Use of GEDI Signal and Environmental Parameters to Improve Canopy Height Estimation over Tropical Forest Ecosystems in Mayotte Island

Abstract

Canopy height is a fundamental parameter for describing forest ecosystems. GEDI is a spaceborne LiDAR system that was designed to measure vegetation’s vertical structure at a global scale. This study evaluates the accuracy of GEDI-derived canopy height estimates over complex tropical forests in Mayotte Island (Overseas France) characterized by moderate height and biomass levels as well as a relatively steep terrain. The influence of GEDI signal and environmental parameters (canopy height, beam sensitivity and slope) on height estimates was assessed. Linear as well as non-linear approaches were implemented using the GEDI L2A product to estimate canopy height. Empirical models were trained on reference data derived from airborne LiDAR scanning. The results showed that using regression models built on multiple GEDI metrics yielded improved accuracies compared to a direct estimation from a single GEDI height metric. Canopy height, beam sensitivity and terrain slope were found to have a significant impact on the height metrics derived from GEDI waveforms. Conversely, both linear and non-linear regression models produced unbiased and stable estimates.