Geoderma 97 Ž2000. 367–391
www.elsevier.nlrlocatergeoderma
Use of combined digital elevation model and
satellite radiometric data for regional soil mapping
Endre Dobos a,) , Erika Micheli b, Marion F. Baumgardner c ,
Larry Biehl c , Todd Helt c
a
Department of Geography and EnÕironmental Sciences, UniÕersity of Miskolc,
3515 Miskolc-EgyetemÕaros,
Hungary
´
b
Department of Agrochemistry and Soil Science, Godollo
¨ ¨ ˜ Agricultural UniÕersity,
2103 Godollo,
¨ ¨ ˜ Hungary
c
Department of Agronomy, Purdue UniÕersity, West Lafayette, IN 47907, USA
Received 2 October 1998; received in revised form 30 June 1999; accepted 20 July 1999
Abstract
Previous reports demonstrated that data from air- and spaceborne sensors are appropriate for
delineation of soil patterns. Also, many attempts have been made to use digital elevation model
ŽDEM. for deriving soil information. However, little is known about the potential use of low
spatial resolution satellite and digital elevation data in small-scale soil mapping. A case study was
conducted to assess the use of integrated terrain and Advanced Very High Resolution Radiometer
ŽAVHRR. databases for small-scale soil pattern delineation. The main objective was to test the
effect of the addition of terrain descriptor data to the AVHRR data set on the classification results.
Two database were used for the study. The first one was purely AVHRR data and contained the
five basic AVHRR channels and the normalized difference vegetation index ŽNDVI. of five
different dates, while in the second database the AVHRR data was complemented with a DEM, a
curvature, a slope, an aspect and the potential drainage density layers. The performance of these
two databases when employed to derive soil information was compared. These databases were
then further processed using the Discriminant Analysis Feature Extraction ŽDAFE. function
Žwhich is based on a canonical analysis procedure., and were then classified using the Fisher
linear discriminant, and the ECHO spectral–spatial classifiers. Based on the results, it was
concluded that the two reflective bands, the middle infrared, the two thermal bands and the NDVI
provided a relatively wide range of detectable soil information. The use of single images or small
)
Corresponding author. Tel.: q36-46-565-111; fax: q36-46-362-972.
E-mail address: [email protected] ŽE. Dobos..
0016-7061r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 0 1 6 - 7 0 6 1 Ž 0 0 . 0 0 0 4 6 - X
368
E. Dobos et al.r Geoderma 97 (2000) 367–391
dimensional AVHRR data sets Žless then 10 layers. does not result in acceptable performances,
while the use of multispectral and multitemporal databases improved the classification performance very significantly. However, the purely AVHRR-based model could not always delineate
soil variations related to terrain differences, and resulted in an overall classification performance
of 49.1%. Digital elevation and terrain descriptor data were essential in the model for achieving
acceptable results. In the second part of the study an integrated AVHRR-terrain database was
used, where five terrain layers were added to the 30 AVHRR channels. Two different spatial
resolutions were compared, 500 m and 1 km, respectively. The use of elevation, slope, aspect and
curvature as differentiating criteria often lead to a satisfactory result in terrain characterization,
particularly in large-scale mapping. However, with those variables extracted from DEM of
physiographically complex areas, e.g., — where plain areas and mountainousrhilly regions occur
together in the same study — often lose their ability to delineate soil variations of the level lands.
Beyond these terrain descriptors we implemented a new function, called potential drainage density
ŽPDD. to improve the performance of the model on the plain areas. The classification accuracy of
the integrated AVHRR-terrain database was improved significantly over the case when only
AVHRR data was in the model. The classification performances of the three different resolution
images were 87.3% for the 500-m resolution image and 70.1% for the 1-km resolution image.
q 2000 Elsevier Science B.V. All rights reserved.
Keywords: AVHRR, GIS; DEM; soil-landscape; spatial variability; spatial modeling; remote
sensing
1. Introduction
In 1986 the International Society of Soil Science initiated a project, called
SOTER to create a World Soil and Terrain Digital Database. This process is
ongoing and depending on the financial resources the SOTER database will soon
be completed. However, distribution and quality of soil data are not homogeneous over the world. There are regions where the soil and terrain data, essential
to complete the basic SOTER, are limited in quality or density or even missing.
In many areas, field data are yet to be collected. Secondary data sources from
which soil and terrain information may be extracted could be utilized for
efficient completion of the database.
A review of literature reveals two possible data sources to aid in the SOTER
process: coarse spatial resolution satellite data and digital elevation data. These
data have worldwide coverage and provide essential supplemental support in
characterizing the soil-forming environment.
The coarse spatial resolution satellite data are provided by the Advanced Very
High Resolution Radiometer Ž AVHRR. . However, a new European instrument
called Vegetation has been launched and, in the near future the Moderate
Resolution Imaging Spectroradiometer ŽMODIS. will be operational. These will
provide better spectral resolution and absolute location accuracy. Therefore, this
study is not only the evaluation of the AVHRR data, but also a preliminary
study of the potential use of these kinds of satellite data.
E. Dobos et al.r Geoderma 97 (2000) 367–391
369
Numerous studies have been carried out to evaluate the potential use of
AVHRR data for soil pattern recognition at small scale Ž Vettorazzi et al., 1995;
Dobos, 1998; Odeh and McBratney, 1998. . The 1-km pixel size of AVHRR is
roughly equivalent to the range of scales — 1:500,000 to 1:1 million, the
primary scales used in the SOTER database. This relatively coarse resolution is
still useful for studying global processes and phenomena without the difficulties
of secondary generalization and the loss of detail from more costly large-scale
images. On the other hand, it is often difficult to identify a unique soil category
in 1-km2 pixels, thus, an in situ generalization of pixel areas is being done when
AVHRR data are used. This fact has to be considered when the classes are
defined. The two reflective bands Žthe red and the near infrared channels., the
middle infrared and the two thermal bands of the AVHRR provide a relatively
wide range of detectable land surface information. That is why these data have
been used extensively for vegetation, ecoregion and land cover mapping and
modeling in addition to the meteorological applications for which the AVHRR
instrument was designed Ž Ehrlich et al., 1994; Zhu and Evans, 1994; Foody et
al., 1996; Maselli et al., 1996; Rogers et al., 1997; Yang et al., 1997. .
