Forest Biomass Change Estimation Using Multi-Acquisition SAR and GEDI

In this paper we present the results of a pilot project determining year-over-year above ground woody biomass (AGB) change from high resolution RADARSAT-2 C-band synthetic aperture radar (SAR) imagery, GEDI LiDAR data, and in-situ field data. Textural classification of the SAR imagery complements the sparse coverage of GEDI and field AGB measurements permitting high resolution biomass mapping over large areas. By assigning biomass values to the derived classes in temporally filtered SAR images we can robustly estimate annual changes in AGB. We illustrate the fidelity of this approach with results over a densely forested site.