# Exploring the impact of ice storm on urban forests and branch fall using mobile LiDAR

Karl Montalban, Thierry Moszkowicz, Annick St-Denis, Christian Messier,

Martin Béland

► To cite this version:

Karl Montalban, Thierry Moszkowicz, Annick St-Denis, Christian Messier, Martin Béland. Exploring the impact of ice storm on urban forests and branch fall using mobile LiDAR. Urban Forestry & Urban Greening, 2026, 116, pp.129223. 10.1016/j.ufug.2025.129223. hal-05468046

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Submitted on 20 Jan 2026

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# Exploring the impact of ice storm on urban forests and branch fall using mobile LiDAR

Karl Montalban<sup>a</sup>, Thierry Moszkowicz<sup>a,b</sup>, Annick St-Denis<sup>c</sup>, Christian Messier<sup>c</sup>, Martin Béland<sup>a</sup>

December 8, 2025

<sup>a</sup> Digital Forest Lab, Department of Geomatics Sciences, Laval University, Quebec City, QC, Canada

<sup>b</sup> Computer Vision and Systems Laboratory, Department of Electrical and Computer Engineering, Laval University, Quebec City, QC, Canada

<sup>c</sup> Centre d'Étude de la Forêt, Département des Sciences biologiques, Université du Québec à Montréal, Montréal, Québec, Canada

**Keywords:** Ice storm, Mobile Scanning LiDAR, Quantitative Structure Modelling, Change detection, Branch failure.

## Highlights

* LiDAR data evaluates tree damage after 2023 ice storm in Montréal (Québec, Canada).

* Over 1,200 trees were scanned and reconstructed using QSM.

* Voxel-based change detection identifies fallen branches from 3D point clouds.

* Fallen branches show greater cumulative length and total surface area than intact ones.

## Abstract

Ice storms are a major threat to urban trees, often causing severe structural damage and increasing risks to public safety, power lines and property. However, large-scale and quantitative analysis of branch-level failure factors is lacking. This study aims to bridge this gap by proposing a novel methodology to automatically identify and analyze broken branches following a major ice storm. We utilized Mobile Laser Scanning (MLS) data acquired before and after the April 2023 ice storm in the Town of Mount Royal, on the island of Montréal, Québec. Our proposed voxel-based change detection method identified branches present before the storm but absent after. We then generated over 1,200 individual tree 3D structural reconstructions using Quantitative Structure Models (QSMs) to extract detailed metrics (including cumulated length, total surface area, angle, attachment radius and distance from attachment to center-of-mass) for both intact and fallen branches. The proposed methodology showcase that fallen branches exhibit significantly greater cumulative length and total surface area than intact branches (high ratio of mean values and p-value < 10<sup>-5</sup>), while other metrics do not show statistical separation. The proposed MLS-QSM workflow demonstrates promising results for large-scale, automated urban tree risk assessment, building on initial insights into storm-related failure factors. However, further validation is required to refine predictive capabilities. This study lays the groundwork for future research aimed at improving preventive pruning strategies and mitigating storm-related hazards in urban forests.

## 1 Introduction

Ice storms are a recurrent natural hazard in several parts of the world, often causing severe damage to urban infrastructure and vegetation (GulfNews, 2023). During the spring season or a warm period in the winter season, specific atmospheric conditions, such as the combination of near-freezing surface temperatures and warm clouds, can lead to freezing rain. When this precipitation accumulates on surfaces, a layer of ice forms and adds considerable weight to the supporting structures. Trees are particularly vulnerable in such conditions: once the load
exceeds the mechanical resistance of their branches, breakage can occur. In urban settings, falling branches pose a significant threat to public safety and infrastructure, such as power lines, leading to high economic costs and service interruptions (HydroQuebecNews, 2023). To mitigate these risks, companies (namely Hydro-Québec in our case) routinely conduct pruning operations, especially for trees near electric lines or those displaying visible signs of structural weakness or disease. However, pruning is done systematically on all branches close to the electric lines or based on the general knowledge and experience of arborists. Improving our understanding of the factors contributing to branch failure at both the tree and branch levels can help optimize pruning strategies and better allocate maintenance resources.

Recent advances in remote sensing have enabled new means for assessing tree properties (Brandt et al., 2025). In particular, LiDAR (Light Detection and Ranging) systems can produce high-definition 3D point clouds that capture the external geometry of trees with high accuracy (Beland et al., 2019; Disney et al., 2018). One promising application of LiDAR acquisitions is multi-temporal change detection, where point clouds acquired at different times are compared to identify structural changes in the environment (Fekete & Cserep, 2021; Hirt, Xu, Hoegner, & Stilla, 2021). Among LiDAR systems, Mobile Laser Scanning (MLS) offers a practical balance between coverage and resolution (Beland et al., 2019). MLS systems acquire point clouds while navigating through the area of interest, typically using vehicle-mounted or backpack-mounted platforms. This approach enables the efficient collection of high-density data over large areas, making it suitable for applications in urban forestry (Holopainen et al., 2013). Nevertheless, MLS data can be subject to certain limitations, including occlusions, noise, and one-sided views, limiting tree reconstruction. In the context of ice storms, coupling MLS modality and multi-temporal acquisitions enables the detection of missing tree components, such as broken branches, and allows for quantification of the resulting loss in wood volume.

The influence on tree crown structure and the capacity of a tree to accumulate ice on its branches has been studied by Nock et al., (2016). Using Terrestrial Laser Scanning (TLS) and in situ branch measurements, they propose an ice accretion model depending on the crown tree structure in the form of voxels. Though, no precise information is given concerning branch characteristics leading to failure. To analyze tree structure from point clouds, Quantitative Structure Modelling (QSM) methods are commonly employed. These techniques approximate the geometry of trees using geometric primitives (mostly cylinders) fitted to the point cloud data (Du, Lindenbergh, Ledoux, Stoter, & Nan, 2019; Raumonen et al., 2013; Fan, Nan, Dong, Su, & Chen, 2020; Jan et al., 2021; Delagrange, Jauvin, & Rochon, 2014) and facilitate the measurement of structural attributes, such as trunk, crown and branch metrics, essential for assessing mechanical properties and risks of failure (Hartley, Jayathunga, Morgenroth, & Pearse, 2024). For example, QSM volumetric representations have been used for tree mechanical stability studies (Jackson et al., 2019; Moravcik, Vincur, & Rozova, 2021; Tsugawa et al., 2022). Interested in wind-based failure, (Jackson et al., 2019) presents a Finite Element Model (FEM) applied to multiple QSM reconstructions from TLS acquisitions. They successfully modeled the mechanical strain produced by a static wind force, enabling tree failure prediction. A TLS tree point cloud was used by (Moravcik et al., 2021) to manually construct a 3D-CAD model reconstruction (similar to a QSM), and they then used an FEM model and simulated different loads to analyse the tree mechanical stress. An FEM method was also proposed by Tsugawa et al., (2022). Using two different trees with an MLS LiDAR setup, they constructed QSM models and evaluated the mechanical stress inside the tree supporting its own weight. Despite ongoing research on tree stability and failure, relatively few studies have focused on branch-level breakage of urban trees and especially in ice storm conditions. While tree-level parameters including diameter at breast height (DBH), height, species, or crown shape, may influence vulnerability (Auckland, Cooper, & Varlow, 1992), branch-specific features such as slenderness, curvature, or orientation can also play a decisive role. Therefore, it is crucial to obtain precise branch-level data to better understand failure mechanisms and develop predictive models.

