气候变化领域集成服务门户(CCPortal): Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data

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DOI	10.1016/j.rse.2020.112122
	Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data
	Aguirre-Gutiérrez J.; Rifai S.; Shenkin A.; Oliveras I.; Bentley L.P.; Svátek M.; Girardin C.A.J.; Both S.; Riutta T.; Berenguer E.; Kissling W.D.; Bauman D.; Raab N.; Moore S.; Farfan-Rios W.; Figueiredo A.E.S.; Reis S.M.; Ndong J.E.; Ondo F.E.; N'ssi Bengone N.; Mihindou V.; Moraes de Seixas M.M.; Adu-Bredu S.; Abernethy K.; Asner G.P.; Barlow J.; Burslem D.F.R.P.; Coomes D.A.; Cernusak L.A.; Dargie G.C.; Enquist B.J.; Ewers R.M.; Ferreira J.; Jeffery K.J.; Joly C.A.; Lewis S.L.; Marimon-Junior B.H.; Martin R.E.; Morandi P.S.; Phillips O.L.; Quesada C.A.; Salinas N.; Schwantes Marimon B.; Silman M.; Teh Y.A.; White L.J.T.; Malhi Y.
发表日期	2021
ISSN	00344257
卷号	252
英文摘要	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 oversimplify our understanding of ecosystems responses 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 from 2434 individual trees across the tropics using a standardised methodology. The functional traits and vegetation censuses were collected from 47 field plots in the countries of Australia, Brazil, Peru, Gabon, Ghana, and Malaysia, which span the four tropical continents. The spatial positions of individual trees above 10 cm diameter at breast height (DBH) were mapped and their canopy size and shape recorded. Using geo-located tree canopy size and shape data, community-level trait values were estimated at the same spatial resolution as Sentinel-2 imagery (i.e. 10 m pixels). We then used the Geographic Random Forest (GRF) to model and predict functional traits across our plots. We demonstrate that key plant functional traits can be accurately predicted across the tropicsusing the high spatial and spectral resolution of Sentinel-2 imagery in conjunction with climatic and soil information. Image textural parameters were found to be key components of remote sensing information for predicting functional traits across tropical forests and woody savannas. Leaf thickness (R2 = 0.52) obtained the highest prediction accuracy among the morphological and structural traits and leaf carbon content (R2 = 0.70) and maximum rates of photosynthesis (R2 = 0.67) obtained the highest prediction accuracy for leaf chemistry and photosynthesis related traits, respectively. Overall, the highest prediction accuracy was obtained for leaf chemistry and photosynthetic traits 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. © 2020 Elsevier Inc.
英文关键词	Image texture; Pixel-level predictions; Plant traits; Random Forest; Sentinel-2; Tropical forests
语种	英语
scopus关键词	Biodiversity; Decision trees; Forecasting; Forestry; Photosynthesis; Remote sensing; Space optics; Tropics; Diameter-at-breast heights; Environmental change; Environmental conditions; Environmental disturbances; Functional characteristics; Photosynthetic traits; Rapid transformations; Remote sensing information; Ecosystems; biodiversity; disturbance; ecosystem response; environmental change; forest ecosystem; modeling; satellite data; spatiotemporal analysis; Australia; Brazil; Gabon; Ghana; Malaysia; Peru
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179081
作者单位	Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, United Kingdom; Biodiversity Dynamics, Naturalis Biodiversity Center, Leiden, Netherlands; Department of Biology, Sonoma State University, 1801 East Cotati Avenue, Rohnert Park, CA 94928, United States; Department of Forest Botany, Dendrology and Geobiocoenology, Faculty of Forestry and Wood Technology, Mendel University in Brno, Brno, Czech Republic; Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia; Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam, Netherlands; Laboratoire d'Écologie Végétale et Biogéochimie, CP 244, Université Libre de Bruxelles, Brussels, Belgium; Living Earth Collaborative, Washington University in Saint Louis, St. Louis, MO, United States; Center for Conservation and Sustainable Development, Missouri Botanical Garden, St. Louis, MO, United States; Herbario Vargas (CUZ), Escuela Profesional de Biol...
推荐引用方式 GB/T 7714	Aguirre-Gutiérrez J.,Rifai S.,Shenkin A.,et al. Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data[J],2021,252.
APA	Aguirre-Gutiérrez J..,Rifai S..,Shenkin A..,Oliveras I..,Bentley L.P..,...&Malhi Y..(2021).Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data.Remote Sensing of Environment,252.
MLA	Aguirre-Gutiérrez J.,et al."Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data".Remote Sensing of Environment 252(2021).