# Automatic detection of in-stream river wood from random forest machine learning and exogenous indices using very high-resolution aerial imagery

Gauthier Grimmer, Romain Wenger, Germain Forestier, Valentin Chardon

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Gauthier Grimmer, Romain Wenger, Germain Forestier, Valentin Chardon. Automatic detection of in-stream river wood from random forest machine learning and exogenous indices using very high-resolution aerial imagery. Environmental Modelling and Software, 2025, 190, pp.106460. 10.1016/j.envsoft.2025.106460. hal-05131638

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# Automatic detection of in-stream river wood from random forest machine learning and exogenous indices using very high-resolution aerial imagery

Gauthier Grimmer<sup>a</sup>, Romain Wenger<sup>aID,*</sup>, Germain Forestier<sup>b</sup>, Valentin Chardon<sup>aID</sup>

## ARTICLE INFO

Dataset link: https://github.com/g-grimmer/RiverDetectWood

**Keywords:**

In-stream automatic wood detection

Machine learning

Aerial imagery

River wood metrics

Spatial inference

## ABSTRACT

River wood (RW) plays a key role in shaping aquatic and riparian habitats while influencing sediment and water dynamics. This study presents the first automated RW detection model using Random Forest classification and near-infrared aerial imagery on the Meurthe River. By progressively incorporating exogenous indices, the model achieved recall, precision, and F1-scores between 12%-39%, 90%-94%, and 21%-54%, respectively. Validation on the Loire, Doubs, and Buëch rivers confirmed robust detection rates (75.41-86.57%) after filtering. The model also estimated RW characteristics, including length, diameter, area, and volume, with high accuracy post-calibration. These findings demonstrate the potential of remote sensing and AI for RW monitoring, providing an efficient decision-support tool for river management and habitat conservation.

## Software availability

* **Software name:** RiverDetectWood

* **Developer:** Gauthier Grimmer, Romain Wenger

* **Year first official release:** 2024

* **Hardware requirements:** Multicore processor

* **System requirements:** Windows, Linux, MacOS

* **Program language:** Python

* **Program size:** < 1 MB for the code and 15 MB for the test set

* **Availability:** https://github.com/g-grimmer/RiverDetectWood

* **License:** CC BY-NC 4.0 License

* **Documentation:** README in GitHub repository and detailed installation and usage instructions.

## 1. Introduction

River wood as well as sediment supply and water flow, is fundamental to the geomorphological functionality of rivers, allowing the development of a rich diversity of aquatic and riparian habitats and associated biocenoses (Wohl et al., 2019). However, river wood was frowned upon because of its potential to aggravate floods and increase the risk of destabilizing transverse structures in rivers (Le Lay and Piégay, 2007). These shared impacts lead to the development of a balanced management of river wood in watercourses by preserving participating river wood in a way that is beneficial to the river ecosystem (Beechie and Sibley, 1997; Bilby, 2003; Benke and Wallace, 2010; Dolloff et al.,

2003), and by clearing river wood presenting risks to the latter (Wohl et al., 2019). In France, since the floods of 1993, river wood has considered a factor to be taken into account in river management. Article 23 of the Barnier Law of 2 February 1995 stipulates that every riparian owner of a watercourse must ensure that its natural morphology and functioning (natural size and depth) are maintained by cutting back and pruning vegetation, maintaining the banks and, in particular, removing river wood deposits in the active watercourse that would impede its natural flow (Piegay and Gurnell, 1997).

Remote sensing and advances in Artificial Intelligence (AI) offer new prospects to study environmental changes, particularly river systems (Piégay et al., 2020). Optical remote sensing, such as the World-View constellation (Very High Spatial Resolution, VHSR), which uses both visible and near-infrared spectral data, have been used to map the locations of isolated trunks and log jams (LJ) in streams (Atha, 2014; Marcus et al., 2003). Other studies use aerial imagery (through the use of UAVs) coupled with a Digital Elevation Model (DEM) to extract river wood location manually (Helm et al., 2020). Some studies combined Machine Learning (ML) and remote sensing to detect river wood. For example, Sendrowski and Wohl (2021), Esse et al. (2022) used support Vector Machine (SVM) and Maximum Likelihood, respectively. More recently, Breiman (2001), Recking (2020) used Random Forest (RF) methods. This latter have been also applied on ground cameras to detect and characterize floating river wood during floods (Benacchio et al., 2017) and on aerial photographs (Smikrud and Prakash, 2006).

