<figure><img src="image_1.png" /></figure>

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

Manon Collard<sup>a ID,*</sup>, Olivier Martin-Ducup<sup>b ID</sup>, Nicolas Mellado<sup>c</sup>, Laurent Larrieu<sup>a,d</sup>, Fabien Laroche<sup>a</sup>, Nicolas Gouix<sup>e</sup>, Antoine Brin<sup>f</sup>, Pierre Gonin<sup>g ID</sup>, David Sheeren<sup>a ID</sup>

## ARTICLE INFO

### Keywords:

- Remote sensing

- LiDAR

- Airborne laser scanning

- Forest structure

- Biodiversity

- Habitat index

## 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 km² 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.

## 1. Introduction

Forest ecosystems provide a significant number of services to society. Beyond the production of various resources (wood, energy, food) and the recreational functions they offer, these ecosystems play a crucial role in biogeochemical cycles (e.g. C, N, O) and, consequently, in climate regulation. However, the ability of forest ecosystems to provide these services depends on the biodiversity they harbour, among other forest attributes and environmental factors (Felipe-Lucia et al.,

2018). Throughout history, anthropogenic pressures (habitat fragmentation, intensive exploitation, management focused on timber production) have significantly impacted the biodiversity of these ecosystems. Implementing methods for systematic evaluation and monitoring of forest biodiversity in large areas is a priority to ensure conservation, sustainable management and understand forest response to environmental changes.

<footnote><sup>a</sup> University of Toulouse, INRAE, UMR DYNAFOR, Castanet-Tolosan, France</footnote>

<footnote><sup>b</sup> CIRAD, UMR AMAP, Montpellier, France</footnote>

<footnote><sup>c</sup> University of Toulouse, CNRS, UMR IRIT, Toulouse, France</footnote>

<footnote><sup>d</sup> CNPF-CRPF Occitanie, Tarbes, France</footnote>

<footnote><sup>e</sup> Conservatoire d'Espaces Naturels d'Occitanie, Mercuès, France</footnote>

<footnote><sup>f</sup> University of Toulouse, Ecole d'Ingénieurs de PURPAN, UMR DYNAFOR, Castanet-Tolosan, France</footnote>

<footnote><sup>g</sup> CNPF-IDF, Castanet-Tolosan, France</footnote>

<footnote>☆ This article is part of a Special issue entitled: 'ForBiodiv_Indicat_Monitor' published in Forest Ecology and Management.</footnote>

<footnote>* Corresponding author.
E-mail address: manon.collard@inrae.fr (M. Collard).</footnote>
The knowledge of the ecological quality of forest ecosystems and their conservation status remains poorly understood today, despite long-term monitoring systems, large-scale initiatives and National Forest Inventories (NFI) (Chirici et al., 2012; Heym et al., 2021; Burrascano et al., 2025). Evaluating forest ecological quality is complex. Two approaches are generally adopted (Noss, 1990; Lindenmayer et al., 2000; Burrascano et al., 2025): (1) field inventories that assess the abundance and diversity of multi-taxonomic species or indicator species (direct taxa-based indicators) or (2) the record of key characteristics of composition and structure that are favourable to the potential presence of multiple species (indirect habitat-based or structure-based indicators). In practice, direct evaluation of forest biodiversity through systematic taxonomic surveys is rare in the context of forest management and taxon identification is time consuming, even though it is the only way to accurately reflect actual biodiversity levels. It is often limited to monitoring endangered species or taxa present in national forest inventories, which are primarily focused on the measurement of structural variables (Burrascano et al., 2025). The direct evaluation method assumes the ability to determine a list of priority taxa to monitor (i.e. "bioindicators"), which remains controversial (Noss, 1999; Lindenmayer et al., 2000) and impose to consider multi-taxon sampling. The target species or group of species cover only partially the diversity of forest organisms, leading to a partial assessment of biodiversity (Burrascano et al., 2025). Moreover, knowing which species are present and in what numbers does not necessarily offer guidance on how to improve management practices (McElhinny et al., 2005). It also requires forest managers to have extensive taxonomic expertise, in addition to dedicating significant time to surveys, to accurately identify observed species (Cosović et al., 2020).

To overcome these constraints, in the operational context of forest management, the indirect approach has been proposed and is the most widespread today (Lindenmayer et al., 2000; Paillet et al., 2024). According to this approach, the ecological quality of forests is assessed based on the diversity of composition, structure, and the presence of naturalness attributes that depend on the intensity of management (McElhinny et al., 2005). These proxies (surrogates) and their heterogeneity are assumed to support the taxonomic diversity of a wide range of species, considering that structural complexity provides greater niche space (Gao et al., 2015; Heidrich et al., 2023; Zeller et al., 2023).

Various composite indicators have been proposed to indirectly approach potential biodiversity in temperate forests. Many of these rely on data available in forest inventories (Meersschaut and Vandekerkhove, 1998; Geburek et al., 2010; Chirici et al., 2012; Storch et al., 2018). At the stand level, McElhinny et al. (2005) identified a set of key structural attributes to construct these indicators and synthesised various indices of structural complexity derived from them (cumulative score, average score, or score based on a non-linear combination of structural attributes). Some of these indicators incorporate a landscape dimension. This is the case with the "habitat hectares" approach (Parkes et al., 2003) which considers the size of a patch of remnant vegetation in the neighbourhood, the degree of connection between areas of remnant vegetation, and the distance to the nearest "core area". The FOrest Biodiversity Index (FOBI), developed more recently for assessing the biodiversity potential of the United Kingdom's public forests, is another example (Bellamy et al., 2024).

The ecological quality of forests can also be approached using the Index of Biodiversity Potential (IBP) developed in France in 2008 (Larrieu and Gonin, 2008). This index was specifically designed to provide a tool to assist managers in adopting an integrative approach to biodiversity-friendly forest management, as conceptualised by Kraus and Krumm (2013). It evaluates, at the scale of a forest stand, the capacity to host animal and plant species. It indirectly estimates potential taxonomic diversity based on 10 key factors identified as favourable to biodiversity, with 7 related to management and 3 related to context. The ecological relevance of these factors was assessed by several studies

based on multi-taxa data (Larrieu et al., 2019; Zeller et al., 2022; Rota et al., 2025). The IBP has become a cost-effective diagnostic tool widely used and has continued to evolve since its creation (Larrieu et al., 2025). Initially developed for temperate forests in France (with a specific version for Mediterranean environments), it is now used in more than 16 countries.

Currently, the IBP is only evaluated through in-situ surveys. A score is assigned for each factor, and these are then aggregated to obtain a final score. Its repeated estimation over large areas remains challenging, like most other indicators mentioned.

Remote sensing has long been used to monitor forest environments (Boyd and Danson, 2005). Research has primarily focused on detecting forests themselves, their state and dynamics related to natural or anthropogenic processes, their species composition, characterising stand structure by estimating dendrometric variables (e.g. average height, diameter, volume, basal area), or estimating functional traits (phenology, biophysical and biochemical properties). Several reviews have been produced on one or more of these aspects (e.g. Boyd and Danson (2005), Masek et al. (2015), Fassnacht et al. (2016), White et al. (2016), Coops et al. (2021), Toivonen et al. (2023)). These works are continually renewed with the advent of new, increasingly powerful sensors (improved spatial, spectral, and temporal resolution) that push initial limits. The arrival of LiDAR data also marks an important turning point by adding a three-dimensional perspective.

From a biodiversity perspective, a number of so-called essential variables (Essential Biodiversity Variables - EBVs) can be approached through remote sensing. Ten candidate variables have been identified (Skidmore et al., 2015), and more recently, around thirty remote sensing products have been listed as priorities for improving biodiversity monitoring (Skidmore et al., 2021). Among these are habitat maps and their structure, which refers to a set of characteristics such as vegetation height, vegetation type, vegetation cover fraction, or the presence of deadwood. These characteristics directly echo certain factors of the IBP, which can be seen as a proxy for an EBV, more easily accessible to forest managers.

There are many studies seeking to identify the best proxies offered by remote sensing data to qualify the structure of forest habitats in order to correlate them with associated biodiversity (Bergen et al., 2009; Coops et al., 2016; Toivonen et al., 2023). However, few studies have focused on predicting an indirect multi-factor indicator of potential forest biodiversity, and no attempt has been made to predict the IBP using remote sensing, even though it is an indicator based on structural variables that provides valuable multi-taxon information. Rather than following the more classical approach of directly comparing LiDAR structural metrics with taxon-specific data, our study compares these metrics with an index used as a surrogate indicator for multi-taxa biodiversity. This index is based on surveys that are more readily available than traditional taxonomic inventories in a forest management context, which are often difficult to obtain in large quantities, across a wide range of taxa, and require specialised naturalist skills expertise.

We hypothesise that the IBP can be approached through remote sensing by combining the spatial heterogeneity of the spectral information provided by 2D optical imagery and the vertical heterogeneity of the point cloud provided by LiDAR data. Some IBP factors can be well estimated with LiDAR data. This is the case for structural complexity (Seidel et al., 2020; Atkins et al., 2023), open environments identified through gaps (Silva et al., 2019), and very large diameter living trees (Korhonen et al., 2016; Kramer et al., 2016). Other factors are much more uncertain, although research is ongoing. These include standing and fallen deadwood (Pesonen et al., 2008; Jarron et al., 2021; Marchi et al., 2018) and tree-related microhabitats (Santopuoli et al., 2020), which are related to the ecological maturity of trees, as well as habitats related to aquatic environments and rocky areas under cover (Ørka et al., 2022).

