporated into standard assessment of forest vigor (Innes 1993). There has been a great deal of interest in the use of other methods, one of which have been considered to be more quantitative than visual assessments called non-visual assessment of individual trees. However, in many cases it is costly and/or time consuming and frequently involves destructive sampling. Remote sensing lies somewhere in between visual assessment and non visual assessment. The most successful application have involved in use of vegetation sensitive wavelength and this section had therefore been separated from the visual assessment section. More recently, there has been a great deal of interest in the use of remote sensing to quantify forest change. Many early studies were conducted using black and white or color IR imagery alone; the later has been particularly useful. The data obtained from such studies are particularly amenable to statistical treatment and a number of techniques have been developed for looking at distribution of asymptotic and symptomatic trees. In-spite of high cost of data collection the role of remote sensing in surveying and monitoring had increased dramatically.
Technology trends in the forest resource assessment Information need in forestry basically involve characterizing the location, area, and status of the forest resource and the change in spatial and time domain. These information needs are not met entirely by traditional techniques because those were not practical or economically sound to devote more effort to human intensive menstruation activities. Thus the new technological augmented for the information requirement (Czaplewski 1999; Wynne & Carter 1997). The forest managers require timely and accurate geospatial information on forest conditions and management practices at site specific and regional scales. At the same pace, the science and technologies associated with forest management are evolving rapidly. Geospatial technologies, such as remote sensing, Geographical Information System (GIS), and
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Global Positioning System (GPS) provides vital support to collect, analyze and store all sort of geospatial information. Vegetative characteristics derived from remotely sensed data are particularly important for forest assessment. The level of detail depends largely on the spatial and spectral resolution of the imagery used. Four common attributes derived from remote sensing are canopy structure, tree size class, percent crown closure and vegetation composition. These four attributes combined with other core data layers, support analysis for numerous resource issues.
Aerial photography Aerial photograph has proved to be a valuable aids in many of the forestry activities which can be substantially change the methods by which forester can obtain the needed information. Aerial photographs can be assessed with ocularly or with the help of some measuring device to map canopy density. The accuracy of the canopy density depends on the scale of aerial photographs. The larger the scale of photographs more is the accuracy. In order to use the scale, the interpreter slides the scale placed besides the aerial photographs under the stereoscope up and down till standard corresponds with the photographs. Forest density class intervals depends on the nature of the photo quality and scale of photographs. However, the canopy density estimation using aerial photographs could not become popular due to difficulty in their procurement and high cost. Early work included automation of air photo interpretation, the first multistage sampling scheme for forest inventory, spectral reflectance studies on healthy and stressed trees (Rohde & Olson 1970; Weber & Olson 1967), and forest change detection (Colwell & Weber 1981) (Table 1).
Satellite remote sensing Satellite remote sensing has played a pivotal role in generating information about forest cover, vegetation type and land use changes (Roy 1993). The increase in processing speed and the compression techniques for digital storage have made digital imaging available to anyone. One advantage of digital imagery for natural resource managers is that it can be enhanced on the computer to bring out details of interest – whether vegetation stress, species composition, or growth and volume. The standardization of ground sampling methods, un-
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FOREST RESOURCE ASSESSMENT
Table 1. Ground resolution, photographic scale and film type to measure certain selected basic resource parameters. Parameters
Ground Resolution
Film type (1)
Smallest scale for measurement
Platform (2)
Stand size
0.3
BW
1: 9500
MAP, LAP
Tree height
0.3
BW
1: 9500
MAP, LAP
Crown diameter
0.3
BW
1: 9500
MAP, LAP
Crown area
0.3
BW
1: 9500
MAP, LAP
Number of trees
0.3
BW
1: 9500
MAP, LAP
Dead, declined trees
0.3
CIR
1: 6400
LAP
Cover types
3.0
CIR
1: 92000
HAP
BW
1: 125000
HAP
CIR
1: 1500
LAP
Species Composition
0.1
(1) BW – Panchromatic (Panatomic B410): IR Infrared (Eastman Kodak Infrared Aerographic 2424) CIR – Color Infrared (Aerochrome Infrared 2443) (2) LAP – Low altitude photography; 150 – 3660 m MAP – Medium altitude photography; 3660 – 9200 m HAP – High altitude photography; 9200 – 19800 m.
derstanding of spectral and temporal responses of vegetation have brought acceptance of the application of satellite remote sensing data in forest inventory and mapping (Roy & Joshi 2000a, 2000b).