<footnote><sup>a</sup> LIVE UMR 7362 CNRS, University of Strasbourg, 3 rue de l'Argonne, Strasbourg, 67000, France</footnote>

<footnote><sup>b</sup> IRIMAS UR 7499, University of Haute-Alsace, 12 rue des Frères Lumière, Mulhouse, 68000, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: romain.wenger@live-cnrs.unistra.fr (R. Wenger).</footnote>
Although these methods are effective, they are generally limited in terms of their class typology. Indeed, the two studies cited above work on a binary class typology (river wood and Water or river wood and No river wood) which does not allow the specificity of the terrain to be taken into account. On the other hand, some studies use a multi-class approach, but this does not provide good predictive accuracy (Marcus et al., 2003). However, RF classification methods are attractive due to the high generalization capabilities and low amount of resource needed to implement it (Breiman, 2001) compared to recent Deep Learning (DL) approaches (Zhu et al., 2017) which has been widely use in a vast range of application (Sharifi and Hosseingholizadeh, 2020; Nejad et al., 2022) with various sensors (Esmaeili et al., 2023; Jalayer et al., 2023; Sharifi and Felegari, 2022). These approaches, while effective in terms of accuracy, required a lot of reference data to achieve usable results (Mohammadi and Sharifi, 2021). To our knowledge, no datasets where available to train convolutional neural networks on different river system, making them complex to use to face our objectives.

Currently, no study detects automatically river wood on large reach length (several decades of river kilometres) and simultaneously on multiple river systems from remote sensing data. This gap explains why it is still difficult for river managers to achieve sustainable and integrated management of wood in rivers. The main objective of this work is to develop and evaluate an automatic river wood (RW) detection model based on RF classification using high-resolution near-infrared aerial imagery and calculate river wood metrics. This study targets to fill this gap by (i) the application of a RF supervised classification model for the automatic detection of RW in the Meurthe watercourse on 120 kilometre of river length using orthophotographs from the Institut National de l'Information Géographique et Forestière (IGN) with fine spatial resolution (0,2 m pixel size), (ii) test the capacity of the model to detect automatically RW on three other river systems (spatial inference) sush as the Buëch, Doubs and Loire rivers and (iii) evaluate the capacity of the model to characterize shape, area and volume of deposited RW on the four rivers.

## 2. Study sites

The Meurthe River is a sub-tributary of the Rhine River in the Vosges mountains starting from the col de la Schlucht and joins the Moselle River at Pompey just downstream of Nancy (Fig. 1). The total length of the Meurthe River and the catchment size equals to 160.6 km and 3085 km², respectively. The upstream part of the Meurthe flows from the Vosges massif towards the Lorrain plateau (Fig. 1). The river slope for the upstream part from it source to Saint-Dié, for the medium part from Saint-Dié to Lunéville and for the downstream part from Lunéville to Nancy equals to 2.08%, 0.15% and 0.07% respectively. The mean annual precipitation equals 1100 mm and 800 mm on the Vosges mountains and Lorrain plateau, respectively (Corbonnois et al., 2011). The discharge equals 35.70 m³/s at the Laneuville-devant-Nancy gauging station (1985–2024; Fig. 1).

Three additional rivers were selected for validation purposes through spatial inference (Wenger et al., 2023). In order to test the model's applicability to diverse river typology, it is necessary to conduct experiments with rivers that differ from the Meurthe.

The Buëch River is a gravel-bed braided, established in a wide channel. It is a tributary of the Durance River (Brousse et al., 2020). In fact, this watercourse can be subject to strong floods that can transport significant sediment loads (Gautier, 1994). The hydrological dynamics of the system can be described as comprising two distinct phases: a low discharge phase occurring in April, and a relatively high discharge phase observed in October and November (Hemmelder et al., 2018). The discharge is equal to 14.1 m³/s at the Serres Gaugin station (1964–2014) (Brousse et al., 2020).