Ehrlich et al. Ž1994. summarized the application of National Oceanic and
Atmospheric Administration ŽNOAA.-AVHRR data for environmental monitoring. They concluded that all five channels have found some level of use for land
cover studies. The AVHRRrNDVI data are the most commonly used data
source. In particular, the multitemporal NDVI data sets have found wide use to
describe vegetation phenology. Thermal bands have also been employed for
surface temperature mapping and land cover discrimination, especially in tropical rain forests. The thermal bands are often superior to vegetation indices for
land cover discrimination.
The spatial and temporal variations in NDVI have been studied by numerous
researchers and were found to be linked with temperature and precipitation
regimes ŽSchultz and Halpert, 1993; Di et al., 1994; Yang et al., 1997. , plant
evapotranspiration ŽCihlar et al., 1991; Yang et al., 1997. , root zone soil
moisture ŽNarasimha Rao et al., 1993. and soil physical properties Ž LozanoGarcia et al., 1991; Yang et al., 1997. .
Yang et al. Ž1997. used NDVI for ecoclimatic mapping in Nebraska. They
concluded that NDVI–climate relations are stronger where vegetation is developed on soils with low root zone available water holding capacity and high
permeability. Temporal variability of NDVI was found to be linked closely to
the temperature regime, while the NDVI–precipitation and the NDVI–
evapotranspiration relationships exhibited time lags.
Vettorazzi et al. Ž 1995. studied the utility of AVHRR data to characterize
regional soil patterns through the use of the two reflective bands and the NDVI.
They performed several unsupervised classifications and concluded that AVHRR
data are useful in the delineation of small-scale soil patterns. They used one
image from April and another from the peak of the growing season at the time
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E. Dobos et al.r Geoderma 97 (2000) 367–391
of maximum crop canopy. The results indicated that the NDVI has less
importance than the first two bands. However, in the mid-summer AVHRR
image case Žmaximum crop canopy. the NDVI provided useful supplemental
information related to soil patterns.
AVHRR-type data have not been used routinely for soil characterization.
However, its utility in differentiating between different kinds of parent materials
Žusing the thermal bands. , and different kinds of vegetation Žthrough the
vegetation indices. has been demonstrated Ž Short and Stuart, 1982; Zhu and
Evans, 1994; Foody et al., 1996. .
Foody et al. Ž1996. reported high correlation between remotely sensed
radiation and the biophysical properties of tropical forests, particularly with the
middle- and thermal-infrared channels Žy0.87,y 0.88 correlation coefficient
with tree density.. They found better performance for vegetation indices containing data acquired in the middle- and thermal-infrared than with the widely used
NDVI.
These two phenomena Žparent material and vegetation. refer to two of
Jenny’s soil forming factors Ž Jenny, 1941. . Some spectral variation could be due
to the physiographic characteristics of an area, which could produce different
results even for the same natural phenomenon. Integrating terrain information to
the AVHRR data can eliminate this problem on a certain level.
The time factor, which refers to the age of the soil surface, is mainly a
function of the age of the deposit or the ‘‘time zero’’ when the exposure of the
surface began. The latter directs the erosion and depositional processes. This
factor could influence significantly the kind and condition of vegetation, so the
NDVI in some manner may reveal some information related to these phenomena. If an integrated database of satellite and DEM data is used, only the
macroclimatic factor, among Jenny’s soil forming factors, is missing. However,
the spatial variation of the 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, the integrated database has the
capability of delineating areas of the same soil-forming environment.
Remotely sensed data are greatly influenced by terrain variability. However,
it still does not represent all the soil variability that occurs in the landscape. As
has been suggested by numerous researchers, Ž Franklin, 1987; Frank, 1988;
Leprieur et al. 1988; Lee et al., 1988; Yuan et al., 1994, 1995. satellite data have
to be complemented with terrain information which can be used 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 Ž DEM. have world wide coverage, and
definitely help to characterize the soil forming environment.
Digital terrain data have been used for soil feature prediction by many
researchers Ž Moore et al., 1993; Bell et al., 1994; Gessler et al., 1995; Chaplot
et al., 1998; Florinsky and Kuryakova, 1998..
E. Dobos et al.r Geoderma 97 (2000) 367–391
371
Catenary soil development occurs in many landscapes in response to the way
water moves through and over the landscape. Furthermore, terrain attributes can
characterize these flow paths and the interactions with the soil attributes. Moore
et al. Ž 1993. found significant correlation between quantified terrain attributes
and measured soil attributes. Slope and wetness index 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.
Bell et al. Ž1994. carried out a study to combine a statistically based
soil-landscape model and geographic information system Ž GIS. to create soil
drainage class maps. The landscape attributes used were parent material, terrain
and surface drainage feature variables. The model they used could produce
drainage class maps with an accuracy of 67% at a scale of 1:20,000. Gessler et
al. Ž1995. developed a statistical soil-landscape model to predict soil attributes.
They used different terrain attributes, such as plane curvature, compound
topographic index, upslope mean plane curvature to predict the depth of the A
horizon and the solum, absence or presence of the E horizon in an area with a
uniform geology and geologic history. The percent reduction in deviance was
around 65% on average.
Biggs and Slater Ž1998. carried out a medium scale soil survey with the use
of soil landscape and digital elevation model. They used a 15-m DEM and its
derivatives, namely the slope, curvature, topographic wetness index Ž TWI. ,
relative elevation and slope position. The rapid soil attribute map with a scale of
approximately 1:100,000 enhanced field validation and increased mapping confidence. Until recently, no low spatial resolution DEM data has have been used
for small-scale soil mapping.
Odeh et al. Ž1995. 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, e.g., parent material, vegetation, etc. can be included into
the model and thus the accuracy of the predicted variables can be increased.
