Mapping the Above-Ground Biomass of Rhizophora apiculata plantation Forests Using PlanetScope Imagery in Thanh Phu Nature Reserve, Vietnam.

Estimating forest biomass is a necessary task to assess the carbon sequestration potential and storage, which serves to evaluate the environmental value of forests. It is possible to develop models for predicting forest biomass by combining remote sensing, field data and statistical models. In this study, we assessed the feasibility of estimating aboveground biomass (AGB) of Rhizophora apiculata plantation forests in Thanh Phu Nature Reserve by combining PlanetScope imagery and field inventory data obtained from 55 sample plots. Furthermore, we investigated the potential to enhance the accuracy of AGB estimation by applying four statistical models, generalized linear model (GLM), generalized exponential model (GEM), support vector machine (SVM) and random forest (RF). Our results showed that incorporating gray—level co-occurrence matrix (GLCM) texture features led to a more robust AGB estimation compared to using only spectral bands or vegetation indices. The RF model achieved the highest accuracy of AGB estimation based on the combination of spectral bands, vegetation indices and the optimum texture features, with an R2 value of 0.77. In addition, the spectral and texture features of the green and near-infrared bands were also important in predicting AGB. Finally, the results indicated that PlanetScope imagery has a great potential for mapping the AGB of R. apiculata plantations in mangrove forests, with relatively good accuracy (e.g., 78.26 and 86.36% for low and high values, respectively). [ABSTRACT FROM AUTHOR]