Fusion of Complementary Information of SAR and Optical Data for Forest Cover Mapping using Random Forest Algorithm

Abstract

We developed a methodological framework for accurate forest cover mapping of Shivamogga taluk, Karnataka, India using multi-sensor remote sensing data. For this, we used Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data. These datasets were fused using principal component analysis technique, and forest and non-forest areas were classified using a random forest (RF) algorithm. Backscatter analysis was performed to understand the variation in γ 0 values between forest and non-forest sample points. The average γ 0 values of forest were higher than the non-forest samples in VH and VV polarizations. The average γ 0 backscatter difference between forest and non-forest samples was 8.50 dB in VH and 5.64 dB in VV polarization. The highest classification accuracy of 92.25% was achieved with the multi-sensor fused data compared to the single-sensor SAR (78.75%) and optical (83.10%) data. This study demonstrates that RF classification of multi-sensor data fusion improves the classification accuracy by 13.50% and 9.15%, compared to SAR and optical data.