Rights statement: This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 252, 2020 DOI: 10.1016/j.rse.2020.112122
Accepted author manuscript, 1.54 MB, PDF document
Available under license: CC BY-NC-ND
Rights statement: This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 252, 2020 DOI: 10.1016/j.rse.2020.112122
Accepted author manuscript, 2.6 MB, PDF document
Available under license: CC BY-NC-ND
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data
AU - Aguirre-Gutiérrez, J.
AU - Rifai, S.
AU - Shenkin, A.
AU - Oliveras, I.
AU - Bentley, L.P.
AU - Svátek, M.
AU - Girardin, C.A.J.
AU - Both, S.
AU - Riutta, T.
AU - Berenguer, E.
AU - Kissling, W.D.
AU - Bauman, D.
AU - Raab, N.
AU - Moore, S.
AU - Farfan-Rios, W.
AU - Figueiredo, A.E.S.
AU - Reis, S.M.
AU - Ndong, J.E.
AU - Ondo, F.E.
AU - N'ssi Bengone, N.
AU - Mihindou, V.
AU - Moraes de Seixas, M.M.
AU - Adu-Bredu, S.
AU - Abernethy, K.
AU - Asner, G.P.
AU - Barlow, J.
AU - Burslem, D.F.R.P.
AU - Coomes, D.A.
AU - Cernusak, L.A.
AU - Dargie, G.C.
AU - Enquist, B.J.
AU - Ewers, R.M.
AU - Ferreira, J.
AU - Jeffery, K.J.
AU - Joly, C.A.
AU - Lewis, S.L.
AU - Marimon-Junior, B.H.
AU - Martin, R.E.
AU - Morandi, P.S.
AU - Phillips, O.L.
AU - Quesada, C.A.
AU - Salinas, N.
AU - Schwantes Marimon, B.
AU - Silman, M.
AU - Teh, Y.A.
AU - White, L.J.T.
AU - Malhi, Y.
N1 - This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 252, 2020 DOI: 10.1016/j.rse.2020.112122
PY - 2021/1/1
Y1 - 2021/1/1
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
KW - Biodiversity
KW - Decision trees
KW - Forecasting
KW - Forestry
KW - Photosynthesis
KW - Remote sensing
KW - Space optics
KW - Tropics
KW - Diameter-at-breast heights
KW - Environmental change
KW - Environmental conditions
KW - Environmental disturbances
KW - Functional characteristics
KW - Photosynthetic traits
KW - Rapid transformations
KW - Remote sensing information
KW - Ecosystems
KW - biodiversity
KW - disturbance
KW - ecosystem response
KW - environmental change
KW - forest ecosystem
KW - modeling
KW - satellite data
KW - spatiotemporal analysis
KW - Australia
KW - Brazil
KW - Gabon
KW - Ghana
KW - Malaysia
KW - Peru
U2 - 10.1016/j.rse.2020.112122
DO - 10.1016/j.rse.2020.112122
M3 - Journal article
VL - 252
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
SN - 0034-4257
M1 - 18
ER -