Investigating the Influence of GEDI Vegetation Penetration on Canopy Height Estimation

This paper evaluates GEDI's canopy height estimation accuracy in dense tropical forests located in Mayotte Island. It examines GEDI's ability to penetrate canopies and detect the ground, which is crucial for reliable estimates. The study tests the use of a single GEDI height metric (rh_95) in comparison with regression models using various GEDI metrics to enhance accuracy. Beam sensitivity plays a pivotal role, as it impacts significantly GEDI return waveforms and the subsequent derived height estimates. In the context of our study, GEDI tends to underestimate heights above 15 meters. Regression models outperform rh_95, mitigating the impact of beam sensitivity and canopy height (RMSE decreasing from 6.6 m to 5.5 m, bias going from -1.9 m to 0.0 m). They provide unbiased estimates, offering improved accuracies regardless of these factors. This study emphasizes GEDI's limitations and highlights regression models' potential to refine canopy height estimations in complex ecosystems where signal penetration is challenging.