Shirazi, M., Zehtabian, G., Matinfar, H., Alavipanah, S. (2012). Evaluation of LISS-III Sensor Data of IRS-P6 Satellite for
Detection Saline Soils (Case Study: Najmabad Region). Desert, 17(3), 277-289. doi: 10.22059/jdesert.2013.35260
M. Shirazi; Gh.R. Zehtabian; H.R. Matinfar; S.K. Alavipanah. "Evaluation of LISS-III Sensor Data of IRS-P6 Satellite for
Detection Saline Soils (Case Study: Najmabad Region)". Desert, 17, 3, 2012, 277-289. doi: 10.22059/jdesert.2013.35260
Shirazi, M., Zehtabian, G., Matinfar, H., Alavipanah, S. (2012). 'Evaluation of LISS-III Sensor Data of IRS-P6 Satellite for
Detection Saline Soils (Case Study: Najmabad Region)', Desert, 17(3), pp. 277-289. doi: 10.22059/jdesert.2013.35260
Shirazi, M., Zehtabian, G., Matinfar, H., Alavipanah, S. Evaluation of LISS-III Sensor Data of IRS-P6 Satellite for
Detection Saline Soils (Case Study: Najmabad Region). Desert, 2012; 17(3): 277-289. doi: 10.22059/jdesert.2013.35260
Evaluation of LISS-III Sensor Data of IRS-P6 Satellite for
Detection Saline Soils (Case Study: Najmabad Region)
3Assistant Professor, University of Lorestan, Khoram abad, Iran
4Professor, University of Tehran, Tehran, Iran
Abstract
Soil Salinity has been a large problem in arid and semi arid regions. Preparation of such maps is useful for Natural resource managers. Old methods of preparing such maps require a lot of time and cost. Multi-spectral remotely sensed dates due to the broad vision and repeating of these imageries is suitable for provide saline soil maps. This investigation is conducted to provide saline soil maps with sensor LISS-III of IRS-P6 satellite data, in Najmabad of Savojbolagh. Satellite images belonging to 25 June 2006. For enhancement of images, salt Indices, Digital Elevation Model (DEM), False Color Composite imageries (FCC) and Principal Component Analysis (PCA), were used. Supervised classification method includes Box classifier, Minimum Distance, Minimum Mahalanobis Distance and Maximum Likelihood classifier, DEM, PCA1, PCA4 and Saline Indices (SI) were used. After classification, the class map salinity S0, S1, S2, S3 S4, were prepared. The results shows highest overall accuracy and kappa coefficient for the maximum Likelihood classifier estimate, respectively 99% and 97% and the lowest overall accuracy and kappa coefficient for PCA1 estimate, respectively 1% and 0% were obtained. Using Digital Elevation Model (DEM) also due to the difference in height position to the separation of saline lands is usefully. Most spectral interference related to non-saline soils and low saline soil. From among indices INT2 and PVI greatest ability to segregate is salty soils(especially classes S0 and S1).
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