Habitat quality assessment of temperate forest ecosystems: An airborne LiDAR-based approach to predict the Index of Biodiversity Potential (IBP) at large scale

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 2 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. • Remote sensing data demonstrate their potential for large-scale IBP assessment. • LiDAR structural metrics strongly correlate with the IBP management factors. • Canopy height, heterogeneity and biomass are key structural traits linked to the IBP. • The IBP is mapped across all forests of the Ariege Natural Park, French Pyrenees.

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Publication Details

Journal
Forest Ecology and Management
Published
2025-11-06
DOI
https://doi.org/10.1016/j.foreco.2025.123305
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Habitat quality assessment of temperate forest ecosystems: An airborne LiDAR-based approach to predict the Index of Biodiversity Potential (IBP) at large scale

Forest Ecology and Management
Remote Sensing and LiDAR Applications
article

Habitat quality assessment of temperate forest ecosystems: An airborne LiDAR-based approach to predict the Index of Biodiversity Potential (IBP) at large scale

article en

Abstract

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 2 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. • Remote sensing data demonstrate their potential for large-scale IBP assessment. • LiDAR structural metrics strongly correlate with the IBP management factors. • Canopy height, heterogeneity and biomass are key structural traits linked to the IBP. • The IBP is mapped across all forests of the Ariege Natural Park, French Pyrenees.

Forest Ecology and ManagementVol. 601
Centre National de la Recherche Scientifique (FR), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), Université Toulouse III - Paul Sabatier (FR), Université Toulouse-I-Capitole (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Ecole d'Ingénieurs de PURPAN (FR), Centre National de la Propriété Forestière (FR), Genomics and Biotechnology of the Fruits Laboratory (FR), Institut de Recherche en Informatique de Toulouse (FR), UMR Botanique et Modélisation de l’Architecture des Plantes et des végétations (FR), Comportement et Écologie de la Faune Sauvage (FR), Université Toulouse - Jean Jaurès (FR), Génétique Physiologie et Systèmes d'Elevage (FR), Institut Polytechnique de Bordeaux (FR), Dynamiques et écologie des paysages agriforestiers (FR)
Région Occitanie Pyrénées-Méditerranée, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement
Life in Land
Openalex Percentile: Top 49%
Remote Sensing and LiDAR Applications
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