Spatial Characterization of Woody Species Diversity in Tropical Savannas Using GEDI and Optical Data
Developing the capacity to monitor species diversity worldwide is of great importance in halting biodiversity loss. To this end, remote sensing plays a unique role. In this study, we evaluate the potential of Global Ecosystem Dynamics Investigation (GEDI) data, combined with conventional satellite o...
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2025-01-01
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| author | Franciel Eduardo Rex Carlos Alberto Silva Eben North Broadbent Ana Paula Dalla Corte Rodrigo Leite Andrew Hudak Caio Hamamura Hooman Latifi Jingfeng Xiao Jeff W. Atkins Cibele Amaral Ernandes Macedo da Cunha Neto Adrian Cardil Angelica M. Almeyda Zambrano Veraldo Liesenberg Jingjing Liang Danilo Roberti Alves De Almeida Carine Klauberg |
| author_facet | Franciel Eduardo Rex Carlos Alberto Silva Eben North Broadbent Ana Paula Dalla Corte Rodrigo Leite Andrew Hudak Caio Hamamura Hooman Latifi Jingfeng Xiao Jeff W. Atkins Cibele Amaral Ernandes Macedo da Cunha Neto Adrian Cardil Angelica M. Almeyda Zambrano Veraldo Liesenberg Jingjing Liang Danilo Roberti Alves De Almeida Carine Klauberg |
| author_sort | Franciel Eduardo Rex |
| collection | DOAJ |
| description | Developing the capacity to monitor species diversity worldwide is of great importance in halting biodiversity loss. To this end, remote sensing plays a unique role. In this study, we evaluate the potential of Global Ecosystem Dynamics Investigation (GEDI) data, combined with conventional satellite optical imagery and climate reanalysis data, to predict in situ alpha diversity (Species richness, Simpson index, and Shannon index) among tree species. Data from Sentinel-2 optical imagery, ERA-5 climate data, SRTM-DEM imagery, and simulated GEDI data were selected for the characterization of diversity in four study areas. The integration of ancillary data can improve biodiversity metrics predictions. Random Forest (RF) regression models were suitable for estimating tree species diversity indices from remote sensing variables. From these models, we generated diversity index maps for the entire Cerrado using all GEDI data available in orbit. For all models, the structural metric Foliage Height Diversity (FHD) was selected; the Renormalized Difference Vegetation Index (RDVI) was also selected in all species diversity models. For the Shannon model, two GEDI variables were selected. Overall, the models indicated performances for species diversity ranging from (R<sup>2</sup> = 0.24 to 0.56). In terms of RMSE%, the Shannon model had the lowest value among the diversity indices (31.98%). Our results suggested that the developed models are valuable tools for assessing species diversity in tropical savanna ecosystems, although each model can be chosen based on the objectives of a given study, the target amount of performance/error, and the availability of data. |
| format | Article |
| id | doaj-art-edb2e6d1eb1f4df4a80f6c2e9d61b915 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-edb2e6d1eb1f4df4a80f6c2e9d61b9152025-01-24T13:48:27ZengMDPI AGSensors1424-82202025-01-0125230810.3390/s25020308Spatial Characterization of Woody Species Diversity in Tropical Savannas Using GEDI and Optical DataFranciel Eduardo Rex0Carlos Alberto Silva1Eben North Broadbent2Ana Paula Dalla Corte3Rodrigo Leite4Andrew Hudak5Caio Hamamura6Hooman Latifi7Jingfeng Xiao8Jeff W. Atkins9Cibele Amaral10Ernandes Macedo da Cunha Neto11Adrian Cardil12Angelica M. Almeyda Zambrano13Veraldo Liesenberg14Jingjing Liang15Danilo Roberti Alves De Almeida16Carine Klauberg17Department of Forestry Engineering, Federal University of Paraná-UFPR, Curitiba 80050-380, PR, BrazilForest Biometrics and Remote Sensing Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USASpatial Ecology and Conservation (SPEC) Laboratory, School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USADepartment of Forestry Engineering, Federal University of Paraná-UFPR, Curitiba 80050-380, PR, BrazilNASA Postdoctoral Program Fellow, Goddard Space Flight Center, Greenbelt, MD 20771, USAUS Department of Agriculture, Forest Service, Rocky Mountain Research Station, 1221 South Main Street, Moscow, ID 83843, USAFederal Institute of Education, Science and