Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data
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 inf...
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| Format: | Article |
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MDPI AG
2021-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/3/442 |
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| author | Khaldoun Rishmawi Chengquan Huang Xiwu Zhan |
| author_facet | Khaldoun Rishmawi Chengquan Huang Xiwu Zhan |
| author_sort | Khaldoun Rishmawi |
| collection | DOAJ |
| description | 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 (<i>r</i><sup>2</sup> = 0.8; RMSE = 3.35 m), CFC (<i>r</i><sup>2</sup> = 0.79; RMSE = 0.09), PAI (<i>r</i><sup>2</sup> = 0.76; RMSE = 0.41), and FHD (<i>r</i><sup>2</sup> = 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. |
| first_indexed | 2024-03-09T03:32:15Z |
| format | Article |
| id | doaj.art-31f4fdc95af24c5fb00cc2220ab7c57a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T03:32:15Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-31f4fdc95af24c5fb00cc2220ab7c57a2023-12-03T14:53:23ZengMDPI AGRemote Sensing2072-42922021-01-0113344210.3390/rs13030442Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS DataKhaldoun Rishmawi0Chengquan Huang1Xiwu Zhan2Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USADepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USANOAA-NESDIS Center for Satellite Applications and Research (STAR), 5830 University Research Court, College Park, MD 20740, USAAccurate 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 (<i>r</i><sup>2</sup> = 0.8; RMSE = 3.35 m), CFC (<i>r</i><sup>2</sup> = 0.79; RMSE = 0.09), PAI (<i>r</i><sup>2</sup> = 0.76; RMSE = 0.41), and FHD (<i>r</i><sup>2</sup> = 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.https://www.mdpi.com/2072-4292/13/3/442VIIRS-NOAA 20GEDI Ecosystem LiDARvegetation 3D structurerandom forest regression models |
| spellingShingle | Khaldoun Rishmawi Chengquan Huang Xiwu Zhan Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data Remote Sensing VIIRS-NOAA 20 GEDI Ecosystem LiDAR vegetation 3D structure random forest regression models |
| title | Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data |
| title_full | Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data |
| title_fullStr | Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data |
| title_full_unstemmed | Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data |
| title_short | Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data |
| title_sort | monitoring key forest structure attributes across the conterminous united states by integrating gedi lidar measurements and viirs data |
| topic | VIIRS-NOAA 20 GEDI Ecosystem LiDAR vegetation 3D structure random forest regression models |
| url | https://www.mdpi.com/2072-4292/13/3/442 |
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