A regional scale soil mapping approach using ... - ScienceDirect

JAG

A regional scale soil mapping AVHRR and DEM data Endre Dobosl,

Luca Montanarella’J,

1 University of Miskolc, Department of Physical Geography 565-1 1 l/2314; e-mail: ecodobosQgold.uni-miskolc.hu)

approach

Thierry

and Environmental

University,

KEYWORDS: Soil mapping,

Department

of Agrochemistry

DEM, AVHRR, Remote

Sciences, Miskolc-Egyetemvdros, Institute,

sensing

forestry,

the food

extensive

Agriculture

usrng Advanced

Very

High

Resolution

the

Radiometer

(AVHRR) and digital elevation data. This method was employed

toring

soil-forming

environment.

An Integrated,

45-layer

build

AVHRR-

and

Governmental interest relies

studies

and

at various

an

and region-

on numerous to a great

of information.

systems need to acquire models

all require

and international in global

information

quality

scientific

and the preserva-

for example,

Decision-making

existence

long-term

in an

earlier study in Hungary for a much smaller area and a significantly different

up-to-date

resources.

accurate soil data.

industry,

quality,

also have a strong

al, reliable,

The aim of this study was to test a method for small-scale soil mapin Italy

and Regional Information

production

range of data.

agencies

There is an increasing need for reasonably accurate small-scale soil databases. The compilation of a continental or global-scale soil data-

ping

3515 Hungary (phone: +36-

and Soil Science, Godolld Pater K. 1, 2100 Hungary

ABSTRACT

and thematically

- Issue 1 - 2001

using integrated

tion of environmental

base requires a lot of spatially

3

NPgre2 and Erika Michel?

2 European Commission, Directorate General, Joint Research Center, Space Applications Systems, European Soil Bureau, I-21020, lspra (VA), Italy 3 Godollo Agricultural

Volume

l

extent

on

Furthermore,

natural

earth

natural

resource

moni-

observation

data to

scales.

terrain database was used for the study, including a digital elevation model (DEM), slope, curvature,

aspect, potential

drainage

and the five bands of AVHRR data for eight different were processed

using the Discriminant

(DAFE) function,

which is based on a canonical likelihood

that have identical soil classification. FAO-revised groupings

coverage,

numerous

the

were classified using

Program

(IGBP)

but differ

Planet

in the level of

while

the

other

set represented

major

were selected using the Bhattachryya were then

classified.

pared. The performance data-based

Program

selection method

and

System

sets were com-

of the purely AVHRR and purely terrain-

images, respectively,

were also interpreted.

indicate that the terrain descriptors classification.

feature

The results of the different

However,

the

The results

alone are not sufficient

feature

selection

&

Winkler, Sensing

Global [SAI,

AVHRR data alone, test class performances

of 49.8 percent

and 48.6 percent (SU) were achieved. Integration the AVHRR database and 2.8 percent).

produced

relatively

When using (MSG)

The best test class performances

Resource 19981,

European

The European

namely

ground

and 51.7 and 54.4 respectively

this work)

spring period (April-May),

on the DAFE-transformed

images.

to be from

while the most abundant

the

bands were the

monized

INTRODUCTION quality

information

Joint

Agriculture on

of our

natural

Soil Database

lives,

we

resources.

need

good

Agriculture,

30

under

initially

and corrected

19981,

Monitoring

from

(FIRE)

Remote

Sensing

1: 250,000

(which

provides

source

for continuation

nations,

database

and the the

Center

to

European

ongoing

and

coverage

extended

to 26 West,

Central

backof

known

on the development

as

of a harfor the soil

at a scale of 1:1,000,000.

as part of the institutional

Research

the

an EU program

geographical

cover of 18 European the

aspects

Monitoring [SAI,

[ESB, 19981.

was initiated

It was developed

many

19961, (MARS)

1 :I,OOO,OOO and

and also a potential

MARS. It focused

visible-red (band 1) and bands 3 and 4.

To support

(CEO) [SAI,

on the Environment

Environmental Information

Soil Database

Research Observation

(4.6

51.4 for the FAO’s SUs and 54.4 for the MSGs on the basic image, AVHRR bands were found

Earth

were achieved

when all available channels were used for the classification,

The most informative

and

Forests

and

(TREES) [SAI, 19981, the Fire in

Forest Monitoring

(FIRS) [SAI, 19981 and the

of terrain data into

small improvements

by

to

Databases

Earth Observation

Remote

[Megier

Global

Change

19931,

(CORINE)

ing their importance

characterization.

for

for

Biosphere

Digital

Global

of Information

selected the DEM and its derivatives among the first ones, highlightfor soil-landscape

19931, US

(EOS), Centre

19931,

Terrain

19981, Co-ordination

Tropical

always

[USGCRP,

and

including and

et a/, 19941, Mission

(USGCRP) [USGCRP,

Agriculture

for soil

algorithms

(MTPE)

has instigated

activities,

Geosphere

[Townshend

(SOTER)[ISRIC,

soil

(MSG). The best 10, 15, 20, 25, 30, 35, 40 and 45 layers

and continental

Soils

and comprehensive

resources

International

Earth

National

One set was based on the soil units (SU) of the

legend,

global

instance

sets were chosen

for consistent

on environmental

analysis procedure.

classifier. Two training

geographic

demand

information

Analysis Feature Extraction

Two types of images (basic and transformed) the maximum

The growing

density,

dates. The data

Directorate Statistical of

this

support

from

General Office.

database

and East European

VI -

Work will

is be

coun-

Mapping

tries,

JAG

soils using AVHRR and DEM data

mainly

ing national,

those that are members of or wish to join

regional

l

Volume

or global-scale

3 - Issue 1 - 2001

land information

becomes increasingly time consuming and expensive using traditional methods, while database consistency is also difficult to maintain due to varying mapping approaches of contributing field surveyors. Thus, smallscale databases are rarely based on primary (field) data collection, but on the generalization of existing largerscale maps. However, such an approach entails difficulties. First of all, a method of data generalization has to be developed and then the compilation problems arising from data dissimilarity must be solved. Lack of data compatibility and the missing data limit the comprehensiveness of the database. Both issues call for more soil information, which may require too much effort. A potential shortcut is the use of secondary (non-soil) data sources, which have to meet the following requirements: they have to (1) contain extractable soil information; (2) have global coverage; and (3) be consistent and comprehensive both in time and space. A review of literature reveals two possible data sources: coarse spatial resolution satellite data and digital elevation data. These data have worldwide coverage and provide support to characterize the soil-forming environment.

the EU and their neighboring countries. Long-term intentions of the project to extend its database development activities to include all countries in the world [King & Thomasson, 19961. The nomenclature used in this project was developed for the FAO “Soil Map of the World.” This database has been successfully used for monitoring crop performance [Vossen & Meyer-Roux, 19951 and estimating environmental degradation risks [Giordano et al, 19911. However, the uncertainties attached to the 1: 1,OOO,OOOscale should be estimated to identify areas with inadequate information and to avoid misuses of the information. Due to the limitations of this small-scale database, a new mapping program at a scale of 1:250,000 was proposed by the scientific advisory committee of ESB. It is still unclear whether this larger-scale database needs to cover all of Europe or just concentrate on priority regions and An even larger- scale soil representative “windows.” database, at 1:50,000 scale, is being discussed by the committee, because more detailed soil information is required for many situations.

