Caracterização espectral e fusão de dados lidar e hiperespectrais coletados por drone para estimar a biomassa acima do solo de florestas secundárias da mata atlântica

Abstract

Tropical forests play a fundamental role in the global carbon cycle, biodiversity conservation, soil and water preservation, and provide a wide range of ecosystem services. Therefore, improving tropical forest monitoring using data collected by a remotely piloted aircraft (RPA) is crucial to ensuring these services. In this context, this study aimed to evaluate the use of hyperspectral data collected by an RPA to characterize the vegetation of secondary forest fragments of the Atlantic Forest at different successional stages. Additionally, another objective was to combine LiDAR and hyperspectral data to enhance the estimation of aboveground biomass (AGB) and to spatialize these estimates in the studied areas. To achieve this, all tree individuals (D > 5 cm) were identified and inventoried in 30 field plots (30 × 30 m each) across five forest remnants located in the southern region of Espírito Santo state. Aerial point clouds and hyperspectral image cubes were generated for all analyzed fragments simultaneously with the field forest inventory. Subsequently, traditional metrics and metrics derived from the Fourier transform of canopy height were estimated from the point clouds, along with spectral information, including reflectance values and vegetation indices, for each plot. The successional stages of the analyzed secondary forest fragments could be distinguished using hyperspectral data collected by RPA. In the context of secondary tropical forests, characterized by high structural variability and different successional stages, the integration of LiDAR and hyperspectral data resulted in minimal improvements in AGB estimation accuracy. In some cases, data fusion did not improve the results compared to models based solely on LiDAR, indicating that spectral information did not significantly contribute to enhancing AGB estimates.