These various studies suggest the possibility of predicting the IBP through remote sensing, either in its most complete form (score based
<table><caption>**Table 1**
IBP v3.0 survey sheet for Atlantic, Continental and Alpine contexts (Larrieu et al., 2025).</caption><thead><tr><th>Type of factor</th><th>Factor</th><th>Succinct definition</th><th>Scoring system</th></tr></thead><tbody><tr><td rowspan="3">Compositional</td><td>A: Tree richness</td><td>Number of autochthonous tree genera (dead or living trees)</td><td>1 or 2 genera: 0 points<br>3 or 4 genera: 2<br>5 genera and more: 5</td></tr><tr><td>B: Vertical structure</td><td>Number of vegetation layers (max. 5 layers)</td><td>1 layer: 0<br>2 layers: 1<br>3 or 4 layers: 2<br>5 layers: 5</td></tr><tr><td>C: Standing deadwood</td><td>Number of small snags (SS; 17.5 cm &lt; dbh &lt; 37.5 cm) and large snags (LS; dbh ≥ 40 cm)</td><td>LS/ha &lt; 1 and SS/ha &lt; 1: 0<br>LS/ha &lt; 1 and SS/ha ≥ 1: 1<br>1 ≤ LS/ha &lt; 3: 2<br>LS/ha ≥ 3: 5</td></tr><tr><td rowspan="5">Structural</td><td>D: Lying deadwood</td><td>Number of small logs (SL; 17.5 cm &lt; d &lt; 37.5 cm) and large logs (LL; d ≥ 40 cm); diameter at the larger end</td><td>LL/ha &lt; 1 and SL/ha &lt; 1: 0<br>LL/ha &lt; 1 and SL/ha ≥ 1: 1<br>1 ≤ LL/ha &lt; 3: 2<br>LL/ha ≥ 3: 5</td></tr><tr><td>E: Very large trees</td><td>Number of trees with 47.5 cm &lt; dbh &lt; 67.5 cm (LT) and dbh ≥ 70 cm (VLT)</td><td>VLT/ha &lt; 1 et LT/ha &lt; 1: 0<br>VLT/ha &lt; 1 et LT/ha ≥ 1: 1<br>1 ≤ VLT/ha &lt; 5: 2<br>VLT/ha ≥ 5: 5</td></tr><tr><td>F: Habitat-trees (HT)</td><td>Number of live trees with at least one tree-related microhabitat (reference list of tree-microhabitats to observe)</td><td>&lt;1 HT/ha: 0<br>≥ 1 and &lt; 2 HT/ha: 1<br>≥2 and &lt; 6 HT/ha: 2</td></tr><tr><td>G: Openness</td><td>% per ha of open areas (clearings, edges and other areas with a well-developed herb layer composed of flowering plants)</td><td>At least 6 HT/ha: 5<br>0%: 0<br>&lt; 1% or &gt; 5%: 2<br>1 to 5%: 5</td></tr><tr><td>H: Temporal continuity of the woody state</td><td>Presence of the stand on an ancient map (19th c.)</td><td>Absence: 0<br>Edge of an ancient forest: 1<br>Ancient forest partially cleared or replanted after tillage: 2<br>Ancient forest never cleared: 5</td></tr><tr><td rowspan="2">Structural context</td><td>I: Wet macrohabitats</td><td>Number of wet-habitat types (reference list of macrohabitats to observe)</td><td>none: 0<br>only 1 type: 2<br>2 or more types: 5</td></tr><tr><td>J: Rocky macrohabitats</td><td>Number of rocky-habitat types (reference list of macrohabitats to observe)</td><td>none: 0<br>only 1 type: 2<br>2 or more types: 5</td></tr></tbody></table>

on all 10 factors) or in a more reduced form (management IBP calculated on 7 factors or a more restricted group of factors).

In this study, we investigate the relationships between metrics derived from remote sensing data and the IBP management factors to assess, on one hand, whether these relationships align with existing literature on the factors that can be well estimated using remote sensing data (e.g., structural complexity, gaps, large diameter living trees). On the other hand, we aimed to determine whether it is possible to predict the IBP either in its complete or reduced form with satisfactory predictive performance using these metrics as features in a model.

We gathered a dataset of IBP ground truth, comprising 1536 plots across France with LiDAR coverage. We computed remote sensing metrics and, for the first time, analysed their relationships with the IBP management factors. Predictive models were then calibrated using both regression and classification machine learning algorithms. Finally, we produced the first large-scale map of the IBP management score over an 890 km² area within the forests of the Ariege Pyrenees Regional Natural Park.

## 2. Material and methods

### 2.1. IBP definition and reference data

The IBP is an indirect indicator that integrates ten key factors: 7 compositional and structural factors related to forest management and 3 other factors related to historical and environmental context (Table 1). It includes native tree species richness (factor A), vertical structure of vegetation (factor B), standing and fallen deadwood (factors C and D), very large living trees (factor E) and canopy gaps or open areas (factor G). Context-related factors include forest continuity over time

(factor H) and two non tree-related habitats (factors I and J). A score of 0, 1, 2, or 5 is assigned to each factor, using a scoring system with a scale calibrated by expert judgment. The global IBP score ranges from 0 to 50, resulting in the aggregation of the IBP management score (i.e. the addition of the 7 related factors of a total score of 35) and the context score (i.e. the addition of the 3 related factors of a total score of 15). The detailed field survey sheet and methodology for calculating scores are provided in Table 1. In this study, we used individual raw IBP factor values for exploratory correlation analyses with remote sensing data only. For IBP modelling, we predicted the overall IBP management score and an integrative score based on a more restrictive set of management factors (i.e. B, E, G and B, E, G, F). None of the factors were predicted individually. Focusing on the IBP management score rather than the global IBP score was motivated by its higher relevance for testing relationships with structural and spectral metrics derived from remote sensing data, unlike factors H, I, and J. Indeed, the historical context factor (H) involves referring to ancient maps which we are not using in this study. Additionally, the wet and rocky microhabitats factors (I, J) deal with characteristics that may not be visible in LiDAR-HD data due to occlusion.

We gathered eight IBP reference datasets. A total of 1750 one hectare (56-m radius) forest circular plots were collected as ground truth (Table 2). These datasets come from IBP surveys conducted as part of several research projects or by partners (CNPF, CEN Occitanie, PNR Ariege). For instance, Dataset 4 comes from the database used to calibrate the ten factors of IBP using large-scale multi-taxa data (Larrieu et al., 2019). Dataset 6 and Dataset 7 are surveys carried out as part of the characterisation of mature Pyrenean forests, in mountainous (Savoie et al., 2015) and lowland (Gouix et al., 2019) areas.
<table><caption>**Table 2**
Number of IBP plots and LiDAR cover by dataset.</caption><thead><tr><th>Dataset</th><th>IBP survey year</th><th>Nb of IBP plots</th><th>LiDAR acquisition year and season (with nb of IBP plots covered)</th></tr></thead><tbody><tr><td>Dataset_1</td><td>2021-2024</td><td>70</td><td>may-july 2021 (n = 19), april-september 2022 (n = 51)</td></tr><tr><td>Dataset_2</td><td>2022-2023</td><td>28</td><td>july-october 2022 (n = 11), february 2023 (n = 17)</td></tr><tr><td>Dataset_3</td><td>2019</td><td>44</td><td>august-september 2021 (n = 26), july-october 2022 (n = 18)</td></tr><tr><td>Dataset_4</td><td>2012-2013</td><td>478</td><td>june-october 2021 (n = 58), february-october 2022 (n = 181), february-august 2023 (n = 40), uncovered (n=199)</td></tr><tr><td>Dataset_5</td><td>2010-2015</td><td>53</td><td>july-august 2021 (n = 9), june-october 2022 (n = 29), uncovered (n=15)</td></tr><tr><td>Dataset_6</td><td>2008-2024</td><td>620</td><td>may-october 2021 (n = 390), july-october 2022 (n = 230)</td></tr><tr><td>Dataset_7</td><td>2013-2024</td><td>354</td><td>june-september 2021 (n = 138), may-august 2022 (n = 216)</td></tr><tr><td>Dataset_8</td><td>2021</td><td>103</td><td>september 2021 (n = 4), july 2022 (n = 99)</td></tr><tr><td>Total:</td><td></td><td>1750</td><td>or 1536 with LiDAR data (214 uncovered)</td></tr></tbody></table>

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Geographical distribution of the IBP management scores across the reference plots, with the prediction area selected to test the model (green square). The size of the circles does not represent the real size of the plots. The prediction area corresponds to the forests in the Ariege Pyrenees Regional Natural Park.</figcaption></figure>

**Table 2** summarises the number of plots in each dataset as well as their survey year and LiDAR coverage status. Plots are distributed across the whole French territory, with a higher abundance in mountainous areas (Fig. 1). The violin plot showing the distribution of the IBP scores reveal a marked imbalance in the data, with half of the plots having a score of 27 or more out of 35, and 75% having a score above 21 (Fig. 2). The datasets with the largest sample size (Dataset_6 and Dataset_7) have a significant impact on this imbalance, with a median score of 29. Dataset_1 and Dataset_2 show lower values (median score between 15 and 20) but they represent approximately only 5% of the total number of plots. High IBP management scores are found in mountainous areas (French Pyrenees in the south, Mercantour in the Alps), whereas the plains are characterised by low scores (e.g. in Champagne, north-east).