THE

USE

OF

COARSE

SPATIAL

RESOLUTION

SATELLITE

The work described in this paper aimed to test a method using integrated AVHRR and DEM data for deriving smallscale soil maps in support of the European Soil Database. The final product can be used parallel to the one created with the conventional method or as complementary information to help delineate patterns on soil maps. Comparison of the conventionally made database and the one created through the use of AVHRR-terrain data can highlight areas with potentially inadequate information and help solve problems that occur along national boarders due to the lack of successful harmonization.

The coarse spatial resolution satellite data are provided by the Advanced Very High Resolution Radiometer (AVHRR). Recently, a new European instrument called Vegetation was launched (Table 1) and, in the near future, the Moderate Resolution Imaging Spectroradiometer (MODS) will be operational. These will provide better spectral resolution and absolute location accuracy. Therefore, this study is not only about the evaluation of AVHRR data, but also a preliminary study of the potential use of these new kinds of satellite data.

BACKGROUND INFORMATION Natural resource inventories are conventionally done using ground-based and aerial surveys. However, collect-

Numerous studies have been carried out to evaluate the potential use of AVHRR data for soil pattern recognition on a small scale [Vettorazzi et al, 1995; Dobos, 1998;

TABLE 1:

Features

Four spectral bands - blue: 0.43-0.47 urn - red: 0.61-0.68 pm Calibration:

Operation

FOR SOIL MAPPING

Vegetation InStrUment feature5

Radiometric

Geometric

IMAGES

Features

- near-IR: 0.78-0.89 urn - SWIR: 1.58-I .75 pm

- inter-band and multidate: better - absolute: better than 5 %

than 3 %

Spatial Resolution: 1 km across entire FOV Cross-track viewing up to 50” Distortion: - multispectral < 0.1 km - absolute locational accuracy: 0.5km Global coverage: almost daily Central archiving and processing (solid state onboard memory and X-band Worldwide receiving station network (L-band downlink)

31

multidate: c 0.3 km HRVIR c 0.3 km

downlink

to receiving

station)

Mapping soils using AVHRR

Odeh & McBratney, kilometer

1998;

coarse

1:500,000

resolution

ondary

generalization large-scale

difficult

and the single

tive

(the red and the

infrared

provide

The

respect

more

crop

near-infrared

wide

AVHRR

channels),

the

teristics

and modeling, for

in addition

which

the

AVHRR

instrument

carried

He

information

found

depends

of the band,

the acquisition conditions

conditions effect

The thermal

[I9941

concluded

data

summarized

that all five channels

use for land cover studies. most

commonly

the

for environmental

used.

have found

index were found

soil classes. He concluded

and their

spatial

In particular,

phenology.

employed cover

for

Thermal

surface

bands

especially

bands

are often

variability

AVHRR-type

data are the

mapping

thermal

been

and

in tropical

ferentiating

vege-

also

studied

by numerous

linked

with

[Schultz

variations

researchers

temperature

& Halpert,

et a/, 19971, root

zone

19931 and soil physical

land

in NDVI have

to be

They

[Cihlar

soil moisture

concluded

where

that

et al,

forests,

Parent

with

tree

variability

Temporal closely

regime,

and the NDVI-evapotranspiration

tionships

exhibited

time

Vettorazi

et a/ [I9951 studied

tive

regional

pervised

classifications

the utility

while

&

that

of small-scale

correlation

between properties

the middle-

-0.88

and ther-

correlation

Vegetation

indices

coeffi-

containing

and thermal-infrared

than the widely

chan-

used NDVI.

several

and vegetation

factors

[Jenny,

refer

to two

19411. Some spectral

be due to the physiographic could

with

of Jenny’s

produce

different

phenomenon.

varia-

characteristics results

Integrating

terrain

the AVHRR data can eliminate

of

even for infor-

this problem

level.

to The time

the

face,

rela-

of AVHRR data to

using the two

and concluded

in the delineation

the

[Short

Foody et al, 19961.

with

(-0.87,

better

material

on a certain

and high

“time

bands and the NDVI. They performed

are useful

mation

lags.

soil patterns

(through

demonstrated

high

particularly

density).

the same natural

relations

of NDVI was found

to the temperature

NDVI-precipitation

characterize

in dif-

(using the

and the biophysical

in the middle-

an area, which

in

on soils with

capacity

reported

channels

nels performed

et al,

mapping

NDVI-climate

permeability. be linked

materials

et al,

[Narasimha

developed

water-holding

for soil

of the data

of vegetation

been

sensed radiation

data acquired

et al,1991; Yang

[Lozano-Garcia

low root-zone-available

kinds has

et al [I9961

soil forming

vegetation

and

Foody

cient

regimes

et a/ [I9971 used NDVI for ecoclimatic

Nebraska.

infor-

and this

used routinely

1982; Zhu & Evans, 1994;

tion could

are stronger

been

the utility

Stuart,

mal-infrared

1991; Yang eta/, 19971. Yang

non-soil

response

to the soil class identi-

kinds of parent

indices)

of tropical

been

found

precipitation

properties

have not However,

bands),

vegetation

rainforests.

Di et al, 1994; Yang

19971, plant evapotranspiration

pixel

have a sig-

Due to the large

to the vegetation

and were

and

1993;

the

contribution

between

remotely and temporal

data

characterization.

indices for land cover discrimination. The spatial

response.

into

and condition

fication.

multitemporal

have

superior

is incorporated

makes an important

of They

used to describe

temperature

discrimination,

The thermal

the

on the spectral

to be

that the

some level of

The AVHRR/NDVI

NDVI data sets have been widely tation

application

monitoring.

area at

bands (particu-

pixel size of 1 km x 1 km, much primarily mation

of the image

of the scene, the type

of the vegetation

designed

date

of

charac-

of the observed

larly band 3) and the vegetation thermal

of the soilamount

on the spectral

Maselli et a/,1996; Rogers et al, 1997; Yang et al,19971. et a/

supplemental

the

nificant

Ehrlich

with

(maximum

analysis

that

[Ehrlich et a/, 1994; Zhu & Evans, 1994; Foody et al, 1996;

NOAA-AVHRR

crop

has less

However,

image

useful

out a statistical

the best for predicting

applica-

was

NDVI

bands.

provided

the peak

maximum

the

AVHRR

the time of data acquisition.

mapping

to the meteorological

two

NDVI

of

to soil patterns.

and the environmental

land surface

and land cover

first

relationship.

extractable

reflec-

That is why these data have been used extenecoregion

the

related

Dobos [I9981

at

bands of the AVHRR

range of detectable

and one from time that

mid-summer

canopy),

information

April

at the

indicated

the

the

Volume 3 - Issue 1 - 2001

l

kilo-

is done

The two

than

to

from

season results

importance

global of sec-

it is often

generalization

and the two thermal

sively for vegetation, tions

canopy.

from

hand,

the classes are defined.

a relatively

information.

