Abstract:
Burned area extraction from optical imageries often has a major problem, that is the
presence of atmospheric particles. A potential alternative for burned area extraction is using SAR
imageries, those are relatively free of atmospheric interference. The purpose of this research was
to explore change detection capabilities of Sentinel-1 SAR polarimetry for burned area
extraction. The imagery used is dual-polarized Sentinel-1 (VV,VH). A number of polarimetric
transformation methods are applied for the purpose of change detection. Those are, single cross polarized log ratio, single co-polarized log ratio, dual polarized combination log ratio, dual
polarized multiple log ratio, and dual polarized ratio log ratio. For comparison, the Relativized
Burn Ratio (RBR) method was applied to the Sentinel-2 MSI imagery. The Otsu thresholding is
then applied to separate the burned area and the unburned area. The results of the research
showed that the single cross-polarized log ratio (ln(
0VHt1/
0VHt2)) transformation method was
the most accurate method. This method has an overall accuracy of 88.7665% (Kappa 0.7567). It
is more accurate than Sentinel-2 RBR, which has an overall accuracy of 81.8470% (Kappa
0.6383). Cross validation between Sentinel-1 SAR change detection and Sentinel-2 RBR does
not show a significant correlation. The highest correlation coefficient achieved is only 0.25. This
shows that burned area extraction between change detection from SAR imageries and RBR from
optical imageries has a different mechanism. SAR change detection tends to detect changes in
surface roughness, while NIR-based RBR tends to extract changes in leaf chlorophyll conditions