1.1. 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 Ž e.g., Weismiller et al.,
1977; Shasby and Carneggie, 1986; Franklin, 1987; Frank, 1988; Lee et al.,
1988; Leprieur et al., 1988; Yuan et al., 1994, 1995..
Loveland et al. Ž1991. suggested that the effect of physiographic variation on
spectral data can be reduced through stratification of a large area into smaller
regions. Zhu and Evans Ž1994. used this technique in the production of the ‘‘US
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E. Dobos et al.r Geoderma 97 (2000) 367–391
forest type and percent forest cover map’’. A similar physiographic stratification
technique was utilized in the classification of potential old growth forest in the
Pacific Northwest of USA Ž Congalton et al., 1993. . The disadvantage of this
method is the need for edgematching and of refinement of final classes and
categories. Along the edges of the stratification units, there is likely to be some
unconformity due to the lack of absolute classification categories, and the not
necessarily coherent way of class interpretation within the different classification
units.
Another weakness of this approach to data integration is that the gradually
changing natural phenomena are represented with borderlines and the possibility
of the utilization of continuous surface information as a whole is missing. The
rapid development of GIS in the last two decades has made possible the
‘‘direct’’ data integration when the data sources with different origins are used
together simultaneously. This way permits a much better exploitation of the
DEM data. Weismiller et al. Ž 1977. used Landsat and topographic data to make
an inventory of soils in Missouri. However, they did not attempt to relate soil
cover to soil type. Franklin Ž 1987. reported a 46% to 75% improvement of
classification accuracy when he used Landsat MSS data with DEM-derived
landscape descriptor layers for classification of landscape classes.
In this study our primary objective was to evaluate the use of integrated
satellite and terrain data in global scale soil inventories. Our previous studies
have demonstrated the potential use of AVHRR data for small-scale soil pattern
delineation ŽDobos, 1998. . In this study we are focusing on the use of the
integrated AVHRR-terrain data, and will emphasize the potential improvement
on the final classification results and also the problems that can occur in small
scale soil inventories based on remotely sensed and digital elevation data. The
main objective was to test the effects of integrating terrain data to the AVHRR
data set on the classification results.
2. Materials and methods
2.1. The study area
Two study areas were used in this study. In the first phase of the study, the
entire area of Hungary was used as a study site, while in the second phase a
subset was taken.
2.1.1. The soils of Hungary
Hungary covers an area of 93,000 km2 in the Carpathian basin of Central
Europe. This area is the transitional zone of the two main zonal soils of the
E. Dobos et al.r Geoderma 97 (2000) 367–391
373
region, the Brown Forest soils and the Chernozem soils. Due to the significantly
higher precipitation the Brown Forest soils Ž Typic and Ultic Haplustalfs. are
more common in the western part of Hungary and in the mountains than in the
eastern part where the annual precipitation is 200 to 300 mm less. Accordingly,
the vertical soil zones appear higher in elevation in the eastern region than in the
west. On higher elevated plateaus of the mountains, where mainly consolidated
parent materials, such as limestone, dolomite, basalt, andesite or rhiolite occur,
lithomorph soils have formed Ž Lithic and Entic Haplumbrepts, Lithic and Typic
Rendolls.. These lithomorph soils differ from each other in many physical and
chemical aspects depending on the parent material on which they formed, but
usually support similar vegetation and typically have forest cover and were
grouped together into the rendzinas and erubase soils class. The Brown Forest
soils occupy the highest elevations. Descending from this zone, the clay
illuviation process weakens, and the soils there are called Ramann-type Brown
Forest soil ŽTypic Haplumbrept.. The transition zone between the plain lowland
and the mountains, where the organic materials are more strongly concentrated,
is comprised mainly of Chernozem–Brown Forest soils ŽTypic Haplustolls and
Hapludolls.. On the plains, depending on the kind of the parent material,
different kinds of soil have formed. In loess covered areas Chernozems are
typical ŽPachic and Typic Calciustolls. . The soils formed on finer textured
parent material or in shallow depressions and concave surfaces generally show
hydromorphic properties and are called Meadow–Chernozems Ž Aquic Calciustolls and Haplustolls. or Meadow soils ŽTypic Endoaquolls, Aquic Hapluderts. .
In the central part of the great plain, where the saline groundwater reaches the
soil surface and the annual evaporation is higher than the precipitation, salt-affected soils ŽMollic Natraqualf, Aquic and Typic Natrustolls, Typic Natraquerts,
Typic Natraquolls, Aquertic Hapludolls. exist. On the Danube–Tisza Interfluve,
sandy parent materials dominate with some loess inclusions. The soils of these
areas vary between the Blown sand Ž Typic Ustipsamments and Quartzipsamments. and the more or less fixed Humous Sandy soils ŽTypic Ustipsamment
and Psammentic Haplumbrepts..
2.1.2. Study area of the Matra-region
´
In the second phase, when integrated AVHRR-terrain data was used, a
smaller study area was selected. The need for representation of mountainous
areas as well as plain areas played an important rule in choosing the location.
The study area is located at the ‘‘Matra-region’’
in Hungary, 20 km east of
´
Budapest. The area, which lies in the transition zone between the NorthHungarian Mountain range and the Great Hungarian Plain, is 96 = 96 km and is
extremely heterogeneous in many features. The elevation of the area varies
between 80 and 1014 m above sea level. The northern part is mountainous. Soils
of the Matra
´ mountains were developed from neutral and acidic volcanic
material. The western part is a hilly region, called the Godollo
¨ ¨ ˜ Hilly region,
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E. Dobos et al.r Geoderma 97 (2000) 367–391
which is mainly of loess parent material, while the south-eastern part is the flat
Great Hungarian Plain, mainly with variable fluvial deposits.
The heterogeneity is reflected in the soil types Ž Fig. 1. . At higher elevations
in the mountains and on eroded slopes lithomorphic soils are common especially: Rankers and Erubase soils or Lithic and Entic Haplumbrepts Ž Soil Survey
Staff, 1994. . On descending from the mountains are the Brown Forest soils with
clay illuviation, Ramann-type Brown Forest soils and Chernozem–Brown Forest
Fig. 1. The study area. ŽA. The soil map of Hungary Žbased on the HunSOTER database.; ŽB. The
soils of the ‘‘Matra
´ region’’ pilot area.