This rela-

AVHRR data are used. This fact has to be

when

bands

growing

soil classes in one square

considered middle

of the

for studying

loss of detail

They used one image

to the

the difficulties

On the other

pixels; thus, an “in situ”

pixel level when

equivalent

to 1:1,000,000.

without

images.

to identify

meter

et a/, 20001. The one-

is still useful

processes and phenomena costly

Dobos

pixel size of AVHRR is roughly

range of scales from tively

JAG

and DEM data

when

could significantly

of vegetation, related

of the

and depositional influence

deposit

surface

or

began.

processes.

the kind and con-

so the NDVI may reveal some infor-

to parent database

only the macroclimatic

32

of the age of the

the exposure

directs the erosion

an integrated

AVHRR data soil patterns.

refers to the age of the soil sur-

The latter

mation

unsu-

zero,”

which

a function

This factor dition

reflec-

factor,

is mainly

material, of satellite

factor,

vegetation

and time.

If

and DEM data is used,

among

Jenny’s soil forming

Mapping soils using AVHRR

and DEM data

factors, is missing. However, the spatial variation

JAG . Volume 3 - Issue 1 - 2001

of the

a scale of approximately 1:100,000, which enhanced field validation and increased mapping confidence. Until

vegetation can explain some of the climate variation as well. If the area of the study site is “small enough” to assume the macroclimatic effects to be homogeneous, then the integrated database will allow delineation of areas characterized by the same soil-forming environment.

recently, no low spatial resolution for small-scale soil mapping.

Odeh et a/ [ 19951 compared geostatistical methods with classical statistical methods by integrating soil-landform interrelationship. They found that regression kriging generally performs best. However, there is no single best method for all predicted variables. Due to the flexibility of the regression kriging, more ancillary information (eg, parent material, vegetation, etc.) can be included in the model and thus the accuracy of the predicted variables can be increased.

Remotely sensed data are greatly influenced by terrain variability. However, these data still do not represent all the soil variability that occurs in the landscape. As has been suggested by many researchers [Franklin, 1987; Lee et a/, 1988; Frank, 1988; Leprieur et al, 1988; Yuan et a/, 19951, satellite data have to be complemented with terrain information to correct satellite data distortions arising from topographic variations of the landscape and to provide additional data for soil-landscape modeling. Both data sources, the satellite and the digital elevation data (DEM), have worldwide coverage and help to characterize the soil- forming environment. THE

USE OF DIGITAL

TERRAIN

DATA

DEM data were used

THE

USE

OF INTEGRATED

SATELLITE

AND

TERRAIN

DATA

FOR SOIL MAPPING

Many attempts have been made to complement the satellite data sources with topographic information for mapping natural resources [eg, Weismiller et al, 1977; Shasby & Carneggie, 1986; Franklin, 1987; Lee et a/, 1988; Frank, 1988; Leprieur et a/, 1988; Yuan et al, 19951.

FOR SOIL MAPPING

Digital terrain data have been used for soil feature prediction by many researchers [Moore et al, 1993; Bell et al, 1994; Gessler et a/, 1995; Chaplot et al, 1998; Florinsky & Kuryakova, 19981. Catenary soil development occurs in many landscapes in response to the way water moves through and over the landscape. Terrain attributes can characterize these flow paths and the interactions with the soil attributes. Moore et al [I9931 found significant correlation between quantified terrain attributes and measured soil attributes. Slope and wetness indices were the terrain attributes most highly correlated with surface soil attributes. They accounted for about one-half of the variability in A horizon thickness, organic matter content, pH, extractable P, and silt and sand contents.

Loveland et a/ [I9911 suggested that the effect of physiographic variation on spectral data can be reduced through stratification of a large region into smaller areas. Zhu & Evans [I 9941 used this technique in the production of the “U.S. forest type and percent forest cover map.” A similar physiographic stratification technique was used in the classification of potential old growth forests in the Pacific Northwest of the United States [Congalton et al, 19931. The disadvantage of this method is the need for edge matching and refinement of final classes and categories. Along the edges of the stratification units, some unconformity is likely to occur due to the lack of absolute classification categories and possible incoherence of class interpretation within the different classification units.

Bell et a/ [I9941 combined a statistically based soil-landscape model and a geographic information system (GIS) to create soil drainage class maps. The landscape attributes used were parent material, terrain and surface drainage feature variables. The model produced drainage class maps with an accuracy of 67 percent at a scale of 1:20,000. Gessler et a/ [ 19951 developed a statistical soillandscape model to predict soil attributes. They used different terrain attributes, such as plane curvature, compound topographic index, and upslope mean plane curvature to predict the depth of the A horizon and the solum,and the absence or presence of E horizon in an area with a uniform geology and geologic history. The reduction in deviance was around 65 percent on average. Biggs & Slater [ 19981 carried out a medium-scale soil survey with the use of digital elevation model. They used a 15 m DEM and its derivatives, namely the slope, curvature, topographic wetness index (TWI), relative elevation and slope position. They produced a soil attribute map at

Another weakness of this approach of data integration is that the gradually changing natural phenomena are represented with boundaries, and thus the possibility to use continuous surface information as a whole is missing. The rapid development of GIS in the last two decades has made “direct” data integration possible, when data sources of different origin are used simultaneously. This permits a much better exploitation of the DEM data. Weismiller et al [I9771 used Landsat and topographic data to make a soil inventory in Missouri. However, they did not attempt to relate soil cover to soil type. Franklin [I9871 reported a 46 to 75 percent improvement of classification accuracy when he used Landsat MSS data with DEM-derived landscape descriptor layers for classification of landscape classes. In our study, the primary objective was to evaluate the use of integrated satellite and terrain data for global-scale

33

Mapping

soil inventories.

potential

Previous

studies

have demonstrated

use of AVHRR data for small-scale

delineation 20001.

