A modelling framework to estimate canopy height at local scale using optical and radar images paired with GEDI measurements in Mediterranean-like landscapes
The near-worldwide coverage of spaceborne LiDAR data from the GEDI offers unprecedented opportunities for mapping canopy height (CH). Notwithstanding the sensitivity of the GEDI to forests’ vertical structure, it provides sparse sampling measurements, which hinder gap-free mapping. Several machine and deep learning models that resort to optical, radar, and GEDI have been tested to produce gap-free CH maps. Not all factors affecting the accuracy and consistency of these GEDI-fused products have been explored. Specifically, the sampling fraction coverage and the geolocation correction of footprints on-orbit positions have not been deeply studied. In this article, a collection of data from 15 study areas, characterised by Mediterranean landscapes, had their CH mapped resorting to Sentinel-1/2, ALOS-2, ancillary data, and a locally fitted extreme gradient boosting regressor. The produced maps had an average %RMSE of 32.41%, outperforming the other two global products in Mediterranean regions. Additionally, the geolocation correction of the GEDI footprints was limited to 1.23 percentage points. This experiment was able to demonstrate three key points: (1) the importance of including InSAR in the optical/SAR synergy; (2) the reduced impact of collocating GEDI footprints at the track level; and (3) the benefits of increasing GEDI-footprint coverage using multi-year data are hindered by temporal variation effects.