Mapping large-scale pantropical forest canopy height by integrating GEDI lidar and TanDEM-X InSAR data

Abstract

NASA's Global Ecosystem Dynamic Investigation (GEDI) mission provides billions of lidar samples of canopy structure over the Earth's temperate and pantropical forests. Using the GEDI sample data alone, gridded height and biomass products have been created at a spatial resolution of 1 km or coarser. However, this resolution may be too coarse for some applications. In this study, we present a new method of mapping high spatial resolution forest height across large areas using fusion of data acquired by GEDI and TanDEM-X (TDX) Interferometric Synthetic Aperture Radar (InSAR). Our method utilizes GEDI waveforms to provide vertical profiles of scatterers needed to invert a physically-based InSAR model to solve for canopy height. We then use 2-year GEDI canopy height and adaptive wavenumber (k_Z)-based calibration models to reduce errors in the inverted canopy height caused by the limited penetration capability of the X-band signal in dense tropical forests and the impact of terrain. We apply this novel method over large areas including Gabon, Mexico, French Guiana and most of the Amazon basin, and generate continuous forest height products at 25 m and 100 m. After validating against airborne lidar data, we find that our canopy height products have a bias of 0.31 m and 0.46 m, and a root mean square error (RMSE) of 8.48 m (30.02 %) and 6.91 m (24.08 %) at 25 m and 100 m respectively, for all sites combined. Compared to existing data products that integrate GEDI with passive optical data using machine learning approaches, our method reduces bias, has a lower RMSE, and does not saturate for tall canopy heights up to 56 m. A key feature of this study is that our canopy height product is complemented with an uncertainty of prediction map which provides information on the predictor's uncertainty around the actual value —an advancement over the standard error maps used in earlier studies, which provide uncertainty around the expectation of the predicted value. This integration approach enables the first-ever accurate and high-resolution mapping of forest canopy heights at unprecedented large areas from GEDI and TDX InSAR data fusion, serving as an essential foundation for pantropical aboveground biomass mapping.