Assessing multiple remotely sensed data and model-assisted inference for biomass estimation of an Atlantic Forest fragment

Forest biomass quantification (Mg ha⁻¹) is essential for ecosystem monitoring, especially in areas under anthropogenic pressure, such as Atlantic Forest fragments. This study aimed to compare remote sensors in biomass mapping and population stock estimation of an Atlantic Forest fragment. Ten 0.1 ha plots were randomly distributed within a 17 ha fragment. Data from Sentinel-1 (S1), Sentinel-2 (S2), digital aerial photogrammetry (DAP), and their fusion were evaluated for the construction of predictive models. Linear models with two predictors were fitted: one for each sensor and another using data fusion, selecting the best predictors among all. The models were applied to estimate stand biomass using a regression estimator. The data fusion model showed the best predictive performance (RMSE = 41%), while the DAP-based model had the highest error (RMSE = 64%). However, the most accurate population estimate was obtained with the S2-based model (SE = 21 Mg ha-1), with a relative efficiency 7% higher compared to the traditional inventory (SE = 22 Mg ha-1). Estimates based on DAP, S1, and fusion were less accurate than those from the field inventory. The selected metrics, such as vegetation indices (S2) and textural metrics (S1), reflected the sensors' sensitivity to canopy structure and foliage abundance. DAP showed limitations, possibly due to its low canopy penetration. It is concluded that although the data fusion between DAP and S2 produced the best model for biomass mapping, S2 alone proved more advantageous for population estimates in forest fragments with limited sampling.