Journal of Geography  & Natural Disasters
Open Access

Abstract

Evaluation of IRS1D-LISS-III and Landsat 8-OLI Images for Mapping in Maroon Riparian Forest, Iran

Firoozynejad M and Torahi AA

In order to compare the mapping by IRS1D-LISSIII and Landsat 8-OLI data in Riparian forest of Maroon Behbahan of Iran, the small window of panchromatic and multispectral images of IRS1D-LISSIII, and Landsat 8-OLI satellites data have been selected at Maroon riparian forest. Quality of data and radiometric error has been checked. Using 25 ground control points, geometric correction, whose accuracy was less than 5.0 pixels, has been implemented. Classification of images has been performed by supervised method using Maximum Likelihood and SVM algorithms for seven classes on the original bands. Moreover, Jeffreys-Matusita Method has been employed in order to test the separability of classes. Considering to the results, it can be concluded that IRS1D-LISS-III and Landsat 8-OLI data have suitable ability for mapping Maroon riparian forest as well as classification of forest to separate land use. Overall accuracy of classification obtained by OLI images and using SVM algorithm on original bands was 92/95. Furthermore, kappa coefficient was 0/85 percent which was the best result. In general, it should be notified that according to the presented study, OLI sensor can be considered as a more accurate method than LISS Ш in order to map Maroon riparian forests.