E. Dobos et al.r Geoderma 97 (2000) 367–391
375
soils Ž Typic and Ultic Haplustalfs, Typic Haplumbrept, Typic Haplustolls and
Hapludolls, respectively. . The Great Plain with its continental climate is predominantly of Chernozem and Chernozem–Meadow Ž Aquic Haplustolls. ,
Meadow ŽTypic Endoaquolls., Alluvial Ž Typic and Aquic Udifluvent. and
salt-affected soils ŽMollic Natraqualf, Aquic Natrustolls. , their differences being
determined largely by the parent material and depth to the ground water.
2.2. The data
2.2.1. The AVHRR data
The primary data used in this project are from the AVHRR on the NOAA
polar orbiting weather satellites. Selected 10-day composite images were downloaded from the 1-km AVHRR Global Land Data Set of the US Geological
Survey EROS Data Center at Sioux Falls, SD ŽTownshend et al., 1994. . All five
spectral bands were used. The characteristics of the NOAArAVHRR system are
shown in Table 1. The 10-day composites are made up of picture elements of
Table 1
NOAArAVHRR System Characteristics Žfrom Ehlrich et al., 1994.
Sensor characteristics
Spectral Bandwidth
Radiometric Resolution
IFOV Žnadir.
View Angle
Swath Width
Ž1. 580–680 nm
Ž2. 735–1100 nm
Ž3. 3550–3930 nm
Ž4. 10,300–11,300 nm
Ž5. 11,500–12,500 nm
10 bits Ž1024 level.
1.1 km
55.48 ŽIFOVs6 km at swath edge. a
2700 km
Platform characteristics
Orbit
Altitude
Inclination
Period
Equat. crossing time b
Repeat cycle
Global Frequency Coverage
a
Near-polar, Sun-synchronous
833–870 km
98.7
102 min
0730 and 1930 Ževen numbered satellites.
1400 and 0200 Žodd numbered satellites.
12 h
1–2 days
The most usable within the swath of 2700 km is the area within "158. At 158, the area
covered by a pixel is approximately 1.5 km and the repeated coverage for this reduced swath
width is about 6 days.
b
Greenwich standard time Ž1430 ascending and 0230 descending local time4..
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E. Dobos et al.r Geoderma 97 (2000) 367–391
the certain channel Ž pixels. that have the maximum NDVI value out of the
10-day period for the given pixel ŽMaximum Value Composite — MVC. . The
basic priorities of the data selection were the followings: free of clouds; the lack
of snow cover; the representation of different stages of vegetative growths. The
periods of 10-day composite data chosen for this study were 1–10 May, 11–20
August and 11–20 September 1992, and 1–10 June, and 21–30 September
1993. These data were preprocessed. The raw images were first radiometrically
calibrated and georeferenced with the use of ground control points and DEM.
Hence, the spatial accuracy Ž the spatial deviation of a pixel from its geographically correct position. of the recommended 1000 m could be achieved. After the
computation of NDVI, the final step was the atmospheric correction for Ozone
and Rayleigh Scattering Ž Eidenshink and Faundeen, 1994. . The AVHRR data
have an original resolution of 1.1 km. These were resampled into 1-km spatial
resolution. The data were then stacked into six-layer images data sets for each
date containing the five channels and the NDVI. A transformation into the
Hungarian Unified Projection System ŽEOV. was performed to provide compatibility with the reference maps and digital elevation data. This transformation
was done in a way suggested by Eidenshink and Faundeen Ž 1994. , using
hydrologic features from vector data sets in EOV projection. Characteristic
points and features were selected from both the image and the vector coverage.
Within the ARCrINFO register command ‘‘links’’ between the point pairs were
established. An average of 13 links were selected, and then an affine transformation was applied to calculate the amount of scaling and rotating required to align
the image to map coordinates. Following the transformation the 30-layer stack
was formed using the nearest neighbor resampling method.
2.2.2. The ‘‘TIM’’ database (Soil Monitoring and Information System of Hungary)
TIM is part of the Hungarian Environmental Monitoring System created and
maintained since 1995 ŽVarallyay
et al., 1995. . This point-vector database
´
consists of 1236 soil profile descriptions. The locations of these points were
selected as representative points of the natural landscape units of Hungary, so
the database can be considered a realistic characterization of soil resources of
the country. Besides the detailed soil description data, it contains numerous
soil-physical and -chemical measurement data for monitoring the soil changes in
time as a result of anthropogenetic and natural processes.
The soil classes are based on the Hungarian Soil Classification System
ŽVarallyay
et al., 1995. . However, some regrouping of the original class system
´
was done to optimize the performance of the model. Some major soil types had
to be merged together while in other cases transition classes among the major
types were added to improve the thematic resolution of the model. The classes
are: Stony soils ŽLithic and Typic Ustorthents. ; Blown sand Ž Typic Ustipsamments and Quartzipsamments.; Humous Sandy soils ŽTypic Ustypsamments and
E. Dobos et al.r Geoderma 97 (2000) 367–391
377
Psammentic Haplumbrepts. ; Rendzina and Erubase ŽLithic and Typic Rendolls,
Lithic and Entic Haplumbrepts. ; Brown Forest soils with clay illuviation Ž Typic
and Ultic Haplustalfs. ; Ramann-type Brown Forest soils ŽTypic Haplumbrepts. ;
Sandy Forest soils Ž Argic Ustipsamments. ; transition classes between the Chernozem and Brown Forest soils, Chernozem–Brown Forest soils Ž Typic Haplustolls and Hapludolls. ; Chernozem Ž Pachic and Typic Calciustolls. ; transition
classes between the Chernozem and Meadow soils, Chernozem–Meadow soils
ŽAquic Calciustolls and Haplustolls. ; Salt-affected soils Ž Mollic Natraqualfs,
Aquic and Typic Natrustolls, Typic and Aeric Halaquepts. ; Meadow and Peaty
soils ŽTypic Endoaquolls and Aquic Hapluderts. ; and the Alluvial soils ŽTypic
and Aquic Udifluvents. .