High resolution sensor The SPOT Panchromatic image (10 m on each side); IRS 1C/1D PAN data (5.8 m) and Space Imaging, OrbImage and Earth watch offer greatly increased spatial resolution of 1 m panchromatic and 4 m multispectral pixels are as good as the aerial photograph. Satellite imagery with high spatial resolution gives forests a new set of mapping and monitoring options. The analysis of the data set for the vegetation mapping was found to be satisfactory. The linear plantation having width of 7-8 m and cluster of 3-4 trees were easily identifiable. It is capable of mapping the avenue plantation and barren lands within the urban areas.
Medium resolution sensor In the recent years, since 1972, there has always been at least one multispectral satellite with medium spatial resolution in operation and frequently more than one. Landsat, SPOT, IRS, MOS and Okean are among the satellite in this class. These data sets are potential enough to provide the information in 1: 250,000 scale. It is assessed to provide information in multilevel to the forest managers for rapid forest cover mapping, detailed
forest cover mapping in divisional level and enable monitoring of rapid changes in the forests due to forest fire, shifting cultivation etc. The merged data set (Multispectral - High resolution) provides better feasibility and enhanced capability to evolve new programmes in the forest management sector, especially in the forest inventories and microplanning like Joint Forest Management (JFM) activities. The detailed information at compartment and village level scales the need to protect forest and call for the involvement of forest dwellers in forest management. Landsat thematic mapper, multidate data has been used to map secondary forests, agriculture lands and old growth forest in the Talamanca Mountain tropical forest range in southern Coast Rica (Helmer et al. 2000). Mutistage sampling approach using remote sensing provides most reliable estimate of forest resource stockings. Singh & Roy (1990), estimated volume of individual forest types and further grouped into utility classes using Landsat TM (1: 50,000 scale) image.
Moderate/low resolution sensor In the context of regional/national change research priorities, moderate resolution sensors assume higher responsibility of producing wide coverage. This imposed logistical and financial constraints in formulating and executing wide field sensor like WiFS to monitor climate oriented
ROY, DUTT & JOSHI
changes with an acceptable resolution of 200 m. WiFS provides advantage of covering very large area in single IFOV (Instantaneous Field of View) avoiding any illumination difference. The excellent repetivitvity of wide field sensor gives an unique edge in phenology based characterization and physiological process modeling. Wide field sensor is the first of its kind to be in space shortcomings in AVHRR scale would be overcome efficiently. Semi-arid landscape of Central India (Roy 1983), Western India (Singh et al. 1999a, 1999b, Roy et al. 2001) and Western Himalayas (Joshi et al. 2001a; 20001b) has been studied using temporal WiFS data set. The integration of three distinct phenological stages makes it possible to stratify forest and land cover types.
