Abstract

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 results suggest that the use of ancillary data that are sensitive to biomass, such as heights from TDX, with observations of ecosystem structure from GEDI, has the potential to both improve accuracies and/or provide finer spatial resolution, than what is achievable through either mission by itself. We further demonstrate that the GEDI data themselves may be used to parameterize models that invert height from TDX, leading to more accurate height maps for use in biomass estimation. Lastly, our results illustrate the benefits of using parametric estimation theory to quantify uncertainties from both sampling and modeling errors that arise in the fusion of multi-modal remote sensing data at various spatial scales.

Keywords

Forest biomass; Fusion; Lidar; GEDI; InSAR; TanDEM-X; Hybrid inference; Hierarchical model-based inference

Published in

Remote Sensing of Environment
2019, Volume: 232, article number: 111283
Publisher: ELSEVIER SCIENCE INC

SLU Authors

• Saarela, Svetlana

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Ståhl, Göran

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Remote Sensing
Forest Science


Publication identifier

DOI: https://doi.org/10.1016/j.rse.2019.111283

Permanent link to this page (URI)

https://res.slu.se/id/publ/102284