| Record |
| Author |
Molto, Q.; Herault, B.; Boreux, J.-J.; Daullet, M.; Rousteau, A.; Rossi, V. |
| Title |
Predicting tree heights for biomass estimates in tropical forests -A test from French Guiana |
Type |
Journal Article |
| Year |
2014 |
Publication |
Biogeosciences |
Abbreviated Journal |
Biogeosciences |
| Volume |
11 |
Issue  |
12 |
Pages |
3121-3130 |
| Keywords |
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| Abstract |
The recent development of REDD+ mechanisms requires reliable estimation of carbon stocks, especially in tropical forests that are particularly threatened by global changes. Even though tree height is a crucial variable for computing aboveground forest biomass (AGB), it is rarely measured in large-scale forest censuses because it requires extra effort. Therefore, tree height has to be predicted with height models. The height and diameter of all trees over 10 cm in diameter were measured in 33 half-hectare plots and 9 one-hectare plots throughout northern French Guiana, an area with substantial climate and environmental gradients. We compared four different model shapes and found that the Michaelis-Menten shape was most appropriate for the tree biomass prediction. Model parameter values were significantly different from one forest plot to another, and this leads to large errors in biomass estimates. Variables from the forest stand structure explained a sufficient part of plot-to-plot variations of the height model parameters to improve the quality of the AGB predictions. In the forest stands dominated by small trees, the trees were found to have rapid height growth for small diameters. In forest stands dominated by larger trees, the trees were found to have the greatest heights for large diameters. The aboveground biomass estimation uncertainty of the forest plots was reduced by the use of the forest structure-based height model. It demonstrated the feasibility and the importance of height modeling in tropical forests for carbon mapping. When the tree heights are not measured in an inventory, they can be predicted with a height-diameter model and incorporating forest structure descriptors may improve the predictions. © Author(s) 2014. CC Attribution 3.0 License. |
| Address |
Université de Liège, Liège, Belgium |
| Corporate Author |
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Thesis |
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| Publisher |
European Geosciences Union |
Place of Publication |
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Edition |
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| ISSN |
17264189 (Issn) |
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| Notes |
Export Date: 3 July 2014; Correspondence Address: Molto, Q.; Université des Antilles et de la Guyane, UMR Ecologie des Forêts de Guyane, Kourou, France; email: quentin.molto@gmail.com |
Approved |
no |
| Call Number |
EcoFoG @ webmaster @ |
Serial |
550 |
| Permanent link to this record |
