Scientific registration number: 1347 Symposium N o : 17 Presentation: Oral

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1 Scientific registration number: 147 Symposium N o : 17 Presentation: Oral The use of integrated AVHRR and DEM data for small scale soil mapping Utilisation intégrée de données AVHRR et MNT pour la cartographie des sols à petite échelle DOBOS Endre 1, MICHELI Erika 2, BAUMGARDNER Marion F.,BIEHL Larry, HELT Todd 1 University of Miskolc, Department of Geography and Environmental Sciences Miskolc- Egyetemváros 5, Hungary 2 Gödöllõ Agricultural University, Department of Soil Science, Gödöllõ Páter Károly u. 1., Hungary Purdue University, Department of Agronomy, West Lafayette, IN477, USA Introduction Conventional large scale maps are often produced by interpolation and generalization of point data into polygons. For those areas, where the number of reference points are limited, the accuracy of these databases is questionable. Transitional zones among different types of mapped features are not well represented either. With advances in satellite remote sensing and geographic information systems (GIS), it has become feasible to characterize land and produce thematic maps for a very large area. The general objective of this study is to evaluate the use of small scale satellite imagery as a potential data source for delineating meaningful soil information in support of small scale soil mapping, such as the SOTER. The only global scale soil map of the world was created during the period 1- by FAO at a scale of 1:5 million. This map was digitized and in spite of its limitations, is being used for global change studies and land evaluation with limited levels of satisfaction. There is a need for a new, more accurate, larger but still global scale soil map of the world. Thus, a project, called SOTER, was initiated to create a new, uniform global scale soil and terrain digital database from existing soil information (Van Engelen, 1). Unfortunately, complete soil and terrain information is not available from numerous regions of the world. Previous research has demonstrated that data from airand spaceborne sensors are appropriate for soil characterization at large scale. Also, many attempt were made to use digital elevation data for deriving soil information. However, little is known about the potential use of small scale satellite data, such as AVHRR (Advanced Very High Resolution Radiometer) in extracting soil information. The aim of this study is to analyze and evaluate AVHRR data using together with different spatial resolution digital elevation data for small scale soil characterization. 1

2 Why AVHRR data...? One can ask, why we want to use AVHRR data. There are many, much more detailed satellite data sources available with much higher spatial and spectral resolution. However, the higher the spatial and spectral resolution, the longer computational time and the greater the requirement for data storage capacity. Furthermore, a generalization would be needed to resample the data into the appropriate scale. The one kilometer pixel size of AVHRR is roughly equivalent to the 1 to 5, and 1 to 1 million scale, the scale of the SOTER database. This relatively coarse resolution makes it 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 type in one square kilometer pixels, thus, an 'in situ' generalization of pixel areas is being done when AVHRR is used. This fact has to be considered, when the classes are defined. The two reflective bands and the three thermal bands provide a relatively wide range of detectable land surface information. That is why these data have been extensively used for vegetation, ecoregion and land cover mapping and modeling in addition to its meteorological applications for which the AVHRR instrument was designed (Zhiliang Zhu et al. 14). AVHRR-type data have not been used for soil characterization yet, however, its capability in differentiating between different kinds of parent materials (using the thermal bands), and different kinds of vegetation (through the NDVI) has been demonstarted (Zhiliang Zhu et al., 14). These two phenomena refer to two of Jenny's soil formation factors. Some spectral variation is due to the physiographic characteristics of the area what can cause a different show-up even for the same natural phenomena. Stratification of the large areas into smaller regions has been suggested as a way to reduce the effect of physiographic variations in spectral data. When doing so, the output maps representing those smaller regions have to be merged to create the final output and the problem of edgematching has to be handled somehow. The relief or landform can be characterized with the use of digital elevation data as well. With the use of integrated satellite and DEM data, that problem of edgematching can be avoided. The time factor, what refers to the age of the soil surface, is mainly the function of the date of the deposition or the 'time zero' when the exposition of the surface began, and the landform, which directs the erosional and depositional processes. These processes can make a real difference in the kind and the condition of the vegetation so the NDVI -in some level- can show some of this information. If we use an integrated database of satellite and DEM data, only the climatic 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 extent of the study site is "small enough" to permit us to disregard the climatic effects, the integrated database has the capability of delineating areas having the same soil forming environment. The soils of the study area The demand for representing mountainous areas as well as plain areas played an important rule in choosing this location. The size is by 1 kilometer and lies in the transition zone between the North-Hungarian Mountain range and the Great Hungarian Plain. The area is extremely heterogeneous in many features. The elevation of the area varies between 8 and 114 meters above sea level. The northern part is mountainous, mainly the Mátra mountains, made up of neutral and acidic volcanic material, the western 2