Active sensor Radar technology was first developed during World War II to locate aircraft and ships. The all weather capability radar and all time capability of active radar is an important attribute for monitoring applications or use in areas of chronic cloud cover. Imaging radar has been used for forest applications since the 1960s, mostly in experiments and research. The prime applications are land cover mapping (based on structure), estimation of forest attributes (harvest, fire and blowdown), estimation of forest attributes (average tree height, basal area, biomass, and timber volume) and monitoring of regrowth (Roy et al. 1994a, 1994b). The next generation of spaceborne earth imaging radar promises to change the situation, with multipolarized and high-resolution modes that are much more appropriate for forestry. The principal uses of radar will be forest structure and moisture and the information largely complementary to that obtained by optical technique (Dobson 2000). Lidar – light detecting and ranging as a remote sensing technique is analogous to radar, but it uses lidar light. It directly measures vertical forest structure, is breakthrough in technology with many forestry applications. Using the laser equivalent of radar, lidar instruments accurately estimate important forest structural characteristics as canopy heights, stand volume, basal area and above ground biomass. Lidar instruments have been accurately described canopy heights in temperate deciduous, pine, Dougals-fir, and dense tropical wet forests (Dubayah et al. 1997; Dubayah et al. 2000) Recent studies have demonstrated that
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lidar wave forms are sensitive to these structural changes through forest succession. The vertical distribution of intercepted surfaces has also been used to examine the volumetric nature of Douglasfir-western hemlock (Leesky et al. 1999). Since the sub canopy vegetation height is function of species composition, climate and site quality, the results can be used for land cover classification, habitat mapping and forest wildlife management (Ralph et al. 2000).
Hyperspectral remote sensing Hyperspectral remote sensing deals with the recording spectral information of the ground objects by means of some device like spectroradiometer, or some air or space born sensors having higher spectral range with very narrow band width. With the advances in computer and detector technology, the new field of imaging spectroscopy has developed (Merton 1999). Imaging spectroscopy is a new technique for obtaining a spectrum in each position of a large array of spatial positions so that any one spectral wavelength can be used to make a coherent image (data cube). High spectral and spatial resolution provides the ability to classify tree stands within a forest. Precise classification of species by stand is possible. The benefit of hyperspectral remote sensing is that the information about the ground objects can be recorded in a very narrow spectral range hence minute alterations can be mapped.
Videography The videography is used to detect potato disease from the air, using a monochrome video camera modified and filters to record near infrared radiation two decade ago. Gausman et al. (1983) found air borne videography a useful tool to demonstrate interactions of near infrared radiation a useful tool to demonstrate interactions of near infrared radiation with plant leaves. Vicek & King (1984) found that multiband video could discriminate subtle differences in spectral signatures of vegetation. Stutte & Stutte (1990) concluded that video in digital form could provide a valuable input to an expert system for stress identification and quantification. Niedrauer (1991) found mutispectral video useful for distinguishing conditions and feature of soils and vegetation (Table 2).
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FOREST RESOURCE ASSESSMENT
Table 2.
Overview of remote sensing application opportunities for forest management. Application
Scale/ Resolution
Frequency
Cost
Limitation
Aerial Photography Pancromatic True Color Color Infrared B & W Infrared
+++ +++ +++ ++
++ ++ ++ ++
++ ++ ++ ++
++ +++ +++ +++
++ ++ ++ ++
Scanning : Air MSS Hyperspectral
+++ +++
++ ++
++ ++
+ ++
++ ++
Scanning : Space
++
+
+
+
+
Radar : Air
++
++
++
++
++
Radar : Space
+
+
+
+
++
Lidar/laser : Air
+
++
++
++
++
Videography : Air
+
++
++
++
++
Type/Sub-type
+ Low; ++ Medium; +++ High.
Methodologies for forest cover assessment/monitoring
lyst to use different band combination for visual interpretation on screen (Roy & Murthy 1984).
Visual interpretation – heads down
Digital classification
In this technique image interpreter systematically examines the images and frequently, other supporting materials such as maps and reports of field visit keeping in view the elements of visual interpretation proposed by Olson (1960). An image interpretation key is proposed to facilitate the interpretation work by the analyst based on the requirement of the task and natural features present on the satellite data. The visual interpretation technique has been widely used for forest cover mapping (Anon. 1983). The methodology is subjective and varies with the aptitude of interpreter (Roy 1993).