2.2.3. Digital eleÕation data
Due to the high cost of digital elevation data for the entire country, a subset
was used for this study. The data used covers a 96 = 96 km area at the transition
zone of the North Hungarian Mountain region and the Great Hungarian Plain.
These data were extracted with the use of the ARCrINFO topogridtool command from six of the digitized map sheets, numbers 76, 77, 66, 67, 56, 57 of the
1:100,000 scale topographic map. The pixel size of the DEM is 100 = 100 m.
The topogridtool command uses an interpolation method specifically designed
for the creation of a hydrologically correct digital elevation model. It is based on
the ANUDEM program developed by Hutchinson Ž 1993. . However, these
interpolation algorithms can result in small imperfections, such as the sinks or
level peaks, unpredictably low or high values in the scene. These potential
problem sources had to be identified and eliminated with the use of the fill
commands within the ARCrINFO. There are numerous methods for assessing
the accuracy of a DEM. However, all of these require ground truth elevation
data for comparison. In this study, there were no available field observed
elevation data, so the accuracy assessment was made in a different way. First, a
drainage network was created based on the DEM, and it was visually compared
with existing drainage network line coverage. This comparison indicated that the
digital elevation data does not contain significant imperfections.
2.2.4. HunSOTER database
The Hungarian SOTER database, the HunSOTER, was created at a scale of
1:500,000 ŽVarallyay
et al., 1994. . It contains digitized polygons referring to the
´
physiographic characteristics and parent material of the delineated area. Soil
information occurs on the second level as soil associations with a given
proportion of the individual soil type within the unit. The attribute database is
derived mainly from the 1:100,000 scale agrotopographic maps ŽVarallyay
and
´
Molnar,
´ 1989. and the profile information of TIM database. HunSOTER is
available for the entire country, but only with a limited number of attribute data.
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E. Dobos et al.r Geoderma 97 (2000) 367–391
This database is the reference or ‘‘ground truth’’ database used for comparison with the results from the AVHRR classification. However, some major
generalization had to be made in order to make it unambiguously interpretable
from the point of view of soil types. The polygons have no direct soil
association information assigned to them. The dominant soil type, that is of
largest spatial extent within the polygon, was selected and assigned to the
polygon. This generalization means some loss of information. However, due to
the use of the Major Types and partly the Types level of the Hungarian Soil
Classification system for the definition of the classification classes, many of the
soil Types within the soil-associations are in a close genetic relationship with
each other. These refer to the same or very similar classification classes obtained
by classification of the digital data. The general accuracy of this study-oriented
SOTER database was checked with the TIM reference profiles.
2.3. Methods
The study was in two separate phases. The main goal of the first phase was to
evaluate and quantify the usefulness of AVHRR-type data for soil identification.
In the second phase digital elevation and terrain attributes, such as slope, aspect,
curvature and potential drainage density ŽPDD. layers were integrated into the
model. This integrated database was evaluated at two different resolutions.
2.3.1. Classifications using the AVHRR data alone
In the first phase of the study no elevation data were used in the model. The
AVHRR images of Hungary were the only data sources for the classifications.
The AVHRR images contained 30 layers, i.e., 5 layers Ž dates. for each channel
Žvisible-red, near infrared, mid infrared, two thermal bands and the NDVI. . The
normality of the class distributions was checked using the Shiparo–Wilk test
ŽShiparo and Wilk, 1965. . Based on the results reported by Dobos Ž 1998. the
Discriminant Analysis Feature Extraction Ž DAFE. method, what is based on a
canonical analysis procedure Ž Richards, 1993. was used to reduce the dimensionality of the data from the original 30 and to increase the separability for the
classes. This linearly transformed image was later used as a basis of the
classification. In this part of the study, the entire area of Hungary was used to
get a more generally interpretable result. The TIM database reference profiles
were used for training the classifiers. Thirteen soil classes were formed to
represent the soils of Hungary Žsee the Section 2.2.2. for the list of the classes. .
Of the entire study area, 1.2% was labeled as training pixels. Due to limited
number of training pixels, the weighted Fisher linear discriminant Ž Raudys and
Jain, 1991. classifier was used to achieve the best results. The weights were
based on the natural extent of the certain soil type and were taken from the
HunSOTER database ŽVarallyay
et al., 1994. . For evaluation purposes test areas
´
E. Dobos et al.r Geoderma 97 (2000) 367–391
379
were selected from the HunSOTER database and compared with the results of
the classifications. The percentage of the correctly classified pixels was calculated and recorded as overall accuracy. The same calculations were done for
each of the 13 classes and a confusion matrix was generated for more detailed
examination of the results.
2.3.2. Classification using the integrated AVHRR-DEM database
In the second phase of the study, a subset of the Hungarian images was taken,
and a digital elevation model, a slope percentage, a curvature, an aspect and a
potential drainage density ŽPDD. layer were integrated into the image set in
resolutions of 500 m and 1 km, and thus two data set were produced. The
performance of the two different resolution data sets Ž1 km, 500 m. were
compared. The creation of the slope, aspect and the curvature coverages were
made with the slope, aspect and curvature functions of the ARCrINFO’s GRID
package Ž Environmental Systems Research Institute Ž ESRI. , 1997. . The PDD
layer was created by following the method described by Dobos Ž 1998. . As a
reference map, from which the training and test samples were taken, the
HunSOTER database was resampled at the same resolution as that of the
corresponding layerstacks.
DAFE method was used to reduce the dimensionality of the data from the
original 35 and to increase the separability for the classes. This linearly
transformed image was later used as a basis of the classification.
The number of classes was somewhat decreased for this smaller area, because
for the classes with smaller extent, or the ones with lower spectral diversity, the
number of different observations was fewer than the minimum required and thus
matrix singularity occurred. In both examples of 500-m and 1-km resolution
several classes were merged: the Chernozem with Chernozem–Meadow classes;
the classes of Meadow and Peaty soils with the salt-affected soils; and finally
the Stony soils class with the Rendzina and Erubase classes. The same image
processing tools were used for this part of the study as those were used in the
first phase. Unlike, in the first phase, the training pixels were selected manually.
