Estimation of aboveground biomass in Atlantic Forest fragments using hyperspectral-RPA data: assessing the effect of spatial resolution and shadowed pixels

Secondary tropical forests are essential for biodiversity conservation and carbon sequestration. However, estimating aboveground biomass (AGB) in these ecosystems remains challenging due to their structural complexity and the spectral interference caused by canopy shading. This study demonstrates the potential of hyperspectral data acquired by a remotely piloted aircraft, HS-RPA, for modelling AGB in forest remnants at different successional stages in the Atlantic Forest. Specifically, it investigates how variations in spatial resolution and canopy shading affect the accuracy of biomass estimates, providing insights for optimizing remote sensing approaches in structurally complex tropical forests. Thirty permanent plots (30 m × 30 m) were established in four forest remnants in southern Espírito Santo, Brazil. Hyperspectral images were acquired in the 397–1002 nm range, with native resolution of 0.11 m, later resampled to 0.5 m and 5 m. Four scenarios were analysed combining different spatial resolutions and the presence or absence of shadow masking. Regression models indicated that both spectral bands and vegetation indices, particularly Red-edge Vegetation Stress Index (RVSI) and Photochemical Reflectance Index (PRI), contributed to AGB estimation. The best performance was obtained in the scenario with 5 m resolution and without shadow masking (adjusted R2 = 0.78; RMSE = 50.50%). Notably, models without shadow masking outperformed those with shaded pixels removed, indicating that shadow exclusion did not improve predictive accuracy. Shaded areas may capture structural nuances of the canopy, providing complementary information on vegetation heterogeneity. In this study, the spatial resolution of 5 metres also promoted greater spectral stability by smoothing small-scale variations. Overall, airborne hyperspectral data for AGB estimation highlight important aspects related to spatial resolution and illumination conditions in tropical forest monitoring. The results obtained provide valuable support for the development of more accurate methodologies aimed at biomass estimation, contributing to the improvement of forest monitoring strategies.