3 part is a hilly region, called the Gödöllõ Hilly region, mainly with loess parent material, while the south-eastern part is the flat Great Hungarian Plain, mainly with variable fluvial deposits. Great heterogeneity reflected in the soil types as well. At higher elevations in the mountains and areas subject to erosion, lithomorphic soils are common: Rankers and Erubase soils (Lithic and Entic Haplumbrepts). Descending from the mountains through the brown forest soils (Typic Haplumbrept, Typic and Ultic Haplustalfs, Typic Haplustolls and Hapludolls), we arrive at the continental climate wastelands of the Great Plain. Depending on the parent material and depth to the groundwater, the dominant soil types are the Chernozem-Meadow (Aquic Haplustolls), Meadow (Typic Endoaquolls), Alluvial (Typic and Aquic Udifluvent) and Salt-effected soils (Mollic Natraqualf, Aquic Natrustolls). The Data 1. AVHRR Data Primary data used in this project are from the Advanced Very High Resolution Radiometer (AVHRR) on the National Oceanic and Administration (NOAA) polar orbiting weather satellites. The 1-day composite images downloaded from the 1-km AVHRR Global Land Data Set of the U.S. Geological Survey EROS Data Center at Sioux Falls, South Dakota. Cloudfree coverage of Hungary from five different dates (May, August and September of and June, September of 1) were used. 2. Digital Elevation Data Unfortunately, DEM for the entire country would have been excessively expensive to buy. The data I use instead covers a 1 by km area at the transition zone of the North Hungarian Mountain region and the Great Hungarian Plain and was extracted from a digitized 1:1, scale topographic map. The pixel size of the DEM is 1 meter.. Agrotopographic database: This database was created on the scale of 1:1.. It contains digitized soil polygons and provide additional attributes referring to the physical and chemical characteristics of the soil. Method A subset of the AVHRR images for the study area was taken, and a digital elevation model, a slope percentage, a curvature and a drainage density layer were added to the image set. This layerstacks with 1 km 2 pixel size was then further resampled into 5 and 1 meter resolution, and the three different resolution dataset (1 km, 5 and 1 meter) were compared in many way. The creation of the slope and the curvature coverage was made with the slope and curvature functions of the ARC/INFO s GRID package. The drainage density image represents the drainage way length within a certain area. As a reference map, where the training and test samples were taken from, we used the digitized Agrotopographic database resampled into the same resolution as that of the corresponding layerstack. Four different base image (layerstack) were used for the supervised classifications,(i) the raw image, (ii) the raw image with enhanced statistics, (iii) DAFE transformed image with enhanced statistics and (iiii) the DBFE transformed image with enhanced statistics (Richard, 1, Lee and Landgrebe, 11, Behzad and Landgrebe, 1). Three classifier, the maximum likelihood, the Fisher linear

4 discriminant and the ECHO were used with unequal class weights. The weights were taken from the Agrotopgraphic database. The classifications were performed for all third channel numbers. The channels used for the given selection numbers were selected with using the Bhattacharya feature selection stepwise method. The percentage of the training samples are the followings: for the 1 km image 1%, for the 5 meter image 2% and 1% and for the 1 meter pixelsize image 4% and 1 %. In the 1 meter pixel size and 1% training area case two different sampling scheme were compared. The number of the training field in the first case was 45 while in the second it was 114, what means a much dispersed sampling scheme. The test fields were selected based on the Agrotpographic database. The percentage of the correctly classified test pixels were calculated and recorded as overall accuracy. The software we used were the Arc/Info, Erdas Imagine, Multispec and SAS. Result and Discussion The aim of this study was to analyze the different resolution DEM and DEM-extracted layers when used with the 1 km satellite data. The main question was whether it is rewarding to use higher resolution data or not, due to its disadvantages, such as the huge storage capacity and the much longer computational time. The original 1 km pixel size AVHRR channels were resampled to 1 and 5 meter. For the 1 meter pixel size case three different training set were created, one which represents 4% of all the pixels, and two with 1 % training pixels. The 1% ones differ from each other in the number of training fields. In the first case there were 45, while in the second there were 114 training fields. The more the training fields the more dispersed the distribution of the pixels and so the better chance to correctly estimate the probability distributions of the classes. Figure 1. and 2. shows the corresponding results for the 1 m pixel size case. The increase of the training pixel percentages resulted a significant increase in the classification accuracy. The 1 % case accuracy line keeps going up, while the 4 percent one become saturated about the channels case and then starts to decline. An even higher increase in the classification accuracy was obtained when the number of training fields was changed from 45 to 114, while the numbers of training pixels were unchanged (Figure 2.) The probability distributions of the soil type classes always have a relatively high variance, so the value range of a certain class can be very broad. The means are very close to each other and the overlays of the classes are significant. That is why the sampling schemes are very important. A more distributed, equally sampled training set has a much better representation of the given soil than the training sets made up of bigger training fields. An indicator of the validity of the sampling scheme was that in many cases in the 5 meter image, the overall testing accuracy was higher than the overall training one. This situation is quite interesting, because the first impression is that we got a good result out of a less accurate data source. However, the training accuracy performance refers not only the quality of the training sample set but also to the complexity and difficulty of the separation of the given class set. What happened here is probably a good estimation of the means and a little bit higher variances for the training set - more extreme value, but balanced on the two side 4

