Aboveground biomass estimation in dry forest in northeastern Brazil using metrics extracted from sentinel-2 data: Comparing parametric and non-parametric estimation methods

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Caatinga is a dry forest, which occupies almost one million square kilometers in the Brazilian territory, representing more than 11% of the territory. However because of the extensive areas to be inventoried and the difficulties in collecting data from this natural vegetation using traditional techniques, the operation becomes very costly, often resulting in low sampling intensity owing to cost and time constraints. Therefore, remote sensing tools have become very useful for minimizing the difficulties caused by traditional methods. Given this context, our study aims to evaluate the performance of spectral and texture variables extracted from an MSI/Sentinel-2 image in the estimation and mapping of AGB in a fragment (natural vegetation area) of Caatinga vegetation in northeastern Brazil. The original reflectance values, the ratio between spectral bands, vegetation indices, texture metrics (extracted from the 3 × 3, 5 × 5, 7 × 7, and 9 × 9-pixel windows), and fraction images (proportion of the vegetation, soil, and shadow/water components) were associated with AGB. These variables were derived from a forest inventory with systematic sampling composed of 40 plots of 900 m2 each. We evaluated the estimates using a parametric model of multiple linear regression (MLR) and a non-parametric model of an artificial neural network (ANN). The texture metrics (angular second moment, correlation, entropy, and contrast), extracted in a 7 × 7-pixel window, were the best predictors. The parametric model was the most accurate for AGB estimation. The MLR and ANN techniques showed bias validation of 2.58% and 2.79%, and relative root mean square errors of 12.02% and 16.60%, respectively. AGB estimates ranged from 1.49 Mg ha−1 to approximately 73 Mg ha−1 on the MLR and ANN maps. The textural metrics showed the potential to accurately predict AGB, providing valuable information for the use of open access data. Such information is very useful in the management of natural resources in large areas, such as the Caatinga vegetation. The use of optical data for AGB estimation in the region is relatively unexplored and this study contributes to filling that gap or better understand their application for deriving AGB in a dry forest in the semiarid region of Sergipe.