Detection of land cover changes in El Rawashda Forest, Sudan: A systematic comparison

This study compares six change detection techniques to study land cover change associated with tropical forest (El Rawashda forest reserve, Gedaref State, Sudan). For this site, Landsat 7 Enhanced Thematic Mapper (ETM+) data acquired on March 22, 2003 and Aster data acquired on February 26, 2006 were used. The change detection techniques employed in this study were Post-Classification Comparison (PCC), image differencing of different vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI) and Transformed Difference Vegetation Index (TDVI)), Principal Component Analysis (PCA), Multivariate Alteration Detection (MAD), Change Vector Analysis (CVA) and Tasseled Cap Analysis (TCA). As field validation data did not exist for 2003, a manual classification was performed, then a change map was conducted to locate and identify change in vegetation. This change map was used as a reference to quantitatively assess the accuracy of each change-detection techniques. Based on accuracy assessment, the most successful technique was the PCC technique with an accuracy of 94%. This was followed by the MAD technique with an accuracy 88.8%. However, among vegetation indices techniques, TDVI stood out as better than NDVI and SAVI in its ability to accurately identify vegetation change.