In this study, we use MLS data acquired before and after a major ice storm to investigate the factors contributing to branch breakage in urban trees. Using a voxel-based change detection approach to extract branch data of QSM reconstructions, we hypothesize that branch characteristics (e.g., length, orientation, radius) influence the susceptibility of the branch to ice-related failure. The specific objectives of this study are to (1) construct a dataset of point clouds and QSM reconstructions of individual urban trees before and after an ice storm; (2) perform 3D change detection between epochs to quantify the volume of lost wood due to branch breakage; and (3) extract and compare the branch characteristics of fallen and intact branches.
# 2 Materials and methods

## 2.1 Study area and data

### 2.1.1 Town of Mount Royal

The study site is located in the Town of Mount Royal, or Mount Royal, on the island of Montréal agglomeration, in the province of Québec in Canada. A map of the study area is shown in Figure 1. This site is known for having suffered major damage to its urban forest during the specific ice storm event of April 5th 2023, following a 15–20 *mm* freezing rain precipitation (GulfNews, 2023). This district contains a large variety of trees, *color*bluewith a dominance of *Acer platanoides*. Figure 3 shows the repartition of tree species within the area included in the dataset.

### 2.1.2 MLS point clouds

The dataset consists of LiDAR point clouds acquired by an MLS system from Jakarto, a company specialized in Digital Twins and AI-Driven Asset Inventories (Jakarto, 2025a). Their system is composed of two LiDAR scanners (Z&F 9012) oriented at -45° and 45° on each side of a vehicle. Two different epochs were considered: 27th of November 2022 and 8th of April 2023. These two dates were chosen because the ice storm took place between these dates, and there are no leaves on the trees during this period to affect the point clouds. As a result, it is easier to both assess the tree structure and compare trees. A total of approximately 2.5 km² was covered which represents 43.7 Go and 33.7 Go of point cloud data for epochs 2022 and 2023, respectively (the size difference lies in trajectory differences during the acquisitions, as shown in Figure 1).

<figure><img src="image_2.png" /><figcaption>Figure 1: Town of Mount Royal study area, showing the different routes taken to produce the MLS dataset. Colors refer to the paths of the MLS vehicle: blue for 2022, green for 2023, and before and after the ice storm event, respectively.</figcaption></figure>

The point clouds consist of georeferenced 3D coordinates with point intensity, gps time and a class label (vegetation, pole, cable, ground, building, car, noise), from a classification performed by the dataset provider. Figure 2 shows a point cloud taken from the dataset, along with different classes of urban environment objects as well as noise points which will have to be deleted.
<figure><img src="image_3.png" /><figcaption>Figure 2: Example of point cloud from the MLS dataset, colors indicate different classification labels.</figcaption></figure>

### 2.1.3 Tree inventory

The fragility of trees and the risk of branch breakage in the event of an ice storm may depend closely on the tree species, each of them being more or less vulnerable to different stresses. In addition to point clouds, the dataset is extended with a tree inventory provided by Mount-Royal. It is composed of tree species, Diameter at Breast Height (DBH) and coordinates. Using centroid proximity between individual tree point clouds (obtained following the methodology described in 2.2.1) and coordinates from the inventory, it is possible to label the species of each tree. The repartition of tree species in the final dataset is represented in Figure 3. The *Unknown* label represents trees where no label can be given using centroid proximity. Multiple factors can cause such miss-classification, from imprecisions in the inventory coordinates or obsolescence of its information. A validation was carried out by comparing some of the tree point clouds with the assigned species; however, optimizing the labelling of tree species, notably by reducing these *unknown* classifications, constitutes future work of this study.

<figure><img src="image_4.png" /><figcaption>Figure 3: Repartition of tree species in the paired and labelled tree dataset. Only the top 5 species are displayed in the legend. The *Unknown* label represents trees which cannot be labelled by the method described in 2.1.3.</figcaption></figure>
## 2.2 Methodology

This section describes the steps followed to extract branch metrics from the MLS dataset. The first objective was to obtain individual trees, this step is described in section 2.2.1. Using these individual and paired tree point clouds, we performed change detection to assess the quantity of lost material for each tree. The method is based on voxelization and the DBSCAN clustering algorithm (section 2.2.2). Then, QSM structural representations (using two different methods: Adtree and TreeQSM) of the trees were created and extended with the *aRchi* R package (section 2.2.3). Finally, using change detection results and QSMs, we extracted fallen and intact branches (section 2.2.4). This complete process is illustrated in Figure 4.

<figure><img src="image_5.png" /><figcaption>Figure 4: Methodology diagrams illustrating the complete workflow to produce the branches and tree data metrics. The input data is represented with two green blocks, consisting of individual and paired trees available for the two different epochs (2022 and 2023). Blue blocks represent the processing steps, with details about the methods in orange. The white blocks represent the output results.</figcaption></figure>

### 2.2.1 Extracting individual trees

To obtain individual and paired trees (the tree point clouds for both 2022 and 2023 epochs are linked), a point cloud processing pipeline was used. This is described in Figure 5.

