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


Affiliations
1 University of Agricultural and Horticultural Sciences, Shivamogga College of Forestry, Ponnampet, Kodagu 571 216, India
 

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.

Keywords

Forest Cover, Mapping, Multi-sensor Data Fusion, Principal Component Analysis, Remote Sensing, Random Forest Algorithm.
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  • Fusion of Complementary Information of SAR and Optical Data for Forest Cover Mapping using Random Forest Algorithm

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Authors

Naveen Veerabhadraswamy
University of Agricultural and Horticultural Sciences, Shivamogga College of Forestry, Ponnampet, Kodagu 571 216, India
Guddappa M. Devagiri
University of Agricultural and Horticultural Sciences, Shivamogga College of Forestry, Ponnampet, Kodagu 571 216, India
Anil Kumar Khaple
University of Agricultural and Horticultural Sciences, Shivamogga College of Forestry, Ponnampet, Kodagu 571 216, India

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.

Keywords


Forest Cover, Mapping, Multi-sensor Data Fusion, Principal Component Analysis, Remote Sensing, Random Forest Algorithm.

References





DOI: https://doi.org/10.18520/cs%2Fv120%2Fi1%2F193-199