In the digital classification technique all the pixels in image are categorized into land cover classes or themes. It is conducted in two different modes. Supervised classification, in which the analyst ‘supervises’ the pixel categorization process by specifying, to the computer algorithm, numerical descriptors of the various land cover type present in a scene. The different algorithms used for supervised classification are minimum-distance-tomean, parallelepiped, and Gaussian maximum likelihood classifier (Roy et al. 1982). The other mode Unsupervised approach the image data are first classified by aggregating them into the natural spectral grouping or clusters, present in the scene. The different algorithms commonly used for unsupervised classification are ‘K-means’, ISODATA clustering and texture/roughness based classification. Hybrid classification involve aspects of supervised or unsupervised or both and is aimed to improving the accuracy or efficiency of the classification process. Guided clustering is a hybrid approach that has shown quite effective results in some natural circumstances. Hierarchical classification technique is something in between supervised and unsupervised classification aimed to get best land use/land cover map with maximum accu-
Screen digitization – heads on It is most recent technique used commonly at present. In this method the digital multi-spectral images are visualized on-screen for thematic interpretation purpose and direct delineation of the theme on the screen. The scale selected for interpretation is corresponding to minimum mapping units and with diameter reaches to linear features. During screen digitization, at first the linear features are delineated followed by boundary of group/class and then interspersed features. Unlike visual image interpretation it facilitates the ana-
ROY, DUTT & JOSHI
racy. The emerging fields for digital image classification are rule based (Roy et al. 1995). Spectral mixture analysis, fuzzy classification and neural network techniques (Foody & Arora 1997).
Initiative on global forest cover assessment The Forest Resource Assessment of the FAO presented a global analysis of the distribution of forest ecosystem in 1990, as well as changes during 1980-90. The study was conducted for three separate regions: temperate forests in developed countries; tropical forests in developing countries and non-tropical forests in developing countries. The FRA 1990 assessment of the tropical zone was conducted using two complementary approaches. The first was based on statistical analysis of existing, reliable forest inventory data from different countries. The second approach made use of multidate observations of forest cover using highresolution satellite images for 1980 and 1990 at statistical chosen sample locations. The sample frame used by the FAO in this assessment was based on the Landsat World Reference System –2 (WRS-2). The WRS-2 system is well known and is used to identify Landsat scenes based on a system of paths and rows. It provides a convenient sampling frame for a sample of Landsat scenes. In order to overcome problems of data compatibility due to differences in the base years and standardize national level data, the assessment of tropical forests incorporated demographic evolution and ecological zoning to model forest cover change. The International Geosphere Biosphere Programme (IGBP), the US Geological Survey (global land cover), the Commission of the European Community’s TREES project (tropical moist forest), NASA’s PATHFINDER programme, FAO’s AFRICOVER project and the Woods Hole Research Center are using remote sensing techniques to map forest or land cover digitally at global and/or regional level. The PATHFINDER project covers only the tropical moist forests with high-resolution satellite data. The classification is simple, including forest and non-forest as the main categories. The TREES, IGBP, Woods Hole efforts and US Geological Survey rely on low-resolution (AVHRR) imagery. Earlier Global Area Coverage (GAC) NOAA-AVHRR data of 1-km/4-km resolution has
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been used to produce continental scale vegetation map (Townshend et al. 1987; Tucker et al. 1985).
NASA's Landsat pathfinder project In 1990 NASA, in conjunction with the USEPA and USGS, began a prototype procedure for using large amounts of high-resolution satellite imagery to map the rate of tropical deforestation. This activity builds on experience gained during a proofof-concept exercise as part of NASA's contribution to the International Space Year/World Forest Watch Project. It focused initially on the Brazilian Amazon, and has now been expanded as part of NASA's Earth Observing System activities to cover other regions of the humid tropical forests. This project has succeeded in demonstrating how to develop wall-to-wall maps of forest conversion and regrowth. The project is now in the process of extending its initial proof-of-concept to a large-area experiment across Central Africa, Southeast Asia and the entire Amazon Basin. The project is acquiring several thousand Landsat scenes at three points in time -- mid 1970s, mid 1980s, and mid 1990s -- to compile a comprehensive inventory of deforestation and secondary growth to support global carbon cycle models. Methodology and procedures have been identified. Although this exercise is being implemented for most of the tropics, it is not an operational global program. In principle it will provide an initial large-scale prototype of an operation program.