<figure><img src="image_6.png" /><figcaption>Figure 5: Point cloud processing pipeline to obtain paired and labelled individual trees. The input and output data are represented with two green blocks as they are available for the two different epochs of 2022 and 2023. They consist of the initial point clouds from the MLS system and individual and paired trees generated at the end of the processing pipeline and used as input for the branch extraction methodology presented in Figure 4. Each pair of blue blocks represents a processing step executed two times, one for each epoch. Orange blocks give details concerning the processing steps.</figcaption></figure>

The total point clouds are first segmented into 7 zones of interest of a few 100 m² each because of the large size of the total surface area, point cloud density and computation consideration. Then, the provided semantic classification (shown in Figure 2) allows non interesting points labelled as ground, car, poles, electric lines and road signs to be deleted. It is important to note that the building class sometimes includes data from trees that should be kept for optimal data quality, which is crucial for further branch reconstruction and analysis. To segment trees individually, we used the *R* package *lidUrb* (Lecigne, 2022). This step is important to enable tree change detection between epochs (section 2.2.2), leading to fallen branches extraction (section 2.2.4). Based on the work by Wang, Liang, Mofack, and Martin-Ducup, (2021), this method takes point cloud clusters as input and the algorithm returns a segmented tree. To generate the input clusters, we used the Connected Components plug-in in CloudCompare;
<figure><img src="image_7.png" /><figcaption>(a) Tree point clouds of 2022 (orange) and 2023 (white).</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(b) Tree point clouds with *Lost voxels* before DBSCAN outlier filtering.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(c) Tree point clouds with *Lost voxels* after DBSCAN outlier filtering.</figcaption></figure>

<caption>Figure 6: Illustration of the change detection process for an *Acer saccharinum* tree. Orange points represent the 2022 point cloud and white points the 2023 point cloud. Green voxels highlight the detected *lost voxels*.</caption>

its parameters were set empirically as *octree level* = 10 and *Min. points per component* = 10⁵. Automatic tree segmentation is imperfect, especially when trees are close together. We performed extensive manual adjustments to improve the quality of the segmented tree point clouds. These previous steps were executed for each epoch individually and, a pairing method (based on the proximity of the tree centroids) was used to link together trees from the two different epochs. This resulted in 1218 paired tree point clouds.

### 2.2.2 Change Detection by voxel difference

To evaluate the extent of degradation experienced by trees between the two epochs and ultimately detect broken branches, the change detection process relies on the voxelization of the tree point clouds. Each individual tree point cloud is converted into a 3D grid composed of small and equally sized cubes called voxels. A voxel is considered occupied if one or more points from the point cloud fall within its volume. This representation allows for a structured and spatially consistent comparison of tree geometry across time. To detect changes, both point clouds (from the 2022 and 2023 epochs) were voxelized using the same grid origin and voxel size, ensuring that each voxel had the same coordinates on both sides. The change detection algorithm then compared the occupancy status of each voxel between the two epochs:

* *Stable voxel*: present in both 2022 and 2023

* *New voxel*: present in 2023 but absent in 2022

* *Lost voxel*: present in 2022 but absent in 2023

A *Stable voxel* indicates that no change is found between the two trees at this exact position. A *New voxel* typically represents noise, minor growth or misalignment of the tree point clouds. Although the quantity of *New voxels* encapsulates information which could be of interest in future work, they are not considered in this paper. Finally, a *Lost voxel* is interpreted as a structural loss between the trees, likely due to a fallen branch. Figure 6 illustrates the change detection process through the detection of *Lost voxels*. The voxel size is set at 0.25 *m* for Figures 6a and 6c as well as for the presented results in section 3. The two versions of the tree point clouds are shown in Figure 6a in orange and white, for 2022 and 2023, respectively. A dominance of orange indicates points only present in the 2022 version, i.e., potentially points from fallen branches. The voxel difference method detects these regions with the presence of *Lost voxels*, displayed in green in Figure 6b.

The tree point cloud quality (resolution, completeness) is prone to high variability because of the nature of the MLS acquisition (occlusions, alignments, noise). Change detection using voxels is impacted by these imperfections and can generate false positive and negative voxel detections. For instance, areas that appear as *Lost voxels* may actually be due to occlusions or misalignment rather than true structural loss. Equally, misalignments between the two point clouds are likely to cause erroneous detections.

To overcome such errors and mitigate false positive detections caused by noise and misalignment, we implemented a clustering filter to distinguish between meaningful clusters of lost voxels (likely representing actual branches) and scattered, isolated voxels (likely representing noise). This filtering was performed using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. It operates directly on the 3D coordinates of the voxel centers and classifies voxels based on two key parameters: a search radius (ε), which defines the maximum
distance between two neighbouring voxels, and the minimum number of neighbours (MinPts) required for a voxel to be considered part of a cluster rather than an outlier. The search radius parameter is set to respect at least 12 vertex-connected voxels (Chen, She, Li, Zhang, & Tan, 2020) using equation 1 where $v_{size}$ is the voxel size used for both trees. The additional $(1/2) v_{size}$ is set to increase voxel connectivity tolerance and comes from empirical improvements. This method successfully removes isolated voxels as shown in Figure 6c but also has its limitations. Parameters are set empirically and can cause different results considering the variety of trees in the dataset. Future work should focus on a sensibility analysis of these parameters and determine ways to adapt their values to the different tree species.

$$ \epsilon = \sqrt{3} v_{size} + (1/2) v_{size} \qquad (1) $$

To assess quantitative change between the two trees using the *Lost voxels* (posterior to the DBSCAN outlier removal step), different methods could be considered. First, the number of *Lost voxels* could be normalized by the total number of voxels of the tree in 2022, returning a ratio of lost material through voxel count. Second, LiDAR points originally located inside the *Lost voxels* could be retrieved and the same ratio of lost material could be measured but this time through point count (normalized by the total number of points of the tree 2022). Note that using multiple voxel sizes and accumulating the resulting points inside has been shown to improve the results for this point count approach. And finally, the *Lost voxels* could be used to extract the structural branch portions using the output of the structural reconstruction steps, described in the following section. The results presented in this paper focus on this last extraction procedure.

### 2.2.3 Structural reconstruction

A common method to extract tree structural features from point clouds is to convert tree point clouds into a set of cylinders which all together represent the entire 3D tree structure; this method is called Quantitative Structure Modelling (QSM).

