Résumé
Acacia mearnsii is an invasive pioneer species that has become widely established on Réunion Island in the Indian Ocean. Its use as an energy biomass offers a dual advantage: reducing the ecological pressure caused by its spread while supporting the development of a local and renewable resource. Nevertheless, its exploitation must rely on an integrated framework that balances production objectives with sustainable forest management, habitat conservation, and biodiversity preservation. Addressing these challenges, the project combines field measurements, remote sensing data, and modelling approaches to map the resource, quantify its potential biomass supply, and account for the complex stand structures influenced by disturbances such as fires and cyclones. The first stage consists of classifying optical satellite imagery (Sentinel-2, Pléiades Neo) to identify and delineate Acacia mearnsii stands. The second stage focuses on developing wood-volume estimation models using national airborne LiDAR data. Additional acquisitions were conducted to improve model accuracy, including drone-based LiDAR surveys that provide fine-resolution airborne information. These flights were coupled with terrestrial LiDAR scans collected on the same plots, yielding high-density point clouds and enabling robust relationships to be established between ground-based measurements and airborne observations. Together, these complementary datasets support the derivation of reliable forest indicators such as height, diameter, volume, and basal area, while enhancing the understanding of stand structure and dynamics. This multi-sensor approach provides a strong methodological foundation for sustainable resource management, helps assess the potential contribution of Acacia mearnsii to the wood-energy sector, and more broadly supports informed decision-making regarding ecosystem services and the trade-offs associated with their management.