Spatial characterization of woody species diversity in tropical savannas using GEDI and optical data

Abstract

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.

This research was funded by the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama Nº 33/2018). Additional support was provided by the São Paulo Research Foundation (FAPESP, grant #2018/21338-3), MCTIC/CNPq Nº 28/2018 (grants #408785/2018-7; #438875/2018-4), CNPq Nº 09/2018 (grant #302891/2018-8), and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES), Finance Code 001. A.P.D.C. was supported by CNPq #88887.373249/2019-00. M.E.F. is a CNPq Research Fellow (grant #315699/2020-5). J. Xiao was supported by NASA (GEDI Science Team: 80NSSC24K0601). C.A. acknowledges the University of Colorado Boulder Earth Lab Grange Challenge for supporting her work.