The Loire is also characterized by its different geomorphological successions, ranging from multiple channels to large meanders (which is the case in the section studied). Its hydrological regime is varied,

ranging from low water levels (19 m³ s⁻<fnref n="1" /> in Blois in 1949) to sometimes devastating floods (6000 m³ s⁻<fnref n="1" /> in Blois in 1866) (Claude, 2012). The Loire River is subject to the influence of two distinct climatic regimes. In the Massif Central region, the river is subject to a mountain climate with Mediterranean influences, while downstream, the climate is oceanic. The extensive geographical range of the River in question gives rise to a total of between eight to twelve distinct hydrological regimes. The principal hydrological regimes are those of rainfall, which affect 50% of the stations, with flows ranging from 2 L/s/km² (in the months of August and September) to 5 L/s/km² (in the months of June and October). The second most prevalent hydrological regime is the pluvio-nival regime, which is characterized by flows of up to 27 L/s/km² (January to May) (Moatar et al., 2022). The study area is located upstream of the confluence with the Allier River, and is therefore subject to a pluvio-nival regime.

The Doubs is a tributary of the Rhône and the Saône. It drains the Jura massif. Along its course, it adopts meandering forms (upstream) that give way to an anastomose system downstream. The Doubs River has a pluvio-nival regime with a mean annual discharge of up to 177 m³/s (Citterio and Piégay, 2009).

## 3. Methodology

### 3.1. Data presentation

In this section, we will present the different data used to perform this research.

#### 3.1.1. Characteristics of IGN orthophotographs

The orthophotographs used in this study originate from the BD Ortho® IRC (for Infra-Rouge fausses Couleurs - Infra-red false colors) dataset, produced by the Institut national de l'information géographique et forestière (IGN) through aerial surveys conducted as part of France's national mapping program. These images are acquired using high-resolution aerial cameras and orthorectified to ensure geometric accuracy. They are publicly available via the IGN Geoportal<fnref n="1" />.

The orthophotographs constitute a mosaic of images, devoid of any enhancement or overlay, and accompanied by georeferencing indications. The spatial resolution of the BD Ortho® IRC is 0.15 m for the Île-de-France region and 0.2 m for the rest of the country. The IRC version of the BD Ortho® is encoded in three bytes (24 bits), corresponding to the three channels that comprise the images (NIR, red, and green) (IGN, 2023).

In this study, the orthophotographs corresponding to the study area encompass the Meurthe watercourse, spanning approximately one hundred kilometres in a linear from Lunéville to Baccarat (Fig. 1), and captured in the year 2018. The orthophotographs are therefore presented in false color composition and comprise three bands: near infrared (NIR) (70–171 nm), red (45–196 nm) and green (49–165 nm).

#### 3.1.2. Data extraction by digitization

The objective of the model is to facilitate the automated identification of river wood within watercourses. In order to achieve this, a four-class typology has been developed. The aforementioned typology comprises the following categories: (1) “River wood”, (2) “Vegetation”, (3) “Sand/gravel bars”, (4) “Water”.

The classes were manually digitized (Kuiper et al., 2023) using 2018 Meurthe River orthophotographs produced by IGN. In the case of the “Vegetation”, “Sand/gravel bars” and “Water” classes, a total of 250 polygons were digitized. The “River wood” class comprises a total of 339 polygons which were identified through visual interpretation.

In order to incorporate as much spectral information as possible into the training set, the digitizing process was conducted as follows: LJ

<footnote n="1">¹ https://geoservices.ign.fr/</footnote>
<figure><img src="image_3.png" /><figcaption>Fig. 1. Location of the study reaches on the four river systems.</figcaption></figure>

(comprising a minimum of two trunks) were digitized using polygons, while isolated trunks were digitized using their median polylines. As the median polyline does no take into account sufficient information, a 10 cm buffer must be applied to it in order to optimize the consideration of the pixels that constitute the IT while limiting the integration of the pixels from the other classes, which could subsequently introduce to much bias into the classification. Thereby facilitating the acquisition of the maximum amount of accurate information while simultaneously reducing errors attributable to manual digitizing (Ulloa et al., 2015).

The width of the data set for the 339 digitized RW polygons ranges from 0.2 (i.e. the size of a pixel) to 20.26 m with an average of 1.34 m and a median of 0.43 m. The length of the data set ranges from 0.92 to 25.64 m with an average of 5.36 m and a median of 3.98 m.

Given the irregular nature of RW polygons, it is not feasible to ascertain their precise length and width. Accordingly, these metrics are measured on the minimal oriented bouding box (MOBB), which represents the smallest oriented bouding box capable of encompassing the entire RW polygon. All the process is developed in Section 3.4.2.

