Challenging the link between functional and spectral diversity with radiative transfer modeling and data

With human induced biodiversity loss, remote sensing (RS) is emerging as a promising tool to map plant biodiversity from space. Yet, it remains unclear which metrics, methods, and sensors could provide the most reliable estimates of plant biodiversity. Here we assessed the potential of RS to infer plant biodiversity using radiative transfer simulations and inversion. We focus specifically on “functional diversity,” which represents the variability of plant functional traits. First, we simulated vegetation communities and evaluated the information content of different functional diversity metrics (FDMs) derived from their optical reflectance factors (R) or the corresponding vegetation “optical traits,” estimated via radiative transfer model inversion. Second, we assessed the effect of the spatial resolution, the spectral characteristics of the sensor, and signal noise on remote sensing-based FDMs. Finally, we evaluated the plausibility of the simulations using Sentinel-2 and DESIS imagery acquired over sites of the FunDivEUROPE network. Results proved that functional diversity can be inferred both by reflectance and optical traits. However, not all the FDMs tested were suited for assessing plant functional diversity from RS.

PACHECO-LABRADOR Javier;  MIGLIAVACCA Mirco;  MA Xuanlong;  MAHECHA Miguel;  CARVALHAIS Nuno;  WEBER Ulrich;  BENAVIDES Raquel;  BOURIAUD Olivier;  BARNOAIEA Ionut;  COOMES David A.;  BOHN Friedrich;  KRAEMER Guido;  HEIDEN Uta;  HUTH Andreas;  WIRTH Christian; 

2022-07-22

ELSEVIER SCIENCE INC

JRC128507

0034-4257 (online),   

https://www.sciencedirect.com/science/article/pii/S003442572200284X,    https://publications.jrc.ec.europa.eu/repository/handle/JRC128507,   

10.1016/j.rse.2022.113170 (online),