According to Table 2, only 1536 plots had LiDAR coverage. Therefore, we applied an initial filter to retain only these plots. Based on the distribution of time lags in our dataset (Figure A.1, in supplementary materials), which shows a clear split between plots with lags under

five years and those above eight years, we excluded the latter to limit the risk of structural changes and outliers. This threshold ensured a sufficient number of plots for training while minimising changes in forest stand dynamics, as lags of around six years can already lead to differences in biomass (Nazset et al., 2011) and can affect species richness, for example in avian communities (Vierling et al., 2014). This second filter resulted in a final dataset of 827 plots.

## 2.2. Remote sensing data

### 2.2.1. LiDAR data

To characterise the canopy structure, we used open LiDAR data provided by the French geographic institute (IGN). The LiDAR HD program aims at covering the entire territory by 2026 and consist of Airborne Laser Scanning (ALS) point clouds with a minimum density of 10 pts/m² and an average of 10 pulses/m² or 40 pts/m² (IGN, 2024). Point clouds are classified according to the ASPRS (American Society for Photogrammetry and Remote Sensing) specifications. The ALS point
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Distribution of IBP management scores by dataset. Only plots with LiDAR-HD coverage are included.</figcaption></figure>

clouds are provided in the form of 1 km × 1 km square tiles in LAZ 1.4 format. Only points corresponding to ground (class 2) and vegetation (class 3 to 5) as well as unclassified (class 1) that mostly correspond to ground or low vegetation, were kept.

Point clouds of each IBP plot were extracted from the LiDAR HD corresponding tile and normalised by height using a nearest neighbour algorithm. To clean the point cloud of remaining outliers in the LiDAR-HD data, we applied a statistical outlier removal filter with conservative values (mean number of neighbours: 8, multiplier: 1).

A set of LiDAR metrics has been calculated within each IBP plot from the 3D point clouds (see Section 2.4). To determine some metrics such as plant area density (PAD) and plant area index (PAI), three-dimensional (voxel) grids were created with a horizontal resolution of 10 m and a vertical resolution of 0.5 m. This size was deemed sufficiently large to match the tree scale and was considered appropriate given the point density of the ALS data.

### 2.2.2. Multispectral imagery

In order to complement the structural variables provided by the LiDAR data, we added basic spectral variables from aerial image data available at the national scale. Normalised Difference Vegetation Index (NDVI) was computed from IGN BDORTHO® v2.0 IRC (infrared colour) orthoimages at 1 m resolution. Each pixel is coded on 3 bytes (24 bits) corresponding to the 3 near-infrared, red and green channels. Images are acquired during the summer season and updated every 3–4 years. They were downloaded by accessing the corresponding IGN webservice in Python.

Here, NDVI could inform on the health status and abundance of vegetation. We expect NDVI to be related to the presence of deadwood (factors C and D). Additionally, NDVI could be informative about large trees (factor E) and flowering open areas (factor G) (Lalechère et al., 2024).

### 2.2.3. Dynamic Habitat Indices

In order to introduce information on the intra-annual dynamics of vegetation, we added the Dynamic Habitat Indices (DHIs) (Coops et al., 2008). DHIs are remote sensing indices that can inform essential

biodiversity variables (EBVs), such as species richness or distribution, and have good potential for general biodiversity science (Radeloff et al., 2019; Bonthoux et al., 2018). They provide information to describe seasonality and productivity of vegetation. The DHIs are calculated from the curve obtained from time series of vegetation index images (e.g. NDVI or EVI, the Enhanced Vegetation Index), which captures seasonal variations over time, resulting in the following three metrics:

- Cumulative DHI, i.e. the area under the curve for a year.

- Minimum DHI, i.e. the minimum value of the curve of a year.

- Seasonal DHI, i.e. the coefficient of variation of the curve of a year.

DHIs were included in the study because they provide complementary information about the temporal dynamic of vegetation which can be related to plant functional types (e.g. broadleaf trees) and thus, to the vegetation composition (factor A). A 2018 DHI product calculated from Sentinel-2 annual time series at 10 m spatial resolution was used. This product was provided by THEIA, the french land surface data and services hub of IR Data Terra (https://www.theia-land.fr) and produced by the CESBIO laboratory. The product contained additional components that were slightly different from the original definition, i.e. 5 bands: minimum, maximum, mean, standard deviation and coefficient of variation of NDVI.

### 2.2.4. Aboveground biomass

Intuitively, vegetation biomass increases in the presence of large-diameter trees (IBP's factor E) and when openness (factor G) decreases. Additionally, we expect biomass variability to be higher in transition areas within the plot, such as those with canopy gaps. Therefore, we included biomass data in our study. We used data on the above-ground biomass of trees across Europe at 30-m resolution, derived from 2019 remote sensing data (Liu et al., 2023). The values were converted from kg/pixel of 30 m × 30 m into ton/ha.

### 2.2.5. Topographic data

Environmental variables are proven to have a significant impact on forest management. In particular, topography can be related to the
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Global Overview of the Method. The circled numbers correspond to the respective section numbers. Several metrics are derived from remote sensing data at the plot level. Following preliminary analysis, a machine learning step is performed to train and evaluate predictive models of the IBP. Finally, the models are used to predict the IBP on a forest area in Ariege.</figcaption></figure>

attributes of old-growth forests that are characterised by the abundance of dead wood, large trees, presence of native species as well as heterogeneous horizontal and vertical structure (Lalechère et al., 2024), meaning high IBP management scores. Topographic variables were therefore included as they could improve the predictive accuracy. These variables consist in slope, aspect and elevation descriptive statistics (mean, max, min, standard deviation, coefficient of variation). For these, we used Digital Terrain Models (DTM) from IGN RGE ALTI® data at 1 m resolution, accessed using an IGN webservice.

## 2.3. General approach

The detailed workflow including all the steps to derive the predictors from the remote sensing data and to create the model to predict and spatialise the IBP management score is shown in Fig. 3.

## 2.4. Computing remote sensing metrics

We computed a set of remote sensing metrics for its hypothetical ability to describe the IBP management score and its factors. As many relevant metrics as possible were calculated, with the goal of maximising the representation of relationships with IBP, while increasing the chances of having metrics that are not collinear with each other. The choice of the metrics was based on the three main components of variation of canopy structural traits: canopy height, vertical complexity and canopy openness (Fahey et al., 2019).

The selected metrics are summarised in Table 3. The structural metrics (height, vertical layering and horizontal openness descriptors) were derived from LiDAR data. They have been supplemented with spectral metrics, topographic metrics and complementary canopy descriptors from biomass data. All calculations were implemented in Python using the PDAL library for LiDAR data (PDAL Contributors, 2025) and GDAL library for raster data (Rouault et al., 2025).

To ensure compatibility with the scale of the IBP surveys, we calculated the metrics based on the extent of the 1-ha plots. Plot-scale metrics are commonly used in studies characterising EBVs with LiDAR data. It provides a satisfactory description of variability within the stand (Valbuena et al., 2014, 2020). According to Atkins et al. (2023), a spatial grain size between 25–75 m is sufficient to infer leaf area, canopy cover and canopy arrangement metrics, while a grain size between 30–150 m sufficiently captures canopy height metrics. Therefore, our circular plot size based on a radius of 56-m which corresponds to a grain size of 50 m when transposed to a square shape seems adapted for capturing LiDAR vegetation metrics, while being defined primarily by the IBP study scale. This plot size was also identified as the best to predict large diameter tree density (Kramer et al., 2016).

