Survey of capability of remote sensing indices for enhancement of land cover in arid areas (case study: Najmabad)

Recently there is a great deal of interest in the quantitative characterization of temporal and spatial vegetation patterns with remotely sensed data for the study of earth system science. One of important methods for extracting information from satellites image is use of indices. In this study for enhancement of land cover in region of northwest Tehran near Hashtgerd some indices such as BI, MIRV2, GREENNESS, TVI, VNIR, MND، NIR, OSAVI, RA, NDVI, IR1, MSI IPV, MSAVI, SAVI, TSAVI, PD322, BI, INT1, INT2, PVI, SI1, SI2, SI3, GEMI, WDVI Are used. Most of study area covers by density of vegetation (such as irrigation farming and vegetation cover around streams) and bare lands. The results have shown that TSAVI, DVI, IPVI, RA, NIR, IR1 Indices have the most effective efficiency for vegetation enhancement and SI2, BI, TVI, PVI, INT1, SI3, SI2 indices have the most effective efficiency for salinity surface. This study addressed that all of vegetation indices except DVI have correlation more than 0. 8 and DVI has correlation around 0. 4 with others. Meanwhile all of salinity indices have more than 0. 9 correlations with each other. As conclusion, this study has shown that IRS satellites image have high accuracy for providing land cover map by use of vegetation indices, also use of salinity indices having high capability for salinity surface can be used for providing salinity maps, meanwhile vegetation indices with high correlation can be used instead each other for providing vegetation maps.