### 3.1.3. Spectral characteristics of the extracted data

In order to evaluate the separability of the elements identified, for the training set (TrS) and the test set (TeS), the NIR, red, and green spectral values were extracted for each class, and their mean spectral signatures were subsequently generated. This allows for the evaluation of spectral trends within a single class and the verification of separability between established classes.

## 3.2. Random forest supervised classification

In this section, we detailed the supervised algorithm used, the different hyperparameters and the exogenous indices derived from the aerial images.

### 3.2.1. Random forest

Random Forest (RF) is a machine learning (ML) algorithm widely used for classification tasks. It operates as an ensemble method, combining multiple decision trees to achieve a more robust and accurate prediction compared to individual trees. Each tree in the RF is trained on a randomly sampled subset of the data (bagging), and splits at each node are determined by considering a random subset of features (Breiman, 2001). This randomization reduces overfitting and increases the generalizability of the model. The final prediction is obtained by aggregating the predictions of all constituent trees, typically through majority voting for classification tasks (Xiang et al., 2023).

RF was chosen for its robustness in handling small training datasets and its relatively low computational cost compared to deep learning models. Unlike deep convolutional networks, RF does not require extensive labeled datasets and is less prone to overfitting, making it an appropriate choice for our application. Furthermore, the feature importance ranking provided by RF enables better interpretability, which is crucial in environmental studies.

The RF model developed in this study is based on a per-pixel approach, which classifies each pixel individually based on its spectral and textural features. This approach is particularly advantageous for delineating fine objects such as in-stream RW, where object-based methods (OBIA – Object-Based Image Analysis) might struggle to capture detailed spatial variations (Sendrowski and Wohl, 2021). Although per-pixel classification has the drawback of introducing “salt-and-pepper” noise due to pixel-level variability, it provides better results for detecting fine-scale features (Blaschke, 2010) especially when the objective is then to calculate several RW metrics.

RF's key strengths include its ability to model non-linear relationships, handle large datasets with many features, and maintain robustness against noise and imbalanced datasets. The inclusion of multiple decision trees reduces the variance of predictions and ensures that the model is not overly reliant on any single subset of the data (Recking, 2020). Additionally, the algorithm supports feature importance ranking, which allows the identification of the most influential spectral and
<table><caption>**Table 1**
Pixel distribution by class (in %) for training and test set.</caption><thead><tr><th>Classe</th><th>Training set</th><th>Test set</th></tr></thead><tbody><tr><td>River wood</td><td>1.97</td><td>1.83</td></tr><tr><td>Vegetation</td><td>40.21</td><td>17.36</td></tr><tr><td>Sand/Gravels</td><td>16.81</td><td>6.37</td></tr><tr><td>Water</td><td>41.01</td><td>74.44</td></tr></tbody></table>

textural indices used in the classification process and can then help the stakeholder to understand the learning process.

In this study, RF was optimized using a Bayesian optimization approach (Tree Structured Pazern Estimator algorithm, see Section 3.2.3), adjusting key hyperparameters such as the number of trees (967 after TPE), the maximum depth of each tree (18 after TPE), and the minimum samples required to split a node (10 after TPE). These parameters were fine-tuned to balance computational efficiency and classification accuracy. This optimization ensures that the model performs effectively across different environmental conditions and spectral characteristics of the study area.

### 3.2.2. Training set and test set data

Data sets for model training and validation were constructed using a “geographical stratification” (Wenger et al., 2022; Maxwell et al., 2021). The model TrS was selected for around 85% of the Meurthe River channel, while the remaining 15% of the linear section was dedicated to model application. As can be seen in Table 1, the dataset is imbalanced in terms of class distribution. RW covers about 2% of the total dataset and due to the “geographical stratification”, the distribution of each class cannot be perfectly split between the training and test set. However, this approach ensures a high generalization capacity as we tested the RF over an “unseen” area of the river.

### 3.2.3. Optimization of model hyperparameters using the TPE algorithm

While numerous test protocols exist for optimizing RF model parameters (Albertini et al., 2024), the use of a Tree Structured Pazern Estimator (TPE) algorithm was deemed the most appropriate (Liao et al., 2024). The RF algorithm accounts for a number of hyperparameters that enable its optimization. Five of these were selected for consideration: n_estimators, max_features, max_depth, min Samples Split and min Samples Leaf.

