Pantropical modelling of forest functional traits using Sentinel-2 remote sensing data

Tropical forest ecosystems are undergoing rapid transformation as a result of changing environmental conditions and direct human impacts. However, we cannot adequately understand, monitor or simulate tropical ecosystem responses to environmental changes without capturing the high diversity of plant functional characteristics in the species-rich tropics. Failure to do so can result in oversimplified understanding of responses of ecosystems to environmental disturbances. Innovative methods and data products are needed to track changes in functional trait composition in tropical forest ecosystems through time and space. This study aimed to track key functional traits by coupling Sentinel-2 derived variables with a unique data set of precisely located in-situ measurements of canopy functional traits collected to a standardised methodology from field plots in countries spanning the four tropical continents (Australia, Brazil, Peru, Gabon, Ghana, and Malaysia). The spatial positions of individual trees above 10 cm DBH were mapped and their canopy size and shape recorded. From these data, community-level trait values were estimated at the same spatial resolution as Sentinel-2 imagery (i.e. 10m pixels). We use a geographical version of random forest to model and predict functional traits across our plots in the tropics. We demonstrate that key plant functional traits can be measured at a pantropical scale using the high spatial and spectral resolution of Sentinel-2 imagery in conjunction with climatic and soil related information. Pixel texture parameters were found to be key components of remote sensing information for predicting functional traits across tropical forests and woody savannas. Leaf thickness ( R 2 =0.52) obtained the highest prediction accuracy among the morphological and structural traits and leaf carbon content ( R 2 =0.70) and A max ( R 2 =0.67) obtained the highest prediction accuracy for leaf chemistry traits and photosynthesis related traits, respectively. Overall, the highest prediction accuracy was obtained for leaf chemistry and photosynthetic traits (A max and A sat ) in comparison to morphological and structural traits. Our approach offers new opportunities for mapping, monitoring and understanding biodiversity and ecosystem change in the most species-rich ecosystems on Earth. Modeling tropical montane forest biomass, productivity and canopy traits with multispectral remote sensing data.

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