Résumé
The Index of Biodiversity Potential (IBP) assesses the forest stand’s capacity to host species based on 10 structural, compositional, and environmental factors. Widely used by French forest managers, its reliance on in-situ surveys limits large-scale applications. While LiDAR-derived metrics can finely describe forest structure, their relationship with the IBP remains unexplored.We aimed to study these relationships with the IBP management factors, some of which reflect forest structure such as the number of large trees and vertical strata. Using a dataset of 1536 IBP plots across France, we computed LiDAR-derived structural metrics along with other variables (e.g., topographic, spectral). We then analysed their statistical relationships with the IBP factors, and calibrated predictive models using both regression and classification machine learning algorithms. Finally, we mapped the IBP management score for the first time over a 890 km area within the forests of the Ariege Pyrenees Regional Natural Park (France).The results revealed strong correlations between the IBP management score, its factors, and remote sensing metrics. LiDAR-derived metrics describing canopy height and vertical complexity were particularly important for prediction, as well as biomass and topographic metrics. Our best model, with an RMSE of 5.24 ± 0.63, predicts IBP within 5 points—a threshold beyond which variations reflect actual changes in species richness within the forest stand.These findings emphasise the relevance of remote sensing data, in particular LiDAR, for describing structural field metrics. They demonstrate that remote sensing offers a viable approach for large-scale IBP assessment.