The European commission TREES Project: low resolution survey The Tropical Ecosystem Environment Observations by Satellites (TREES) project is currently being implemented as a demonstration of the feasibility of applying space observation techniques to monitoring of tropical forest areas (their extent world-wide and the distribution of various types) and for developing early warning, or alarm, systems which provide information on areas of particularly rapid change. This project, being sponsored by the European Commission, utilizes global coverage with coarse resolution sensor systems such as the AVHRR, which provide daily coverage over large areas. It also focuses on the use of thermal sensors for the detection of fires, and incorporates other indicators of deforestation. The project uses these data in conjunction with sample
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FOREST RESOURCE ASSESSMENT
higher spatial resolution data from a range of space sensors (e.g. Landsat).
NOAA – AVHRR
SPOT VEGETATION (VGT) Spot Vegetation is highly used for the global and continental land cover dynamics study. For this purpose a study was taken to develop operative methodology to monitor resources of Boreal forests over extensive areas (http: //www.vtt.fi/aut/ rs/docs/annamicnual95/node13.html). SPOT VGT imagery is highly effective for discrimination burned forest, owing to the inclusion of a 1.65 µm SWIR channel that is sensitive to vegetation and water content (Fraser et al. 2000). Monitoring natural disasters and 'hot spots' of land cover change with SPOT VEGETATION data to assess regions at risks was found to be a priority for global change research and for policies aimed at mitigating the impact of these changes.
MODIS data set for global/continental level studies A new era of global remote sensing began with the launch of the NASA Terra satellite on December 18, 1999. The first major platform of the Earth Observing System (EOS), the Terra satellite has five sensors on board, of which the Moderate Resolution Imaging Spectroradiometer (MODIS) is the most useful for forestry application with viewing capability of daily. MODIS has 36 spectral bands with three nested spatial resolution of 250 m, 500 m and 2 km. Global Forest Resource Assessment 2000 for North America is proposed using MODIS to measure certain indicators of forest sustainability at the spatial scales of continents and broad eco-floristic zones (http: //www.fs.fed.us/rm/ftcol/ rwu4804/nafc.htm). To the end user, the most significant advantage MODIS offers is an array of biophysical product (Table 3). Table 3.
Regional forest cover assessment
Data from the Advanced Very High Resolution Radiometer (AVHRR) are available from the US Geological Survey, with promising capabilities for evaluating global change (Eidenshink & Faundeen 1998). The central Africa region map was derived from NOAA-AVHRR observation using a fusion of Local Area Coverage (LAC, 1 km), Global Area Coverage (GAC, 8 km), and ancillary information (Laporte et al. 2000). Examples of AVHRR applications in the tropics include Achard & Estreguil (1995), Estreguil & Lambin (1996), and Jeanjean & Achard (1997) in tropical southeast Asia; Santos (1995), Di Maio & Setzer (1997) and Batista et al. (1997) in Amazonian; Rogers et al. (1997) in Nigeria; Srivastava et al. (1997) in India and Laporte et al. (1995) in Zaire. In particular NDVI data have proven promising for evaluating and monitoring both temporal and spatial changes of vegetation at a regional scale (Kastens et al. 1999; Reed & Yang 1997).
IRS - Wide field sensor
As the demands of regional studies evolve from simple qualitative and quantitative monitoring the lacunae with regard to sensor calibration, image navigation, cloud screening, variable spatial resolution, on broad data storage etc. limit the use of NDVI from NOAA-AVHRR as a direct measure of biospheric processes. Vegetation monitoring at a spatial scale finer than NOAA-AVHRR is essential to understand regional scale processes controlling landscape dynamics. The resource perspective for national planning can be attained at a temporal frequency essential, by such spatial resolution. The excellent repetitive of IRS - Wide Field sensor give unique edge on phenology process modeling. A spatial resolution of 200-250 m has been found out Biophysical variables computed from MODIS data.
