Abstract
Accurate information on the global distribution and the three-dimensional (3D) structure of Earth’s forests is needed to assess forest biomass stocks and to project the future of the terrestrial Carbon sink. In spite of its importance, the 3D structure of forests continues to be the most crucial information gap in the observational archive. The Global Ecosystem Dynamics Investigation (GEDI) Light Detection and Ranging (LiDAR) sensor is providing an unprecedented near-global sampling of tropical and temperate forest structural properties. The integration of GEDI measurements with spatially-contiguous observations from polar orbiting optical satellite data therefore provides a unique opportunity to produce wall-to-wall maps of forests’ 3D structure. Here, we utilized Visible Infrared Imaging Radiometer Suite (VIIRS) annual metrics data to extrapolate GEDI-derived forest structure attributes into 1-km resolution contiguous maps of tree height (TH), canopy fraction cover (CFC), plant area index (PAI), and foliage height diversity (FHD) for the conterminous US (CONUS). The maps were validated using an independent subset of GEDI data. Validation results for TH (r2 = 0.8; RMSE = 3.35 m), CFC (r2 = 0.79; RMSE = 0.09), PAI (r2 = 0.76; RMSE = 0.41), and FHD (r2 = 0.83; RMSE = 0.25) demonstrated the robustness of VIIRS data for extrapolating GEDI measurements across the nation or even over larger areas. The methodology developed through this study may allow multi-decadal monitoring of changes in multiple forest structural attributes using consistent satellite observations acquired by orbiting and forthcoming VIIRS instruments.
Highlights
We investigate the potential of extrapolating Global Ecosystem Dynamics Investigation (GEDI)-derived forest structure attributes into 1-km resolution spatially-contiguous maps of tree height (TH), canopy fraction cover (CFC), plant area index (PAI), and foliage height diversity (FHD) for the conterminous US (CONUS) using Visible Infrared Imaging Radiometer Suite (VIIRS) annual metrics data
When the distribution of the differences between the VIIRS-derived and the GEDI-derived values were plotted for strata representing intervals drawn from the range of each canopy attribute, it was noticed that the canopy height model underestimated GEDI-derived canopy height data for tall (>27 m) forests and the Plant Area Index model underestimated the GEDIderived Plant Area Index at index values greater than 2 (Figure 3)
This study demonstrated the potential of using random forest regression models and VIIRS data to extrapolate the GEDI-derived forest structure attributes into wall-to-wall maps of Canopy height (CH), CFC, PAI, and FHD
Summary
The subsequent changes in forest biomass are thought to dominate changes in net terrestrial carbon flux, altering the global carbon cycle, affecting species diversity, and changing Earth’s climate [1,2,3]. The total amount of carbon contained in forest’s biomass and subsequently the future state of the terrestrial Carbon sink remain highly uncertain [4]. Constraining this uncertainty is paramount to safeguarding the future of our planet not least through informing sustainable local forest management policies, forest-based natural climate solutions, and international carbon emission reduction initiatives [5,6,7,8]
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