Deforestation detection using scattering power decomposition and optimal averaging of volume scattering power in tropical rainforest regions

Abstract

Synthetic aperture radar (SAR) has been a powerful tool for deforestation detection in tropical rainforests. Polarimetric SAR (POLSAR) data are acquired in a quad-polarization mode, and L-band POLSAR data in particular are one of the few SAR data types that preserve the dielectric properties and structures of the scatterer. POLSAR data consist of 2 × 2 scattering matrices and consequently offer superior target recognition compared with dual-polarization data. In this study, we applied scattering power decomposition suitable for detecting deforestation in near real-time to POLSAR data obtained from the Earth observation sensor Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2). The reflection symmetry condition is known to apply to natural distributed objects (i.e., the cross-correlation between co- and cross-polarization data is zero). Inspired by this, we theoretically and experimentally examined the volume scattering power component to distinguish natural forests from the surrounding area. Two important results were verified for natural forests: as the window size of the ensemble average increases, (i) the coherency matrix approaches a simple theoretical form and (ii) the volume scattering power becomes dominant among the scattering power components. Based on these results, we constructed an algorithm that applies scattering power decomposition for detecting deforestation. We produced reference data using high-resolution optical images and evaluated the performance of the derived deforestation map in the Amazon natural forest when employing various window sizes for the ensemble average. At an optimal window size of 15 × 15 pixels, the deforestation detection performance reached a user's accuracy of 94.9% ± 1.5%, a producer's accuracy of 72.3% ± 1.2%, and a kappa coefficient of 0.816 ± 0.0039. Sparse trees left after logging increased the volume scattering power and reduced the producer's accuracy. The proposed algorithm can contribute to deforestation detection with slightly lower accuracy than that of the annual map provided by Global Forest Change. Further, the proposed algorithm is robust to the seasonal variations in tropical rainforests and temporal variations in the deforestation process. Consequently, the proposed algorithm employing the six-component scattering power decomposition method can be utilized in near real-time without considering applicable areas in tropical rainforests. Subsequently, we applied our algorithm to dual-polarization data, which are acquired by PALSAR-2 much more frequently than POLSAR data. The false detection rate did not increase when using the dual-polarization data; however, the omission error increased considerably compared with that when using POLSAR data owing to the low total power obtained from the dual-polarization data.