In the research aim is to evaluate the Jennes algorithm for landform classification in salt dome of Korsia of Darab plain, Iran. The Jennes's approach uses a multi-scale approach by fitting a quadratic polynomial to a given window size using least squares. In the study used window size of 3*3 and 10*10. Input data for landform classification is digital elevation model (DEM) with resolution of 30 m. After prepared landform classification map for the study area, used geology map. The results show that the evaluated method can be helpful in the predictive mapping of geology. The algorithm of landforms classification proposed by Jennes seem to be the most applicable method.

The use of remote sensing for rapid and accurate evaluation of phenomena, specially land covers is very important. In this study, for modeling and estimated of salt dome was used visible atmospherically resistant index (VARI), difference vegetation index (DVI), enhanced vegetation index (EVI), green difference vegetation index (GDVI), normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), soil adjusted vegetation index (SAVI), infrared percentage vegetation index (IPVI) by Landsat 8 ETM? bands vegetation in the salt dome of Korsia of Darab plain, Iran in 2015 years. By software ENVI preprocessing, processing, geometric and atmospheric corrections were performed, and then vegetation index for the study area was calculated. Also ArcGIS 10.2 software for mapping of area vegetation was applied. Then relationship between Vegetation Indices and salt dome of Korsia were determined. The results show that the value of indices were high in the agriculture field for VARI, DVI, GDVI and IPVI and low value for other indices. Also the results show that cannot used IPVI and OSAVI for determination of soil salinity. DVI and SAVI close to 0.1 and 0.2 is represent soil salinity respectively. Also soil salinity have EVI and GDVI close to 0.14. Finally VARI and NDVI have value of lower that 0 in soil salinity. The comparison of vegetation indices show that change values were same for SAVI, EVI, GDVI and DVI in salt dome. Finally the results show that EVI, GDVI, OSAVI and SAVI are suitable for prediction and modeling of salt dome in the study area.

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