Résumé
In the context of its energy transition, Reunion Island aims to develop a wood-energy sector to provide local, renewable fuel to power plants and reduce reliance on imported resources. Acacia mearnsii, an invasive exotic species, is identified as the main potential source of wood-energy on the island, but it is still poorly documented and characterized. Therefore, an accurate assessment of this resource is a key step in this effort. The successive tropical storms led to complex stands with numerous inclined or entangled trees, making the resource modeling challenging.This study aims to explore methods based on high-density LiDAR to improve the resource evaluation of Acacia mearnsii. Two sub-objectives were identified: 1) build quantitative predictive models for estimating basal area, wood volume, and biomass from LiDAR variables using an Area Based Approach (ABA), and 2) produce a map of different types of stands to locate most suitable stands for mechanized exploitation, i.e. excluding stands with entangled trees.Fieldwork was carried out in 2022 in the “Hauts Sous le Vent” forest, at an altitude of about 1500 m in the West of the island. Allometric equations specific to Acacia mearnsii were established and density measurements were taken on a sample of wood collected to convert volume and biomass. One hundred plots were inventoried, ten of which were fully measured to establish the allometries, while UAV LiDAR data were collected.Preliminary comparison of basal area surface models for straight stands and for all stands showed discrepancies of residual errors (see Figure). Ongoing work aims to extract tree inclination proxies from LiDAR data in order to improve models to predict stand characteristcs and contribute to stand classification. The study demonstrates the potential of high-density LiDAR data to better characterize Acacia mearnsii stands and provides valuable information for the wood-energy sector on the Reunion island.