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Article

Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data

1
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
2
NOAA-NESDIS Center for Satellite Applications and Research (STAR), 5830 University Research Court, College Park, MD 20740, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(3), 442; https://doi.org/10.3390/rs13030442
Received: 24 November 2020 / Revised: 13 January 2021 / Accepted: 25 January 2021 / Published: 27 January 2021
(This article belongs to the Section Forest Remote Sensing)
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. View Full-Text
Keywords: VIIRS-NOAA 20; GEDI Ecosystem LiDAR; vegetation 3D structure; random forest regression models VIIRS-NOAA 20; GEDI Ecosystem LiDAR; vegetation 3D structure; random forest regression models
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MDPI and ACS Style

Rishmawi, K.; Huang, C.; Zhan, X. Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data. Remote Sens. 2021, 13, 442. https://doi.org/10.3390/rs13030442

AMA Style

Rishmawi K, Huang C, Zhan X. Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data. Remote Sensing. 2021; 13(3):442. https://doi.org/10.3390/rs13030442

Chicago/Turabian Style

Rishmawi, Khaldoun, Chengquan Huang, and Xiwu Zhan. 2021. "Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data" Remote Sensing 13, no. 3: 442. https://doi.org/10.3390/rs13030442

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