Improving aboveground biomass density mapping of arid and semi-arid vegetation by combining GEDI LiDAR, Sentinel-1/2 imagery and field data

Abstract

Accurate estimates of forest aboveground biomass density (AGBD) are essential to guide mitigation strategies for climate change. NASA's Global Ecosystem Dynamics Investigation (GEDI) project delivers full-waveform LiDAR data and provides a unique opportunity to improve AGBD estimates. However, global GEDI estimates (GEDI-L4A) have some constraints, such as lack of full coverage of AGBD maps and scarcity of training data for some biomes, particularly in arid areas. Moreover, uncertainties remain about the type of GEDI footprint that best penetrates the canopy and yields accurate vegetation structure metrics. This study estimates forest biomass of arid and semi-arid zones in two stages. First, a model was fitted to predict AGBD by relating GEDI and field data from different vegetation types, including xeric shrubland. Second, different footprint qualities were evaluated, and their AGBD was related to images from Sentinel-1 and -2 satellites to produce a wall-to-wall map of AGBD. The model fitted with field data and GEDI showed adequate performance (%RMSE = 45.0) and produced more accurate estimates than GEDI-L4A (%RMSE = 84.6). The wall-to-wall mapping model also performed well (%RMSE = 37.0) and substantially reduced the underestimation of AGBD for arid zones. This study highlights the advantages of fitting new models for AGBD estimation from GEDI and local field data, whose combination with satellite imagery yielded accurate wall-to-wall AGBD estimates with a 10 m resolution. The results of this study contribute new perspectives to improve the accuracy of AGBD estimates in arid zones, whose role in climate change mitigation may be markedly underestimated.