Some of the metrics have been derived both at the scale of the entire point cloud within the plot and at the scale of each vegetation strata (Table 3). Others are directly dependent on vertical strata (e.g. density proportions). To describe the structure from the point clouds in a way similar to what an IBP survey evaluates in the field, we defined the height bins following the IBP protocol:
<table><caption>**Table 3**
Summary of the computed metrics and the rationale for their selection (based on references).</caption><thead><tr><th>Type</th><th>Acronym</th><th>Metric</th><th>Rationale (related factor)</th><th>Reference</th></tr></thead><tbody><tr><td rowspan="2">Canopy height descriptors</td><td>Zmax, Zmean, Zmed</td><td>Maximum, mean, median vegetation height</td><td rowspan="2">Can reflect the level of maturity of the vegetation, determining the vertical structure (B), tree width (E) and depending on the tree species (A).</td><td rowspan="2">Naesset (2002), Kissling and Shi (2023) and Valbuena et al. (2018)</td></tr><tr><td>ZP10, ... ZP90</td><td>Height percentiles</td></tr><tr><td rowspan="4">Vertical complexity metrics</td><td>Density proportions<sup>a</sup></td><td>Proportion of vegetation points within height bins</td><td rowspan="4">ENL increases with stand height and evenness across strata.</td><td rowspan="4">Ehbrecht et al. (2017, 2016).</td></tr><tr><td>ZLn</td><td>L-moments of height</td></tr><tr><td>ENL (i.e. 2D ENL)</td><td>Effective Number of Layers, also part of the complexity metrics</td></tr><tr><td>Zsd<sup>a</sup>, Zvar<sup>a</sup></td><td>Standard deviation and variance of vegetation height</td></tr><tr><td rowspan="6">Openness, cover metrics</td><td>Zcv<sup>a</sup>, Zskew<sup>a</sup>, Zkurtosis<sup>a</sup></td><td>Coefficient of variation, skewness coefficient and kurtosis coefficient of vegetation height</td><td rowspan="2">Quantify heterogeneity of vertical structure (B).</td><td rowspan="2">Kissling and Shi (2023)</td></tr><tr><td>Lcv, Lskew, Lkurtosis</td><td>L-ratios</td></tr><tr><td>Ziqr</td><td>Interquartile range of the height</td><td rowspan="2">May indicate inequality in tree size and diameter (factors B, E). Height dispersion measurement. Measure of vertical entropy of the forest ecosystem structure.</td><td rowspan="2">Valbuena et al. (2017)</td></tr><tr><td>FHD</td><td>Foliage Height Diversity based on the Shannon-Wiener index</td></tr><tr><td>PAD<sup>a</sup>, PAI, VCI</td><td>Plant Area Density metrics and Vertical Complexity Index</td><td rowspan="2">Can provide information on the heterogeneity of foliage.</td><td rowspan="2">Moudry et al. (2023)</td></tr><tr><td>Icv<sup>a</sup></td><td>Coefficient of variation of intensity</td></tr><tr><td rowspan="3">Spectral metrics</td><td>CC</td><td>Canopy Cover, from a geometric-based approach (see Figure A.2)</td><td>May be anti-correlated with openness (G).</td><td>Korhonen et al. (2011), Taneja et al. (2023)</td></tr><tr><td>CRR</td><td>Canopy Relief Ratio</td><td>Descriptor of canopy arrangement and variability.</td><td>Atkins et al. (2023)</td></tr><tr><td>MOCH, Rugosity</td><td>Mean Outer Canopy Height, standard deviation of canopy height (from first returns)</td><td>Emphasises horizontal canopy variability.</td><td></td></tr><tr><td rowspan="2">Ancillary</td><td>NDVI</td><td>Normalised difference vegetation index (mean, min, max, SD, var, CV)</td><td rowspan="2">May be anti-correlated with the amount of deadwood (C and D) or openness (G), and correlated with species richness (A). Phenological information from NDVI.</td><td rowspan="2">Zielewska-Büttner et al. (2020)</td></tr><tr><td>DHI</td><td>Dynamic Habitat Index min, max, mean, SD, CV</td></tr><tr><td>Topographic metrics</td><td>Biomass</td><td>Biomass mean, SD, CV</td><td>Additional descriptor of canopy structure.</td><td>Liu et al. (2023)</td></tr><tr><td></td><td></td><td>Elevation, slope, aspect statistics (mean, CV)</td><td>The presence of ancient and mature forests (which often have a high IBP) is favoured by steep slopes and high elevation.</td><td>Larrieu et al. (2023)</td></tr></tbody></table>

- Herbaceous, semi-woody vegetation (set to 0 to 0.75 m)

- Very low woody stratum (0.75 to 1.5 m)

- Low woody stratum (1.5 m to 7 m)

- The medium woody stratum (7 to 20 m) was subdivided into 7 to 13 m and 13 to 20 m strata.

- The high woody stratum (> 20 m) was not taken into account as not all plots had points above 20 m in the ALS data.

Plant Area Density (PAD) was computed from voxels (see Section 2.2.1), as in Martin-Ducup et al. (2025). Expressed in $m^2 m^{-3}$, PAD defines ‘the total one-sided total plant surface area within a given volume’ (Nguyen et al., 2022). By definition, PAI, which is the sum of PAD at all heights, could be indicative of vertical stratification, as well as tree diameters and canopy openness.

PAD statistics (mean, min, max, standard deviation - SD, coefficient of variation - CV) were calculated at the scale of the entire plot, aggregated over all voxels. Vertical and horizontal heterogeneity metrics were also calculated, as they are potentially informative about biodiversity (Heidrich et al., 2023). For each horizontal strata of voxels in the plots, and for each voxel column, mean, SD and CV were measured, allowing to derive the following metrics: the mean of horizontal SD and CV of PAD, the SD and CV of horizontal mean of PAD, the mean of vertical SD and CV of PAD, and the SD and CV of vertical mean of PAD (Carrasco et al., 2019).

We also computed a PAD-based Vertical Complexity Index (VCI) by replacing the proportions of points per vertical stratum with the PAD values as in Eq. (1), turning the VCI into an index of vertical PAD complexity.

$$VCI_{x,y} = \frac{- \sum_{z=1}^{H} \text{PAD}_{x,y,z}^{\text{scaled}} \ln(\text{PAD}_{x,y,z}^{\text{scaled}})}{\ln(H)} \quad (1)$$

Where $VCI_{x,y}$ is the VCI of the column of coordinates $x, y$, $H$ is the number of voxels in the column, and $PAD_{x,y,z}^{\text{scaled}}$ is the $PAD_{x,y,z}$ value divided by the PAI (sum of all PADs in the column).

In total, we calculated 314 metrics, of which 277 are derived from LiDAR data, 20 from topographic data, 11 from spectral data and 6 from biomass data.

As a preliminary study, we conducted a correlation analysis before training and testing the models, in order to investigate relationships between the remote sensing variables and the IBP management factors individually. For this purpose, we calculated Spearman's rank correlation coefficient $\rho$. Correlations were calculated over 100 iterations, each using a balanced subset of 62 IBP samples per category (matching the least populated category), with the IBP dataset divided into three homogeneous categories based on IBP management scores (0–12, 13–24, and 25–35) to mitigate bias from the over-representation of high IBP values. For each pair of variables, the significance of $\rho \ge |0.1|$ was tested, considering this value as a very weak correlation, and values

<footnote><sup>a</sup> Variable derived both at the scale of the entire point cloud and at the scale of each vertical strata.</footnote>
below as no evidence of correlation. The *p*-value for significance was set to 0.01.

## 2.5. Addressing spatial autocorrelation using Moran's eigenvectors maps

The IBP dataset is composed of multiple merged datasets, with sampling plots distributed in distinct patches, including a high concentration of plots in the French Pyrenees. This uneven spatial distribution can lead to strong spatial autocorrelation, where geographically close plots exhibit similar characteristics and therefore potentially similar IBP values. We aimed to incorporate the IBP spatial structure in our study to mitigate this bias while leveraging it to enhance predictive performance.

For this purpose, we calculated the Moran's eigenvectors maps (MEMs), from Dray (2011). MEMs are a set of orthogonal spatial predictors used to account for spatial autocorrelation. They are derived from a spatial weighting matrix which encodes the spatial relationships between data points based on their geographic locations. By maximising Moran's I, a measure of spatial autocorrelation, these eigenvectors capture spatial structures at different scales, allowing for the integration of spatial effects into predictive models.

We calculated the first 9 MEMs from the plot coordinates directly, as considering a France-level grid would have been too computationally expensive. The analysis was conducted using R (v. 4.4.2), primarily with the *adespatial* package (v. 0.3.25), from (Dray et al., 2024). The MEMs are shown in supplementary materials (Figure A.3).

## 2.6. Model training and testing

We investigated the prediction of several IBP management-based variables. Initially, we focused on predicting IBP classes, as predicting the IBP point by point is not essential. A variation of more than 5 points in the IBP score substantially impacts species richness in terms of magnitude, as discussed by Zeller et al. (2022). Also, classification could potentially achieve better performance and provide a simplified, categorised representation of the IBP management score, which is more suitable for forest managers than regression. Finally, we also addressed the regression task of the IBP management score to study a finer grained representation of the index.

Several discretisation schemes were evaluated to form categories for IBP management scores. First, the IBP scores were discretised into 3 and 4 classes, based on the score distribution (histogram) and ecological relevance, with finer classes for the highest scores. Then, since different stand structures and factor combinations can result in the same final IBP management score, we aimed to predict stand structure in more detail. To achieve this, we examined predictions based on specific IBP components known to be significantly correlated with species richness according to Zeller et al. (2022): canopy gaps (factor G), the number of vertical layers (factor B), large living trees (factor E), and TreMs (factor F). Based on these, we tested models using a recalculated IBP score that incorporates these factors (4-factor IBP), as well as a simplified version that excludes factor F (3-factor IBP). This approach was taken because it is unclear whether factor F directly correlates with LiDAR variables, given that only a few recent studies have demonstrated relationships between ALS-derived variables and TreMS (Parisì et al., 2024; Santopuoli et al., 2020).