[Dobos,

that

Digital

elevation

The digital a resolution

Odeh et al,

of 1 km2 per pixel. The European

emphasize

the

EROS Data Center

the

I” x I” Digital

classification

can occur

potential

improve-

results and highlight

in small-scale

based on remotely

sensed and digital

MATERIALS

METHODS

soil

the

elevation

from

US Defense

inventories

The data AND

tion

study

covers

(1,650,OOO

area

kmz), of which

sents the entire France,

an

1160

48.8

Austria,

Bosnia-Herzegovina. Mediterranean

regions

percent

x

platform

soils

Andosols,

region

and

of

the

Data (DTED) of the

(DMA)

from

Azimuthal

Equal Area

is the average

elevation

Finally,

Equal

the training

Fluvisols and

the

Area

data

were

projection

to

Luvisols,

European

structure

soils

reach

descriptive minimum

attributes

and maximum

altitudes

use. Due to the scale and the resolution,

tics of the Selected

weather

NOAA/AVHRR

IO-day

five spectral calibration

composite

bands were was applied

The characteris-

are shown

images

were

to reduce

composites

NDVI

value

in

lowing erage

channel

the

Composite-MVC).

Dobos

near

the

separability

or near field

capacity.

data

for this study

bers are shown selected

between

an original

in Table

layer image tors).

Value

growths;

capacity.

According

of soils with the image

copious

3. Eight

the

instrument

features

Spectral Bandwidth

1. 2. 3. 4. 5.

Radiometric Resolution IFOV (nadir) View Angle

10 bits (1024 levels) 1.1 km 55.4” (IFOV = 6 km at swath

Swath Width

edge 1 (i) 2700 km

use of

with

of IO-day

no

soils at

composite

identification different

Platform

to

was acquired

rainfall,

and their

and soil

580-680 nm 735-l 100 nm 3550-3930 nm 10300-l 1300 nm 11500-12500 nm

of I. 1 km. The images were with

the

data set (40 AVHRR and five terrain

The geographic

projection

was

the

Orbit Altitude Inclination Period Equator crossing time (ii)

Near-polar, Sun-synchronous 833870 km 98.7 102 min 0730 and 1930 (even numbered satellites) 1400 and 0200 (odd numbered satellites)

Repeat cycle Global Frequency Coverage

12 hours l-2 days

were resam-

use of the

The data were then stacked

Characteristics

num-

dates

1995 and 1998. The AVHRR data have

resolution method.

have maximum

(Maximum

was based on the fol-

The periods

pled to a 1 km resolution neighbor

ele-

to field

a few days after

chosen

Unit

free of clouds and snow cover; cov-

AVHRR band 3 is best when more than

or off board

made up of picture

period

to soil types

is the Soil Typological

Sensor Characteristics

All

pix-

stages of vegetative

contents [1998],

AVHRR

the size of the data-

(pixels) that

IO-day

requirements:

produced.

to all water

Data selection

of different

moisture

were

these SMU cor-

and not directly

level

and land-

2.

to the data. The pixel value range

base. A pixel value of 255 was assigned ments of the certain

TABLE 2:

in Table

used. No filtering

was set to O-255 in order els. The IO-day

The second

Units (SMU)

Administration

satellites.

system

(soil units).

are from the AVHRR

and Atmospheric

to soil associations

hierarchic

of SMU are stored,

respond

data

data-

The data

into three

levels. The first level consists of Soil Mapping

Leptosols

(ESD) was

truth”)

classification.

THE DATA

polar orbiting

into

a common

Soil Database

AVHRR

(NOAA)

for an

re-projected

or “ground

of the ESD was organized

for instance

Oceanic

projection). taken

Soil Database

and test (reference

- only general

of the National

co-ordi-

the AVHRR data.

base used for the AVHRR-terrain

soils are

The dominant

data used in this project

Digital

GISCO projec-

value

with

the

geographical

to the standard

(Lambert

l,OOO,OOO scale

and Cambisols.

The primary

and

(DCW) (scale 1 :l,OOO,OOO).

The 1: 1 ,OOO,OOOscale European

Leptosols

dominant

Podzoluvisols,

Albers

Elevation

Agency

from

set is based on

by temperate

Cambisols,

and Phaeozems.

in the Alps are Podzols,

Terrain

transformed

area of 1 kmz. the

are mainly

temperate

Luvisols

system

and parts of

The

Regosols.

In the

were

The elevation

km

Croatia

climates. some Vertisols,

1425

is sea. It repre-

Hungary,

and Luvisols, with Cambisols,

km

The area is dominated

Mediterranean

and

of

area of Italy and Slovenia

Switzerland,

(EDC). This data

Mapping

DEM data

DEM data set for Europe

nates (latitude/longitude)

STUDY AREA

The

the 30”

Chart of the World

data.

3 - issue 1 - 2001

data cover the same study area with

are derived

the

Volume

l

data

elevation

we focus on the use of integrated

data,

of the final

problems

the

soil pattern

1998, Dobos et a/, 2000,

In this paper,

AVHRR-terrain ment

JAG

soils using AVHRR and DEM data

nearest (i) The most usable wlthln the swath of 2700 km is the area wlthln +/- 15”. At 15O, the area covered by a pixel is approximately 1.5 km and the repeated coverage for this reduced swath width is about 6 days.

into a 45descrip-

Albers

Equal

(il) Greenwich standard time (1430 ascendmg and 0230 descending (local time)).

Area projection.

34

JAG

Mapping soils using AVHRR and DEM data

TABLE 3: Layer identification

The European

numbers and the corresponding

Soil Database

l

Volume 3 - Issue 1 - 2001

in a polygon format, 1 km resolution grid format is stored

band or terrain data

which

Layer ID

and reprojected to the Albers Equal Area projection to make the overlay possible with the AVHRR-terrain data.

1

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

Information

DEM SLOPE ASPECT CURVATURE PDD 98 May 08-17 Band 1 98 May 08-17 Band 2 98 May 08-17 Band 3 98 May 08-17 Band 4 98 May 08-17 Band 5 98 June 20-29 Band 1 98 June 20-29 Band 2 98 June 20-29 Band 3 98 June 20-29 Band 4 98 June 20-29 Band 5 97 August 22-31 Band 1 97 August 22-31 Band 2 97 August 22-31 Band 3 97 August 22-31 Band 5 97 April 01-10 Band 1 97 April 01-10 Band 2 97 April 01-10 Band 3 97 April 01-10 Band 4 97 April 01-10 Band 5 96 May 29-June 07 Band 1 96 May 29-June 07 Band 2 96 May 29-June 07 Band 3 96 May 29-June 07 Band 4 96 May 29-June 07 Band 5 95 October 07-16 Band 1 95 October 07-16 Band 2 95 October 07-16 Band 3 95 October 07-16 Band 4 95 October 07-16 Band 5 95 July 06-15 Band 1 95 July 06-I 5 Band 2 95 July 06-15 Band 3 95 July 06-15 Band 4

was converted

into

A soil profile database is connected to the polygon database. This data contains the exact geographic location and all the main descriptive information of the soil profile. These profiles were also used for training the data. METHODS The work

was done

struction;

(2) training

extraction

and reduction

classification; Database

in five main steps: (1) database and test set selection; of dimensionality;

and (5) accuracy

con-

(3) feature

(4) supervised

assessment.

construction

Forty AVHRR bands (five bands for eight different dates) and five terrain descriptor layers, (DEM, slope, aspect, curvature and the potential drainage density) were integrated and a 45-layer, 1 km pixel size AVHRR-terrain image set was formed. The slope, aspect and the curvature layers were created using the slope, aspect and curvature functions of the Unix platform ARC/INFO’s GRID package Version 7.0.3. [ESRI, 19971. The potential drainage density (PDD) layer was created by following the method described by Dobos [I9981 (Figure 1). The image set was projected onto the Albers Equal Area projection. For constructing and processing the database, the ARC/INFO and ERDAS Imagine software were used.