#### QSM

Two QSM reconstruction methods were used in this study: TreeQSM (Raumonen et al., 2013) and AdTree (Du et al., 2019). We used these two state-of-the-art algorithms to compare the reconstruction performance of each of them and evaluate their efficiency for branch extraction. TreeQSM segments the point cloud into small patches and fits cylinders to these segments, effectively modelling the tree structure. The algorithm is open-source and written in MATLAB. It is possible to modify a set of parameters, which makes it potentially adaptable to one's data. This method has been used in many forest analysis applications to estimate biomass (Gonzalez de Tanago et al., 2018; Disney et al., 2018; Calders et al., 2015), classify tree species (Åkerblom, Raumonen, Mäkipää, & Kaasalainen, 2017) or even perform wood/leaf segmentation (Wang, Momo Takoudjou, & Casella, 2020). On the other side, AdTree constructs an initial tree skeleton and refines it by removing adjacent vertices and edges before performing cylinder fitting (Du et al., 2019). In the case of small tree branches, where reconstruction through cylinder is difficult, the AdTree algorithm instead uses allometric rules to provide "plausible estimates for the rest of the tree branches" (Du et al., 2019). The algorithm is also open source, developed in C++ and is operable via a graphical interface, but parameters cannot be modified from the interface. This method has not yet been thoroughly used in forestry applications but is cited in numerous works (Cheng, Jiang, & Zhang, 2025; Ali, Lohani, Hollaus, & Pfeifer, 2025), and the Github is quite active.
<figure><img src="image_10.png" /><figcaption>Figure 7: Point cloud and QSM reconstructions of a *Acer saccharinum* tree.</figcaption></figure>

After comparing both methods, we found that AdTree was better suited to the context of this study. Figure 7 illustrates an example from the dataset, showing the raw point cloud of an *Acer saccharinum* tree (Silver maple) and the corresponding QSM reconstructions using TreeQSM and AdTree. As introduced in section 1, MLS tree point clouds are characterized by irregularities such as occlusions, inhomogeneous point densities and one-sided views (see Figure 8). These issues are typical of MLS data and differ significantly from the high-quality, uniform point clouds usually obtained from TLS systems. Even though we tried multiple sets of parameters, we believe that TreeQSM is originally designed for high-resolution TLS data and thus struggles to produce accurate reconstructions when applied to noisy and incomplete point clouds. Figure 7a shows that the TreeQSM reconstruction is rather chaotic and does not enable proper branch extraction while AdTree appears more robust to such imperfections. This gain in quality is partly due to its tendency to overestimate wood volumes.

<figure><img src="image_11.png" /><figcaption>Figure 8: Illustration of the QSM reconstruction from AdTree, compensating for the MLS scan defaults.</figcaption></figure>

As shown in Figure 8, the volumetric reconstruction of the tree can be overestimated, as the volumes largely encompass the point clouds. While this overestimation may seem like a drawback, it helps preserve the structural continuity of the tree in low point cloud quality areas, making AdTree particularly suitable for the branch extraction task, where maintaining branch integrity is more critical than precise volume estimation.

Finally, only the QSM reconstructions of 2022 trees were used because we were looking for properties indicating fragility and potential branch fall; the structural information posterior to damage was not relevant. Though, one could consider using the 2023 reconstruction as well, for example, for change detection.

### • Tree structure

Multiple informative variables can be drawn from the tree QSM reconstructions: from cylinder properties (length, radius, volume, angle) to tree structural information, relating to the hierarchy between the tree trunk, branches and even QSM cylinders together. Such data enables the segmentation of a tree into sub-elements (axis, segments, nodes, which are rather implicit variables but might be useful to extract specific information) and especially branches.
The TreeQSM algorithm provides this information, including cylinder properties, hierarchy between tree elements, as well as numerous other metrics at the tree-level: concerning trunk, crown and the entire tree (volumes, areas, heights, lengths, and tree-level variables).

On the other hand, QSM reconstructions from AdTree do not include any information natively and the software only produces an `.obj` file containing the branch cylinder coordinates. To benefit from its relatively accurate 3D reconstructions and enable the study of fallen branches, we used open-source R packages and extended the AdTree QSMs with cylinder properties and tree structure information. The `.obj` first had to be converted into a 3D mesh object using the `Morpho` library and function `Morpho::obj2mesh()` (Schlager, 2017). Then, the mesh was converted into a QSM object using `lidurb` library and function `lidUrb::adtree2qsm(min_diameter)` (Lecigne, 2022). For computation consideration, we chose to set the `min_diameter` parameter at 0.02 m. This drastically reduces computation time of the next steps (already quite high) but reduces the resolution of the QSM reconstructions, which is not ideal. For example, this is likely to have an impact on the capacity of the branches to carry ice load, where small radius sections of branches tend to cause proportionality high accreted ice surface (Nock, Jones, & Greene, 2021). Additional work should consider the complete QSM representations and set this parameter at 0 m. Though, the negative influence of removing small diameters can be mitigated by the fact that the precision of the LiDAR measurements and QSM reconstructions are probably not high enough to capture the most accurate details at small radius levels. The `aRchi` R package (Jakarto, 2025b) was then used to extend AdTree QSM reconstructions with hierarchy information and enable branch segmentation (described in section 2.2.4). This package is composed of multiple functions useful when manipulating QSM tree objects. In our case, we used `build_aRchi()` to extend the QSM with cylinders hierarchy (parent, extension) and additional structural information (axis, segment, node and branching order). Finally, the function `Make_Path()` was used to construct the tree paths. These paths correspond to all the possible itineraries between the cylinders of the tree and this step. Though rather time consuming, it is useful to separate the tree into individual branches. For example, processing an 85 MB *Acer saccharinum* Adtree reconstruction through all the steps described in this section, with the 0.02 m threshold, took 18.0 minutes (min), including 3.4 min on `build_aRchi()` and 14.6 min on `Make_path()`. On the other hand, without the 0.02 m threshold, the processing took 1.4 h, including 12.4 min on `build_aRchi()` and 1.2 h on `Make_path()`.

### 2.2.4 Extraction of fallen and intact branches

To extend the approach to the prediction of fallen branches, we studied the statistical difference between fallen and intact branches. In this section, we explain the methodology used to extract individual branches and label them as fallen or intact.

Fallen branches were extracted using the results from the change detection method as well as the 2022 QSM reconstructions and their tree structural information. For each tree, the coordinates and size of *Lost voxels* were kept and used to extract the QSM cylinders inside of them. These cylinders were then grouped into individual branches using the tree structure information. Once the branches were selected, relevant metrics were saved for future comparison: *cumulated length*, *total surface area*, *attachment radius*, *branch angle* and *attachment2com*, the distance between branch start and center-of-mass. *Cumulated length* is the sum of all branch cylinders lengths. The *total surface area* was obtained by summing the surface of all branch cylinders and one cylinder surface was calculated with $2 \cdot \pi \cdot \text{radius}_{\text{cylinder}} \cdot \text{length}_{\text{cylinder}}$. In the context of ice storm conditions and mechanical resistance against carrying ice loads, the *cumulated length* and *total surface area* represent the quantity of wood capable of accumulating ice and thus weight. The *attachment radius* is the radius of the first cylinder of the branch and can be interpreted as the surface holding the branch in relation to its parent element (other branch or trunk). *Branch angle* encompasses the deflection of the branch due to self-weight (Sone, Noguchi, & Terashima, 2006) and, in the case of additional load, influences the projected weight on the branch direction vector and therefore the moment of the force the branch must support (Cannell, Morgan, & Murray, 1988). Finally, the center-of-mass (COM) of a branch was calculated as the weighted average of the coordinates of all its constituent cylinders, where the weights are the volumes of the cylinders. This is expressed as:

$$ COM = \frac{\sum_{i=1}^{n} V_i \cdot p_i}{\sum_{i=1}^{n} V_i} \qquad (2) $$

where $V_i$ is the volume of the i-th cylinder, $p_i$ is the position vector of its centroid, and $n$ is the total number of cylinders in the branch. The *attachment2com* is the distance between the first cylinder centroid position and the branch COM, giving some insight into the branch's mechanical equilibrium.

