VEGETATION CHANGE DETECTION BASED ON IRS AND LANDSAT SATELLITES DATAK. Solaimani, A. Ahmadpour, M. Shokri and J. Ghorbani
The study aims to evaluate the IRS-P6 LISS III and Landsat ETM+ efficiency in plant group identification. In order to achieve this purpose, 143 training samples were collected from a homogenous plant species composition with an area of 3600 m2 (60 x 60 m). Coordinates of these training samples recorded using GPS and transferred to a GIS environment. For satellite data ENVI 4.2 software has used to process and analyses them. Several methods of processing such as; spectral separability, supervised classification and classification accuracy assessment have used in order to gain a satisfy evaluation accuracy. The results of this process indicated that the best separability is related to net farming of Me.sativa and lu.polycarpus -Ar.kopetdaghensis community (1.99 for Landsat data and 2 for IRS). In contrast, the worst results were related to I u.polycarpus-On.cornuta and tu.polyearpus-Ar.kopetdaghensis communities (1.57) for Landsat and Ju.polycarpus-Ar.kopetdaghensis and lu.polvcarpus-Ag.interrnectium communities (1.53) for IRS data. It can be concluded that the satellite data are roughly able to identify plant groups when vegetation communities have a sufficient homogenous, wealthy and ecologically separable zones.
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