The AVHRR-terrain database was overlaid by the HunSOTER, and the training
samples were selected in accordance with the HunSOTER database. Due to a
relatively unlimited number of training pixels, the weighted ECHO spectral–spatial classifier was used ŽKetting and Landgrebe, 1976. . The weights were taken
from the HunSOTER database. The percentages of the study area selected as
training samples for classifying the image at the two different resolutions were
as follows: 13% for the 1 km image, 2% for the 500 m image. These
percentages were the minimum values when no matrix singularity occurred. The
test fields were selected also based on the HunSOTER database, however, the
test and training field set were independent and contained no common pixel. The
evaluation was done in the same way as it was described above.
380
E. Dobos et al.r Geoderma 97 (2000) 367–391
3. Results and discussion
3.1. Results using AVHRR only
In the first part of the study only the AVHRR data covering the entire area of
Hungary was used for the classification. The accuracy assessment of the
AVHRR based classified image was done by comparing our result with the
HunSOTER database. Therefore, to validate our test data, an accuracy assessment of the HunSOTER database was performed to determine and to quantify its
taxonomic accuracy. This database was created through the traditional, expert
knowledge-based method. The accuracy value of the HunSOTER can serve as a
general descriptor of small-scale databases. Its scale of 1:500,000, represents an
overgeneralization. An assessment of the HunSOTER accuracy against the TIM
database of point data revealed taxonomic purity of 49.5%. By taxonomic purity
of a mapping unit or a data base we mean the degree or percentage to which soil
profiles sampled at random — in this case the TIM profiles — match the
mapping unit description in which they occur. This number means that 49.5% of
the pixels from both the TIM and the HunSOTER have the same classification
category assigned to them. One reason of this low value is that the generalization of the HunSOTER is such, that only the dominant soil type was assigned to
the entire mapping unit instead of its original soil association. The accuracy of
the HunSOTER database is probably much higher, but in order to make the
spatial comparison possible for this study this generalization needed to be done.
This fact is considered later when interpreting the classification accuracy of the
different test schemes. Burrough et al. Ž 1971. found the mapping purity ranges
from 45% to 63% for a soil survey map with a scale of 1: 63,360 and between
65% and 86% for a 1:25,000 scale map depending on the complexity of the area
mapped. In the light of these values, the 49.5% taxonomic purity of the
HunSOTER is not unacceptably low. However, these above-mentioned studies
were carried out on a more extensive area of larger scale and the differences
occur at lower categorical levels. In these scales the impurities often differ only
in minor definitive features and do not require different management Ž Bascomb
and Jarvis, 1976.. For the purposes of this study, the HunSOTER had to be
generalized, and thus soils are described on a higher taxonomic level, where the
different classes refers to greater differences between certain soil properties.
Although, the classification accuracy was only 49.1% in the first phase of the
study, when only the TIM profiles data were used for training, the overall
appearance of the classified image was impressive. The actual accuracy was
probably significantly higher but due to the limitations of the test data set
Ž49.5% overall accuracy. it could not be quantitatively confirmed. On the
mountainous and hilly regions the major misclassifications have occurred within
the major soil types, that is the highest hierarchical level of the Hungarian Soil
Classification System. This level of generalization is roughly equivalent with the
E. Dobos et al.r Geoderma 97 (2000) 367–391
381
soil groupings of the FAO ‘‘Soil Map of the World’’ legend Ž FAO-UNESCO,
1994.. On the plain areas, however, the main part of the misclassification have
occurred between the Salt-affected and the Meadow major soil types, while
some ‘‘within-major-soil-type’’ misclassifications was apparent in the case of
the Chernozem soils, too.
The capability of AVHRR for delineating small scale soil patterns was
sometimes limited, because it could not always handle spectral variation due to
topographic differences. Table 2 shows the confusion matrix of the best
classification for the Hungarian image. It clearly shows that the classes of
Ramann-type Brown Forest soils and the Chernozem–Brown Forest soils have a
very low percentage of correctly classified pixels. These soils, however, show a
vertical zonality ŽFig. 2. . The highest areas are usually characterized by the
Brown Forest soils, while at the lower part of the mountain and hill slopes the
Ramann-type Brown Forest soils occur and at the bottom of the hills, which
represent the transition zone between the lowland and the hilly area, the
Chernozem–Brown Forest soils are the typical soils. Table 2 shows that in the
case of the Ramann-type soils the confusion occurs mainly between the Brown
Forest soils class and the Meadow and Peaty soil class. In the Chernozem–Brown
Forest soil class, the sources of the confusion are the Chernozem–Meadow and
the Meadow and Peaty soils classes. Thus, many of the Forest soils extend to the
lowland, where they are not typical. Meadow soils also extend into the hills
where they are not typical. With the use of terrain information, this misclassification could be reduced significantly.
The visual interpretation of the image highlighted another phenomenon,
which is a significant source of misclassification of the soil classes from the
Great Hungarian Plain. This is where the differences in the soil forming
environment, and thus so in the soil types differentiation is mainly due to two
soil forming factors: parent material and topography. The soils of the Great
Hungarian Plain are formed on unconsolidated material, namely loess and
different textured Alluvial and aeolian deposits. The AVHRR data alone separated the different kinds of deposits very well. The sandy regions, the clayey
fluvial deposits, the loess areas were accurately delineated on the classification
results image. However, even on the same kind of parent material, different soil
classes can be found depending on the depth to the saline groundwater. The
elevation differences that results in a different soil class are often less then 1 to 2
m. This soil variability that occurs as a result of a few meters elevation
differences are not well represented on the final image. A vertical zonality is
shown by these Great Plain soils too. In the lowest positions, where the
groundwater level often reaches the soil surface and Ž annually. flushes the
accumulated salt out from the soil, the Meadow soils are typical. On the
relatively higher surfaces, where only the capillary zone reaches the soil surface,
the salt accumulates in the top surface horizons and results in salt-affected soil.