To avoid outliers and build a reliable comparison method, selection steps were required to obtain consistent branch data. We chose to keep branches with a *cumulated length* above 5 m and to remove branches that were too small and often seen as outliers. We also removed branches for which the euclidean distance between their first
and last cylinders was smaller than 1 *m* because they often originated from failures in the QSM reconstruction. Figure 9 illustrates the results of branch extraction for the example *Acer saccharinum* tree, where the detected fallen branches are colored by branch id. We can see that the very tips of the fallen branches are not included as *fallen*; this is because the branch diameter is limited to 2 *cm* during the tree topology reconstruction, as noted in section 2.2.3.

To ensure that only branches absent from the most recent acquisition were retained, we applied a spatial filtering step based on a spherical neighborhood search. For each cylinder forming a branch, the 3D coordinates of its start and end points were compared to the 2023 point cloud using a KD-tree structure. All 2023 points located within a radius *r* around each endpoint were queried, and the number of neighbors was counted. Let *N* denote the minimum number of neighbors required to consider an endpoint as physically connected to the 2023 data. In our implementation, a cylinder was considered attached or partially visible in 2023 if at least one of its two endpoints had $\ge N$ neighbors within radius *r*. Consequently, the entire branch was discarded as a false detection when any of its endpoints was connected to the 2023 point cloud. And, branches for which both endpoints of all cylinders showed fewer than *N* neighbors (i.e., no spatial continuity with the 2023 cloud) were retained as valid detections of fallen or missing structures. The parameters were set empirically to *N* = 1 and *r* = 0.25 *m*. This filtering generally reduces false positives caused by change-detection misclassifications, although in some cases it may remove valid segments and lead to visually incomplete branches.

<figure><img src="image_12.png" /><figcaption>Figure 9: QSM with highlighted fallen branches, colored by branch id.</figcaption></figure>

Finally, cylinders selected as *fallen* were removed for the QSM reconstruction to ensure they could not be selected during the following *intact* branch extraction. To extract *intact* branches, we used the QSM reconstructions and their tree structural information but stripped of *fallen* cylinders. To make the comparison appropriate, we used the same selection rules that were applied in the *fallen* branch extraction (*cumulative length* > 5 *m* and distance between start and end > 1 *m*). The trunk was not considered (filtered by branch order and node metrics from the tree structural information).

# 3 Results

In this section, we first present the results of change detection with the examination of the quantity of lost wood to determine which species are the most affected. Then, branch extraction results are given to assess statistics of fallen and intact branches, opening to a prediction approach. The change detection results encompass 4 different species: *Acer saccharinum* (silver maple), *Acer platanoides* (Norway maple), *Gleditsia triacanthos* (Honey locust) and *Quercus rubra* (Red oak). These 4 species are the most represented in the dataset whereas the other species are not represented enough in the dataset to be part of the results. Then, section 3.2 only reports results for the two most dominant species, *Acer saccharinum* and *Acer platanoides*. We decided to give only these results to ease the reading load and showcase the most consistent findings (more trees and branches). However, results from *Gleditsia triacanthos* and *Quercus rubra* are given as supplementary material in sections 6.2 and 6.3, respectively.
## 3.1 Change detection at the tree level

Both versions of the same tree, before (2022) and after (2023) the ice-storm event, are voxelized using a common voxel grid. For each tree, only *Lost voxels* were kept and used to extract the cylinders corresponding to the detected fallen branches. Using the detected *Lost voxels*, we extracted the QSM cylinders that lay within them and obtained individual branches according to section 2.2.4. Finally, to quantify material loss, we computed the sum of the branches cylinders volume and normalized it by the total volume of the original QSM. Figure 10 depicts the results of lost material for different severity categories.

<figure><img src="image_13.png" /><figcaption>Figure 10: Bar charts of degradation for each species using the lost volume. The degradation severity is segmented into 4 classes: intact trees represent trees with a lost volume ratio between 0% and 0.1%, light impacted trees correspond to a lost volume ratio between 0.1% and 5%, moderate impacted between 5% and 10% and high impacted between 10% and 100%.</figcaption></figure>

The results indicate that the ratio of lost volume varies according to the tree species. Most of them show a majority of small impacted trees (less than 5% of lost volume ratio) but the most represented class is the 0.1%-5% (similarly for all tree species) and not the 0% one. The high concentration of trees in the 0%-5% class encapsulates a fair amount of noise. This noise is probably due to limitations already discussed in 2.1.2 and 2.2.2, namely defects in the point cloud qualities and weaknesses in the voxel change detection method. Nevertheless, *Acer saccharinum* trees show the most damaged trees, with 31.2% of them with more than 5% of loss, following by *Acer platanoides* and its 19.6%. On the other hand, *Gleditsia triacanthos* and *Quercus rubra* only show 3.1% and 6.6%, respectively, with more than 5% of lost volume ratio. These results confirm that species differ in vulnerability, with *Acer saccharinum* showing the highest susceptibility to branch loss. However, the presence of low-level volume loss in many trees also suggests limitations of the voxel detection method and residual noise in the data.