95 July 06-15 Band 5 95 May Ol-10 Band 1 95 95 95 95

(STU), which

May01-10 May01-10 May Ol-10 May 01-10

refers

Band Band Band Band

2 3 4 5

to a certain

soil type.

At this level,

to a SMU is described with its percentage of representation within the SMU. The STU provides the descriptive soil attributes (eg, topsoil texture, parent material). The nomenclature used in this project was developed for the FAO “Soil Map of the World.” The polygons have no direct soil unit (SU) information assigned to them, but rather a predefined soil association. In order to simplify the database for our purposes, the dominant SU, that is, of largest spatial extent within the polygon, was selected and assigned to the polygon. This generalization results in some loss of information. However, since many soil types within the soil associaeach STU belonging

tions mation

are in close genetic is not really

relationship,

the loss of inforFIGURE 1:

relevant.

35

The Potential Drainage Density (PDD)image of Italy

Mapping soils using AVHRR and DEM data

Training

and test sample set selection

Two training first

and test sample

phase, major

soil grouping

classes selected

formed. (MSG)

selected.

In the

legend

In the second

level was

in the first phase were

MSG classes. In both

For assessing

was

phase,

as test pixels,

used and the

grouped

into

Database.

17

option, are

of the

study

was

done

software

developed

Electrical

Engineering,

Purdue

Both the training based on the training

with

at

1:1,000,000

all the representative

Soil Database.

profiles

(from

were

selected

known

and overlaid

a given

polygon

were

cells for

training

under-represented selected

from

classes,

pixels were

cent of the entire

together

purposes.

more

larger-scale

1 :I ,OOO,OOO European training

selected

training

maps

Soil

which

test

European gons.

were

taken

In total,

9160

from

the

samples

were

ACCURACY

the

2.13

veyor

Based

on

the

Discriminant

results

was used to reduce the original

making

Dobos

[1998],

formed

by the method

using successively finally, wise

feature

Classifications

classifications

tion

of the data from

the separability

transformed

statistical

taxonomic

accuracy

purity

survey

is rarely

of a mapping

above.

were

The data were

the-

known.

unit or a data-

layers.

the Bhattacharyya

method

were also performed

[Richards,

Dobos

were

performed

Bascomb

value

that this could

on

& Jarvis

scale maps (up to 1: at lower

the taxonomic

categorical

often

arise only

limit

different

purity

values

of

the use of the

be interpreted

from

itself.

maximum

compared

likelihood

later.

36

[I9981

estimated

the

taxonomic

HunSOTER database

[Varallyay

to be 49.5

This database,

created

The first 10, 15, 25, 35

used for the

and these were

Therefore

65 to

using only the terrain

the classifications image.

and between

and do not require

maps do not necessarily

the purity

19931.

occur

found

for a soil

scale, depending

mapped.

in the larger

features

data to such a degree

step-

area

that

definitive

large-scale

The best layers for these

with

et al. [I9711

levels and that the impurities

management.

the best 10, 20, 25, 30, 35, 40, and,

of the

soil pro-

unit descrip-

45 to 63 percent

scale) soil differences

in minor

to which

a scale of 1: 63,360

concluded

(taxonomic)

classified

Burrough

from

for a map of 1: 25,000

complexity

25,000

per-

set selected

occur.

ranges

map with

[I9761 classifications

they

purity

86 percent

using the training

at random

in which

mapping

for the

image was later used as

likelihood

were

are the existing

match the mapping

on the DAFE transformed

The most

Such maps may be of good

or percentage

19931

data (5 layers) and then only the AVHRR data (40 layers).

and 45 features

process.

base means the degree

the dimensionality

phase,

sur-

of less expensive,

mapping data

stud-

the resource

sources

used ground-truth

and test data

in small-scale

This motivates

for use in the

but their

classification

of the training particularly

expensive.

DATABASE

image

files sampled

selection

In the second

and quantity

for

(DAFE) method,

were selected

&

are assigned

REFERENCE

factors

[Richards,

all the available

classifications

image

Extraction

described

that

map by

by Hudson

procedure

on the basic image

given

and (4) likelihood

Feature

classifications

maximum

described

analysis

the Several

(2) user’s accu-

is “B”,

of the

OF THE

maps and databases.

The term

the

a basis for the classification. Supervised

actual

of the most likely class.

most critical

data

commonly

up

matic

by

45 and to increase

classes. This linearly

the

of the dimensionality

reported

Analysis

based on a canonical

has prop-

that

class (the class in the

[1991];

to search for alternative

reliable

image.

reduction

the

(produc-

DISCUSSION

are the quality

per-

quality, Feature extraction,

assessing

on the thematic

Statistics

ASSESSMENT

Two of the

34,888

1:1,000,000

selected,

given is “B”;

“B”

the pixels

value

RESULTS AND

were

using the inner part of the poly-

of the entire

of

Soil Database)

sets. Field data collection,

Soil Database

0.56 percent

truth)

labeled

image:

the likelihood

image.

samples

“B”,

that the actual

(3) Kappa

ies, is always The

ways

Its

the results

the

and

Overall,

represent

known.

that the classifier

pixel

19871 and Congalton

probability

exactly

Soil

available

case of

samples

(1:250,000)

Database.

selected,

with

In the

image

European

the classifier;

the SU

best

later when

different

the probability

has been

Ramm,

European

the

is not

per-

selected

used: (1) test class performance

the

1:1,000,000

data-

matching

Four

were

the pixel

on the polygon

In the second step, all profiles

its accuracy

racy: the probability

geographic

was

0.56

were

1:1,000,000

database

class (test pixel or ground

The

the soil profile

coordinates,

neighboring

U.S.A.

on the

test

interpreted.

erly labeled

selected

performance,

of pixels (9160)

have to be considered

er’s accuracy):

of

was done in two steps. First,

into the study area, with

of

Indiana,

sets were

European

base) falling database.

use of the

Department

University,

and the test sample

sample set selection

the

the

based

although

limitations

of the results.

MultiSpec

classification number

This

accuracy This part

the

cent of the total

cases, the same pixel set was used

to secure comparability

Volume 3 - Issue 1 - 2001

l

Accuracy assessment

sets were

the SU level of the FAO-revised

used and 56 classes were the

JAG

percent.

using

the

traditional,

method.