Technology of São Paulo-IFSP, Cubatão 11533-160, SP, BrazilFaculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, P.O. Box 15875-4416, Tehran 15418-49611, IranEarth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USAUSDA Forest Service, Southern Research Station, P.O. Box 400, New Ellenton, SC 29809, USAEnvironmental Data Science Innovation and Inclusion Lab (ESIIL), Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USADepartment of Forestry Engineering, Federal University of Paraná-UFPR, Curitiba 80050-380, PR, BrazilTechnosylva Inc., La Jolla, CA 92037, USAAX Spatial Ecology and Conservation (SPEC) Lab, Center for Latin American Studies, University of Florida, Gainesville, FL 32611, USADepartment of Forest Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Lages 88520-000, SC, BrazilForest Advance Computing and Artificial Intelligence Laboratory, Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907, USADepartment of Forest Sciences, “Luiz de Queiroz” College of Agriculture, University of São Paulo (USP/ESALQ), Piracicaba 13418-900, SP, BrazilForest Biometrics and Remote Sensing Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USADeveloping the capacity to monitor species diversity worldwide is of great importance in halting biodiversity loss. To this end, remote sensing plays a unique role. In this study, we evaluate the potential of Global Ecosystem Dynamics Investigation (GEDI) data, combined with conventional satellite optical imagery and climate reanalysis data, to predict in situ alpha diversity (Species richness, Simpson index, and Shannon index) among tree species. Data from Sentinel-2 optical imagery, ERA-5 climate data, SRTM-DEM imagery, and simulated GEDI data were selected for the characterization of diversity in four study areas. The integration of ancillary data can improve biodiversity metrics predictions. Random Forest (RF) regression models were suitable for estimating tree species diversity indices from remote sensing variables. From these models, we generated diversity index maps for the entire Cerrado using all GEDI data available in orbit. For all models, the structural metric Foliage Height Diversity (FHD) was selected; the Renormalized Difference Vegetation Index (RDVI) was also selected in all species diversity models. For the Shannon model, two GEDI variables were selected. Overall, the models indicated performances for species diversity ranging from (R<sup>2</sup> = 0.24 to 0.56). In terms of RMSE%, the Shannon model had the lowest value among the diversity indices (31.98%). Our results suggested that the developed models are valuable tools for assessing species diversity in tropical savanna ecosystems, although each model can be chosen based on the objectives of a given study, the target amount of performance/error, and the availability of data.https://www.mdpi.com/1424-8220/25/2/308Cerradoalpha diversityGEDILiDARimagerymodeling |
| spellingShingle | Franciel Eduardo Rex Carlos Alberto Silva Eben North Broadbent Ana Paula Dalla Corte Rodrigo Leite Andrew Hudak Caio Hamamura Hooman Latifi Jingfeng Xiao Jeff W. Atkins Cibele Amaral Ernandes Macedo da Cunha Neto Adrian Cardil Angelica M. Almeyda Zambrano Veraldo Liesenberg Jingjing Liang Danilo Roberti Alves De Almeida Carine Klauberg Spatial Characterization of Woody Species Diversity in Tropical Savannas Using GEDI and Optical Data Sensors Cerrado alpha diversity GEDI LiDAR imagery modeling |
| title | Spatial Characterization of Woody Species Diversity in Tropical Savannas Using GEDI and Optical Data |
| title_full | Spatial Characterization of Woody Species Diversity in Tropical Savannas Using GEDI and Optical Data |
| title_fullStr | Spatial Characterization of Woody Species Diversity in Tropical Savannas Using GEDI and Optical Data |
| title_full_unstemmed | Spatial Characterization of Woody Species Diversity in Tropical Savannas Using GEDI and Optical Data |
| title_short | Spatial Characterization of Woody Species Diversity in Tropical Savannas Using GEDI and Optical Data |
| title_sort | spatial characterization of woody species diversity in tropical savannas using gedi and optical data |
| topic | Cerrado alpha diversity GEDI LiDAR imagery modeling |
| url | https://www.mdpi.com/1424-8220/25/2/308 |
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