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İlhami Bayramin Ankara University, Agricultural Faculty, Soil Science Department, ANKARA ABSTRACT As a significant and dynamic component of the earth system, soil has come to the forefront of the environmental agenda during the past decade as it has never done previously. Landforms are the product of both long term and short term processes that operate principally in response to climate, water, geology, tectonics and vegetation. A soil-forming factor is anything, which acts or has acted on a parent material of a soil with the potential for changing it. Equation of soil forming factors is very well known and soil is characterized as a function of parent material, climate, organisims, and relief and time. The equation implies that, by looking for changes in one or more of these factors as the landscape is traversed, one can accurately locate boundaries between different bodies of soil. Quantifying and knowing soil-landscape relationships will be very useful information for soil survey studies. In this research, the challenge for soil survey interpretations was to more adequately synthesize the component parts of soil landscapes into segments of space that are meaningful to the soil user in relation to the soil-use decisions he faces, and integrate geological, and land cover information to the landform classes. In this study, non-soil digital data were used to make preliminary soil survey maps of Beypazarı area by computer. Digital Elevation Model (DEM) used for landform classification. The percentage of area where slope is flat or gentle (less than 8%, 4 classes), local relief (maximum minus minimum elevation, 6 classes) and the profile type expressed as relative percentage of flat or gentle slope areas that occur in lowlands or uplands (4 classes) were calculated. Landforms in Beypazarı were grouped into 5 main types: plains, tablelands, plains with hill or mountains, open hills and mountains, and hills and mountains. The landform types were subdivided into 24 classes and 96 subclasses according to the amount of gently sloping land in the area, local relief, and profile type (the amount of gentle slope on lowlands or uplands). The same data set was also used to generate 3D view for the study area and to evaluate the results visually. Digital geological data were overlayed with the landform classification data. After overlying processes, new classes were linked to the land cover classes, obtained from Landsat TM data classification, and re-classified to determine soil profile pits. Completing computer processes, field studies were carried out with global positioning system (GPS). The results show that digital methods were quite well to provide similar patterns for the most of the major landform types and much greater detail for classes and subclasses of landforms. Integrating geological and satellite data with landform classification increased the soil survey efficiency. Indeed, the landforms in any country or area with a DEM could be classified easily and readily. Using RS and GIS technologies and integrating DEM, satellite and digital geological data are very powerful tool for soil surveys. INTRODUCTION As a significant and dynamic component of the earth system, soil has come to the forefront of the environmental agenda during the past decade as it has never done previously. Whether we are considering soil as a source and sink of greenhouse gases, a contaminant of water sources, a medium for production of food for the rapidly expanding human population, a non-sustainable resource under current management systems, or a site of environmental degradation, the community of soil scientists faces a formidable challenge to provide credible, usable and timely soil information to resource manager and policy makers. As members of the first human generation to have the tools to study the Earth as a system, we soil scientists must address seriously our obligation to provide the user community needs to make rational decisions about resource and environmental management and policy. Requests for digitized soils data have increased dramatically during the past decade as government agencies and industries have begun to include geographic information systems (GIS) capabilities in their set of resource management tools, and wish to integrate soils data layers into their resource databases (Bayramin, 1998). LITERATURE REVIEW Hammond (1954) stated that ''land surface configuration is simply a three-dimensional geometry, to which some consideration surface material is usually added and the geometry of finite areas of land surface is usually so complex that it cannot be effectively apprehended as a whole. The usual way of handling such complex phenomena for study is by analysis. The complex is resolved into its component parts, elements or attributes''. According to Kuhn (1970) paradigm is based on ''one or more past scientific achievements that some particular scientific community acknowledges for a time as supplying the foundation for its further practice.'' Trained soil scientists can delineate bodies of soil accurately on the landscape by directly examining less than 1/1000th of the soil below the surface. They can do this because of the validity of the soil-landscape model. A powerful paradigm, it enables soil scientists to make very accurate predictions about their world. Jenny's (1980) well-known soil forming factor