Document Type: Research Paper
MSc Graduate, University of Tehran, Karaj, Iran
Professor, University of Tehran, Tehran, Iran
Professor, University of Tehran, Karaj, Iran
Assistant Professor, University of Lorestan, Khoram abad, Iran
In order to assess the satellite data for soil investigation, ASTER digital data 20 June 2006, field study and phisiochemical properties of soil, were analyzed. All landcover classes including soils are classified based on
morphological and physico-chemical characteristics. Images were geocorrected and photomorphic units were selected based upon visual interpretation and sampling in study area. The images was classified using maximum likelihood algorithm; with eight approaches. The classified image was compared with the ground truth map. The lowest classification accuracy was achieved by optimum index factor (OIF) approaches and hence application of OIF for discrimination of soils was not effective way. The results showed that best index is not only efficient and other information such as DEM (digital elevation model) with the spectral combination increase the accuracy of classification and Kappa coefficient. Salinity Indexes (SI) and Normalized Soil Index (NDSI) and Brightness Index (BI) were useful for discrimination of the soils in the study area. Typic Haplocambids showed the maximum reflection due to bright color. In addition, the minimum value was related to the Typic Torriorthents class, because of dark gravels. The result showed ASTER data can differentiate Typic Haplocambids from Typic Torriorthents, Typic Haplosalids and Typic Haplogypsids in arid lands.