## 3.2 Branch extraction and comparison

In this section, we give the results drawn from the branch extraction procedure, looking for statistical indicators about branch fragility. The number of fallen and intact branches available for this analysis is shown in Table 1 as well as the percentage of fallen branches, indicating a prior knowledge about how much branches each species is susceptible to lose. These results indicate that *Acer saccharinum* is more vulnerable to ice storms than the other three species.
<table><caption>Table 1: Branch extraction results per species. Radius classes R0–R3 represent increasing radius bins in *m*: R0 = [0, 0.05], R1 = [0.05, 0.1], R2 = [0.1, 0.15], R3 = [0.15, 0.2]</caption><thead><tr><th>Metric</th><th colspan="4">Acer saccharinum</th><th colspan="4">Acer platanoides</th><th colspan="4">Gleditsia triacanthos</th><th colspan="4">Quercus rubra</th></tr></thead><tbody><tr><td>Intact branches</td><td colspan="4">11049</td><td colspan="4">54042</td><td colspan="4">12824</td><td colspan="4">5980</td></tr><tr><td>Fallen branches</td><td colspan="4">853</td><td colspan="4">1541</td><td colspan="4">372</td><td colspan="4">110</td></tr><tr><td>% of Fallen branches</td><td colspan="4">7.2%</td><td colspan="4">2.8%</td><td colspan="4">2.8%</td><td colspan="4">1.8%</td></tr><tr><td>Radius class</td><td>R0</td><td>R1</td><td>R2</td><td>R3</td><td>R0</td><td>R1</td><td>R2</td><td>R3</td><td>R0</td><td>R1</td><td>R2</td><td>R3</td><td>R0</td><td>R1</td><td>R2</td><td>R3</td></tr><tr><td>Intact branches</td><td>1512</td><td>5480</td><td>2743</td><td>1314</td><td>8322</td><td>28122</td><td>12062</td><td>5536</td><td>1794</td><td>6711</td><td>2975</td><td>1344</td><td>835</td><td>3122</td><td>1367</td><td>656</td></tr><tr><td>Fallen branches</td><td>156</td><td>438</td><td>197</td><td>62</td><td>292</td><td>823</td><td>320</td><td>106</td><td>149</td><td>203</td><td>19</td><td>1</td><td>48</td><td>46</td><td>10</td><td>6</td></tr><tr><td>% of Fallen branches</td><td>9.4%</td><td>7.4%</td><td>6.7%</td><td>4.5%</td><td>3.4%</td><td>2.8%</td><td>2.6%</td><td>1.9%</td><td>7.7%</td><td>2.9%</td><td>0.6%</td><td>0.1%</td><td>5.4%</td><td>1.5%</td><td>0.7%</td><td>0.9%</td></tr></tbody></table>

The mean Diameter at Breast Height (DBH) values for the studied tree species, derived from the AdTree QSM reconstructions, are as follows: *Acer saccharinum* (0.78 m), *Acer platanoides* (0.72 m), *Gleditsia triacanthos* (0.61 m), and *Quercus rubra* (0.41 m). These measurements indicate that the selected trees are generally mature and of substantial size. However, the red oaks (*Quercus rubra*) exhibit notably smaller DBH values, suggesting they may be younger or more vigorous compared to the aging maples. This size discrepancy could potentially explain the observed pattern of fewer fallen branches in oaks, as their relatively smaller and healthier canopies might be less prone to structural failure.

The analysis focuses on the specific metrics of *cumulated length*, *total surface area*, *angle*, *radius branch attachment* and *attachment2com*. For a fair comparison between branches, we split the branch dataset into classes of attachment radius at branch start from 0 to 0.2 *m* by steps of 0.05 *m* and these classes are labelled R0, R1, R2 and R3 (as seen in Table 1). The origins of branch failure may vary according to branch radius at the start of the branch because it represents the attachment surface with the rest of the tree and potentially its capacity of carrying weight.

Now, the results are presented for each metric of interest with RainCloud plots. This kind of plot enables the visualization of the data using 3 different chart types: the distribution density, a scatter plot and a box plot. The difference between *fallen* and *intact* branches mean values is highlighted in red.

Finally, the p-value between the *fallen* and *intact* data is reported, computed using the Mann-Whitney U test. It indicates the probability that the observed difference between the two distributions could have arisen by chance and thus gives statistical insight into the separability of the two classes. This test was chosen because it is non-parametric and does not require the assumption of normality. We believe that this test is valid since the data are independent, they come from two distinct groups (different branches and trees), the metrics have similar distributions and there are no tied values since our metric values are continuous.

Figures 11 and 12 present the RainCloud plots for the *cumulated length* and *total surface area* metrics, where each radius and branch class are represented as well as the two species of *Acer saccharinum* and *Acer platanoides*. In terms of mean values, Figure 11 shows a common tendency where fallen branches have, for every radius class and tree species, higher *cumulated length* values compared to intact ones. The observation is similar for median values (in the middle of the box plots). This supports a statistical separation between the two classes, which is confirmed by the significant low p-values. Finally, the distributions of fallen branches are flatter than the intact branches, where an inflation close to the minimum value is seen. Similarly to the *cumulated length* results, the *total surface area* values indicate a common trend towards higher values for fallen branches, except for the higher radius class of *Acer platanoides* where the observation is less pronounced. As the metrics of cylinder length and surface are correlated, it is expected to have this common observation. Though, we note that the *total surface area* metric shows p-values indicating a stronger separation than with the *cumulated length*, meaning that the radius of the cylinders (used for the surface calculation) has a positive impact on differentiating *fallen* and *intact* branches.

The other metrics of *branch angle*, *attachment radius*, and *attachment2com* do not show similar results. Their results do not indicate any particular distinction between the fallen and intact branch classes, with distributions relatively similar, ratio between mean values close to 1 and relatively high p-values. The Rain Cloud plots of these metrics are therefore not displayed here but given as supplementary material in section 6.1.
<figure><img src="image_14.png" /><figcaption>(a) *Acer saccharinum*</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>(b) *Acer platanoides*</figcaption></figure>

Figure 11: Comparison of branch *cumulated length* between fallen and intact branches across radius classes for *Acer saccharinum* and *Acer platanoides*. Raincloud plots show distribution, mean (red points), median (horizontal bar inside the box plot) and p-value from the Mann-Whitney U test.
<figure><img src="image_16.png" /><figcaption>Figure 12: Comparison of branch *surface* between fallen and intact branches across radius classes for *Acer saccharinum* and *Acer platanoides*. Raincloud plots show distribution, mean(red points), median (horizontal bar inside the box plot) and p-value from the Mann-Whitney U test.</figcaption></figure>

# 4 Discussion

## 4.1 QSM reconstruction on MLS Data

A key contribution of this work is the application of QSM algorithms to MLS data, despite its known limitations in resolution and completeness compared to TLS. While TreeQSM struggles under these conditions—often producing incorrect reconstructions and branch extractions—AdTree demonstrates a clear advantage. Our results show that AdTree successfully reconstructs tree structures from MLS data, delivering visually accurate reconstructions and reliable branch extraction. Although AdTree tends to overestimate the volume of tree elements—a limitation for
applications requiring precise volume estimates, such as biomass assessment (Gonzalez de Tanago et al., 2018) —its robustness to irregular and noisy data makes it particularly well-suited for MLS applications. This trade-off highlights the importance of our findings, as most QSM studies rely on high-resolution TLS data (Åkerblom & Kaitaniemi, 2021). By demonstrating the feasibility of using QSMs with MLS, we contribute on a large-scale, operational tree structure analysis, particularly in contexts where TLS is impractical. Future work could bridge the gap between volume estimation and visually accurate reconstructions by validating AdTree's volume estimates against ground-truth measurements from in-situ data. Such efforts would further solidify the applicability of MLS-based QSMs in forestry and urban tree management.