Going further up on the plain surface, the Chernozem soils becomes dominant as
382
Table 2
Test class performance of the 1-km resolution AVHRR image
Class name
Percent
correct
1
2
3
4
5
6
7
8
9
10
11
12
13
14
96.0
0.0
51.5
0.8
22.4
3.2
47.5
1.2
59.2
69.9
28.6
26.0
23.8
Overall performance Ž1771r3606. s 49.1%
a
Results are not available.
Number
samples
Class numbers
1
2
3
4
5
6
7
8
9
10
11
12
13
14
746
10
130
125
147
189
99
170
480
505
262
461
282
716
9
0
1
110
91
5
17
4
1
0
94
78
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
67
64
0
0
2
1
1
1
3
15
0
14
0
1
1
1
0
0
0
1
0
0
0
0
5
0
0
0
33
3
0
0
2
1
0
4
3
5
0
4
0
0
6
7
3
1
0
1
2
4
1
0
5
1
0
0
47
0
3
0
0
0
6
0
0
0
1
1
3
0
2
6
2
1
1
4
0
0
1
1
1
20
0
46
284
102
6
75
40
0
0
2
1
0
0
13
27
147
353
14
95
6
0
0
0
44
0
0
0
3
0
11
75
10
0
3
1
37
11
1
46
25
54
17
33
156
120
74
0
0
13
0
0
20
0
17
12
1
6
45
67
2
0
0
0
0
0
0
0
0
0
0
0
0
3606
1126
2
154
18
51
33
63
21
576
658
143
578
181
2
E. Dobos et al.r Geoderma 97 (2000) 367–391
Brown Forest soil
Stony soils
Blown Sand
Humous Sandy soil
Rendzinas, Erubase
Ramann forest soil
Sandy forest soil
Chernozem-Brown s.
Chernozem soils
Chernozem-Meadow
Salt-effected soil
Meadow and Peaty s.
Alluvial soils
Background, Water a
Total
Class
number
E. Dobos et al.r Geoderma 97 (2000) 367–391
383
Fig. 2. The 3D model of the pilot area overlain with the HunSOTER database.
salts accumulate in the deeper horizons with the topsoil remaining free of salt.
The topographic differences that occur on this almost flat surface are not easy to
characterize with the commonly used terrain descriptor factors, such as the
elevation, slope, aspect and curvature. Furthermore, the different scales of the
zonalities in the mountainous areas and on the plains makes the problem even
more difficult to resolve. In the mountainous areas each zone represents at least
a hundred meters or more in elevation, while on the plain areas a zone often
represents elevation ranges of only 1 or 2 m or sometimes even less than a
meter.
3.2. Soil classification with integrated AVHRR-DEM data
Modeling the terrain with the help of DEM is a promising tool for the
characterization of the soil-landscape. The use of the aspect and absolute
elevation for the identification of the soil zones on hilly and mountainous areas
has been quite successful. However, this may contribute little in the soil pattern
refinement on the plain areas. Dobos Ž 1998. introduced a new terrain descriptor,
namely the potential drainage density ŽPDD. function. This function uses an
artificially created drainage-line network that can be generated based on the
DEM of an area. It assumes that there is no infiltration into the soil, and thus
100% of the water flows on the surface and will form drainage lines after
reaching a certain size of catchment area. This drainage line network is used for
characterizing the surface topography, where the relative differences in the
384
E. Dobos et al.r Geoderma 97 (2000) 367–391
Fig. 3. The classified images of the pilot area: ŽA. Using AVHRR alone; ŽB. using the integrated
AVHRR-terrain database.
Class name
Ramann-type Brown F. s.
Chernozem-Brown F. s.
Ranker
Sandy Forest soil
Chernozem soil
Alluvial soil
Blown sand
Meadow and Peaty soil
Brown Forest soil
Humous Sand
Total
Class
number
Percent
correct
Number
samples
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
84.9
89.4
89.8
60.8
95.6
41.0
47.9
95.3
99.1
89.5
977
1090
98
153
1235
378
146
1383
449
381
6290
829
32
2
0
0
3
5
0
4
0
875
115
974
8
4
8
36
17
10
0
2
1174
2
4
88
0
0
0
0
0
0
0
94
0
0
0
93
0
0
4
0
0
2
99
0
51
0
3
1181
19
0
36
0
2
1292
4
5
0
4
6
155
1
14
0
0
189
2
9
0
3
0
0
70
1
0
7
92
0
8
0
44
40
163
4
1318
0
27
1604
7
1
0
0
0
0
0
2
445
0
455
18
6
0
2
0
2
45
2
0
341
416
Overall performance Ž5494r6290. s87.3%
Class numbers
E. Dobos et al.r Geoderma 97 (2000) 367–391
Table 3
Test class performance of the 500-m resolution integrated AVHRR-DEM image
385
386
Class name
Brown Forest soil
Ramann-type Brown F. soil
Chernozem-Brown Forest s.
Ranker soil
Sandy Forest soil
Humous sandy soil
Blown sand
Chernozem soil
Meadow and Peaty soil
Alluvial soils
Total
Class
number
Percent
correct
Number
samples
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
93.6
76.9
67.4
60.0
13.3
83.2
3.2
78.7
75.4
17.5
171
295
224
55
60
143
31
352
386
120
1837
160
12
0
1
0
0
0
0
0
1
174
10
227
17
11
5
1
0
0
0
11
282
1
20
151
9
0
0
0
1
0
2
184
0
1
1
33
0
0
0
0
0
1
36
0
0
0
0
8
0
0
1
0
0
9
0
22
6
0
5
119
22
0
0
3
177
0
1
0
0
0
6
1
0
0
0
8
0
1
32
0
27
9
4
277
91
7
448
0
3
10
0
15
7
3
72
291
74
475
0
8
7
1
0
1
1
1
4
21
44
Overall performance Ž1288r1837. s 70.1%
Class numbers
E. Dobos et al.r Geoderma 97 (2000) 367–391
Table 4
Test class performance of the 1 km resolution AVHRR-DEM image
E. Dobos et al.r Geoderma 97 (2000) 367–391
387
elevations are very small. The drainage line density increases when the surface
is somewhat concave and the relative elevation of a given cell is lower than the
majority of its neighboring cells. These spots refer to the low-lying areas. For
areas where the surface form is more like convex, and thus the elevation of their
cells are likely higher than the majority of the surrounding cells, the PDD is
relatively low. The hypothesis is that this function, when combined with the
layer of absolute elevation, slope, aspect and curvature could increase the
classification accuracy using the AVHRR dataset, particularly where the soils
show vertical zonality. Fig. 3 shows the classification results of the pilot area for
the two cases, using AVHRR with Ž Fig. 3A. and without Ž Fig. 3B. terrain data.