One explanation

alization

procedure

purity

of

the

et al, 19941 and found like the

it

ESDB, was

expert-knowledge-based

for this low value

is the gener-

used in HunSOTER, in which

the orig-

Mapping

JAG

soils using AVHRR and DEM data

inal soil association was replaced with the dominant

Volume

3 - Issue 1 - 2001

In both cases the best results were achieved when

soil

type and the soil type was assigned to the entire mapping unit. The accuracy of the original (non-modified) HunSOTER database is probably much higher, but generalization was necessary to allow for spatial comparison. The reference base of this study was the 1 :I ,OOO,OOO scale ESD, which was modified for the purposes of this study in the same manner as the HunSOTER was modified in Dobos’ study [1998]. The dominant soil type, instead of the original soil association, was assigned to the entire mapping unit. Taxonomic purity of the ESDB was estimated (not calculated) to be between 45 and 55 percent on the basis of the similarity between HunSOTER and the ESD in terms of scale, data structure and the method of data modification. This fact must be considered when interpreting the classification accuracy of the different test schemes. Because of the lack of better quality data, the ESD was used in this study for testing the results. RESULTS OF THE CLASSIFICATIONS

Two different training and test data sets were used in the framework of this study, one representing SUs according to FAO-revised legend, and the second representing MSGs [FAO, 19941. The two data sets cover the same areas, so that the results were comparable. In the first phase of the study, classifications were performed on the basic image, while in the second phase the original image was linearly transformed with the DAFE function.

FIGURE 2:

l

The higher the number of channels involved in the classification, the better the separability within the classes. Every new channel that is used for the classification provides new information and more chances to make the training classes more distinguishable. However, higher training field performance does not necessarily mean higher test performance. As the number of the channels increases, more statistics have to be estimated with the same number of training pixels, which can decrease the accuracy of the estimates, and the percentage of correctly classified test pixels. This effect decreases the test performance when the number of training pixels is not high enough to achieve accurate estimates of class statistics (Hughes phenomenon). In this study, we used 2.13 percent of the image pixels for training the classifier to avoid a severe occurrence of the Hughes phenomenon.

Classified image of the SU level (A) and the 1: 1,OOO,OOO European Soil Database

37

all

available channels were used for the classification, namely 51.4 for the SU level and 54.4 for the MSG level on the basic image, and 51.7 and 54.4 respectively on the DAFE transformed images. The classified image of the MSG level is shown in Figure 2. Dobos [I9981 has reported that the use of DAFE can significantly increase the classification result. In this study, we found little or no increase with the use of the DAFE. However, the increase in classification accuracy due to the increasing dimensionality was much higher in the lower dimensions and reached the top values much earlier than the ones obtained without using DAFE.

(B) of Italy

Mapping

soils using AVHRR and DEM data

However,

at

divided

SU

into

curve

showed

when

adding

sification.

level,

a saturation more

class needed

the

higher,

of the and

pixels

were

while

classification

a lower

the saturation

number

to estimate

was much

training

increase

of

training

the second

minimizing

trend

order

site outcome.

the SU level (Figure

less

class performance

pixels

per

bigger

the

test

pixel

classes were

higher

number

-Major

some

Luvisols),

soil groupings

were

of pixels.

while

After

others

units

by increasing

distribution

of the Cambisols, were

situation

the

changed Leptosols,

same

Vertisols,

by a

the SUs into

(Cambisols.

remained

the

represented

regrouping

large

as SU classes (Fluvisols,

cient

much

classes. The test

balanced

became

the test

was

in the SU case. However,

extent

huge classes can decrease - - - -Soil

the

to Cambisols

classes

between

statistics

among

the MSG class, this relatively and

the oppo-

for the SU level. The rea-

balanced

SU classes belonging

for the MSG

showed

in the unbalanced

due to the large geographic

Test class performances

higher

Kappa

numbers

relatively

3 - Issue 1 - 2001

for the MSG level than for

for the MSG level than

of the

(Figure 3).

was

5). The difference and

son for this can be found

phenomenon

Volume

Kappa statistics

They were lower

was

class statistics

the Hughes

l

the test class performance

than for the SU level,

to the set used for the clas-

case,

because

the shape

trend

channels

In the MSG

expressed,

where

56 classes, the

JAG

size as they

Andosols).

Such

the value of the Kappa coeffi-

the subtrahend

in both

terms

of the

the

AVHRR

equation. 10

20 25 30 35 40 Number of channels

FIGURE 3: Test class performance the basic images

The effect

45

database

of the MSG and SU levels for

levels.

of integrating

Three

supervised

layers (layers out a study to develop

soil map for Hungary

with

Hungarian

obtained

49.8

percent

(user’s accuracy). current

suggest

50-60

low figures

percent

reflect

and the data,

as

a

maximum

This value

study

around

AVHRR and terrain

SOTER map

data.

ground

when

but also the quality

cent

value

descriptors (Figure

(MSG)

into

the

examining

Podzoluvisol, lowest were

the

Fluvisol

accuracy;

the

classified

Cambisol

the

and chemical

class, absorbing

properties.

all other

due to spectral

cient

of

number

Podzoluvisol

for

the

and

SEPARABILITY

STUDIES

portion

of

regions

The

In this tion layers.

no distinct of

Chernozem, The

bands

was

class for the pixels was 70.7 and 65.4 percent

percent

of the for the

The most 15 percent) Planosol

most

Bhattacharyya

the

usefulness

when

the “sea”

important

the

the

slope,

neglected

when

selec-

individual

(or background)

layers were

derivatives,

feature of the

class was

DEM and curvature

the

“sea”

to the class set. The most informative

were

These trends

are very easy to identify

values

slightly

Arenosol,

the

descriptor

binations

always

the first likelihood

data

(over

used

most

band

bands (bands

were of the

of terrain

1 (visible-red)

and the

three

its and

class

AVHRR thermal

3-5) (Table 5).

and

delineated.

The average

perdata

respectively.

to assess

PDD. These layers were

Phaeozem,

and can be almost

Except

was added

close class

Arenosol

we

method

terrain

the

of the test pixels accuracy

study,

used, the

or an insufficase

of 49.8 AVHRR

performance

in the classes of Regosol,

the

classes

soils varying

In the

Planosol.

the use of AVHRR data

correctly

(Table 4).

classes with

The best classification classes

Leptosol

Chernozem-Phaeozem with

and Histosol

to the Luvisol class, a relatively

of genesis.