The TPE algorithm is a Bayesian algorithm based on “model-based sequential global optimization” (Liao et al., 2024). In other words, the TPE algorithm is based on the estimation of new conditional probabilities, which are derived from previous observations produced by the model (Liao et al., 2024).

$$p(x|y) = \begin{cases} \ell(x) & \text{if } y < y^* \\ g(x) & \text{if } y \ge y^* \end{cases} \quad (1)$$

In Eq. (1), the TPE algorithm models $p(x|y)$ from two densities with respect to a threshold value $y^*$. The density $\ell(x)$ is formed by the observations of $\{x^{(k)}\}$ such that the corresponding loss at $f(x^{(k)})$ is less than the threshold value $y^*$. The density $g(x)$, on the other hand, is formed by the remaining observations (Bergstra et al., 2011). To apply the TPE algorithm on the RF model, the Hyperopt library was used (Bergstra et al., 2015).

### 3.2.4. Normalized Difference Vegetation Index

The Normalized Difference Vegetation Index (NDVI) (Tucker, 1979) represents the initial index employed in the test protocol for the development of the supervised classification model. This is equivalent to the normalized difference between the NIR band and the red band (Jeevalakshmi et al., 2016).

One of the reasons for employing NDVI as a supplementary index in conjunction with the image spectral bands is its capacity to differentiate between disparate classes, whether natural or otherwise (Zaidi

et al., 2017). Indeed, although NDVI is typically employed to examine vegetated regions (Mohammadpour et al., 2022), it can also be used to differentiate between water bodies, which exhibit negative values, unvegetated soil or anthropogenic areas (Jeevalakshmi et al., 2016; Zaidi et al., 2017).

The formula for NDVI is given by:

$$NDVI = \frac{NIR - RED}{NIR + RED} \quad (2)$$

where NIR represents the near-infrared reflectance and RED represents the red reflectance.

### 3.2.5. Brightness index

Although the Brightness Index (BI) is frequently employed in the study of arid and semi-arid zones, it is of particular interest for the distinction between sand and gravel bars and long-deposited RW, which may be dry and thus cause confusion (Maimouni et al., 2011).

The formula for the Brightness Index is given by:

$$Brightness\ Index = \sqrt{Red \times Red + NIR \times NIR} \quad (3)$$

where Red represents the red reflectance and NIR represents the near-infrared reflectance

### 3.2.6. GLCM homogeneity and entropy

The Gray Level Co-occurrence Matrix (GLCM) indices were developed by Robert Haralick with the objective of highlighting different patterns of heterogeneity and surface roughness in imaging (Haralick et al., 1973; Park and Guldmann, 2020). The indices are calculated on the basis of the image texture derived from the gray level. In order to calculate the latter, the image (which constitutes a matrix of continuous numbers) is transformed into an integer matrix through the process of discretization, whereby the pixel values are divided over a range of n pixels of equal width, thus creating a matrix of different gray levels. Subsequently, the pixel values are transformed into a single gray level. Subsequently, GLCM values are calculated on the discretized matrix by counting the frequency of the different gray levels, thereby establishing spatial relationships between pixels at various angles (from 0° to 135°). Subsequently, the GLCM is normalized. The various indices, such as homogeneity (HOM) or entropy (ENT), are calculated (Park and Guldmann, 2020) using the following equation:

The formula of normalized GLCM is given by:

$$P(i, j | d, \theta)_{\text{norm}} = \frac{P(i, j | d, \theta)}{\sum_{i,j} P(i, j | d, \theta)} \quad (4)$$

where i and j are the gray levels in the image, d is the distance between the considered pixels, θ is the angle (direction) between the pixels.

The formula for HOM is given by:

$$Homogeneity\ (HOM) = \sum_{i,j=0}^{N-1} \frac{P_{ij}}{1 + (i - j)^2} \quad (5)$$

where $P_{ij}$ represents the element in the co-occurrence matrix, and N is the number of distinct intensity levels.

The formula for ENT is given by:

$$Entropy\ (ENT) = \sum_{i,j=0}^{N-1} P_{i,j} (-\ln P_{i,j}) \quad (6)$$

where $P_{i,j}$ is the joint probability of gray levels i and j in the GLCM.

