Spatial Characterization of Woody Species Diversity in Tropical Savannas Using GEDI and Optical Data
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
Affiliations
Franciel Eduardo Rex
Department of Forestry Engineering, Federal University of Paraná-UFPR, Curitiba 80050-380, PR, Brazil
Carlos Alberto Silva
Forest Biometrics and Remote Sensing Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USA
Eben North Broadbent
Spatial Ecology and Conservation (SPEC) Laboratory, School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA
Ana Paula Dalla Corte
Department of Forestry Engineering, Federal University of Paraná-UFPR, Curitiba 80050-380, PR, Brazil
Rodrigo Leite
NASA Postdoctoral Program Fellow, Goddard Space Flight Center, Greenbelt, MD 20771, USA
Andrew Hudak
US Department of Agriculture, Forest Service, Rocky Mountain Research Station, 1221 South Main Street, Moscow, ID 83843, USA
Caio Hamamura
Federal Institute of Education, Science and Technology of São Paulo-IFSP, Cubatão 11533-160, SP, Brazil
Hooman Latifi
Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, P.O. Box 15875-4416, Tehran 15418-49611, Iran
Jingfeng Xiao
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Jeff W. Atkins
USDA Forest Service, Southern Research Station, P.O. Box 400, New Ellenton, SC 29809, USA
Cibele Amaral
Environmental Data Science Innovation and Inclusion Lab (ESIIL), Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA
Ernandes Macedo da Cunha Neto
Department of Forestry Engineering, Federal University of Paraná-UFPR, Curitiba 80050-380, PR, Brazil
Adrian Cardil
Technosylva Inc., La Jolla, CA 92037, USA
Angelica M. Almeyda Zambrano
AX Spatial Ecology and Conservation (SPEC) Lab, Center for Latin American Studies, University of Florida, Gainesville, FL 32611, USA
Veraldo Liesenberg
Department of Forest Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Lages 88520-000, SC, Brazil
Jingjing Liang
Forest Advance Computing and Artificial Intelligence Laboratory, Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907, USA
Danilo Roberti Alves De Almeida
Department of Forest Sciences, “Luiz de Queiroz” College of Agriculture, University of São Paulo (USP/ESALQ), Piracicaba 13418-900, SP, Brazil
Carine Klauberg
Forest Biometrics and Remote Sensing Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USA
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 (R2 = 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.