Arenosol,

were

It acts as a col-

similarities

pixels.

class, a significant

were classified obtained

training

the

class.

class, with

After the integration

in user’s accuracy

were

that

for soil classi-

(SU) using the

6-45)

data

indicate

are not sufficient

database,

the

to these

AVHRR-terrain

percent

that

Cambisol

is a very heterogeneous

48.6

increases

found

(layer

in the second

6). Test class performances

and

showed

found

classes

belonging

into

class statistics

in terms

it was

Vertisol

pixels

mainly

a lot in physical lector

results, and

descriptor

The results

by 4.6 and 2.8 percent.

significant When

alone

AVHRR

increased

of the test database.

performed

data sets, while

integrated

alone were achieved.

is used. Such of the methods

were

the terrain

1-5) and the AVHRR channels

for classification.

fication

in the

maximum

this technique

terrain

accuracy

and the one obtained

not only the limitation

employed

he

into

the SU and the MSG

set. first

used in two separate

phase the 45-layer

Using

truth,

classification

a theoretical

were

a small-scale

data

in both

classifications

using the same training

Dobos [ 19981 carried the

terrain

was also studied

were

were

layers selected

when

the best layer com-

for the classification.

layers were

MSG level

of the selected

selected,

38

AVHRR layers were

algorithm

In the SU case, all terwhile

level only the DEM and PDD layers were

for the SU level (Figure 4). Interestingly,

In general,

by the layer selection

the DEM and its derivatives.

rain descriptor

likely

recognized

selected

from

on the

MSG

selected.

Most

the spring

peri-

Mapping

soils using AVHRR and DEM data

JAG

l

Volume

3 - Issue 1 - 2001

FIGURE 4: Probability images of the MSG level (A) and the SU level (B) (red, yellow and blue indicate high, intermediate probability values, respectively)

Soil Unit level

Soil Unit level 60 50 l Test class

40

pedbmance

&? 30 20

n Kappa statistics

,

IO

20

25

30

35

and low

HTest class perform ante n Kappa statistics

10 0

1

40

45

Number of channels

Major Soil Groupinglevel

Major Soil Grouping level

10 20 25 30 35 40 45 Number of channels

FIGURE 5: Test class performance basic images

FIGURE 6: Test class performance of AVHRR, DEM (terrain descriptors) and AVHRR-DEM images

and the Kappa values for the

39

Mapping

JAG

soils using AVHRR and DEM data

TABLE 4: Producer’s

and user’s accuracy User’s accuracy

Producer’s

Leptosols

69.9

47.9

Luvisols

54.4

57.5

Cambisols

63.8

43.6

Fluvisols

10.3

56

Chernozems

79.8

71.4

Gleysols

27.6

61.8

Podzols

32.3

49.4

Podzoluvisol

19

39.5

Regosols

29.3

13.7

Arenosols

72.5

78.7

Planosols

72.8

79.2

Phaeozems

61.8

94.9

Andosols

45.1

57.5

Vertisols

10.5

14.7

Histosols

33.9

62.3

Solonchaks

Results are not available

Backqround

100

TABLE 5: The best 20

accuracy

100

layers in terms of class separability (when

used individually) Soil Unit

level

Order

Layer ID Major Soil Grouping level Without the use With the use of “sea” class of “sea” class

1

DEM

DEM

CHl-95MAY

2

SLOPE

SLOPE

CH I-950CT.

3

CURVATURE

CURVATURE

CHI-96MAY

4

CHI-95MAY

CHI-95MAY

CHI-95JULY

5

PDD

PDD

CHl-97AUG.

Volume

3

- Issue 1 - 2001

CONCLUSIONS The results of this study show that AVHRR data and DEM derivatives, from national to continental level surveys, are promising tools for geographers and soil surveyors. AVHRR data are often used in land cover studies, but their usefulness in soil studies has not yet been proven. This study demonstrates their “power” for characterizing the soil-forming environment and delineating soil patterns, particularly when other ancillary data, capable of describing the soil-landscape such as DEM, slope, curvature and PDD, are used together. The predictive power of AVHRR and similar low spatial resolution satellite data could be further improved with the development of soil sensitive filters. Potential improvements can be expected when using better quality data provided by satellites that have been launched recently (Vegetation) have been launched recently (Vegetation, MODIS). NOAA/AVHRR was originally designed for meteorological purposes and its application to Earth observation is hampered by system limitations. For instance, the pixel size is not uniform across the entire FOV; geometric distortion affects multidate registration; the loss of radiometric accuracy due to atmospheric absorption is significant. The Vegetation instrument provides a more advanced data source, designed specially for monitoring the Earth’s environment and natural resources. Better data quality may significantly improve the performance of low spatial resolution satellite data in small-scale soil inventories. Performance can also be improved by using better spectral resolution data, such as with MODIS. The 36 bands of MODIS represent a wide range of land information that may be used for soil inventories, among other applications.

of the MSG level

Class name

l

6

CH3-950CT.

CHI-97APRIL

CHl-98MAY

7

CHl-95JULY

CH5-950CT.

CHl-97MAY

8

CH4-950CT.

CH3-95JULY

CHI-98JUNE

9

CH I-97APR.

CH4-950CT.

CHZ-9SOCT

10

CHI-97AUG.

CHI-95JULY

CHZ-95JULY

11

CHI-96MAY

CH3-950CT.

CH5-950CT.

12

CHl-98MAY

CHI-98MAY

CH4-950CT.

13

CHI -950CT.

CH3-97AUG.

CHZ-95MAY

REFERENCES

14

CH5-950CT.

CHl-950CT.

CHZ-96MAY

Bascomb, C.L. & Jarvis, M.G., 1976. Variability in three areas of the Denchworth soil map unit. I. Purity of the map unit and property variability within it. Journal of Soil Science 27: 420-437.

15

CH3-95JULY

CHl-96MAY

CHZ-97AUG

16

CH4-95JULY

CH4-95JULY

CH3-950CT.

17

CH3-97AUG.

CHI-97AUG.

CH5-95JULY

18

CH5-98MAY

CH5-65MAY

CHZ-97APR.

19

CH4-98MAY

CH4-95MAY

CH5-97AUG.

20

CH4-95MAY

CH4-97AUG.

CH4-95JULY

Bell, J.C., R.L. Cunningham & M.W. Havens, 1994. Soil drainage class probability mapping using a soil-landscape model. Soil Science Society of America Journal 58: 464-470. Biggs, A. & B. Slater, 1998. Using soil landscape and digital elevation models to provide rapid medium scale soil surveys on the Eastern Darling Downs, Queensland. Proceedings, 16th World Congress of Soil Science, 20-26 August 1998, Montpellier, France.

od (April-May), while the most abundant bands were the visible-red (band 1) and bands 3 and 4. These results coincide with those reported by Dobos et al [ 19981, who studied the statistical relationship between soil types and AVHRR data. They found that the thermal bands (particularly band 3) and the vegetation index were the best for predicting soil classes. They also studied the correlation between each of the AVHRR channels and found that NDVI shows a relatively high correlation with channel 1 of AVHRR. Therefore, NDVI was not used in the current study, although the integration of NDVI into the database could have slightly improved the final result.

