Statewide Forest Canopy Cover Mapping of Florida Using Synergistic Integration of Spaceborne LiDAR, SAR, and Optical Imagery

被引:1
|
作者
Schlickmann, Monique Bohora [1 ]
Bueno, Inacio Thomaz [1 ]
Valle, Denis [2 ]
Hammond, William M. [3 ]
Prichard, Susan J. [4 ]
Hudak, Andrew T. [5 ]
Klauberg, Carine [1 ]
Karasinski, Mauro Alessandro [6 ]
Brock, Kody Melissa [1 ]
Rocha, Kleydson Diego [7 ]
Xia, Jinyi [1 ]
Vieira Leite, Rodrigo [8 ]
Higuchi, Pedro [9 ]
da Silva, Ana Carolina [9 ]
Maximo da Silva, Gabriel [1 ]
Cova, Gina R. [4 ]
Silva, Carlos Alberto [1 ]
机构
[1] Univ Florida, Sch Forest Fisheries & Geomat Sci, Forest Biometr Remote Sensing & Artificial Intelli, Silva Lab, POB 110410, Gainesville, FL 32611 USA
[2] Univ Florida, Sch Forest, Remote Sensing Lab, Quantitat Ecol Conservat & Remote Sensing Lab Vall, POB 110410, Gainesville, FL 32611 USA
[3] Univ Florida, Agron Dept, Plant Ecophysiol Lab, Ecophys Lab, Gainesville, FL 32611 USA
[4] Univ Washington, Sch Environm & Forest Sci, Seattle, WA 98195 USA
[5] USDA, Forest Serv, Rocky Mt Res Stn, Moscow, ID 83843 USA
[6] Univ Fed Parana, BIOFIX Res Ctr, Dept Forest Engn, BR-80210170 Curitiba, Brazil
[7] Univ Florida, Sch Forest Fisheries & Geomat Sci, Global Forest Dynam Lab, Gainesville, FL 32611 USA
[8] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA
[9] Santa Catarina State Univ, Forest Engn Dept, Av Luiz de Camoes,2090 Conta Dinheiro, BR-88520000 Lages, Brazil
基金
美国食品与农业研究所;
关键词
data fusion; forest structure estimation; GEDI data; machine learning models; southern forests; ABOVEGROUND BIOMASS; VEGETATION INDEX; AIRBORNE LIDAR; REMOTE; LANDSAT; SATELLITE; CLIMATE; GROWTH; BRAZIL;
D O I
10.3390/rs17020320
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 R2 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 R2 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.
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页数:32
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