Finally, we addressed the following five primary machine learning tasks:

1. Classification of IBP management scores:

- according to 3 classes: [0, 15], (15, 25], (25, 35)

- according to 4 classes: [0, 15], (15, 25], (25, 30], (30, 35)

2. Classification of a subset of the IBP management score:

- 3-factor IBP (factors B, E, G) according to 3 classes [0, 7], (7, 12], (12, 15)

- 4-factor IBP (factors B, E, G, F) according to 3 classes [0, 10], (10, 15], (15, 20)

3. Regression of IBP management scores

We used Support Vector Machine (SVM) for the classification tasks (SVC algorithm) as well as the regression task (SVR algorithm). Our choice to use SVMs was based on their strong performance with high-dimensional data compared to tree-based algorithms, which was a crucial consideration given the large number of features in our dataset. In addition, SVM algorithms are robust to class imbalance when appropriate class weights are applied, in contrast to tree-based methods that often require resampling strategies. We first employed the Sequential Forward Floating Selection (SFFS) technique (Pudil et al., 1994) to identify the optimal subset of features (i.e. combination of remote sensing variables and MEMS) and reduce data dimension. SFFS is an iterative method that begins by evaluating each individual feature using an internal machine learning algorithm (e.g. SVM) with cross-validation to assess predictive performance. It then sequentially adds the feature that most improves model performance, forming and testing combinations with an increasing number of features. After each addition, SFFS allows for the potential removal of previously included features if their exclusion enhances overall performance. This “floating” aspect ensures that the feature set remains optimal at each step. Through this approach, SFFS efficiently explores the feature space, ultimately selecting a subset that maximises predictive accuracy while minimising redundancy.

SVM models were tuned and trained according to Figure A.4 (in supplementary materials). We tuned the regularisation parameter (C), the kernel type, the kernel bandwidth ($\gamma$) in a search space starting with the following settings: $C = \{0.01, 0.1, 1, 10, 100\}$, $\gamma = \{\text{radial}, \text{sigmoid}, \text{polynomial}, \text{linear}\}$, $\varepsilon = \{0.0001, 0.001, 0.01, 0.1, 1, 100, 1000, 1/n_{\text{features}}\}$, $1/(n_{\text{features}} \times \text{var}(X))$ with X the feature matrix of the training data and $n_{\text{features}}$ its number of features. To manage the class imbalance issue, we set the SVM weights inversely proportional to class frequencies. We tuned the internal SFFS SVM using the same search space as the main (external) SVM while also tuning the number of selected features in increments of 10, from 10 to 100.

During the tuning, the models were evaluated through 5-folds cross-validation schemes. For classification, we performed cross-validation in a way that preserves the class distribution within each fold, while also repeating the cross-validation process multiple times to enhance the robustness and reliability of the model assessment (using the Repeated-StratifiedKFold function in the *scikit-learn* Python package (Pedregosa et al., 2011)). For regression, we used a similar strategy without preserving an equal distribution of classes (RepeatedKFold in *scikit-learn*).

Finally, models were scored on a balanced and independent test set, not seen during feature selection nor tuning. The full process was repeated 10 times to account for variability in model performance, with a train/test split ratio of 90/10 (see Figure A.4).

## 2.7. Model explainability through SHAP values

We aimed to understand how each feature influenced the predictions. However, SVMs do not directly provide information on feature importance, unlike algorithms such as random forests but which must be interpreted with caution (Strobl et al., 2007). To address this, we computed SHAP (SHapley Additive exPlanations) values, which provide interpretability by quantifying the impact of each feature on the prediction for a set of samples (Lundberg and Lee, 2017). SHAP values provide insights into both the relative importance of features (through the magnitude of SHAP values) and the direction of their effect (through the sign of the SHAP values). We computed SHAP values using 500 samples randomly selected from our full IBP dataset.
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Highest Spearman correlation coefficients between LiDAR metrics, IBP factor measurements and IBP management score. Only variables with $ρ > 0.45$ with at least one of the IBP factors are represented. Factor A: number of native tree species; factor B: number of vertical strata; factor C: number of large standing deadwood pieces; factor D: number of large lying deadwood pieces; factor E: number of very large living trees; factor F: number of TreM-bearing trees; factor G: percentage of open, flowering areas. Blue means negative correlation, red means positive correlation, * means correlation significance ($p$-value < 0.01).</figcaption></figure>

## 2.8. Prediction and mapping of the IBP management score at large scale in the Ariege PNR

To test the models' behaviour on a large-scale prediction, we chose to study predictions in the area of the Ariege Pyrenees Regional Natural Park (PNR). This choice was motivated by the fact that this region is known to partners of the research unit, who will be able to assess the prediction and potentially visit the field to validate it. Also, in the absence of knowledge of the models' range of validity, a prediction in a geographical area close to the training data is likely to be more reliable.

We created a grid at 100 m spatial resolution (i.e. with an area of 1 hectare, to match the IBP assessment area), as a subset of the ecological study grid at 1 km resolution of the INPN (French national inventory of natural heritage). We then filtered out the cells that were not exclusively forest ones, with a canopy height greater than 3 m according to the data from Liu et al. (2023). In this way, the defined study area includes 888 km² of forest out of the 2000 km² surface area of the Ariege Park (see Fig. 1).

The best selected machine learning models (SVC3-IBP and SVR-IBP) were used to predict the IBP management score for each grid cell. Both regression and classification tasks were carried out, using the SVM regressor and the 3-class SVM classifier.

As the Ariege predictions have not yet had the opportunity to be validated in the field (beyond the test samples available across the entire territory), we defined the validity domain of the predictions in a first instance. We used Novelty indices (Mesgaran et al., 2014) that determine whether the samples are in the model's training space or whether the model has had to extrapolate, based on the Mahalanobis distance between training data (i.e. its feature values) and projected data (i.e. feature values of the predicted data). Projected data that fall outside the univariate training space exhibit Type 1 Novelty (NT1), while projected data within the training space but with covariate combinations not captured in the training samples exhibits Type 2 Novelty (NT2). NT1 values range from negative infinity to 0, with values less than 0 indicating extrapolation. NT2 provides an additional test for areas where NT1 equals 0, with values ranging from 0 to infinity. Areas with NT2 values between 0 and 1 do not show extrapolation, whereas areas with NT2 values greater than 1 indicate extrapolation. The Novelty index calculation has been implemented in Python.

# 3. Results

## 3.1. Correlations between the IBP factors and remote sensing metrics

The IBP management score is highly and positively correlated ($ρ > 0.5$) with canopy height (Zmax, ZP99, ENL), heterogeneity in the upper canopy (Zsd in 13–20 m stratum), elevation (min, max and mean

elevation) and steepness (mean, and standard deviation of slope) (Fig. 4). The number of vertical strata (factor B) as well as the number of very large living trees (E) show comparable correlation results. Moreover, these two factors as well as the standing and lying deadwood factors (C and D) were highly positively correlated to the IBP management score with a $ρ$ ranging from 0.73 to 0.77.

The number of both large standing and lying deadwood pieces is mainly correlated with topographical context variables (high elevation and slope, $ρ > 0.5$).

The number of native tree species (factor A) and the number of TreM-bearing trees (F) do not exhibit strong correlations with remote sensing variables neither with the IBP management score ($ρ = 0.37$ for both). The strongest correlation observed with remote sensing variables is negative, specifically with the proportion of vegetation points in the herbaceous layer ($ρ = -0.36$ and $-0.44$ for A and F, respectively).

Conversely, the proportion of flowering open areas (factor G) is positively correlated with the proportion of vegetation points in the herbaceous stratum ($ρ = 0.51$). It is also significantly negatively correlated with the canopy cover and with the percentage of returns above 1.5 m ($ρ = -0.46$ and $-0.47$ respectively). However, note that this factor is not correlated with the IBP management score ($ρ = 0.02$).

Correlation analysis provides a first insight into variable relationships, though it captures only univariate and linear associations. To gain a more comprehensive understanding of feature influence, we further examine feature importance through SHAP values and SFFS results (Section 3.3).

## 3.2. Performance of the predictive models

For all models, the tuning process consistently selected the Radial Basis Function (RBF) kernel, indicating the presence of non-linear relationships among the features. The performance of the SVM models for each classification task and for regression is shown in Fig. 5. For SVC, F1 scores and AUC-ROC (area under the ROC curve) scores are reported as quality indicators. The 4-class classification model (SVC4-IBP) yielded the weakest performance among SVC models, with a low F1 score of 0.49 and an average AUC of 0.75. The best SVC model is the one predicting three IBP classes based on four IBP management factor (i.e SVC3-BEFG) with an average F1 score and AUC of 0.61 and 0.76 respectively. However, the model predicting three IBP classes based on all IBP management factors (i.e SVC3-IBP) also showed a strong and comparable predictive power with an average F1 score and AUC of 0.56 and 0.76 respectively. More detailed results on SVC3-IBP model are provided in supplementary materials in order to analyse the confusion matrix (Figure A.5) and shows confusion mainly between neighbouring classes, with the extreme classes being the best classified.
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** F1 scores and AUC-ROC scores of the different classification estimators on an independent test set. The nomenclature is the following: `<estimator type><number of predicted classes>—<target>`. SVC standing for SVM classifiers, SVR standing for the SVM regressor, IBP standing for the IBP management score, BEG for the score recalculated from B, E, G factors, and BEFG for the score recalculated from B, G, E, F factors. AUC-ROC: area under the false positive - true positive curve. F1 score: trade-off metric (harmonic mean) between precision and recall. $R^2$: proportion of variance explained by the model. As different score types have different meanings, they are not inter-comparable. Scores range from 0 (poor) to 1 (excellent).</figcaption></figure>

For SVR, the coefficient of determination $R^2$ and the Root Mean Square Error (RMSE) are provided. The regression model (i.e SVR-IBP) also shows a strong predictive power by explaining 46% of the variance of IBP management score ($R^2$ = 0.46 ± 0.05). The RMSE of the model is 5.24 ± 0.63 (nRMSE = 15% ± 2%) and therefore reveals that this model predict IBP within 5 points. Details of the prediction error are illustrated in the supplementary materials (Figure. A.6; observed vs. predicted IBP), which shows that predictions are more accurate for higher IBP values.

