Statewide Forest Canopy Cover Mapping of Florida Using Synergistic Integration of Spaceborne LiDAR, SAR, and Optical Imagery
Southern U.S. forests are essential for carbon storage and timber production but are increasingly impacted by natural disturbances, highlighting the need to understand their dynamics and recovery. Canopy cover is a key indicator of forest health and resilience. Advances in remote sensing, such as NA...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/320 |
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| author | Monique Bohora Schlickmann Inacio Thomaz Bueno Denis Valle William M. Hammond Susan J. Prichard Andrew T. Hudak Carine Klauberg Mauro Alessandro Karasinski Kody Melissa Brock Kleydson Diego Rocha Jinyi Xia Rodrigo Vieira Leite Pedro Higuchi Ana Carolina da Silva Gabriel Maximo da Silva Gina R. Cova Carlos Alberto Silva |
| author_facet | Monique Bohora Schlickmann Inacio Thomaz Bueno Denis Valle William M. Hammond Susan J. Prichard Andrew T. Hudak Carine Klauberg Mauro Alessandro Karasinski Kody Melissa Brock Kleydson Diego Rocha Jinyi Xia Rodrigo Vieira Leite Pedro Higuchi Ana Carolina da Silva Gabriel Maximo da Silva Gina R. Cova Carlos Alberto Silva |
| author_sort | Monique Bohora Schlickmann |
| collection | DOAJ |
| description | Southern U.S. forests are essential for carbon storage and timber production but are increasingly impacted by natural disturbances, highlighting the need to understand their dynamics and recovery. Canopy cover is a key indicator of forest health and resilience. Advances in remote sensing, such as NASA’s GEDI spaceborne LiDAR, enable more precise mapping of canopy cover. Although GEDI provides accurate data, its limited spatial coverage restricts large-scale assessments. To address this, we combined GEDI with Synthetic Aperture Radar (SAR), and optical imagery (Sentinel-1 GRD and Landsat–Sentinel Harmonized (HLS)) data to create a comprehensive canopy cover map for Florida. Using a random forest algorithm, our model achieved an R<sup>2</sup> of 0.69, RMSD of 0.17, and MD of 0.001, based on out-of-bag samples for internal validation. Geographic coordinates and the red spectral channel emerged as the most influential predictors. External validation with airborne laser scanning (ALS) data across three sites yielded an R<sup>2</sup> of 0.70, RMSD of 0.29, and MD of −0.22, confirming the model’s accuracy and robustness in unseen areas. Statewide analysis showed lower canopy cover in southern versus northern Florida, with wetland forests exhibiting higher cover than upland sites. This study demonstrates the potential of integrating multiple remote sensing datasets to produce accurate vegetation maps, supporting forest management and sustainability efforts in Florida. |
| format | Article |
| id | doaj-art-e13d2699ec5e4a4a86c5a15ad7c2f8da |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-e13d2699ec5e4a4a86c5a15ad7c2f8da2025-01-24T13:48:06ZengMDPI AGRemote Sensing2072-42922025-01-0117232010.3390/rs17020320Statewide Forest Canopy Cover Mapping of Florida Using Synergistic Integration of Spaceborne LiDAR, SAR, and Optical ImageryMonique Bohora Schlickmann0Inacio Thomaz Bueno1Denis Valle2William M. Hammond3Susan J. Prichard4Andrew T. Hudak5Carine Klauberg6Mauro Alessandro Karasinski7Kody Melissa Brock8Kleydson Diego Rocha9Jinyi Xia10Rodrigo Vieira Leite11Pedro Higuchi12Ana Carolina da Silva13Gabriel Maximo da Silva14Gina R. Cova15Carlos Alberto Silva16Forest Biometrics, Remote Sensing, and Artificial Intelligence Laboratory (Silva Lab)—School of Forest, Fisheries and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USAForest Biometrics, Remote Sensing, and Artificial Intelligence Laboratory (Silva Lab)—School of Forest, Fisheries and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USAQuantitative Ecology, Conservation, and Remote Sensing Laboratory (Valle Lab)—School of Forest, Fisheries and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USAPlant Ecophysiology Laboratory (Ecophys Lab)—Agronomy Department, University of Florida, Gainesville, FL 32611, USASchool of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USAUSDA Forest Service, Rocky Mountain Research Station, Moscow, ID 83843, USAForest Biometrics, Remote Sensing, and Artificial Intelligence Laboratory (Silva Lab)—School of Forest, Fisheries and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USABIOFIX Research Center—Department of Forest Engineering, Federal