Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data

摘要

The Global Ecosystem Dynamic Investigation (GEDI) mission has been successfully launched to the International Space Station (ISS) on December 5th, 2018. While the sampling pattern of GEDI (8 transects with about 600 m across-track spacing) is sufficient to provide accurate biomass maps at the mission's required gridded resolution of 1 km, it is of significant interest to fuse GEDI data with ancillary, wall-to-wall remote sensing data from other sensors to provide increased accuracy, potentially higher resolution, and to fill gaps caused by track spacing, clumping in the ISS orbital tracks, as well as from clouds. In this paper, we examine the utility of combining simulated GEDI lidar observations with acquisitions from DLR's TerraSAR-X/TanDEM-X (TDX) InSAR mission to improve forest biomass estimation at a moderate resolution of 1 km, and to produce biomass maps at a much finer resolution of one hectare. We statistically characterize uncertainties before and after fusing simulated GEDI observations with TDX data across three different biomes: a temperate mixed deciduous forest, a temperate and mountainous coniferous forest, and a tropical forest. We implement the Random Volume over Ground (RVoG) model to derive canopy heights from TDX, using GEDI observations to parameterize key variables in the model. Our uncertainty framework is based on application of what are known as hybrid and hierarchical model-based inference, which allows for the parametric estimation of the variance of biomass estimation using both sampled and modeled data. We found improved accuracies after applying our fusion methods, with uncertainties at 1 km resolution ranging from 11%-20% across the different study sites of the mean biomass from the use of GEDI data alone, compared to 7%-12% achieved after the fusion. At one hectare resolution, comparison of the fusion derived biomass with field plots showed uncertainties associated with mean biomass predictions between 11%-27% across sites. Our resul