The AVHRR image lacks details of soil types on the mountain slopes where
soils different from the classified Brown Forest soil cells for the Great Hungarian Plain occur. The AVHRR-terrain data based image shows a lot more detailed
soil information than the AVHRR image alone.
Tables 3 and 4 show the confusion matrices of the 500 m and 1 km cases.
The Ramann-type Brown Forest soil class and the Chernozem–Brown Forest
soil class had a very low classification accuracy when only AVHRR data were
used, with 3.2% and 1.2%, respectively. The use of integrated AVHRR-terrain
data improved the accuracy percentage. The classification performances were
around 80–90% in the 500-m image and 67–93% in the 1-km image.
The classification accuracy of the Sandy soil classes, such as the Blown sand
and Sandy Forest classes decreased significantly. The AVHRR data in general is
very efficient in identifying the sandy areas. However, these sandy regions are
not necessarily differs in topography from its surrounding area, so the DEM
cannot separate them from the other classes. The low classification accuracy of
these classes shows that the influence of the DEM and terrain descriptors on the
final results is much stronger, than their mathematical proportion. Another
possible reason for this low accuracy can be the size of the area. These two soil
classes have a very low spatial extent, and thus were somewhat difficult to pick
up enough training pixels to define the class characteristics. The classification
results for the Alluvial soil class has a very low accuracy value in all cases,
regardless of which ancillary data were used. This is probably due to the coarse
spatial resolution of the data. This area has only small creeks with a width of a
few meters. This width is much less then a pixel width so the classifier
algorithms could not have been well trained for this class.
4. Summary and conclusions
This case study was carried out to demonstrate and evaluate the use of
AVHRR and integrated AVHRR-terrain databases for small scale soil characterization. The global scale of this study represents a great challenge for the terrain
data users who are engaged in soil survey and mapping. No one has studied the
388
E. Dobos et al.r Geoderma 97 (2000) 367–391
functionality of the terrain descriptor functions extracted from small spatial
resolution terrain data. In this study a major aim was to characterize AVHRR
data as a potential data source for small-scale soil mapping, and identify the
needs and future directions for improving the use of satellite data for soil
classification.
In the first phase of the study only AVHRR data set was used for soil
classification. This database was very efficient in the identification of the major
soil regions of Hungary. The most significant strength of this multitemporal–
multispectral database is that it can separate the different kinds of parent
material fairly well, particularly the unconsolidated ones. However, for a more
detailed classification, AVHRR needs to be complemented with terrain information.
Many previous studies have indicated that remotely sensed data are not
significantly sensitive to the scene variability associated with the physiographic
characteristics of the study area. An interpretation of the confusion matrices of
the classified data revealed that AVHRR data do not fully represent soil
variability associated with terrain position. The results clearly show that the soil
classes on the mountain slopes are less accurately representative of reality.
These soils, however, show vertical zonality. Soils showing vertical zonality can
be characterized more clearly using integrated AVHRR and terrain data. Soils
on a relatively level land often show similar vertical zonality defined by the
depth to the groundwater. This vertical zonality, however, works on a much
smaller scale. On this study site each soil from the mountainous region covers
an area of a hundred or a few hundred meters in elevation, while on the level
land the change in soil type occurs within only a few meters of elevation. This
scale difference requires a different approach of handling. The absolute elevation with the aspect can characterize well the mountain soil zonality, while on
the level land these functions are almost useless. Instead, a new function, the
potential drainage density Ž PDD. was used simultaneously with the two functions mentioned above. This function is designed to highlight relative terrain
differences even on a relatively level land surface. On a physiographically
complex area, where both landform types Ž level lands and mountains. occurs
this integrated terrain descriptor database should be used together with AVHRR
to achieve an acceptable result. With the use of integrated AVHRR-terrain data,
the accuracy percentages increased dramatically. The 500- and 1000-m images
had a very good performance with a range of 67–99% correctly classified pixels
in mountain soil classes, such as the Brown Forest soil with clay illuviation,
Ramann-type Brown Forest soils and Chernozem–Brown Forest soils. Similar
increases were found in the cases of the level land soils, such as the Meadow
and Chernozem soils.
It is important that a qualifying statement be made. The integrated AVHRRterrain data is a very promising tool for small-scale soil inventories. However,
the results and conclusions were based on this case study only. Similar studies in
E. Dobos et al.r Geoderma 97 (2000) 367–391
389
other areas of the world with different climate, vegetation, soils and landuse
practices should be carefully validated.
The results show that using ancillary information such as AVHRR data and
DEM derivatives from the national to continental level surveys is among the
most promising tools for geographers and soil surveyors. The AVHRR data is
often used for land cover studies but its usefulness in soil studies has not yet
been proved. This study is a representative example of its ‘‘power’’ to characterize the soil-forming environment and to delineate soil patterns, particularly when
other ancillary data, capable of describing the soil landscape such as the DEM,
slope, curvature and PDD are integrated to it. The predictive power of AVHRR
and similar low spatial resolution satellite data sources could be further improved with the development of soil sensitive filters. Mention should be made of
the potential improvement of the products derived from these data sources with
the use of better quality data provided by satellites that have been launched
recently ŽVegetation. or will be launched in the near future Ž MODIS. .
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