University of Parana, Curitiba 80210-170, BrazilForest Biometrics, Remote Sensing, and Artificial Intelligence Laboratory (Silva Lab)—School of Forest, Fisheries and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USAGlobal Forest Dynamics Lab—School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USAForest Biometrics, Remote Sensing, and Artificial Intelligence Laboratory (Silva Lab)—School of Forest, Fisheries and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USANASA Goddard Space Flight Center Greenbelt, Greenbelt, MD 20771, USAForest Engineering Department, Santa Catarina State University, Av Luiz de Camões, 2090 Conta Dinheiro, Lages 88520-000, BrazilForest Engineering Department, Santa Catarina State University, Av Luiz de Camões, 2090 Conta Dinheiro, Lages 88520-000, BrazilForest Biometrics, Remote Sensing, and Artificial Intelligence Laboratory (Silva Lab)—School of Forest, Fisheries and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USASchool of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USAForest Biometrics, Remote Sensing, and Artificial Intelligence Laboratory (Silva Lab)—School of Forest, Fisheries and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USASouthern U.S. forests are essential for carbon storage and timber production but are increasingly impacted by natural disturbances, highlighting the need to understand their dynamics and recovery. Canopy cover is a key indicator of forest health and resilience. Advances in remote sensing, such as NASA’s GEDI spaceborne LiDAR, enable more precise mapping of canopy cover. Although GEDI provides accurate data, its limited spatial coverage restricts large-scale assessments. To address this, we combined GEDI with Synthetic Aperture Radar (SAR), and optical imagery (Sentinel-1 GRD and Landsat–Sentinel Harmonized (HLS)) data to create a comprehensive canopy cover map for Florida. Using a random forest algorithm, our model achieved an R<sup>2</sup> of 0.69, RMSD of 0.17, and MD of 0.001, based on out-of-bag samples for internal validation. Geographic coordinates and the red spectral channel emerged as the most influential predictors. External validation with airborne laser scanning (ALS) data across three sites yielded an R<sup>2</sup> of 0.70, RMSD of 0.29, and MD of −0.22, confirming the model’s accuracy and robustness in unseen areas. Statewide analysis showed lower canopy cover in southern versus northern Florida, with wetland forests exhibiting higher cover than upland sites. This study demonstrates the potential of integrating multiple remote sensing datasets to produce accurate vegetation maps, supporting forest management and sustainability efforts in Florida.https://www.mdpi.com/2072-4292/17/2/320data fusionforest structure estimationGEDI datamachine learning modelssouthern forests |
| spellingShingle | Monique Bohora Schlickmann Inacio Thomaz Bueno Denis Valle William M. Hammond Susan J. Prichard Andrew T. Hudak Carine Klauberg Mauro Alessandro Karasinski Kody Melissa Brock Kleydson Diego Rocha Jinyi Xia Rodrigo Vieira Leite Pedro Higuchi Ana Carolina da Silva Gabriel Maximo da Silva Gina R. Cova Carlos Alberto Silva Statewide Forest Canopy Cover Mapping of Florida Using Synergistic Integration of Spaceborne LiDAR, SAR, and Optical Imagery Remote Sensing data fusion forest structure estimation GEDI data machine learning models southern forests |
| title | Statewide Forest Canopy Cover Mapping of Florida Using Synergistic Integration of Spaceborne LiDAR, SAR, and Optical Imagery |
| title_full | Statewide Forest Canopy Cover Mapping of Florida Using Synergistic Integration of Spaceborne LiDAR, SAR, and Optical Imagery |
| title_fullStr | Statewide Forest Canopy Cover Mapping of Florida Using Synergistic Integration of Spaceborne LiDAR, SAR, and Optical Imagery |
| title_full_unstemmed | Statewide Forest Canopy Cover Mapping of Florida Using Synergistic Integration of Spaceborne LiDAR, SAR, and Optical Imagery |
| title_short | Statewide Forest Canopy Cover Mapping of Florida Using Synergistic Integration of Spaceborne LiDAR, SAR, and Optical Imagery |
| title_sort | statewide forest canopy cover mapping of florida using synergistic integration of spaceborne lidar sar and optical imagery |
| topic | data fusion forest structure estimation GEDI data machine learning models southern forests |
| url | https://www.mdpi.com/2072-4292/17/2/320 |
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