Product Land Cover
Spatial Resolution
Temporal Coverage
1 km
Seasonal
Surface Temperature
1 km, 5 km
Daily
Snow Cover
500 m
Every 8 days
Fires, Burned Areas
1 km, 10 km
Every 8 days
Vegetation Indices
250 m, 500 m, 1 km
Every 16 days
Leaf Area Index
1 km
Every 8 days
Vegetation Primary Production
1 km
Every 8 days
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to be best suitable for land cover change processes. WiFS provides regional coverage at very high temporal resolution. It has demonstrated its ability in classifying forest type, land use/land cover, and agriculture production in Indian Sub-continent (Roy & Joshi 2000a) with an accuracy of ~ 80% by using combination of multidate data (Roy et al. 1995). WiFS data has been successfully used to characterize vegetation of different bio-climatic situations (Singh et al. 1999a; Singh et al. 1999b; Roy & Joshi 2000b; Joshi et al. 2001a; Joshi et al. 2001b). Using automated classification studies carried out using WiFS data recommended it for annual forest cover mapping which can save adequate time and money while providing equally efficient forest cover data at 1: 250,000 scale unlike LISS/Landsat. Hence WiFS is ideal for rapid national level forest cover mapping or even at state level for periodic monitoring (Roy & Joshi
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2000b).
Forest monitoring in India Satellite remote sensing data has been extensively used to map forests of tropics whereas up to date data about spatial distribution are absent or lacking. In India, the initial attempt at national level has been on 1: 1 million or 1: 250,000 scale using visual interpretation of false color images. National Remote Sensing Agency for the first time studied 1: 1 million images for the periods 1972-75 and 1980-82 and forests were classified into three categories - closed, mangrove & open/degraded (Anon. 1983). Subsequently, Forest Survey of India (FSI) also used similar technique for the period of 1981-83 for forest mapping on 1: 250,000 scale. The Normalized Difference Vegetation Index (NDVI) is one such ratio, which
Fig. 1. Status of forest cover in India.
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FOREST RESOURCE ASSESSMENT
has been extensively used to generate data of vegetation cover. This experience has been extended in evolving the national programme to monitor national vegetation regularly by Forest Survey of India. Thus information generated has national and regional application and can identify priority human impact areas for more detailed analysis (Figs. 1 & 2).
Vegetation type mapping Forest vegetation types have been described on the basis of physiognomy, structure, function and composition. Remote sensing provides spatial distribution of vegetation types and is considered as prime requisite for vegetation analysis. Some of the classical studies in the India are forest cover mapping in Central India (Roy & Kumar 1986), coherent vegetation classification, vegetation type mapping in Godavari Basin, community description in Andaman and Nicobar (Roy 1989) and resource quality assessment in parts of central India and North East India (Fig. 4).
Satellite remote sensing application in forest and vegetation studies Macro level vegetation cover monitoring Forest cover mapping and monitoring
Analysis of forest disturbance
Satellite remote sensing data have been used to identify vegetation cover and their density. The visual interpretation technique is subjective and depends on the field knowledge and aptitude of the interpreter. The digital classification methods are reported to be more accurate (Roy et al. 1991a). The biophysical indices can reduce the effect of bias and assist in the extraction of significant features of a specified ground object. Forest cover mapping and monitoring methodology needs revision for arriving higher accuracy and reducing efforts in mapping (Fig. 3).
Tropical forests are facing disturbances of varying magnitude in different regions. Due to over extraction of resource, tropical wet evergreen forest are losing their original structure and facing retrogression. Spanner et al. (1989) reported that there is a predictable relationship between spectral response and disturbance classes. Roy (1989) has also observed spectral differences in the spectral ellipse plot of bands 5 and 4 in virgin evergreen forests of Barantang Island extracted during 1972 and 1982. Roy & Unni (1980) observed that it is possible to stratify primary and secondary for-
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Forest Cover in Percentage
20
15
10
5
0
1972-75*
1981-83*
1985-87**
1987-89**
1989-91**
1993-95**
1995-97**
Year
Dense Forest Cover
Total Forest Cover
* NRSA Estimates ** FSI Estimates
Fig. 2. Forest cover estimates in India.
