Document Type : Research Paper




In this study, three methods were evaluated for vegetation mapping. For remote sensing method, in addition to IRS data of LISSIII, Ddigital Elevation Model (DEM) and Normalized Difference Vegetation Index (NDVI) were used for classification of 14 classes of land covers mostly vegetation types using a maximum likelihood algorithm. After comparing of produced vegetation maps, overall accuracy and Kappa index were 82% and 79.43% respectively when only the IRS were used. Whereas, the overall accuracy and Kappa index were increased to 93% and 90.63% respectively, when ancillary data of DEM and NDVI were added.
Slope, slope direction, elevation above sea level, annual precipitation, temperature, and sun radiation were selected as the main physiographic after a broad literature review. Then the relationship between of these six factors with vegetation types was evaluated. so a multivariate logistic regression was used to draw vegetation map of the study area based on the sixth independent variables. The result showed a predicted vegetation map of 47.08% accuracy.Finally, in the morphological method, relationship between three maps of lithology, undulating form of geomorphology and faces with vegetation/land cover were determined using a neural network synthetic approach and predict vegetation map was drawn as the output. The accuracy of resulted map was 39.1%. Comparison of accuracy of vegetation mapping by three methods of RS, physiographic and geomorphological methods revealed that RS method of vegetation/land cover mapping is significantly promising due to a meaningfully higher accuracy even without using ancillary data such as DEM and NDVI in this method.


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