Application of GIS and remote sensing for land use planning in the arid areas of Jordan

Land suitability analysis formed part of a land use planning exercise in a development project aimed at improving agricultural productivity in the transitional Badia region of Jordan. Soil observations and soil maps were available at three levels of detail with differing coverage: level one (1:250,000 scale -complete cover), level two (1:50,000 scale - part cover) and level three (1:10,000 scale very limited cover). The development project selected the FAO Framework for Land Evaluation as the basis for land suitability analysis. This research investigated seven different calculation approaches for the processing of soil observations within soil map polygons using a GIS to derive land suitability ratings. These methods either use the soil observations to calculate the suitability of each soil mapping polygon or an interpolation technique (Voronoi diagram or Triangulated Irregular Network) between observation points. The overall map purity and homogeneity with respect to land characteristics were used to evaluate these methods. The quality of suitability maps varied according to the level of soil mapping and the method of processing the soil observations. The relative performance of the processing methods is discussed and recommendations for each level of mapping are proposed. The results showed that the purity of suitability maps was between 60 and 70% at the highest level of detail. Thus they should be used with caution for site specific analyses. Statements of map quality should be appended to suitability maps. The soil maps and observation points were derived and collected in a previous soil survey programme and georeferenced by map reading before the widespread availability of the Global Positioning System (GPS). When the data were integrated and overlaid on a satellite image within a GIS, a number of inconsistencies in georeferencing the data and in the attributes attached to them were revealed. Investigation and correction of these evolved into a major component of this work. Systematic errors caused by the use of different datums to georeference soil maps and observation points in the Jordan Soil and Climate Information System (JOSCIS) were detected. The map reading procedure also caused unsystematic errors in the locations of soil observations, which were re-measured at a sample of original observation sites using GPS. The correction of the unsystematic errors was not feasible due to the difficulty and cost of relocating all observation points. Errors in the attributes attached to the observation points were caused by survey recording procedures, highlighting the need for an examination of the data before analysis. The systematic and attribute errors were corrected and the implication for suitability analysis examined. The areas and spatial distribution of different suitability classes were affected increasingly as the level of mapping became more detailed. The presence of all these errors was sufficient to create errors in the derived land suitability maps, which could lead to incorrect land use planning decisions. The integration of satellite imagery, soil observations and soil mapping polygons within a GIS was indispensable for quality control of the data. The highest purities of suitability maps using existing soil mapping polygons were between 60% to 70% at level three but they only covered veiy limited areas. This indicated the need to extend mapping at this detail for site-specific planning and if possible, to increase the purity of soil mapping units. This was investigated by integrating satellite imagery and topographic data in a GIS. A 3-D perspective view of a Landsat TM image using an air photo-derived DEM was the most promising way of using the available data. Further research is needed to investigate the interactive use of air photo-derived DEMs and Landsat images, with more focus applied to site specific planning and field verification of the technique. Although this work was necessarily focussed on the issues and problems particular to one data set used in a Jordanian context, a number of general lessons have been learned. Firstly, careful examination of all input data is necessary to eliminate georeferencing and attribute errors. Secondly, overlay of input data onto a geocoded satellite image is extremely useful for detecting potential sources of input data errors and is recommended. And thirdly, GIS is indispensable for investigating existing data for errors and exploring new methods of analysis.