Data from: Allometric models to estimate leaf area for tropical African broadleaved forests

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Sirri, Nelly F.; Libalah, Moses B.; Momo, Stephane T.; Ploton, Pierre; Medjibe, Vincent; Kamdem, Narcisse G.; Mofack II., Gislain; Sonké, Bonaventure; Barbier, Nicolas, 2020, "Data from: Allometric models to estimate leaf area for tropical African broadleaved forests", https://doi.org/10.5061/dryad.63cj030, DataSuds, V1

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Description	Direct and semi-direct estimations of leaf area (LA) and leaf area index (LAI) are scarce in dense tropical forests despite the importance of such measurements to calibrate remote-sensing products, forest dynamics and biogeochemical models (e.g. Dynamic Global Vegetation Models). Extensive and destructive sampling of 61 trees belonging to 13 species spanning all diameter and wood density classes was performed in the semi-deciduous forest of southeastern Cameroon. For each tree, all leaves were weighed, counted for a subsample of branches and LA measured for 10-50 leaves. Allometric models were calibrated to allow semi-direct estimation at tree- and stand-levels, based on forest inventory data (R²=0.7, bias=21.2%, error=39.5%), also on novel tree metrics allowed by remote-sensing like airborne light detection and ranging (R²=0.63, bias=35.1%, error=58.73). Using twenty-one 1-ha forest inventory plots, stand-level estimations of LAI spanned from 4.42-13.99. Models produced at stand-level estimation may be considerably useful to climate-vegetation modelling and remote-sensing communities. (2019-07-22)
Subject	Mathematical Sciences
Keyword	leaf area, allometric models, tropical African broadleaved forests
Related Publication	Sirri, N. F. et al. (2019) « Allometric Models to Estimate Leaf Area for Tropical African Broadleaved Forests », Geophysical Research Letters. American Geophysical Union (AGU), 46(15), p. 8985‑8994. doi: 10.1029/2019gl083514. doi: 10.1029/2019gl083514
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Persistent Identifier	doi:10.5061/dryad.63cj030
Publication Date	2020-08-03
Title	Data from: Allometric models to estimate leaf area for tropical African broadleaved forests
Author	Sirri, Nelly F. (Plant Systematics and Ecology Laboratory (LaBosystE), Department of Biology, Higher Teachers' Training College, University of Yaoundé I, Yaoundé, Cameroon) - ORCID: 0000-0002-1439-1650 Libalah, Moses B. (Plant Systematics and Ecology Laboratory (LaBosystE), Department of Biology, Higher Teachers' Training College, University of Yaoundé I, Yaoundé, Cameroon) - ORCID: 0000-0001-8848-8001 Momo, Stephane T. (Plant Systematics and Ecology Laboratory (LaBosystE), Department of Biology, Higher Teachers' Training College, University of Yaoundé I, Yaoundé, Cameroon) - ORCID: 0000-0002-1226-4826 Ploton, Pierre (AMAP, IRD, CNRS, CIRAD, INRA, Université de Montpellier, Montpellier, France) - ORCID: 0000-0002-8800-3593 Medjibe, Vincent (Commission des Forêts d'Afrique Centrale (COMIFAC), Yaoundé, Cameroon) Kamdem, Narcisse G. (Plant Systematics and Ecology Laboratory (LaBosystE), Department of Biology, Higher Teachers' Training College, University of Yaoundé I, Yaoundé, Cameroon) Mofack II., Gislain (Plant Systematics and Ecology Laboratory (LaBosystE), Department of Biology, Higher Teachers' Training College, University of Yaoundé I, Yaoundé, Cameroon) - ORCID: 0000-0003-3261-1378 Sonké, Bonaventure (Plant Systematics and Ecology Laboratory (LaBosystE), Department of Biology, Higher Teachers' Training College, University of Yaoundé I, Yaoundé, Cameroon) - ORCID: 0000-0002-4310-3603 Barbier, Nicolas (AMAP, IRD, CNRS, CIRAD, INRA, Université de Montpellier, Montpellier, France) - ORCID: 0000-0002-5323-3866
Point of Contact	Use email button above to contact. Sirri, Nelly F. (Plant Systematics and Ecology Laboratory (LaBosystE), Department of Biology, Higher Teachers' Training College, University of Yaoundé I, Yaoundé, Cameroon)
Description	Direct and semi-direct estimations of leaf area (LA) and leaf area index (LAI) are scarce in dense tropical forests despite the importance of such measurements to calibrate remote-sensing products, forest dynamics and biogeochemical models (e.g. Dynamic Global Vegetation Models). Extensive and destructive sampling of 61 trees belonging to 13 species spanning all diameter and wood density classes was performed in the semi-deciduous forest of southeastern Cameroon. For each tree, all leaves were weighed, counted for a subsample of branches and LA measured for 10-50 leaves. Allometric models were calibrated to allow semi-direct estimation at tree- and stand-levels, based on forest inventory data (R²=0.7, bias=21.2%, error=39.5%), also on novel tree metrics allowed by remote-sensing like airborne light detection and ranging (R²=0.63, bias=35.1%, error=58.73). Using twenty-one 1-ha forest inventory plots, stand-level estimations of LAI spanned from 4.42-13.99. Models produced at stand-level estimation may be considerably useful to climate-vegetation modelling and remote-sensing communities. (2019-07-22)
Subject	Mathematical Sciences
Keyword	leaf area allometric models tropical African broadleaved forests
Related Publication	Sirri, N. F. et al. (2019) « Allometric Models to Estimate Leaf Area for Tropical African Broadleaved Forests », Geophysical Research Letters. American Geophysical Union (AGU), 46(15), p. 8985‑8994. doi: 10.1029/2019gl083514. doi: 10.1029/2019gl083514 https://doi.org/10.1029/2019gl083514
Depositor	PEREZ, Jerome
Deposit Date	2020-07-31

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