Burrough, P.A., P.H.T. Beckett & H.G. Jarvis, 1971. The relation between cost and utility in soil survey (I-III). Journal of Soil Science 22: 368-381. Chaplot, V., C. Walter & P. Curmi, 1998. Modelling soil spatial distribution: sensitivity to DEM resolutions and pedological data availability. Proceedings, 16th World Congress of Soil Science. 20-26 August 1998, Montpellier, France. Cihlar, J.. L. St.-Laurent & J.A. Dyer, 1991. Relation between the normalized difference vegetation index and ecological variables. Remote Sensing of Environment 35: 279-298. Congalton, R., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37: 35-46.

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2.8 pour-cent). Les meilleurs performances de ete realisees quand tous les canaux disponibles pour la classification, notamment 51.4 pour les 54.4 pour les MSGs sur les images de base, et pectivement sur les images DAFE transformees. plus informatives AVHRR ont et@ celles de la temps (avril-mai), alors que les bandes les plus visible-rouge (bandel) et les bandes 3 et 4.

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RESUMEN

classes tests ont ont ete utilises SUs de la FAO et 51.7 et 54.4 resLes bandes les periode de prinriches ont ete le

Hay una necesidad creciente de bases de datos de suelos a pequetia escala pero razonablemente precisas. La compilaci6n de una base de datos de suelos a escala continental o global requiere una gran cantidad de datos de suelos que sean precisos desde 10s puntos de vista espacial y tematico. El objetivo de este estudio fue el de probar un metodo de cartografia de suelos a pequefia escala realizada en ltalia mediante uso del radiometro avanzado de muy alta resolution (Advanced Very High Resolution Radiometer, AVHRR) y datos altitudinales en format0 digital. En un estudio previo en Hungria, se aplico este metodo a un area de mucho menor extension y con un ambiente de formacion de suelos significativamente diferente. Para el presente estudio se us6 una base integrada con 45 capas combinando datos de AVHRR y de terreno, incluyendo un modelo digital de elevation (MDE), pendiente, curvatura, exposition, densidad potential de drenaje, y las cinco bandas de datos AVHRR para echo fechas diferentes. Se procesaron 10s datos mediante una funcion de extraction de rasgos por analisis discriminante (Discriminant Analysis Feature Extraction, DAFE), basada en un procedimiento de analisis canonico. Se clasificaron dos tipos de imagen (basic0 y transformado) usando el clasificador de maxima verosimilitud. Se escogieron dos conjuntos de prueba con identica cobertura geografica, pero con distinto nivel de clasification de suelos. Un conjunto estaba integrado por unidades de suelos (SU) de la leyenda revisada de la FAO, mientras que el otro conjunto representaba las agrupaciones de suelos mayores (MSG). Se seleccionaron y clasificaron 10s mejores conjuntos de capas, incluyendo 10, 15, 20, 25, 30, 35, 40 y 45 capas respectivamente, mediante el metodo de Bhattachryya para la selecci6n de rasgos. Se compararon 10s resultados de 10s diferentes conjuntos. Se interpretaron tambien 10s rendimientos obtenidos con las imagenes AVHRR solas y con las imagenes basadas Onicamente en datos de terreno, respectivamente. Los resultados indican que 10s descriptors de terreno solos no son suficientes para clasificacion de suelos. Sin embargo, 10s algoritmos de selection de rasgos siempre seleccionaron el MDE y sus derivados entre 10s primeros, lo que subraya su importancia para la caracterizacion del paisaje edafico. Cuando se utilizaron solamente datos AVHRR, las clases de prueba rindieron 49.8% para 10s MSG y 48.6% para 10s SU. La integration de datos de terreno en la base de datos AVHRR produjo mejoramientos relativamente pequenos (4.6% y 2.8%). Los mejores rendimientos con las clases de prueba se obtuvieron cuando se utilizaron todos 10s canales disponibles para la clasificacion, con 51.4% para 10s SU de la FAO y 54.4% para 10s MSG en la imagen basica, y con 51.7% y 54.4% respectivamente en las imagenes transformadas mediante DAFE. Las bandas AVHRR con mayor information eran las obtenidas en primavera (abril-mayo), mientras que las bandas mds abundantes resultaron ser la banda 1 (rojo visible) y las bandas 3 y 4.

Yuan, D., D. Wort & B. Nassersharif, 1995. The prototype of a knowledge and neural network based image classification system using both remotely sensed and digital elevation data. ACSM/ASPRS Annual Convention and Exposition Technical Papers, 27 February- 2 March 1995, Charlotte, NC, Volume3: 672-683. Zhu, Z-L. & D.L. Evans, 1994. U.S. forest types and predicted percent forest cover from AVHRR data. Photogrammetric Engineering and Remote Sensing 60: 525-533.

RESUME II y a un besoin croissant de bases de donnees des sols a petite echelle d’une precision correcte. La compilation d’une base de donnees des sols continentale ou a une echelle globale exige un tas de donnees spatiales et thematiques precises. Le but de cette etude etait de tester une methode pour une cartographic des sols a petite echelle en ltalie utilisant un radiometre de tres haute resolution en technologie avancee (AVHRR) et des donnees numeriques d’altitude. La methode a ete employee dans une etude anterieure en Hongrie pour une zone beaucoup plus petite et un environnement de formation de sol tres different. Une base de don&es integree AVHRR-terrain de 45 couches a ete utilisee pour I’etude, y compris un modele numerique du terrain (MNT), pente, courbure, orientation, densite du potentiel de drainage et les donnees AVHRR de cinq bandes pour huit dates differentes. Les don&es ont ete traitees en utilisant la fonction d’Extraction de Details par Analyse du Discriminant (DAFE), qui est basee sur un procede d’analyse canonique. Deux types d’images (de base et transformee) ont et@ classees en utilisant la methode de la vraisemblance maximale. On a choisi deux jeux d’essais avec une couverture geographique identique, mais un niveau de classification du sol different. Un des jeux d’essai etait base sur des unites de sol (SU) de la legende FAO revisee, alors que I’autre representait des groupements de sols dominants (MSG). Les meilleurs 10, 15, 20, 25, 30, 35, 40 et 45 couches ont et& selectionnees en utilisant la methode de selection des details de Bhattachryya et ont ete classifiees. Les resultats des differents jeux d’essais ont ete compares. La performance des images exclusivement AVHRR et celle basee sur des donnees exclusivement terrain a egalement ete interpretee. Le resultats indiquent que les descripteurs de terrain seuls ne sont pas suffisants pour la classification des SOIS. Cependant, les algorithmes de selection des details ont toujours choisi le MNT et ses derives parmi les premiers, mettant en valeur leur importance pour la caracterisation sol et paysage. En utilisant des donnees AVHRR seulement, des performances de classification test de 49.8 pour-cent (MSG) et 48.6 pour-cent (SU) ont ete atteints. L’integration de donnees terrain dans la base de donnees AVHRR a produit des ameliorations relativement faibles (4.6 et

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