Scientific registration n : 2180 Symposium n : 35 Presentation : poster MULDERS M.A.

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1 Scientific registration n : 2180 Symposium n : 35 Presentation : poster GIS and Remote sensing as tools to map soils in Zoundwéogo (Burkina Faso) SIG et télédétection, aides à la cartographie des sols au Zoundwéogo (Burkina Faso) MULDERS M.A. Wageningen Agricultural University, Laboratory for Soil Science & Geology, P.O.B. 37, 6700 AA Wageningen, The Netherlands INTRODUCTION The following materials and methods are used for the conventional way of soil mapping (Mulders, 1987): - topographic maps, aerial photographs and environmental data, such as data on petrology, vegetation and land use; - physiographic interpretation of aerial photographs, producing maps on aspects, such as land types, relief, drainage pattern etc. Beside conventional approaches, modern methods include new techniques of Remote sensing image processing and GIS data processing to produce higher accuracy. Different phases are recognized: I) pre-fieldwork, II) first fieldwork, III) digital data processing, IV) final fieldwork, V) final digital data processing and interpretation (Mulders, 1996-a). Where, there is a close relationship between terrain reflection and soils, that is in case of low vegetation coverage (for example under the semi-arid conditions of Northern Burkina Faso with Pa of 600 mm; Mulders, 1996-b), Landsat TM satellite data processing is an important tool for soil mapping. The present study area for soil survey at scale of 1 : , is located in the south of Burkina Faso (Fig. 1). The climate is more humid than in the north of the country, having a Pa of 900 mm and a relatively dense vegetation cover. Different soils are found in this area: Leptosols, Cambisols, Vertisols and soils with an argic B horizon, varying in C.E.C. and base saturation. The area is more or less intensively used for crop growing, cattle grazing and savanna forest reserve. The pattern detected on TM imagery is directly related to land use. Other keys than surface spectral reflectivity have to be used to identify most of the soil units. It is this, which is the subject of the present research, considering phase I-III as mentioned above. METHODOLOGY A DEM (Digital Elevation Model) and layout of drainage system were the basic inputs to the GIS, used in this study. Information extraction from terrain object and remote sensing data formed a basic next step (Fig. 2). The scheme of Fig. 2 is partly adapted from Janssen (1994),

2 who considered the information extraction input to GIS as updating: new data acquisition and interpretation. The different steps followed in distinguishing land elevation classes and land components are detailed in the scheme of Figure 3. Recently automated land classification has got attention of different authors. A DEM has been used to calculate slope and slope aspect to yield a landform facet image by Baral and Gupta (1997). A more complex approach to automated landform classification has been applied by Brabyn (1997). He has used height, slope, relative relief and profile type (indicating flat areas above or below surrounding terrain). The results of automated land classification had a good resemblance with those of the manual methods. The present research is specific in this way that visual interpretation was no alternative to automated land classification, this being mainly due to low slope contrast and monotonous drainage pattern in the study area. Two filters were used to start a first analysis. The final discrimination of land units was based on three steps: application of - filter 1, revealing plateau pattern and high drainage density; - filter 2, revealing aspect and drainage density; - physiographic data on slope, drainage ways and altitude. RESULTS By terrain observation, the following land systems were identified, subdivided on elevation: - High undulating land between 315 m and 360 m; - Steeply dissected land between 315 m and 400 m; - High plateau land between 290 m and 315 m; - Moderately high plateaux and slopes between 270 and 315 m; - Low plateaux and slopes, being located lower than 270 m. The digital elevation model was subdivided into five classes, according to these relevant contourlines: m, m, m, m and < 270 m. Slope classes were calculated from the DEM. The following slope classes were considered discriminative: %, %, 2-5 % and > 5 %. The elevation and slope classes are coded in Fig. 4, respectively: and 4-1. By simple addition values are produced, which are to be red as follows: 11 stands for land lower than 270 m with slope %, 12 for land... etc. In fig. 4, the contour line of 290 m has not been shown, but flat areas are discriminated in the zones of m as well as in the zone of m (codes 21 and 31). A simple discrimination of river valley bottoms > 300 m breadth from adjacent footslopes was not possible in Fig. 4 since valley bottoms cross the land system limiting contour lines. Therefore, broad valley bottoms were discriminated with the aid of Landsat TM imagery, where they are evident by their abundance of vegetation or intensive land use. They are indicated in Fig. 5 together with a lake and the plateaux discriminated in the elevation zones. Now 11, 21, 31 and 41 stand for footslopes, the plateaux having received other codes. Now the next step is the analysis of the drainage pattern. For this two 5x5 filters were used, scanning a drainage system map, with the following multiplication factors:

