Quality assessment and detection of forest area changes using satellite images (Case study: Rustam, Fars)

This paper has been conducted to estimate the detection capability of LandSat satellite data for the detection and qualitative assessment of forest area and vegetation changes, land uses and vegetation percent in Rustam city. In this regard, using Landsat satellite images (1987 and 2010), forest land use map, Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) were obtained by Maximum Likelihood and Supervised Classification algorithms. The results showed that the area of extracted layers of forests with high, moderate and low density as well as barren regions has been estimated as 48.78, 348.67 and 281.42 and 81.68 km2, respectively. Assessing the classification results indicated that the overall accuracy, producer accuracy and user accuracy were given as 94.3, 91 and 95%, respectively and also, Commission error and Ommission error have been computed as 0.09 and 0.05. The highest and lowest producer accuracy estimated as 99 and 80% was related to low-density and high density forests and the highest and lowest percent of user accuracy given as 100 and 87% was attributed to the barren and moderate density forest. Also, comparing maps of vegetation percent and Ratio Vegetation index during 1987 and 2010 has shown no significant changes.