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Title: Mangrove biomass estimation in Southwest Thailand using machine learning
Authors: Jachowski, N.R.A.
Quak, M.S.Y.
Friess, D.A. 
Duangnamon, D.
Webb, E.L. 
Ziegler, A.D.
Keywords: Biomass
Machine learning
Remote sensing
Issue Date: Dec-2013
Citation: Jachowski, N.R.A., Quak, M.S.Y., Friess, D.A., Duangnamon, D., Webb, E.L., Ziegler, A.D. (2013-12). Mangrove biomass estimation in Southwest Thailand using machine learning. Applied Geography 45 : 311-321. ScholarBank@NUS Repository.
Abstract: Mangroves play a disproportionately large role in carbon sequestration relative to other tropical forest ecosystems. Accurate assessments of mangrove biomass at the site-scale are lacking, especially in mainland Southeast Asia. This study assessed tree biomass and species diversity within a 151ha mangrove ecosystem on the Andaman Coast of Thailand. High-resolution GeoEye-1 satellite imagery, medium resolution ASTER satellite elevation data, field-based tree measurements, published allometric biomass equations, and a suite of machine learning techniques were used to develop spatial models of mangrove biomass. Field measurements derived a whole-site tree density of 1313treesha-1, with Rhizophora spp. comprising 77.7% of the trees across forty-five 400m2 sample plots. A support vector machine regression model was found to be most accurate by cross-validation for predicting biomass at the site level. Model-estimated above-ground biomass was 250Mgha-1; below-ground root biomass was 95Mgha-1. Combined above-ground and below-ground biomass for the entire 151-ha stand was 345 (±72.5)Mgha-1, equivalent to 155 (±32.6)MgCha-1. Model evaluation shows the model had greatest prediction error at high biomass values, indicating a need for allometric equations determined over a larger range of tree sizes. © 2013 Elsevier Ltd.
Source Title: Applied Geography
ISSN: 01436228
DOI: 10.1016/j.apgeog.2013.09.024
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