Use of Satellite Data and GIS for Soil Mapping and Capability Assessment

Nature and Science of Sleep 01/2010; 8:104-115.

ABSTRACT This study aims to use the satellite data and Geographic Information System (GIS) to produce the soil map and use the spatial analysis technique to assess the soil capability. The soils adjacent to El-Manzala Lake east of the Nile Delta, Egypt were chosen for this application. To achieve this objective the land surveying, and satellite
data (Landsat ETM and SRTM images) were used in a Geographic Information System to delineate the landforms of the studied area. The correlation between landforms and soil taxonomic units were worked out. The results indicated that the area was dominated by flood plain (33.48 % of the total area), the lacustrine plain (21.52 % of the total area) and the marine plain (3.13 % of the total area). The Water bodies and urban areas exhibit 41.86 % of the total area. The soil properties such as CaCO3 content, texture class, soil depth, salinity, alkalinity, CEC and drainage condition were linked with the different landforms of the studied area. The thematic layers of these data were created in Arc-GIS 9.2 software using the spatial analysis function, and then these layers were matched together to assess the soil capability. The data obtained from the thematic layers indicate that the main limiting factors in the studied area were soil depth, drainage conditions, soil salinity, soil texture, alkalinity and calcium carbonate content. The limiting factors; CaCO3 %, soil depth, drainage condition, salinity and alkalinity were associated with the lacusrtine plain, while the soil texture and CEC were the main limiting factors in the marine plain. The soil depth, drainage condition and soil salinity were the dominating limiting factors of the flood plain.

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    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 (DEM) 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 (AVHRR) 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 (NDVI) 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 (DAFE) function (which 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 dimensional AVHRR data sets (less 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 mountainous/hilly 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 (PDD) 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.
    Geoderma 09/2000; DOI:10.1016/S0016-7061(00)00046-X · 2.51 Impact Factor
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  • Soil Science Society of America Journal 01/1995; 59(2). DOI:10.2136/sssaj1995.03615995005900020034x · 2.00 Impact Factor

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