## 4.2 Voxel-based change detection and branch extraction

The proposed voxel-based change detection method identifies structural loss by detecting the disappearance of voxels between two aligned epochs of MLS tree point clouds. When used jointly with QSM reconstructions, it enables the extraction of branch fall data and supports comparisons between fallen and intact branches. While previous studies have focused on airborne data and change detection at the tree level (Fekete & Cserep, 2021), our study highlights the possibilities of performing urban forest change detection at branch level and on a large scale. The method performs reasonably well overall and is efficient in terms of computation time, but it is sensitive to the chaotic and irregular arrangement of MLS point clouds, voxel discretization effects, and small alignment errors, which leads to false positive detections. These factors can lead to both false positives and false negatives in damage detection. To improve robustness, we introduced a DBSCAN clustering step to filter out isolated voxels. Using this outlier removal method, the results are greatly improved but the parameters were selected empirically and may not generalize well across different species, geometries, or acquisition conditions. Future work should include sensitivity analyses of voxel size and clustering parameters to better assess their impact on detection results and improve reliability. A focus on the tree species could be added to such analysis as it is expected to influence the results. Still, this method enables large-scale and efficient assessment of tree damage, revealing that *Acer-saccharinum* and *Acer-platanoides* were the most heavily affected species during the studied ice storm, which is in accordance with (Luley & Bond, 2006). This tool could provide municipalities with a scalable solution for analyzing storm-related tree damage across urban districts. However, widespread low-level volume loss across many trees indicates potential residual noise and highlights limitations in voxel-based detection accuracy. Future work should prioritize validating this method against ground-truth tree damage data to guide parameter optimization and enhance the accuracy of change detection.

## 4.3 Influence of MLS data quality

The performance of both change detection and Quantitative Structure Model (QSM) methods is fundamentally dependent on the quality of the point clouds used. In an ideal scenario with "perfect" point clouds, one could expect near-flawless QSM reconstructions and highly accurate change detection. However, in the context of MLS acquisitions, data quality is often traded for scalability—a necessary compromise for large-scale applications, such as urban forestry or city-wide tree management, just like airborne acquisitions are similarly employed for extensive or complex areas (Fischer, Jackson, Vincent, & Jucker, 2024). As MLS technology continues to advance in both popularity and efficiency (Vandendaele, Martin-Ducup, Fournier, & Pelletier, 2024; Holopainen et al., 2013; Lin, Holopainen, Kankare, & Hyyppä, 2014), it is increasingly important to evaluate its practical applications, such as those explored in this study. While our work is constrained by the inherent limitations of MLS acquisitions, it contributes to the broader discussion on balancing data quality, scalability, and algorithmic efficiency. By addressing this trade-off, we aim to advance the development of practical, operationally feasible solutions for real-world scenarios, where both change detection and QSM methods must adapt to the realities of MLS data.

## 4.4 Computational considerations of AdTree and aRchi

The *aRchi* R package is crucial in our study to extend AdTree reconstructions with structural information, but this step can be highly demanding in terms of computation time, depending on the tree size. To reduce processing time and process the entire dataset, a cylinder radius threshold of 0.02 *m* was applied. The computation time is improved but the quality of the resulting QSMs is decreased, limiting the precision of subsequent analyses. This obviously has an impact on the branch statistics results so we suggest removing this threshold to obtain the most accurate results. Finally, it would be highly valuable that the extensions made by *aRchi* could be performed by AdTree algorithms, written in C++, to optimize computation time and allow for the processing of full trees.
## 4.5 Interpretation of branch metrics

Different branch metrics (*cumulated length*, *total surface area*, *angle*, *attachment radius*, *attachment2com*) are chosen to explore indicators of branch fragility. Among them, *cumulated length* and *total surface area* emerged as discriminative between *fallen* and *intact* branches. These metrics likely reflect the biomechanical and physical interactions between ice loading and branch structure: longer branches experience higher bending moments, while surface area correlates with ice load capacity. This aligns with biomechanical principles of branch failure under ice loading, where length and surface area influence stress distribution and failure thresholds (Lilly & Sydnor, 1995) Although these two metrics are correlated which makes their common observation predictable, but we believe that it remains informative to consider them separately as the addition of the radius in the surface calculation slightly increases the separability of the *intact* and *fallen* branches. Future work should employ multivariate statistical analyses (e.g., PCA, random forests) to disentangle metric interactions and identify the most robust predictors of branch failure. Other features, such as *branch angle*, *attachment radius*, and *attachment2com*, did not show consistent differences. The results are presented as supplementary material in section 6. Although these metrics can be considered a proxy for a branch mechanical strength (Kane, Farrell, Zedaker, Loferski, & Smith, 2008; Gilman, 2003) and thus have an impact on the mechanical stresses involved with ice loading, the presented results did not support this.

## 4.6 Limitations of the study

No ground truth is available concerning the position of the impacted trees or their branch loss. So, it is not possible to quantitatively evaluate the performance of the change detection and branch extraction methods. Furthermore, any damage suffered by the trees and detected is considered to originate from the ice storm event introduced in section 2.1.1. Pruning carried out between the 2 acquisitions is not considered but could cause bias in the branch comparison method because the branches did not fall naturally. Despite these limitations, the approach proposes an innovative solution by performing change detection on a large scale using MLS data and focuses on branch data with QSM models. By enabling the extraction of branch-level traits associated with failure, the approach provides a data-driven alternative to visual inspection, helping municipalities prioritize pruning and preventive measures based on tree species and structural characteristics.

## 4.7 Future directions for predictive modeling

Considering the limitations posed by MLS acquisitions and data as well as the nature of the AdTree reconstruction technique (over-estimation of volumes and allometric rules), we refrain from drawing conclusions concerning branch failure factors. These findings should be considered as preliminary results as they are not sufficient to build a consistent predictive model. Future work should focus on the evaluation of AdTree branch structural metrics using MLS data compared to accurate in-situ branch characteristics. This would give more insight into the possibility of building predictive models using our approach. Then, to construct appropriate models, more data would be highly beneficial, including of course new generations of MLS point clouds from the same area and others, but also additional data concerning the trees (age, presence of disease) which could have an impact on the tree and branch fragility facing external stress. In addition, this study focuses on a single ice storm event with precise atmospheric conditions. It is expected that the characteristics weather events (precipitation intensity, wind, temperature) have a strong impact on the damage suffered by the trees. Future work should also include weather data to label the damage observed when reporting results.

