Ecology, Environment and Conservation Paper

VEGETATION CHANGE DETECTION BASED ON IRS AND LANDSAT SATELLITES DATA

K. Solaimani, A. Ahmadpour, M. Shokri and J. Ghorbani

Abstract

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.