### 3.3. Selected features and their impact on prediction

Automatic hyperparameter tuning returned an optimal number of remote sensing-based features to select of 30 for both the SVR-IBP and SVC3-IBP models (i.e. 10% of total features), thus allowing significant dimensionality reduction. Fig. 6 shows that the addition of the initial features (up to 5 for both the regression and classification models) significantly improves the cross-validation scores, whereas subsequently added features only marginally improve the scores up to the maximum performance. Specifically, the $R^2$ score of SVR-IBP increases by 0.28 when expanding from 1 to 5 features but gains only 0.09 across the remaining 25 features to reach 0.45. Similarly, the F1 score of SVC3-IBP improves by 0.10 over the first 5 features, while the increase is just 0.05 for the last 25 to reach 0.73. This trend is clearly visible in the SFFS score curves (see Figure A.7 in the supplementary materials). Note that the scores in Figs. 6 and A.7 are cross-validation scores returned by SFFS during calibration (fitting) and not the scores on the test data. Unlike the results shown in Fig. 5, these scores do not reflect predictive performance on unseen data.

The SHAP values calculated from the regression model are given in Fig. 7, and illustrate how each feature contributes to the model's predictions. The SHAP values are generally consistent with the correlation analysis, as features with higher SHAP values also exhibit stronger correlations with the IBP management score. Thus, high biomass, canopy height (i.e. Z_max, Z_P99, ENL), elevation, or DHI (mean, max) are associated with high predicted IBP values. The SHAP values of several features run counter to the correlations, for example the slope or the standard deviation of DHI (DHI_std), where a high feature value is

associated with a low predicted IBP. This suggests the presence of interactions between features (hence the importance of not considering correlations only). Similarly to correlations, PAD and PAI-related metrics stands out less than expected.

The features with the greatest impact on prediction are not necessarily those selected first (e.g., the standard deviation of biomass, i.e. biomass_sd, is selected among the last 10 features). This is due to the functioning of SFFS, which selects variables based on their incremental contribution to the model: some features only become significant when combined with others, while previously chosen features may be replaced by new ones that are correlated with them.

### 3.4. Prediction in the Ariege PNR

Fig. 8 shows the IBP management scores predicted by the SVM regression model (SVR-IBP) on the park's forest areas. We can observe that the prediction displays gradients rather than a salt-and-pepper effect, despite pixels being processed individually. This indicates the model's ability to classify in a gradual manner. There is, however, a clear artifact in a 15 km-wide band to the west of the zone, distinctly visible by a boundary between a yellow/green area to the west and a blue area to the east in Fig. 8.a., with predictions approximately 10 points higher than in the adjacent zone. This artifact is also slightly visible in a 3 km-wide band at the east side.

Fig. 8 illustrates whether the data falls within the model's validity domain. 80% of the pixels in the predicted zone showed no extrapolation, while 19% showed type 1 Novelty (new univariate combinations), and 1% showed type 2 Novelty (new covariate combinations). The model has therefore extrapolated for 20% of the predicted area, and the corresponding predictions should be interpreted with caution. The anomaly to the west does not show a Novelty that is particularly different from the other zones, which indicates that it must be due to a bias in the model.

## 4. Discussion

### 4.1. Relations between the IBP factors and remote sensing variables

Forest structure has been intuitively linked to fauna since the work of MacArthur (1965). Structural characteristics such as complexity,
<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Average SFFS cross-validation scores as a function of the number of selected features for (a) the regression model SVR-IBP and (b) the classification model SVC3-IBP. The displayed scores correspond to those obtained through cross-validation using the SFFS method and not on an independent test set. The y-axis represents the number of features, from 1 to a maximum displayed of 20, and the corresponding features are listed in the x-axis in their order of appearance. The last added features are not displayed as they contribute only negligibly to the score improvement. The colours represent the average scores, also displayed on the right. An empty cell indicates that the feature on the x-axis was not selected for the given number of features on the y-axis, illustrating the floating selection process.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 7.** SHAP values representing the impact of the features on the prediction for 500 random IBP samples from the SVR-IBP model, ordered by mean absolute SHAP value for each feature (i.e. features are sorted from the top to the bottom of the graph by the magnitude of their mean SHAP values). Each point represents the SHAP value for a single sample and a given feature. The higher the absolute value of the SHAP values (i.e. farthest right/left are the points), the greater the impact of the feature on the prediction. A positive SHAP value indicates a positive influence on the prediction, while a negative SHAP value indicates a negative influence. For a given feature, when high feature values (coloured in red) are located to the right of 0, the predicted IBP values increase as the values of this feature increase. Conversely, when low feature values (coloured in blue) are located to the right of 0, the predicted IBP values decrease as the values of this feature increase. For a given feature, if none of its SHAP values are different from zero, and if the colours appear mixed along the horizontal axis, this suggests that the feature has no significant or consistent impact on the prediction. In this graph, $ZL_4$ equals Zkurtosis.</figcaption></figure>

canopy height and cover are now widely recognised as strong determinants of biodiversity over time and space, as they shape micro-climatic refuges and physical habitat (Penone et al., 2019; Lenoir et al., 2017; Fahey et al., 2019).

The IBP integrates these characteristics into its design. In particular, the IBP management score is based on seven factors, five of which are directly related to the spatial organisation of vegetation from the ground to the canopy top (i.e B, C, D, E and G). Our results showed that the correlations between the IBP management score and its factors are clearly the strongest for four of these forest-structure-based factors (B, C, D and E, with $\rho \ge 0.72$). This is very encouraging for predicting the IBP using LiDAR data, which provides a fine 3D description of the vegetation and is therefore well-suited to its potential for predicting biodiversity, in line with the ‘Height variation hypothesis’ recently proposed by Torresani et al. (2023).

Moreover, the LiDAR metrics we identified as relevant and interpretable for forest structure were strongly correlated to the IBP forest-structure-based factors. For instance, ENL, which reflects both the maximum height and the density of vegetation strata, is the LiDAR metric most strongly correlated with factor B (number of strata, $\rho = 0.59$) and factor E (number of large trees, $\rho = 0.55$). The openness factor is also highly correlated with metrics that are interpretable through their relationship with openness or, conversely, canopy cover ($\rho = 0.47$ with the percentage of returns above 1.5 m; $\rho = -0.46$ with canopy cover).

The most important remote sensing metrics for the model according to the SHAP values are among the most correlated with the IBP

management score and the IBP management factor values. The first metrics that stand out are related with the biomass, canopy height ($Z_{max}$, ENL) and vertical heterogeneity ($Z_{kurtosis}$, $Z_{skewness}$). This seems rather relevant, since we had identified these factors as being potentially explanatory of all management factors, except factor F.

These results are consistent with studies which modelled variables equivalent to the IBP factors. For instance, Seidel et al. (2020) modelled the stand structural complexity with ENL being the second most important feature (out of 8). Santopuoli et al. (2020) modelled the sum of microhabitat-bearing trees with the maximum height being the most important feature (by far, with 70% of features importances). Moreover, Kramer et al. (2016) modelled tree diameters with high accuracy from the percentage of areas with a canopy height > 32 m, which is substantially linked to the maximum canopy height.

The model not only characterises IBP in a relevant way through its features, but these features are also ecologically meaningful, as they have a significant impact in modelling several aspects of biodiversity. Toivonen et al. (2023) determined that among several studies, the metrics that stand out as most important for species richness and diversity include canopy height descriptors ($Z_{mean}$, $Z_{max}$). Additionally, Penone et al. (2019) used ENL to quantify vertical variability within the stand, and showed that high ENL values generally implied a high richness and abundance of aboveground trophic groups (with a significant effect on lichens).

It is both consistent and expected that the three main components of variation of canopy structure (i.e. height, openness and vertical
<figure><img src="image_9.png" /><figcaption>**Fig. 8.** Map of (a) the IBP management score predictions in the Ariege Regional Natural Park made using the SVM regression model (SVR-IBP), with (b) the associated Novelty indices. NT1 indicates novelty in a single dimension of the feature space, while NT2 indicates new covariate combinations.</figcaption></figure>

complexity, from Fahey et al. (2019) rank among the most important features in the model. Nevertheless, openness-related metrics were not selected, despite several studies emphasising the important role of canopy cover, such as Atkins et al. (2023) which identified it as the most relevant feature for distinguishing between different structural types of stands. This can be explained by the fact that we selected cover-related metrics for being potentially linked to a single IBP factor (openness, G). However, it turns out that the correlations indicate the lowest impact of this factor on the IBP management score. This weak relationship could stem from a divergence in the definition of openness: while IBP considers only open areas that produce floral resources, it does not account for all canopy openings. This discrepancy may explain why cover-only metrics were not identified as relevant in the IBP modelling.

