Integrating Remote Sensing with SCS and ICONA Models for Mapping Land Degradation in Fars Province
2022
S. Dehghan Farsi | R. Jafari | A.R. Mousavi
The objective of the present study was to investigate the performance of some of the extracted information for mapping land degradation using remote sensing and field data in Fras province. Maps of vegetation cover, net primary production, land use, surface slope, water erosion, and surface runoff indicators were extracted from MOD13A3, MOD17A3, Landsat TM, SRTM, ICONA model, and SCS model, respectively. The rain use efficiency index was obtained from the net primary production and rainfall map, which was calculated from meteorological stations. The final land degradation map was prepared by integrating all the mentioned indicators using the weighted overlay method. According to the ICONA model, 5.1, 9, 47.21, 27.91, and 10.73 percent of the study area were classified as very low, low, moderate, severe, and very severe water erosion; respectively. Overlaying the ICONA map with other indicators showed that very high and high classes, moderate, and low and very low classes of land degradation covered 1.3, 18.7, 70, 0.9, and 9.1 percent of the study area, respectively. According to the results, integrating remote sensing with ICONA and SCS models increases the ability to identify land degradation.
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