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Global Ecology and Biogeography
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publication venue for: Pan-tropical prediction of forest structure from the largest trees
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2000005316
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National Science Foundation, NSF: 164131/2013, 403725/2012-7, 441244/2016-5, DEB 0742830
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funding vehicle for: Pan-tropical prediction of forest structure from the largest trees

Andrew W. Mellon Foundation, AWMF
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funding vehicle for: Pan-tropical prediction of forest structure from the largest trees

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Smithsonian Institution, SI
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funding vehicle for: Pan-tropical prediction of forest structure from the largest trees

Document
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Gordon and Betty Moore Foundation, GBMF
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funding vehicle for: Pan-tropical prediction of forest structure from the largest trees

Continuant
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Pan-tropical prediction of forest structure from the largest trees
related by: authorship_34804
related by: authorship_34795
related by: authorship_34774
related by: authorship_34886
estado: published
related by: authorship_34825
related by: authorship_34840
has funding vehicle: Natural Environment Research Council, NERC: NE/D01025X/1, NE/I02982X/1
related by: authorship_34790
most specific type: Article
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has funding vehicle: Centre de Coopération Internationale en Recherche Agronomique pour le Développement, CIRAD
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page end: 1383
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origen: scopus
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has funding vehicle: Andrew W. Mellon Foundation, AWMF
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has publication venue: Global Ecology and Biogeography
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crisid: 5600
has funding vehicle: Smithsonian Institution, SI
abstract: Aim: Large tropical trees form the interface between ground and airborne observations, offering a unique opportunity to capture forest properties remotely and to investigate their variations on broad scales. However, despite rapid development of metrics to characterize the forest canopy from remotely sensed data, a gap remains between aerial and field inventories. To close this gap, we propose a new pan-tropical model to predict plot-level forest structure properties and biomass from only the largest trees. Location: Pan-tropical. Time period: Early 21st century. Major taxa studied: Woody plants. Methods: Using a dataset of 867 plots distributed among 118 sites across the tropics, we tested the prediction of the quadratic mean diameter, basal area, Lorey%27s height, community wood density and aboveground biomass (AGB) from the ith largest trees. Results: Measuring the largest trees in tropical forests enables unbiased predictions of plot- and site-level forest structure. The 20 largest trees per hectare predicted quadratic mean diameter, basal area, Lorey%27s height, community wood density and AGB with 12, 16, 4, 4 and 17.7%25 of relative error, respectively. Most of the remaining error in biomass prediction is driven by differences in the proportion of total biomass held in medium-sized trees (50–70 cm diameter at breast height), which shows some continental dependency, with American tropical forests presenting the highest proportion of total biomass in these intermediate-diameter classes relative to other continents. Main conclusions: Our approach provides new information on tropical forest structure and can be used to generate accurate field estimates of tropical forest carbon stocks to support the calibration and validation of current and forthcoming space missions. It will reduce the cost of field inventories and contribute to scientific understanding of tropical forest ecosystems and response to climate change. © 2018 John Wiley & Sons Ltd
freetext keyword: carbon; climate change; forest structure; large trees; pan-tropical; REDD+; tropical forest ecology carbon cycle; climate change; data set; deforestation; emission control; forest ecosystem; prediction; tropical forest
related by: authorship_34899
has funding vehicle: WWF International, WWF: ANR‐12‐EBID‐0002
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issue: 11
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volume: 27
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has funding vehicle: CNPq/PQ-2
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Missouri Botanical Garden, MBG
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funding vehicle for: Pan-tropical prediction of forest structure from the largest trees

Article
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Natural Environment Research Council, NERC: NE/D01025X/1, NE/I02982X/1
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funding vehicle for: Pan-tropical prediction of forest structure from the largest trees

Information Content Entity
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Fundação de Amparo à Pesquisa do Estado de Mato Grosso, FAPEMAT: 0589267/2016, 403725/2012‐7, 441244/2016‐5, 457602/2012‐0
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funding vehicle for: Pan-tropical prediction of forest structure from the largest trees

Belgian Federal Science Policy Office, BELSPO
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funding vehicle for: Pan-tropical prediction of forest structure from the largest trees

RDF description of Pan-tropical prediction of forest structure from the largest trees - http://orbis.uaslp.mx/vivo/individual/scopus5599
rights: termsOfUse
date: 2026-08-26T06:55:17
publisher: vivo
type: Document

Centre National pour la Recherche Scientifique et Technique, CNRST
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funding vehicle for: Pan-tropical prediction of forest structure from the largest trees

WWF International, WWF: ANR‐12‐EBID‐0002
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funding vehicle for: Pan-tropical prediction of forest structure from the largest trees

Wildlife Conservation Society, WCS
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funding vehicle for: Pan-tropical prediction of forest structure from the largest trees

CNPq/PQ-2
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funding vehicle for: Pan-tropical prediction of forest structure from the largest trees

Centre de Coopération Internationale en Recherche Agronomique pour le Développement, CIRAD
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type: BFO_0000020
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funding vehicle for: Pan-tropical prediction of forest structure from the largest trees