5 ML test 4%training ML test 1%training Figure 1. Classification performances of the maximum likelihood classifier on the 1 meter pixel size image with the use of 4% and 1% training pixels meter image 45 training field 1 meter image 114 training fields 4 Figure 2. Classification performances for the Maximum likelihood classifier on the 1 meter pixel size image with the use 1% training pixels represented by 45 and 114 training fields.. of the distribution - than for the test set. Therefore in the training set the overlays of the tails of the probability distributions were more significant, and so the training performance was lower than in the test case. An average of % was the absolute increase when the training field numbers was increased. In the 5 meter pixel size case where we followed a point sampling scheme, instead of selecting continuous training fields, the increase of the training pixel number did not resulted a significant increase in the accuracy (Figure.), because the 2% training set already had a very good representation of the soils. However, at higher dimensions, where the Hughes phenomena is getting more expressed, the higher training pixel number can retard the effect of that phenomena. A possible indication of the incompleteness of a training set in number or in representativity is that the Fisher classifier classification performance is higher, than the maximum likelihood one. This trend can be recognized in this study too, where in the 1 km and the 1 meter with 45 training field cases shows lower or equal performance for the maximum likelihood case than for the Fisher classifier. Figure 4. 5

6 shows the classification performances for the three different resolution when 1% of all the pixels are used for training the classifier. It can be seen that the higher the resolution, the higher the classification accuracy. The reason for that can be the better representation of the topographic surface by the higher resolution DEM, however, that is not true for the AVHRR channels, because the resampling of the 1 km pixels into 1 meter size ones would not increase the information contents of the image. The other ML test 2% training ML test 1% training Figure. Comparison of the classification accuracy for the 5 meter pixel size case for two different training set, having 2% and 1% training pixel respectively km 5m 1m 4 Figure 4. Classification performances of three different pixel size cases. possible reason is suggested by the decline of the 1 km image in the high dimension ranges. The percentage of the training pixels were the same for all three cases, but as the pixel size increases the ratio of the training pixel number and the channel number is decreasing what also cause a decrease in the accuracy of the estimation of the class statistics. This fact always has to be considered when comparing different resolution cases.

7 The decrease of the dimensionality was very important to keep the Hughes phenomena unexpressed. It is known that in those cases where the number of classes is lower than the number of features, the DAFE results are not always reliable and the use of DBFE is suggested instead. In this study, the number of classes varied between 1 and 1 while the number of features (channels) were or 4, so the DBFE methods should have been suggested. However it turned out that the majority of the cases we worked with, were appreciated the DAFE transformation much better than the DBFE one. The DAFE could decrease the dimensionality to the third or half of the original one, while the capability of explaining the soil variation was higher than any other set up. Conclusions The results suggested that the use of integrated AVHRR and DEM-derived database can provide a lot of useful information for soil delineation in small scale soil mapping. In this given case our model was capable to reach an up to 8 percent accuracy, depending on the quantity of ground-truth information and the fullness of the terrain data. Our previous studies showed, that the AVHRR data alone cannot represent the soil variability originated from the terrain variation. That model was capable to catch some of these variation, but in general it could not provide satisfactory result. With the use of terrain data together with AVHRR, the model became sensitive to the vertical soil zonality and landscape form, while it is still successful in the discrimination of soil variation originated from the different parent materials or vegetation. We concluded, that the higher the terrain data spatial resolution, the more accurate the final classification result is, however, the computational time is increasing dramatically, too. The sampling scheme is even more important than the quantity of the training pixels. The more dispersed the training fields are within the working area, the better the model performance. The same performance increment can be reached with the use of a tripled number training sample, as when one-third of the training pixels are used, but the size of a certain training area is kept as small as possible. References Behzad, M. S. and D. A. Landgrebe, 14. The effect of unlabelled samples in reducing the small sample size problem and mitigating the Hughes phenomenon. IEEE transactions on Geoscience and remote sensing. Vol. 2, No Pp. Lee, C. and D. Landgrebe, 1. Feature extraction and classification algorithms for high dimensional data. PhD Thesis, Purdue university, December, Richard, J.A., 1. Remote sensing and digital image analysis. An introduction. Springer-Verlag. 1-2 pp. Van Engelen, 1. Global and National Soils and Terrain Digital Databases (SOTER): Procedures Manual. International Soil Reference and Information Centre, Wageningen, The Netherlands. Zhiliang Zhu and D.L.Evans, 14. U.S Forest Types and Predicted Percent Forest Cover from AVHRR Data. Photogrammetric Engineering & Remote Sensing, Vol., No. 5. Keywords: soil survey, GIS, remote sensing Mots clés : cartographie du sol, SIG, télédétection 7