In the development of the RW detection model, the HOM and ENT were calculated as an additional index to improve the distinction between classes. The HOM was calculated on the red band which is one of the most discriminating spectral band between classes. The calculation of ENT is performed using the same spectral band as the HOM calculation.
<table><caption>**Table 2**
Overall accuracy and description of each model-test processed.</caption><thead><tr><th>Test</th><th>Spectral bands and indices used</th><th>Overall accuracy (%)</th></tr></thead><tbody><tr><td>1</td><td>NIR + Red + Green</td><td>86</td></tr><tr><td>2</td><td>NIR + Red + Green + NDVI</td><td>86</td></tr><tr><td>3</td><td>NIR + Red + Green + NDVI + HOM</td><td>89</td></tr><tr><td>4</td><td>NIR + Red + Green + NDVI + HOM + BI + ENT</td><td>92</td></tr></tbody></table>

### 3.2.7. Test protocol

In order to ascertain the optimal supervised classification model for RW detection, a series of four classifications were evaluated (Table 2). To systematically assess the impact of different spectral and textural indices, we performed an ablation study by incrementally adding indices to the model and evaluating its performance. The following configurations were tested:

* **Model 1:** Using only the three available spectral bands (NIR, Red, Green).

* **Model 2:** Adding NDVI to evaluate the role of vegetation differentiation.

* **Model 3:** Incorporating NDVI and GLCM Homogeneity to introduce texture information.

* **Model 4:** Final model integrating NDVI, Homogeneity, Entropy, and the Brightness Index (BI).

### 3.2.8. Models assessment

For all classifications, the confusion matrices and their associated indices are generated. The evaluation of the precision of the different classifications is based on three key metrics: the overall accuracy (OA), recall (Eq. (7)) and precision (Eq. (8)), and finally the F1-score (Eq. (9)) per class. The equations below detail the calculation of these metrics. Overall accuracy is calculated as the ratio of the number of correct predictions to the total number of validation data (Lin and Doyog, 2023).

The formula for Recall is given by:

$$ \text{Recall} = \frac{TP}{TP + FN} \qquad (7) $$

where *TP* is the number of true positives and *FN* is the number of false negatives.

The formula for Precision is given by:

$$ \text{Precision} = \frac{TP}{TP + FP} \qquad (8) $$

where *TP* is the number of true positives and *FP* is the number of false positives.

The formula for F1-score is given by:

$$ \text{F1-score} = \frac{2 \cdot \text{Precision} \cdot \text{Recall}}{\text{Precision} + \text{Recall}} \qquad (9) $$

### 3.3. Towards spatial inference on other rivers

As the objective of the developed model was to be able to perform spatial inference (Wenger et al., 2023) on other rivers, three other rivers were subjected to the supervised classification model developed previously, namely the Buëch, the Doubs and the Loire (Fig. 1). For this purpose, IRC orthophotographs of these rivers were downloaded using an automatic processing chain available on GitHub<fnref n="2" />. The aim is to see if the model can be generalized to these other watercourses, particularly those that are hydrogeomorphologically different from the Meurthe. The script for spatial inference and river wood metrics calculation is available on GitHub<fnref n="3" />.

## 3.4. Model validation and output of river wood metrics

### 3.4.1. Classification correction using connected-component labeling

The use of related components to attenuate noise in the classified image is founded upon the adjacency principle (Perret, 2017), which elucidates the spatial neighborhood relationships that exist between pixels in an image. Subsequently, a Python algorithm is executed on the model's output image to ascertain the related components present therein. For a set of pixels to be considered connected, there must be a link between all pairs of pixels within that set (Perret, 2017).

Subsequently, the image is refined through the automated labeling of related components into eight distinct connexities, delineated by a threshold of 400 pixels. In other words, this cleaning process eliminates small features that are smaller than 400 pixels, assigns the number of the main related component, and reduces noise. Through this final post-processing, we wanted to reduce the “salt of pepper” effect and have the cleanest product possible.

### 3.4.2. Extraction of river wood metrics

To characterize each RW, several metrics were extracted from the classified and corrected images: the length (*L*) of the RW (Ortega-Terol et al., 2014), the diameter (*D*) (Piton et al., 2024), the area (*A*) and the volume (*V*).

