Towards mapping of leaf functional traits across space and time from multispectral satellite imagery
Wednesday, August 4, 2021
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Cesar Hinojo Hinojo. Department of Ecology and Evolutionary Biology, The University of Arizona Teresa Bohner. Department of Ecology and Evolutionary Biology, The University of Arizona Amy Frazier. School of Geographical Sciences and Urban Planning, Arizona State University Nicola Falco. Lawrence Berkeley National Laboratory Bejamin Hemingway. School of Geographical Sciences and Urban Planning, Arizona State University Efthymios Nikolopoulos. Department of Mechanical and Civil Engineering, Florida Institute of Technology Haruko Wainwright. Lawrence Berkeley National Laboratory Brian J Enquist. Department of Ecology and Evolutionary Biology, The University of Arizona, The Santa Fe Institute.
César Hinojo Hinojo
Department of Ecology and Evolutionary Biology, The University of Arizona Tucson, AZ, USA
Background/Question/Methods Determining how the functional composition of communities changes along environmental gradients is the first step toward developing a more mechanistic understanding of climate’s role in driving ecosystem structure and function. Leaf functional traits are important drivers of ecosystem processes and responses. However, due to the difficulty of collecting trait datasets in the field, studies are often restricted to relatively small regions and short time scales, and only one or a few functional traits and climatic variables. Further, ecosystem-level leaf trait data is only available from field measurement sites or estimable at locations wherever and whenever hyperspectral remote sensing images and field calibration data exist. Effectively capitalizing over the long record of broadband remote sensing observations would enable the assessment of variability on leaf traits, ecosystem processes and responses across space and time. Results/Conclusions Here we outline the design of a vegetation index capable of tracking ecosystem mean leaf mass per area (LMA), which we call iLMA. iLMA had a satisfactory performance when tested using radiative transfer model simulations, at continental scale using field data across an elevation gradient in Colorado, NEON sites and forest plots across America (r2 = 0.64), comparable to previous hyperspectral retrieval methods. We show an application of iLMA to assess how LMA has changed over time across a wide range of sites spanning grasslands and different kinds of forests across America. With this approach, we found that the sites that have undergone the largest changes in LMA over the last 35 years tend to be sites with the strongest change in climate. We argue that a similar approach can be applied to retrieve other leaf traits, enabling a continuous monitoring of variation in plant functional diversity across space and time.