Matinfar, H., Sarmadian, F., Alavipanah, S. (2011). Characterization of Soil Salinity in Arid Region of Kashan by ‎Digital Processing of IRS_1D Data. , 2(4), 211-220.
Hamid Reza Matinfar; Fereydoun Sarmadian; Seyed Kazem Alavipanah. "Characterization of Soil Salinity in Arid Region of Kashan by ‎Digital Processing of IRS_1D Data". , 2, 4, 2011, 211-220.
Matinfar, H., Sarmadian, F., Alavipanah, S. (2011). 'Characterization of Soil Salinity in Arid Region of Kashan by ‎Digital Processing of IRS_1D Data', , 2(4), pp. 211-220.
Matinfar, H., Sarmadian, F., Alavipanah, S. Characterization of Soil Salinity in Arid Region of Kashan by ‎Digital Processing of IRS_1D Data. , 2011; 2(4): 211-220.

Characterization of Soil Salinity in Arid Region of Kashan by ‎Digital Processing of IRS_1D Data

مهندسی و مدیریت آبخیز
Article 4, Volume 2, Issue 4, January 2011, Pages 211-220    PDF (2.24 M)
Document Type: Research Paper
Authors
Hamid Reza Matinfar* 1; Fereydoun Sarmadian2; Seyed Kazem Alavipanah3
1Assistant Professor, Faculty of Agriculture, Lorestan University, Khorram Abad, Iran
2Assistant Professor, Soil Science Faculty, Tehran University, Iran‎
3Proffesor, Faculty of Science, Tehran University, Iran‎
Abstract
Soil salinity is one of the main increasing problems of the world. In the recent years application of remote sensing and GIS techniques in order to assess saline soils is used because they bring about vast uniform coverage of ground phenomena in a short time. The images of LISS III sensor of the Indian satellite (IRS) were used in this research. The Brightness index (BI) could discriminate highly saline soils from non –saline and the salinity index (SI) show high potential to separate very high, high and non- saline soils. Results of supervised classification without combination DEM and remote sensing data have overall accuracy of 76%, producer accuracy of 78% and user's accuracy of 82%. While in supervised classification combination of remotely sensed data and DEM have overall accuracy of 98.1% , producer accuracy of 98.28% and user's accuracy of 98.4% .The reason for low accuracy of the classification , before combination of remote sensing and topographic data , can be explained by highly moist saline soils  spectral interference with non-saline soils (soil with 25 to 65% gravels) , but these two soils had different topographic condition with 200 meters elevation difference , thus , with combination of the DEM , this kind of area and other areas with similar conditions have been separated from each other.
Keywords
Brightness index; DEM; GIS; RS; Salinity index
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