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• W4386953659 abstract "Abstract Our ability to infer the impact of land use changes such as deforestation and reforestation on concentrations of atmospheric CO 2 rests upon accurate and spatially resolved estimates of forest structure, namely canopy height, aboveground biomass (AGB) and biomass density (AGBD). Since April 2019, NASA’s Global Ecosystem Dynamic Investigation (GEDI) mission has been collecting billions of lidar waveforms over the Earth’s temperate and pantropical forests. However, GEDI is a sampling mission and there are large gaps between tracks, as well as those caused by clouds. As a result, the standard gridded height products created from this mission are at 1 km resolution which provides nearly continuous coverage, but which may be too coarse for some applications. One way to provide wall-to-wall maps at finer spatial resolution is through fusion with other remotely sensed data that are responsive to ecosystem structure. The TanDEM-X twin satellites (abbreviated as TDX for convenience all through this study) have provided an unprecedented dataset of global SAR interferometry at X-band since 2010 and have been shown to be highly sensitive to height and other ecosystem structure, but with limited accuracy as compared to lidar. Building on our previous research for fusion of TDX and GEDI, we present a new method of mapping high spatial resolution forest heights across large areas using data from these two missions. Our method uses GEDI waveforms to provide the vertical profile of scatterers needed to invert a physically-based model to solve for canopy height. We assess the impact of using profiles generalized over large areas and develop a calibration method based on GEDI canopy heights to improve model performance. Our method reduces regional errors in forest height caused by the limited penetration capability of the X-band signal in dense tropical forests and the impact of terrain slope using adaptive wavenumber ( k Z )-based calibration models and over 2 years of GEDI height observations. In comparison to applying a general country-scale calibration model, the adaptive method selects more representative calibration coefficients for different forest types and landscapes. We apply the method over the entirety of Gabon, Mexico, French Guiana and most of the Amazon basin to produce continuous forest height products at 25m and 100 m. We find that the regional calibration approach produces the best results with a bias of 0.31 m, RMSE = 8.48 m (30.02%) at 25 m and a bias of 0.46 m, RMSE = 6.91 m (24.08%) at 100 m when cross-validated against airborne lidar data. In comparison to existing height data products that have used Machine Learning based approaches to fuse GEDI with passive optical data, such as Landsat and Sentinel-2, our methods produce maps with greatly reduced bias, lower RMSE, and they do not saturate for tall canopy heights up to 56 m. An important feature of this study is that our canopy height product is complemented with an uncertainty of prediction map which is a measure of the predictor’s uncertainty around the actual value rather than the standard error (a square root of estimated variance which quantifies the predictor’s expectation) used by earlier studies. The approach outlined here shows how the integration of GEDI data with TDX InSAR images enables high-resolution mapping of wall-to-wall forest canopy heights, providing an essential foundation for the global mapping of aboveground biomass." @default.
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• W4386953659 date "2023-09-22" @default.
• W4386953659 modified "2023-09-27" @default.
• W4386953659 title "Mapping Large-Scale Pantropical Forest Canopy Height by Integrating GEDI Lidar and TanDEM-X InSAR Data" @default.
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• W4386953659 doi "https://doi.org/10.21203/rs.3.rs-3306982/v1" @default.
• W4386953659 hasPublicationYear "2023" @default.
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