1997-99**
ROY, DUTT & JOSHI
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Fig. 3. Methodology for forest cover assessment.
ests along with shifting cultivation and abandoned shifting cultivation areas. Monitoring of vegetation changes Manual interpretation and change delineation by superimposing two or more time period maps have been carried out by remote sensing scientist since such data became available. A study in forest land use change carried out in Baratang forest division of Andaman and Nicobar Islands highlights the land transformation and its influence (Roy et al. 1991b). The extraction of virgin evergreen forest has brought in a retrogressive successional trend in the vegetation, depending upon the cycle of extraction, among extracted and site quality.
Micro level vegetation analysis Stock mapping Stock map depicts forest type, density, encroachments, cultivation patches regeneration status and an idea about available resources. Visual or digital classification interpretation methodology to update stock maps from high resolution satellite data like IRS – 1C PAN and IKONOS have shown possibility of achieving more informative stock maps than conventional, although the later data set provides more details (Roy & Joshi 2000a). Work carried out at the Space Applications Center, India (1989) demonstrated the use of visual interpretation methodology to update stock maps from high resolution Salyut – 7 MKF – 6 multiband photographs and IRS LISS II data. The
mapping/inventory (Roy et al. 1992), vegetation dynamics, carrying capacity and biomass estimation (Laxmi et al. 1998; Roy et al. 1991a, 1994a). Growing stock estimation Ground based inventories have given way to inventories through aerial photographs and systematic sampling. Forest resource stratification using satellite remote sensing data proved invaluable in carrying out mutliphase sampling. Stratified random sampling based on visually interpreted map from Landsat TM FCC of 1: 50,000 was used by Singh & Roy (1990) in South Andaman forest division of Andaman and Nicobar Islands. Biomass estimation Besides vegetation inventories are required for many decisions in wild land and natural resource management. Estimates of forest biomass are also needed for the determination of site productivity, nutrient cycling and energy potential. Interpretation of aerial photographs or stratification based on satellite images of >1: 50,000 scale combined with appropriately restricted field sampling was viable alternative for extensive time and money requiring conventional methods. Shute & West (1992) have quantitatively analyzed and concluded that contrary to expectation, discrete variable of classified community types are better predictions of plant community production than the same data reduced as continuous variable by tree ordination techniques. Biomass estimation provides the productive
Fig. 4. Temporal vegetation type maps of Meghalaya.
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potential of these grasslands, which has been used for carrying capacity estimation (Roy et al. 1991b, 1994a). Habitat analysis Wildlife habitat analysis is considered most important for management and planning of protected areas. With decreasing size of habitat and increasing fragmentation, it has become essential to develop habitat suitability maps to conserve the habitat intactness. Such an approach for endangered species is a priority task of India’s conservation programs (Roy et al. 1991b, 1995). Ground methods have been used for a long time to evaluate the habitat using various indices based on intensive ground truth. It is felt that the methods have obvious limitations because of the fact that whole area cannot be traversed in one instance, and the information collected may not be accurate. Satellite remote sensing provide most valuable input of spatial database in time domain. Geospatial multicriteria analysis provides an efficient tool for the assessment of habitat. In India, study to evaluate wildlife habitat or any other parameters required for habitat evaluation, using remote sensing data has been also attempted (Roy et al. 1995). Afforestation and social forestry The overall strategy of forest management in developing countries envisages not only a major thrust in the traditional forestry sector, but also ushering in an era of creating fuelwood and fodder resources for the rural population through social forestry programmes. Remote sensing data provide information with respect to extent and location of available lands and its spatial distribution for execution of social forestry schemes. Since these schemes are mostly executed in pockets of wasteland around village in agriculture fields, the data requirements for this purpose are specific. Before the advent of high resolution of data, aerial photographs were the only possible source of data (Laxmi & Dutt 1998). Integrated management strategy Integrated management of rural landscapes needs to be achieved for effective realization of the benefits of social forestry programmes. Wastelands could be priority area for plantation. Contiguous wastelands of different kinds are available and can be treated concurrently. Integration is also needed
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for proper mix of vegetation, sustainable on a particular site with simultaneous attention of water regimes, soil conservation, animal husbandry effective use of fuel alternative energy and fodder sources (Roy et al., 1993, 1994b). The Department of Space under its national programme has undertaken an Integrated Mission of Sustainable Development in selected districts so as to meet critical development constrains (Laxmi & Dutt 1998; Rao et al. 1994).