## 4.8 Applications to urban forestry and climate resilience

Finally, the study's findings have direct applications in urban forestry management and climate resilience planning. Automated damage detection from MLS data can prioritize tree pruning and removal, reducing infrastructure damage and improving public safety during and after storm events. The ability to rapidly assess tree health and damage at city scales is a critical tool for municipalities facing increasing extreme weather events. Moreover, the method's adaptability to other damage types (e.g., windthrow, drought stress) suggests a potential for broader climate adaptation applications. Besides, it could be interesting to produce datasets from different damage types to evaluate potential differences in the fallen branch data. Integrating MLS-based QSMs into urban tree inventories could enable cost-effective, data-driven management strategies, including species selection for resilience and targeted maintenance interventions. A proposed workflow for municipal deployment would include MLS data acquisition, QSM reconstruction, change detection for damage assessment, and integration with GIS and urban forest management systems.
# 5 Conclusion

This article evaluates the characteristics of urban trees and branches impacted by an ice storm event. Using bi-
temporal Mobile Laser Scanning (MLS) data, we propose a voxel-based change detection method and Quantitative
Structure Modelling (QSM) tree reconstructions to assess damage and branch properties. The research effectively
identifies and analyzes branch breakage on approximately 800 trees out of 1,200 studied trees. The vulnerability
of certain tree species is highlighted, with *Acer saccharinum* showing the highest proportion of significant damage
from the ice storm. For *Acer saccharinum* and *Acer platanoides*, the detected fallen branches tend to have signifi-
cantly greater *cumulative length* and *total surface area* compared to intact branches. This suggests that these two
metrics are indicators of a branch failure susceptibility under ice loading, as they can relate to the potential for
ice accumulation and subsequent weight. Conversely, other factors such as branch *angle*, *attachment radius* and
*position of center-of-mass* did not show a consistent correlation with failure in this study. Additionally, limitations
inherent in the MLS data and QSM reconstructions restrain us from drawing clear conclusions of branch failure
factors. Finally, this study demonstrates the successful use of AdTree for reconstructing QSMs from MLS data,
even in the presence of noise and occlusions, where TreeQSM fails to create realistic tree models. By advancing
beyond traditional visual inspections, this data-driven framework provides municipalities with a scalable tool to
inform targeted pruning strategies and proactive risk mitigation. Future work should focus on expanding datasets,
improving metric precision, and integrating additional environmental factors to strengthen the predictive utility of
these findings for urban forest management.

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# 6 Supplementary material

## 6.1 Metrics showing less separability for *Acer saccharinum* and *Acer platanoides*

As mentioned in 3.2, the metrics of *branch angle*, *attachment radius*, and *attachment2com* do not show results indicating separability between *fallen* and *intact* branches. Figures 13,14 and 15 present the RainCloud plots for these metrics For all of them, distributions are relatively similar, the ratio between mean values is close to 1 and p-values are relatively high.

Concerning the *attachment2com* metric, only the R2 class could lead to differentiation for *Acer saccharinum* with relatively small p-value but the distributions are still very similar. On the other side, the results of the Acer platanoides seem to highlight clearer distinction between the two classes but the ratio between the mean and median values of the R0 class are not in accordance and the R2 class for fallen branches seem to contain very high values, which potentially indicate the presence of outliers.

<figure><img src="image_17.png" /><figcaption>(a) *Acer saccharinum*</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>Figure 13: Comparison of branch *angle* between fallen and intact branches across radius classes for *Acer saccharinum* and *Acer platanoides*. Raincloud plots show distribution, mean (red points), median (horizontal bar inside the box plot) and p-value from the Mann-Whitney U test.</figcaption></figure>

<figure><img src="image_19.png" /></figure>
<figure><img src="image_20.png" /><figcaption>Figure 14: Comparison of branch *attachment radius* between fallen and intact branches across radius classes for *Acer saccharinum* and *Acer platanoides*. Raincloud plots show distribution, mean (red points), median (horizontal bar inside the box plot) and p-value from the Mann-Whitney U test.</figcaption></figure>

<figure><img src="image_21.png" /></figure>
<figure><img src="image_22.png" /><figcaption>Figure 15: Comparison of branch *attachment2com* between fallen and intact branches across radius classes. Raincloud plots show distribution, mean (red points), median (horizontal bar inside the box plot) and statistical significance (p-value) for *Acer saccharinum* and *Acer platanoides*.</figcaption></figure>

## 6.2 Branch metrics for *Gleditsia triacanthos*

In 3, Table 1 shows that the number of fallen branches detected for *Gleditsia triacanthos* is relatively low (372) and especially for *R2* = [0, 0.05]*m* and *R3* = [0.05, 0.1]*m* radius classes with respectively 19 and 1 detected fallen branches, leading to statistically inconsistent distributions. This is observed on Figure 16, with 1 data point for the *R3* radius class and multi-modal distributions for the *R2* radius class and for all metrics. As a result, we are not commenting about these radius classes as more data is needed.

However, the lower radius groups are composed of relatively high number of branches, with similar number to the ones of *Acer saccharinum* for example. And, the same behavior can be observed with the *cumulated length* and *surface* metrics between fallen and intact branches, which suggests the same separability interpretation as for *Acer saccharinum* and *Acer platanoides*. The *angle* metric shows interesting p-values and we can see that, although fallen branches are showing some statistical inconsistency with multi-modal distribution, intact branches seem to have higher values. Finally, results from *attachment radius* and *attachment2com* do not show any clear statistical separation.
<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>
<figure><img src="image_26.png" /><figcaption>Figure 16: Comparison of fallen and intact branches across radius classes and selected metrics. Raincloud plots show distribution, mean (red points), median (horizontal bar inside the box plot) and p-value from Mann-Whitney U test for *Gleditsia triacanthos*.</figcaption></figure>

## 6.3 Branch metrics for *Quercus rubra*

The number of fallen branches detected for *Quercus rubra* is very low for all radius classes, as shown in Table 1. As a result, no interpretation should be drawn as most distributions show multi-modal and inconsistent shapes.
<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>
<figure><img src="image_30.png" /><figcaption>Figure 17: Comparison of fallen and intact branches across radius classes and selected metrics. Raincloud plots show distribution, mean (red points), median (horizontal bar inside the box plot) and p-value from Mann-Whitney U test for *Quercus rubra*.</figcaption></figure>