From Trees to Landscapes: Integrating Multi-Platform LiDAR for Structural Assessment of Commercial Macaúba Forests and Carbon Monitoring

Forest structural complexity is a key control on carbon storage, ecosystem functioning, and forest resilience, but its quantification across spatial scales remain challenging, even in managed tropical systems. Commercial plantations of macaúba (Acrocomia aculeata), a native Brazilian palm with increasing relevance for the vegetable oil and bioenergy markets, represent an emerging forest-based bioeconomy whose structural development is still insufficiently described using remote sensing techniques. In this study, we evaluate an integrated remote sensing framework that combine multi-platform LiDAR data (UAV-borne and airborne), multispectral satellite imagery, and field measurements to characterize forest structure and associated carbon stocks in commercial macaúba plantations. High-density LiDAR point clouds were used to derive three-dimensional structural attributes such as canopy height, vertical complexity, and spatial heterogeneity, assessed at both individual-tree and stand scales. These LiDAR-derived metrics were then integrated with satellite time series to support spatial extrapolation and the analysis of structural development and carbon accumulation over time. Relationships between remote sensing metrics and field observations were established using machine learning approaches, enabling robust estimation of aboveground biomass and carbon stocks while maintaining sensitivity to fine-scale structural variability. At the stand scale, the integrated LiDAR–satellite approach achieved coefficients of determination above 0.70 in independent validation, with biomass estimation errors on the order of 10 t ha⁻¹. These results indicate that reliable structural and carbon assessments can be obtained without rely on single-sensor datasets. The analysis highlight the complementary contribution of different LiDAR platforms. UAV-borne LiDAR provide detailed information on canopy and sub-canopy structure at the individual-tree level, whereas airborne LiDAR allow consistent and scalable mapping at the landscape scale. In addition, LiDAR acquisition characteristics, particularly point cloud density, was found to strongly influence the robustness and transferability of structural metrics. By linking individual-level measurements with landscape-scale observations, this multi-platform LiDAR framework advances the assessment of structure and carbon dynamics in planted tropical forests, supporting applications in forest inventory, ecological modeling, and the sustainable management of commercial forest systems associated with climate mitigation and the vegetable oil bioeconomy.