Topographic metrics have great importance in the prediction according to the SHAP values, while showing significant correlations with the IBP management factors. This result is consistent given the

influence of topography on management decisions and highlights the value of incorporating complementary environmental features into IBP modelling. As an example, Munteanu et al. (2022) used elevation and slope in the Anthropogenic pressure index modelling which aims to identify high-conservation-value forests, and these two features alone highly contributed to the model performance (approx. 55% of the total feature importance out of 6 features).

SHAP values also reveal some unexpected effects. We were surprised that DHI_sd negatively affected the prediction. This result is challenging to interpret further, as no IBP factor explicitly accounts for intra-annual variability. One possibility is that this reflects a sampling effect, where certain stand types with low DHI_sd (e.g., high-maturity, conifer-dominated stands exhibiting lower intra-annual variability than deciduous-dominated stands) are overrepresented in our dataset and also tend to have higher IBP scores. At the same time, this result is consistent with findings from Radeloff et al. (2019), who reported that the variation DHI was inversely correlated with the species richness of
several taxa (amphibians, resident birds, and mammals). SHAP values also indicated that NDVI_max and slope_mean negatively affected the prediction. This may reflect that very high NDVI occurs in densely covered but structurally homogeneous stands, typically associated with lower IBP management scores. Similarly, although steep slopes are generally linked to low human impact and thus higher IBP values (Larrieu et al., 2023), it is plausible that extreme slopes correspond to environmentally harsh sites (e.g., erosion-prone or sparsely vegetated) with less deadwood and reduced structural diversity, ultimately leading to lower IBP values.

## 4.2. Performance of the predictive models

Overall, all classifier models (i.e. SVC) showed satisfactory discrimination with AUC-ROC values between 0.7 and 0.8, although the F1 scores between 0.5 and 0.65 suggest that there is still room for improvement in predictive performance. The models with fewer classes (three instead of four) yielded a comparable AUC-ROC but a slightly higher F1 score, which was expected since reducing the number of classes to three lowers the complexity of the classification task. We initially studied classification to achieve satisfactory results, as we anticipated that predicting exact IBP scores (i.e., a regression model such as our SVR) would be highly uncertain. Indeed, the IBP integrates both structural and non-structural factors (or indirectly related to structure such as the indigenous species factor). The operator effect, the potential subjectivity in field perception of certain factors, and the challenge of upscaling them to a one-hectare plot scale (e.g., factor B: the number of vegetation layers) could result in poor point-by-point prediction. However, the regression model performed unexpectedly well and has an uncertainty level that is compatible with the sensitivity of the IBP index to biodiversity. An increase of five IBP points is associated with an increase of 3 to 12% in species richness, depending on the taxon, according to Zeller et al. (2022). For instance, a five-point increase in IBP is associated with an increase in the number of species for groups of birds (7%–9%), moths (3%–9%), lichens (7%–10%) and fungi (7%–9%). Given that the RMSE corresponds to ecologically meaningful differences in species richness, the regression models is more suitable for IBP prediction than classifiers, which exhibit only average performance. Therefore, we recommend using a regression model for the spatial prediction of the IBP. Furthermore, both regression and classification models perform better in high-IBP cases, those most critical to identify for conservation purposes as they are likely to exhibit high ecological maturity.

We expect the scores of our models to be robust, given that we used k-fold cross-validation stratified on IBP scores and accounted for spatial autocorrelation by including MEMs as predictors. However, future work could benefit from using spatially informed data splitting methods, such as spatial cross-validation, to provide even more reliable estimates of model performance (Karasiak et al., 2022).

For the regression model, a $R^2$ value of approx. 0.5 means that 50% of the IBP management score variance is explained by the model. This value can be interpreted differently depending on the context, but the more complex the components of the problem, the lower values are tolerated. It is common to find moderate $R^2$ values in studies modelling biodiversity indices from ALS-derived metrics. Tree species richness, structure and diversity indexes (e.g. Shannon index) can reach $R^2$ values up to 0.8, while biodiversity and taxonomic diversity models generally have $R^2$ values between 0.2 and 0.6 (Toivonen et al., 2023). In this context, our regression model shows satisfactory results.

However, comparing our models' performance with other studies remains challenging, as the IBP integrates multiple complex factors, while most studies typically focus on a limited set of structural or compositional traits. Guo et al. (2017) modelled 8 structure classes from ALS data with an average accuracy of 84% but did not include components that are less directly linked to structure and more tricky to obtain with LiDAR data (i.e. fallen deadwood).

There are strong uncertainties indeed for describing close-to-ground and surface vegetation with Airborne LiDAR data, as reviewed by Gale et al. (2021) in a context of vegetation characterisation for wildfire. As an example, Labenski et al. (2023) explain a small part of the variance in litter and fine dead woody fuels ($R^2$ = 0.27–0.41), but not in coarser dead woody fuels with machine learning models based on remote sensing metrics (i.e LiDAR and optical).

On the other hand, our model can still be compared to the predictive model of the IMAT, a recent indicator of forest maturity based on factors such as large living trees, standing dead wood, and lying dead wood, which are common to the IBP (Lalechère et al., 2024). IMAT was modelled from ALS metrics with an RMSE of 0.12 ± 0.012, or 12% ± 1% error, which is slightly lower than for our model (15% ± 1%). Encouragingly, compared to the IMAT model, our model captures a wider amplitude of predicted values (in relation to the scale of its target variable), which may indicate a better generalisation ability.

## 4.3. Insights from the prediction in Ariege

As a proof of concept, we mapped a 890 km² area in the French side of the Pyrenees corresponding to the PNR (Regional Natural Park) of Ariege. This park aims at integrating biodiversity in forest management approaches; therefore, a comprehensive map of the IBP could be particularly useful to them, as they currently rely on sparse field records. Such spatialised outputs could be especially valuable for forest managers. At the stand scale, the maps may help identify areas with low predicted biodiversity potential, where management actions (e.g., retention of large trees or deadwood) can be prioritised. Conversely, areas with high IBP values could be candidate areas for conservation. The maps can provide an overview of the spatial distribution of IBP scores at both property and broader scales, making it possible to integrate them into forest management plans or local development strategies, supported by appropriate recommendations. In addition, they can help target complementary field surveys with IBP measurements in the most relevant areas, using a sampling stratification tool. IBP maps can also be used to identify suitable locations for strict forest reserves within an integrative management framework, as described by Kraus and Krumm (2013). When doing so, factors such as surface area (ensuring reserves are large enough for long-term effectiveness), forest-level representativeness, and spatial connectivity should be taken into account. At the landscape scale, the maps could help reveal the structure of the forest ecological network by highlighting regional gradients of IBP, potential biodiversity cores and corridors. This is an important step towards improving functional connectivity of the forest network.

The mapping process including the downloading of open data (i.e LiDAR and optical), the preprocessing and the prediction based on the regression model took approximately 1 day on a computing cluster. The IBP management score exhibits a coherent spatial pattern, indicating that the model successfully represent environmental gradients such as the transition between old-growth forests and less mature forests in the lowlands. The range of values taken by the predicted IBP is consistent with the values usually recorded in the field (7–35), which is reassuring regarding the SVMs' ability to generalise from training data, even though the dataset is imbalanced with few low-IBP samples. For the classification model SVC3-IBP, misclassification mainly occurs between adjacent classes, according to the confusion matrix (Figure A.5). This suggests that the SVMs effectively capture the underlying relationships and hierarchy between the classes.

The gradients are consistent with current knowledge of the area, according to the advice of the PNR team. Old-growth forests have high predicted IBP values, which is to be expected. However, field surveys and expert opinion are needed to provide further validation of the prediction. The presence of the Novelty zones, although minor, also remains to be studied. It can be hypothesised that these zones correspond
to metrics describing the structure of more rugged, hard-to-access areas,
for which we have limited reference IBP data.

The artifact visible on the map highlights a significant issue, as it undermines part of the prediction's validity. The most plausible hypothesis was a difference in acquisition dates (2021 on the west, 2022 on the east) except that this did not coincide with the east-side artifact. However, upon cross-referencing with LiDAR-HD metadata, it appears that the boundary corresponds to the use of two distinct instruments (Optech ALTM Galaxy T2000 on the higher-IBP part, RIEGL VQ1560 II on the lower-IBP part). This sensor effect also appears on an additional analysis carried out on the distribution of four LiDAR metrics by sensor (in supplementary materials, Figure A.8), primarily affecting the intensity-based metric. Besides differences in intensity handling between manufacturers, the difference in point density, with the Optech sensor producing denser data than the RIEGL sensor, may also contribute to this bias, as noted by Viedma and Moreno (2025). We did not expect this kind of bias, since all the metrics we computed are intended to be normalised relative to the point cloud (e.g., CV of intensity, PAD based on relative return proportions). Addressing this sensor-related bias is crucial for future research, as it significantly influences the results. One possible approach is the radiometric calibration and normalisation of intensity scales between instruments (Kashani et al., 2015; Fassnacht et al., 2016). Moreover, our additional analyses (see supplementary materials, Figure A.9) revealed a possible influence of acquisition date on LiDAR metrics, which must also be considered in future IBP prediction studies.

