A QUANTITATIVE COMPARISON OF CHANGE-DETECTION TECHNIQUES

Abstract

Six change detection techniques to study land cover change associated with tropical forest (El
Rawashda forest reserve, Gedaref State, Sudan) were examined. 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 are supervised change detection using pixel
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 is not available for 2003, an extensive visual assessment; 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 accuracy assessment of 94%. This was followed by the MAD technique
with calculated accuracy assessment of 88.8%. However, among vegetation indices techniques,
TDVI stood out as better than NDVI and SAVI in its ability to accurately identify vegetation
change.