equation identifies the five factors of soil formation. Soil is characterized as a function of parent material, climate, organisims, and relief and time. The equation implies that, by looking for changes in one or more of these factors as the landscape is traversed, one can accurately locate boundaries between different bodies of soil. Our objectives of the study are; to test the use of non-soil data (DEMs, satellite images, digital geological data) for improving mapping efficiency and quality of soil maps, and to develop a pre-model for soil mapping, especially for countries who need rapid, easily applicable low cost soil mapping methods. Soil survey map units are based on a variety of landscape properties such as soil morphology, substratum type, slope, landform and flooding frequency (Swanson 1990). These properties have been chosen because they affect the land's capabilities and its response to management. However, in the formal classification system that has been used to aid us in defining and describing these map units are based on soil properties alone. He suggested that a system of soil landform units, defined by variety of landscape properties in addition to soil properties be developed for mapping and for communicating information about soil management. According to Hudson (1990); i) Within a soil-landscape unit, the five factors of soil formation interact in a distinctive manner. As a result, all areas of the same soil-landscape unit develop the same kind of soil. ii) Generally, the more different conterminous areas of two soil-landscape units are, the more abrupt and striking the discontinuity separating them. iii) Generally, the more similar two landscape units are, the more similar their associated soils tend to be. iv) Adjacent areas of different soil-landscape units have a predictable spatial relationship one to another. v) Once the relationships among soils and landscape units have been determined for an area, the soil cover can be inferred by identifying the characteristic soil-landscape unit. Klingebiel et al. (1987) investigated the utility of GIS-produced slope maps for soil-survey-related activities. They used 30-m DEMs to produce slope, aspect and elevation maps as pre-maps for third order soil surveys in Idaho, Nevada, and Wyoming. They questioned the use of slope-aspect pre-maps in second-order soil surveys and stated that experienced field soil scientists could improve field mapping using slope-aspect pre-maps. Horvath et al. (1984), using Landsat data, compiled photomaps showing the spatial distribution of the optical density of the spectral characteristics of objects. They found that spectral categories (12 classes) were correlated with parent material, aspect, slope, and spatial variability. The maps compiled were considered to be only supplementary to detailed soil survey techniques. Lee et al. (1988a) combined transformed thematic mapper (TM) data and topographic information from digital elevation model (DEM) to determine soil characteristics of hilly terrain in south western Wisconsin. They found 72% agreement between the soil map and classification used in their research. Stoner and Baumgardner (1981) concluded that characteristic variations in reflectance of surface soil properties may be related to the wetness, soil color, soil texture, soil-moisture regimes, parent material, and vegetation and are useful in separating soils at the higher categories of taxonomy. Su et al. (1989) demonstrated that DEM data could be used with Landsat TM data to benefit second order soil surveys of range land soils. Digital Elevation Model (DEM) data were merged with Landsat TM and SPOT data to delineate soil mapping units within the study area. Soil mapping units from a conventional soil survey were compared with a classified soil spectral map obtained from Landsat TM or SPOT, and DEM derived elevation, slope, and aspect data, using an overall accuracy assessment. The overall accuracy of soil spectral classes from TM and SPOT data was improved after DEM data were merged. Landsat TM data was used for soil survey studies in South Eastern Anatolia Region successfully, with high mapping accuracies of 68 - 94 % (Dinç, 1995). DiPaolo and Hall (1982), in reference to Landsat studies of soils, suggested than in mountain and plateau areas, factors such as elevation, slope and aspect contribute to variation in soil and vegetation and must be considered in any interpretations. MATERIAL In this study, non-soil digital data were used to make preliminary soil survey maps of Beypazarı area by computer. Landsat TM scene, acquired on 9.9.1998, 3-arc second Digital Elevation Model (General Command Mapping) and 3-arc second Digital Geological Data (Mineral Research and Exploration General Directorate) (MTA) for the study area were used. METHODS All data sets were geo-referenced to UTM map projection. In the first step, Landsat TM data were classified to obtain land use and land cover classification. Land use and land cover classes were grouped by USGS system. From this classification urban or built up areas, agricultural lands, range land, forest land, water bodies, barren lands were determined. Geological formation layers for the eleven quads were merged into one data set. DEM were analyzed for landform classification. The technique and schema by Dikau at al (1991) were used for the landform classification (Table 1).   