3 Filter 1: Filter 2: The image produced by filter 1 was simplified such that plateaux with adjacent slopes were clearly visible as well as areas with high drainage density. On screen digitizing produced a first approach to analysis of plateau and drainage pattern. The filter 2 image was combined with a slope aspect map by addition of values and simplification into 16 classes. This image was more complicated than the image of filter 1, and leaded to a second interpretation of the drainage and plateau system. Directional trends were present but not in such a way that they supported discrimination of land units. For consideration of the whole assembly of land components, a 3rd approach was necessary, this time by screen digitizing the image of Fig. 5. The result in the form of discrimination of 61 polygons, representing the land units at this stage of research, is given in Fig. 6. The decision to classify polygons into the same soil catena units will be taken after study of the terrain data base and detailed analysis of terrain configuration. A first test on validity was positive, despite some suggestions for further subdivision. CONCLUSIONS Automated land classification proved to be an adequate tool for mapping physiography in the soil survey project as it is carried out in the Zoundwéogo Province of Burkina Faso. Aspects hidden in visual interpretation, such as plateau limits, change of drainage and plateau pattern, were detected and a physiographic base map with land units and components was composed. Further research, however, should be directed towards automated classification of drainage pattern, since the present approaches were time-consuming and limited in accuracy. REFERENCES Baral, D.J. and Gupta, R.P., Integration of satellite sensor data with DEM for the study of snow cover distribution and depletion pattern. Int. J. Remote Sensing, Vol. 18, No. 18, Taylor & Francis Ltd., London: Brabyn, Lars, Classification of macro landforms using GIS. ITC Journal , Enschede, The Netherlands: Jansen, L., Methodology for updating terrain object data from remote sensing data. Thesis Wageningen Agricultural University, The Netherlands: 173. Mulders, M.A., Remote Sensing in Soil Science. Developments in Soil Science 15, Elsevier, Amsterdam: 379. Mulders, M.A. and Sorateyan, S., 1996-a. GIS and Remote Sensing for mapping soils and erosion hazard in the Kaya region, Burkina Faso. Proc. of the ISSS Int. Symp. (Wgs RS and DM), Ouagadougou 06-10/02/95. ORSTOM éditions, Colloques et Séminaires: Mulders, M.A. and Casterad, A, 1996-b. Preliminary results of processing terrain and Remote Sensing data of the Zablou area (Burkina Faso). Proc. of the ISSS Int. Symp. (Wgs RS and DM), Ouagadougou 06-10/02/95. ORSTOM éditions, Colloques et Séminaires:

4 Keywords : GIS, Remote Sensing, Physiography and Soil Survey Mots clés : SIG, télédétection, physiographie, carte des sols FIGURES: Fig. 1. Location of study area: province of Zoundwéogo. Fig. 2. Information extraction to update GIS.

5 Fig. 3. Flow-chart for physiographic analysis.

6 Fig. 4. Combination of elevation and slope in the province of Zoundwéogo.

7 Fig. 5. Map of Fig. 4, with in addition: plateaux, footslopes, broad valley bottoms, drainage ways and lake.

8 Fig. 6. Physiographic map of Fig. 5 with in addition land units (polygons 1-61).