Mangrove biomass estimation in Southwest Thailand using machine learning

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 151 ha 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 1313 trees ha−1, with Rhizophora spp. comprising 77.7% of the trees across forty-five 400 m2 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 250 Mg ha−1; below-ground root biomass was 95 Mg ha−1. Combined above-ground and below-ground biomass for the entire 151-ha stand was 345 (±72.5) Mg ha−1, equivalent to 155 (±32.6) Mg C ha−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.