Landforms in New Mexico were grouped into 5 main types: plains, tablelands, plains with hill or mountains, open hills and mountains, and hills and mountains. The landform types were subdivided into 24 classes and 96 subclasses according to the amount of gently sloping land in the area, local relief, and profile type (the amount of gentle slope on lowlands or uplands). DEM data were also used to generate 3D view and shaded relief images of the study area to visually compare results obtained from landform classification. The two data sets, geological and topographic, were combined to get soil land units. Each soil land units were analyzed according to their coverage and land cover/use and according to this analyses soil profile pits were determined. Forty-six soil profiles were opened and sampled for the laboratory analyses. Soil survey studies have not been completed yet. Soil land units will be divided into sub-groups according their slopes and aspect groups. Landsat TM image data will be classified and integrated with soil land units to have meaningful soil delineation boundaries. After finishing laboratory analyses soil survey studies will be carried out preliminary soil delineation boundaries will be checked by transecting.  RESULTS AND DISCUSSION Slope, relief and profile layers were produced from DEM data. These layers were combined and 64 landform sub-classes out of 96 landform sub-classes were obtained for the study area. Low Mountains with 14.4% showed the largest area coverage. These landform sub-classes were re-classifed to obtain main landform types of; plains, tablelands, plains with hill or mountains, open hills and mountains, and hills and mountains. Distribution of the main landform types were 4.0%, 0.8%, 6.8%, 21.5%, 67.0% respectively. To determine soil profile pits main landform types and geological layers were integrated. From this integration 59 classes were obtained. These classes were linked to Landsat TM data and 55 soil profile pits were determined (Figure 1). This selection was based on the land use and coverage. During field studies, 47 soil profiles were opened and sampled for laboratory analyses (Table 2).  Soil survey studies has not been completed yet. However, first stage field observations and results show that digital methods with geographic information systems and remote sensing techniques were quite well to provide similar patterns for the most of the major landform types and much greater detail for classes and subclasses of landforms. Integrating geological and satellite data with landform classes increased the soil survey efficiency. Using RS and GIS technologies and integrating DEM, satellite and digital geological data are very powerful tool for soil surveys. Although taxonomic classification seems to be excellent, a system of landscape units, defined by variety of landscape properties in addition to soil properties, would serve the needs of soil survey and land management better than the present system based on classification and soil series. ACKNOWLEDGEMENTS The authors acknowledge Scientific and Technical Research Council of Turkey (TÜBİTAK, project no: TARP 2097) for their support during the course of this ongoing research. REFERENCES Bayramin, I., 1998. Integrating Digital Terrain and Satellite Image Data with Soils Data for Small Scale Mapping of Soils. Ph. D. Thesis. Purdue University, Agronomy Department. 121 pages. Dinç. A. O., 1995. Importance of Satellite Remote Sensing for Developing Countries for Mapping and Monitoring Natural Resources: Examples from Turkey. ACSM/ASPRS Annual Convention & Exposition Technical Papers. Charlotte, NC. Vol:3 pp:741-747. Dikau, R., Brabb, E. E., Mark, R. M., 1991. Landform Classification of New Mexico by Computer. U. S. Dept. of the Interior, U. S. Geol. Survey Report 91-634. Hammond, E. H. 1954, Small-scale continental landform maps: Annals of Assoc. of American Geographers v. 44, p: 33-42 Horvath, E. H., Post, D. F., and Kelsey, J. B., 1984. The relationships of Landsat digital data to the properties of Arizona range lands. Soil Science Society of America Journal. 48, 1331-1334. Hudson , B. D., 1990. Concepts of Soil Mapping and Interpretation. Soil Survey Horizons. 31, 63-73. Jenny, H., 1980. The Soil Resource; Origin and behavior, Ecol. Studies 37. Springer-Verlag, NY. Klingebiel, A. A., Horvarth, H., D. Moore, G. W., and Reybold, U. 1987. Use of Slope, Aspect, and Elevation Maps Derived From Digital Elevation Model Data in Making Soil Surveys Soil Science Society of America, Soil Survey Techniques, SSSA Special Publication, 20, 77-98. Kuhn, T. S., 1970. The structure of scientific revolutions. Univ. of Chicago Press. Lee, K., Lee, G. B., and Tyler, E. J., 1988a. Thematic mapper and digital elevation modelling of soil characteristics in hilly terrain. Soil Science Society of America Journal. 52, 1104-1107. Muller, E., and James, M., 1994. Seasonal variation and stability of soil spectral patterns in a fluvial landscape. International Journal of Remote Sensing, 15:9, 1885-1900. Stoner, E. R., and Baumgardner, M. F., 1981. Characteristic variations in reflectance of soil. Soil Science Society of America Journal, 45, 1161-1165. Su, H., M. D. Ransom, and E. T. Kanemasu., 1989. Detecting soil information on a native prairie using Landsat TM and SPOT satellite data. Soil Science Society of America Journal. 53, 1479-1483. Swanson, D. K., 1990. Soil Landform Units for Soil Survey, Soil Survey Horizons, 31: 17-21. |