Discussion Edge and limitations of remote sensing Classifying vegetation is an important component in the management and planning of natural resources. It provides useful input for classifying habitat and ecological diversity. A limitation with remote sensing satellite is the difficulty in distinguishing fine, ecological divisions between overlapping vegetation communities. However, it has proved only data source for level I or II classification. Remote sensing is also challenging to use in high relief area, often it is difficult to define vegetation classes based on their spectral responses alone, due to the common heterogeneity of the cover type and the factors affecting spectral responses. Satellite images, however, allow for characterization of vegetation classes by a spatially distributed pattern of spectral responses and determine a higher degree of vegetation classes in mountainous regions. Vegetation classification through remote sensing requires two important conditions for its use; the interpreter must have the ability to understand the basic criteria of vegetation classification and unit delineation in satellite images and the satellite sensor must have the ability to act as a surrogate for the landscape properties.
Constraints in the information supply There is a considerable discrepancy between the demand for data and information for certain themes and the ability of existing systems to meet that demand. This indicates a considerable under utilization of existing data sources for accurate forest cover assessment caused by a variety of constraints viz. political, institutional, operational and technical in nature. The accessibility and affordability of existing data and information are the
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major constraints. The other constraints are lack of user friendly technology, inadequate data quality, cloud cover and lack of standardization in methods of data collection and analysis.
The next decade – futuristic vision For past fifty years, most forestry remote sensing data have come from aerial photography plus coarse and moderate resolution mutispectral satellite imagery (AVHRR, Landsat, IRS and SPOT respectively). However, foresters and remote sensing specialists alike are still often perplexed as to how to maximize the efficient use of existing remote sensing (Smith et al. 1999). Soon many more sensors and data products will be available than ever before – developments that make forest managers and policy makers call for action ever more relevant today than in the past. Major changes in satellite remote sensing applications for forest cover assessment can be expected in the next decade, although there are not likely to be as dramatic as the developments that occurred in the period of the mid 1960s to the mid 1970s. At national level, use of remote sensing in forestry, its role to support forest management and strategic planning can be expected to expand. The interfacing of remote sensing and land information systems/geographic information systems and the resulting two-way flow of data may stimulate this strategy. The trend towards the availability of satellite data with finer resolution can be expected to favor its use to replace microscale aerial photographs and development of new and faster methods for merging analogue and digital data. Already, IRS 1C/1D PAN provides data with a resolution of 5.8 m and IKONOS with space resolution of 1 m in panchromatic and 4 m in multispectral, are best available data sets for high level forest assessment and monitoring. In addressing the problem of monitoring changes in forest cover at the continental level, cost will necessitate the use of coarse resolution satellite data for overall coverage and confine fine resolution satellite data to selected sites. The next decade is likely to be a period of consolidation in applying existing remote sensing collection techniques to forest resources, and will be often best achieved using multistage or multiphase
approach. On the other hand, data analysis and the preservation of the analyzed data may require radical changes. Impetus for these changes is already occurring through advances in computer technology and the accumulation of fewer larger quantities of remotely sensed temporal and spatial data related to a world of increasing population pressure. Over the next five years, several earth observation satellites with higher spatial and spectral resolution will be launched, greatly expanding the types of image data available for forest community description and monitoring. The question is no longer whether to use satellite images for forest cover type assessment, but how to choose among the many types available. There remains the question turning the data into information: Is automated classification of pixels by computer algorithms really an improvement over manual interpretation of aerial pixel?
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