4.4. *Limits of the remote sensing data*

In addition to sensors effect, heterogeneity in acquisition dates (months to years) and seasons across the areas may significantly impact the point cloud structure. During the leaf-on period, deciduous dominated stands have higher leaf area density, leading to occlusion of the understory layers, unlike in winter. This distorts the comparison of metrics between seasons, highlighting the importance of filtering LiDAR data by date. In our case, most of the data were acquired during summer, leaf-on period (i.e from the beginning of June to September), which should limit the effect of the phenophases. However, the strong altitudinal gradient in the mapped zone may still influence phenology even within this period and therefore impact the vegetation density related metrics (e.g PAD). Moreover, a significant part of the area was acquired in 2021 while the rest was in 2022, a year notably affected by summer drought and early defoliation.

Large scale national campaigns such as the LiDAR-HD program in France are not conducted frequently, limiting the monitoring the IBP over the long term and on a regular basis. Moreover, our model rely on external product such as the biomass map that may not be updated (Liu et al., 2023), which adds to the limitations of dynamic IBP monitoring.

For the sake of parsimony, it would be interesting to test the model using LiDAR data only, particularly given that NDVI metrics did not significantly improve the score (Fig. 7), and that the biomass is possibly redundant with LiDAR metrics due to high correlations (e.g., maximum biomass correlates with ρ = 0.82 for Z_max and ρ = 0.81 for ENL). We could also derive elevation information from LiDAR data prior to normalisation.

In particular, testing the use of NASA's Global Ecosystem Dynamics
Investigation (GEDI) LiDAR data could help address the previously
mentioned issues related to LiDAR-HD data (Vogeler et al., 2023).
Recent studies highlighted its potential for quantifying and mapping at
large scale, and repeatedly linked the vegetation complexity with sev-
eral forest properties and functions, including biodiversity (Torresani
et al., 2023; Liu et al., 2024).

From a broader perspective, we could explore other remote sensing
data sources as alternatives to LiDAR. For example, radar imagery such
as Synthetic Aperture Radar (SAR) can penetrate the forest canopy and
describe forest 3D structure (Morin et al., 2019; Lalechère et al., 2024).

Using data from space-borne SAR systems like Sentinel-1 could offer
global coverage and frequent updates. Additionally, radar has demon-
strated performance comparable to Airborne Laser Scanning (ALS) for
biodiversity modelling through habitat structure characterisation (Bae
et al., 2019).

4.5. *Limits of the reference data*

Filtering the dataset to retain data closest to the acquisition date helps minimise the discrepancy between field-measured IBP scores and their potential values at the time of LiDAR acquisition. Forest management is the main factor that can make IBP surveys outdated after several years. In old-growth and unmanaged forests, IBP is unlikely to change significantly from year to year due to the stability of forest structure (Arnqvist et al., 2020). However, this is not the case for lowland forests, which are more susceptible to harvesting. We minimised the time lag as much as possible, setting a maximum threshold of five years. While this compromise ensured a sufficient number of plots for training, it also introduced the risk of including outliers—plots where the LiDAR point cloud no longer accurately represents IBP surveys. This represents a limitation of the models.

Another limitation lies in the potential subjectivity of IBP scores as-
signed during field surveys. Gosselin and Larrieu (2020) demonstrated
that the management factors are subject to a moderate observer effect,
introducing noise into the dataset. Although not precisely quantified,
observer precision was initially estimated to be around 10% of the IBP
total score (i.e. 5 points) at the creation of the IBP (Gonin and Larrieu,
2022). However, the data used in this study was primarily collected
by trained experts, which likely improves its reliability (Gosselin and
Larrieu, 2020). Nevertheless, the inherent subjectivity of field assess-
ments introduces a bias that can affect machine learning predictions. To
mitigate this, future work could explore the integration of probabilis-
tic modelling frameworks that explicitly account for observer effect.
Moreover, future developments of the IBP methodology aim to reduce
observer effect, thereby improving the overall quality of future IBP field
data (Gonin and Larrieu, 2022).

Additionally, the filtering process significantly reduced the number of training samples, from 1,750 to just 827 in the final models. This scarcity of samples may hinder the model's ability to capture the full variability of relations between metrics and IBP scores. Even though we accounted for spatial autocorrelation using MEMs, incorporating data from different geographical areas could help mitigate this limitation and further improve the model's generalisation ability. In particular, the models we have trained are not yet suitable for Mediterranean forests. For these, the IBP factor thresholds have been specifically adapted to the Mediterranean biogeographic region (Gonin and Larrieu, 2012) but we did not have enough plots with this definition of IBP to incorporate them appropriately into the model. Nevertheless, this is a short-term prospect, given that the relationships between IBP factors and various taxonomic groups have been recently validated (Rota et al., 2025). More broadly, the model's generalisability beyond the French context remains uncertain. In addition to differing IBP scoring systems, forest management practices, stand structures and composition can vary significantly across countries and forest types. Therefore, applying the model to other regions without local calibration or adaptation would likely lead to inaccurate predictions. Future work should consider training region-specific models, incorporating more diverse datasets, or adapting the model, to improve robustness across biogeographic contexts (Hamrouni et al., 2021; Ma et al., 2024).

The dataset is also imbalanced, with high IBP scores being over-
represented (the class distribution in the SVC3-IBP model was 9%,
25%, and 66% for the first, second, and third classes, respectively).
To address this, we applied class weighting to prevent the model from
being biased towards overrepresented classes. However, the model was
still trained on a limited diversity of feature combinations representing
low IBP cases.
Moreover, in the French Pyrenees, IBP values (capped at a maximum management score of 35) quickly reach a ceiling due to the presence of old-growth forests with a high density of snags, logs, and TreMs (Fig. 2). This means that several plots have an IBP of 35 but different structures and gradients of maturity. Training a model on data with saturated IBP scores may limit its ability to distinguish gradients within high-IBP areas, potentially leading to an underestimation of ecological maturity. Training a predictive model for IBP management scores in an unbounded mode (not capped at a score of 35) by including a full inventory of logs, snags, and TreMs could help capture finer variations. However, unbounded field surveys remain scarce because they are limited to a research context, whereas bounded IBP surveys are used by practitioners in a forest management context and are therefore more widely available. This underscores the necessity of pursuing the acquisition of higher-resolution field data for research purposes. Such detailed surveys are essential for better understanding the tenuous link between forest structure, as derived from remote sensing data, and forest functions, such as providing habitat for biodiversity.

## 5. Conclusions

In this study, we investigated for the first time the relationships between the IBP and variables derived from remote sensing data, particularly LiDAR. We identified significant correlations between the IBP management score, its factors, and remote sensing metrics. The most important features for prediction are the LiDAR metrics characterising canopy height and vertical complexity, combined with biomass data and topographic metrics. Additionally, we present a predictive model for the IBP management score that achieved satisfactory predictive performance, with an uncertainty level compatible with the IBP's 5-point sensitivity threshold for biodiversity (RMSE = 5.24 ± 0.63). This model enabled us to map biodiversity potential across a large area in the French Pyrenees.

A great step forward would be to explore the potential of using airborne remote sensing data which are widely available on a regular basis and could enhance dynamic monitoring capabilities — such as GEDI (LiDAR) or the soon coming European mission “biomass” (P-band RADAR). Furthermore, in a follow-up study, the model will be extended to other temperate forests across Europe, as well as to Mediterranean forests, to improve its generalisation ability and expand its applicability beyond temperate ecosystems.

### CRediT authorship contribution statement

**Manon Collard:** Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Olivier Martin-Ducup:** Writing – review & editing, Writing – original draft, Supervision, Resources, Methodology, Investigation, Data curation, Conceptualization.
**Nicolas Mellado:** Supervision, Resources, Methodology, Investigation, Conceptualization.
**Laurent Larrieu:** Writing – review & editing, Resources, Investigation, Data curation.
**Fabien Laroche:** Resources, Methodology, Investigation, Conceptualization.
**Nicolas Gouix:** Resources, Data curation.
**Antoine Brin:** Resources, Data curation.
**Pierre Goinin:** Resources, Data curation.
**David Sheeren:** Writing – review & editing, Writing – original draft, Supervision, Resources, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization.

### Acknowledgements

This study was funded through the “Tel-IBP” project (“Prediction and spatialisation of IBP using optical remote sensing and LiDAR”), which receives financial support from the AAP ETI 2024 program at Toulouse INP. We thank the colleagues from the CNPF, the CEN Occitanie, the PNR Ariege and J.-M. Savoie (DYNAFOR / EI Purpan) for helping us gather different sets of IBP plots to successfully carry out this project. We also thank M. Fauvel (INRAE/CESBIO Lab.) to provide us the DHI components derived from Sentinel-2 time series.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.foreco.2025.123305.

### Data availability

The raw/processed data required to reproduce the above findings cannot be shared at this time as the data also forms part of an ongoing study.

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