TY - JOUR
T1 - Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data
AU - Aguirre-Gutiérrez, Jesús
AU - Rifai, Sami
AU - Shenkin, Alexander
AU - Oliveras, Imma
AU - Bentley, Lisa Patrick
AU - Svátek, Martin
AU - Girardin, Cécile A.J.
AU - Both, Sabine
AU - Riutta, Terhi
AU - Berenguer, Erika
AU - Kissling, W. Daniel
AU - Bauman, David
AU - Raab, Nicolas
AU - Moore, Sam
AU - Farfan-Rios, William
AU - Figueiredo, Axa Emanuelle Simões
AU - Reis, Simone Matias
AU - Ndong, Josué Edzang
AU - Ondo, Fidèle Evouna
AU - N'ssi Bengone, Natacha
AU - Mihindou, Vianet
AU - Moraes de Seixas, Marina Maria
AU - Adu-Bredu, Stephen
AU - Abernethy, Katharine
AU - Asner, Gregory P.
AU - Barlow, Jos
AU - Burslem, David F.R.P.
AU - Coomes, David A.
AU - Cernusak, Lucas A.
AU - Dargie, Greta C.
AU - Enquist, Brian J.
AU - Ewers, Robert M.
AU - Ferreira, Joice
AU - Jeffery, Kathryn J.
AU - Joly, Carlos A.
AU - Lewis, Simon L.
AU - Marimon-Junior, Ben Hur
AU - Martin, Roberta E.
AU - Morandi, Paulo S.
AU - Phillips, Oliver L.
AU - Quesada, Carlos A.
AU - Salinas, Norma
AU - Schwantes Marimon, Beatriz
AU - Silman, Miles
AU - Teh, Yit Arn
AU - White, Lee J.T.
AU - Malhi, Yadvinder
N1 - Funding Information:
This work is a product of the Global Ecosystems Monitoring (GEM) network (gem.tropicalforests.ox.ac.uk). J.A.G. was funded by the Natural Environment Research Council (NERC; NE/T011084/1 and NE/S011811/1) and the Netherlands Organisation for Scientific Research (NWO) under the Rubicon programme with project number 019.162LW.010. The traits field campaign was funded by a grant to Y.M. from the European Research Council (Advanced Grant GEM-TRAIT: 321131) under the European Union‘s Seventh Framework Programme (FP7/2007-2013), with additional support from NERC Grant NE/D014174/1 and NE/J022616/1 for traits work in Peru, NERC Grant ECOFOR (NE/K016385/1) for traits work in Santarem, NERC Grant BALI (NE/K016369/1) for plot and traits work in Malaysia and ERC Advanced Grant T-FORCES (291585) to Phillips for traits work in Australia. Plot setup in Ghana and Gabon were funded by a NERC GrantNE/I014705/1 and by the Royal Society-Leverhulme Africa Capacity Building Programme. The Malaysia campaign was also funded by NERC GrantNE/K016253/1. Plot inventories in Peru were supported by funding from the US National Science Foundation Long-Term Research in Environmental Biology program (LTREB; DEB 1754647) and the Gordon and Betty Moore Foundation Andes-Amazon Program. Plots inventories in Nova Xavantina (Brazil) were supported by the National Council for Scientific and Technological Development (CNPq), Long Term Ecological Research Program (PELD), Proc. 441244/2016-5, and the Foundation of Research Support of Mato Grosso (FAPEMAT), Project ReFlor, Proc. 589267/2016. During data collection, I.O. was supported by a Marie Curie Fellowship (FP7-PEOPLE-2012-IEF-327990). GEM trait data in Gabon was collected under authorisation to Y.M. and supported by the Gabon National Parks Agency. D.B. was funded by the Fondation Wiener-Anspach. W.D.K. acknowledges support from the Faculty Research Cluster ‘Global Ecology’ of the University of Amsterdam. M.S. was funded by a grant from the Ministry of Education, Youth and Sports of the Czech Republic (INTER-TRANSFER LTT19018). Y.M. is supported by the Jackson Foundation. We thank the two anonymous reviewers and Associate Editor G. Henebry for their insightful comments that helped improved this manuscript.
Funding Information:
This work is a product of the Global Ecosystems Monitoring (GEM) network ( gem.tropicalforests.ox.ac.uk ). J.A.G. was funded by the Natural Environment Research Council (NERC; NE/T011084/1 and NE/S011811/1 ) and the Netherlands Organisation for Scientific Research (NWO) under the Rubicon programme with project number 019.162LW.010 . The traits field campaign was funded by a grant to Y.M. from the European Research Council (Advanced Grant GEM-TRAIT: 321131 ) under the European Union ‘s Seventh Framework Programme ( FP7/2007-2013 ), with additional support from NERC Grant NE/D014174/1 and NE/J022616/1 for traits work in Peru, NERC Grant ECOFOR ( NE/K016385/1 ) for traits work in Santarem, NERC Grant BALI ( NE/K016369/1 ) for plot and traits work in Malaysia and ERC Advanced Grant T-FORCES ( 291585 ) to Phillips for traits work in Australia. Plot setup in Ghana and Gabon were funded by a NERC Grant NE/I014705/1 and by the Royal Society-Leverhulme Africa Capacity Building Programme . The Malaysia campaign was also funded by NERC Grant NE/K016253/1 . Plot inventories in Peru were supported by funding from the US National Science Foundation Long-Term Research in Environmental Biology program (LTREB; DEB 1754647 ) and the Gordon and Betty Moore Foundation Andes-Amazon Program. Plots inventories in Nova Xavantina (Brazil) were supported by the National Council for Scientific and Technological Development (CNPq) , Long Term Ecological Research Program (PELD) , Proc. 441244/2016-5 , and the Foundation of Research Support of Mato Grosso (FAPEMAT) , Project ReFlor, Proc. 589267/2016 . During data collection, I.O. was supported by a Marie Curie Fellowship ( FP7-PEOPLE-2012-IEF-327990 ). GEM trait data in Gabon was collected under authorisation to Y.M. and supported by the Gabon National Parks Agency. D.B. was funded by the Fondation Wiener-Anspach. W.D.K. acknowledges support from the Faculty Research Cluster ‘Global Ecology’ of the University of Amsterdam . M.S. was funded by a grant from the Ministry of Education, Youth and Sports of the Czech Republic ( INTER-TRANSFER LTT19018 ). Y.M. is supported by the Jackson Foundation . We thank the two anonymous reviewers and Associate Editor G. Henebry for their insightful comments that helped improved this manuscript.
Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2021/1
Y1 - 2021/1
N2 - 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.
AB - 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.
KW - Image texture
KW - Pixel-level predictions
KW - Plant traits
KW - Random Forest
KW - Sentinel-2
KW - Tropical forests
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UR - http://www.scopus.com/inward/citedby.url?scp=85092472356&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2020.112122
DO - 10.1016/j.rse.2020.112122
M3 - Article
AN - SCOPUS:85092472356
VL - 252
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
SN - 0034-4257
M1 - 112122
ER -