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

dc.contributor.authorRishmawi, Khaldoun
dc.contributor.authorHuang, Chengquan
dc.contributor.authorZhan, Xiwu
dc.date.accessioned2023-11-07T17:49:10Z
dc.date.available2023-11-07T17:49:10Z
dc.date.issued2021-01-27
dc.description.abstractAccurate 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.
dc.description.urihttps://doi.org/10.3390/rs13030442
dc.identifierhttps://doi.org/10.13016/dspace/0gn2-o5et
dc.identifier.citationRishmawi, 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.
dc.identifier.urihttp://hdl.handle.net/1903/31294
dc.language.isoen_US
dc.publisherMDPI
dc.relation.isAvailableAtCollege of Behavioral & Social Sciencesen_us
dc.relation.isAvailableAtGeographyen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectVIIRS-NOAA 20
dc.subjectGEDI Ecosystem LiDAR
dc.subjectvegetation 3D structure
dc.subjectrandom forest regression models
dc.titleMonitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data
dc.typeArticle
local.equitableAccessSubmissionNo

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