In recent studies (Piton et al., 2024), RW digitizing is carried out using polylines, enabling more representative measurements of the ground, such as length. In this article, RW were digitized from polygons (and polylines which were transformed into polygons using a buffer of 0.1 m). Also, to measure the desired metrics, both length and diameter are calculated using the MOBB. The area (*A*) was calculated from the RW irregular polygon. The area of the MOBB (*A*<sub>b</sub>) of the RW was also calculated, in order to correct the calculation of the volume (*V*) of the RW. To calculate the volume, the formula developed by Piton et al. (2024) was used. It is as follows:

$$ V = L * D^2 * \frac{\pi}{4} \qquad (10) $$

where *L* is the RW length and *D* is the RW diameter.

Because the length and the diameter have been calculated from the MOBB of each RW polygon, hence the need to make a correction to the formula to best estimate the exact volume of RW. To do this, a correction factor (*c*) is calculated using the ratio between the area of the RW polygon and the area of the RW MOBB:

$$ c = \frac{A}{A_b} \qquad (11) $$

where *A* is the RW area and *A*<sub>b</sub> is the MOBB area.

The correction factor is then applied to the formula of Piton et al. (2024):

$$ V = c * L * D^2 * \frac{\pi}{4} \qquad (12) $$

This reduces the volume calculated on the MOBB to the RW.

The values predicted by the model were compared with the reference values estimated from the digitized data in order to assess the accuracy of the model to predict the 4 metrics discussed above. The Root Mean Square Error (RMSE) and Mean Average Error (MAE) are calculated because their are commonly used metrics for evaluating models. As RMSE is calculated as the square root of the mean square error, whereas the MAE is the mean of the absolute deviations between the reference and predicted values (Hodson, 2022).

$$ \text{RMSE} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2} \qquad (13) $$

$$ \text{MAE} = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i| \qquad (14) $$

According to Wang and Lu (2018), a low MAE value indicates that the predicted values are close to the actual values of the reference ones, while the RMSE provides information on the accuracy of the predicted values and therefore on their potential errors. The lower the RMSE value, the more accurate the predicted values.

<footnote n="2">² accessible via https://github.com/r-wenger/Download-BDOrtho21/tree/main</footnote>

<footnote n="3">³ https://github.com/g-grimmer/RiverDetectWood</footnote>
<table><caption>**Table 3**
Recall, Precision and F1-score (%) for various spectral bands and indices used for each model-test processed.</caption><thead><tr><th rowspan="2">Test</th><th colspan="3">River wood</th><th colspan="3">Vegetation</th></tr><tr><th>Recall</th><th>Precision</th><th>F1-score</th><th>Recall</th><th>Precision</th><th>F1-score</th></tr></thead><tbody><tr><td>1</td><td>13</td><td>90</td><td>23</td><td><strong>97</strong></td><td><strong>99</strong></td><td><strong>98</strong></td></tr><tr><td>2</td><td>12</td><td>90</td><td>21</td><td>96</td><td>99</td><td>98</td></tr><tr><td>3</td><td>14</td><td><strong>94</strong></td><td>24</td><td><strong>97</strong></td><td>99</td><td>98</td></tr><tr><td>4</td><td><strong>39</strong></td><td>92</td><td>54</td><td>97</td><td>99</td><td>98</td></tr></tbody></table>

<table><thead><tr><th rowspan="2">Test</th><th colspan="3">Water</th><th colspan="3">Sand/Gravels</th></tr><tr><th>Recall</th><th>Precision</th><th>F1-score</th><th>Recall</th><th>Precision</th><th>F1-score</th></tr></thead><tbody><tr><td>1</td><td>85</td><td><strong>100</strong></td><td>92</td><td>68</td><td>58</td><td>63</td></tr><tr><td>2</td><td>86</td><td><strong>100</strong></td><td>92</td><td>67</td><td>75</td><td>71</td></tr><tr><td>3</td><td>88</td><td><strong>100</strong></td><td>94</td><td><strong>70</strong></td><td>90</td><td>78</td></tr><tr><td>4</td><td><strong>95</strong></td><td>99</td><td><strong>97</strong></td><td>70</td><td>92</td><td><strong>79</strong></td></tr></tbody></table>

# 4. Results

## 4.1. Spectral signatures of training set and test

The “Water” class exhibited pronounced absorption across all three bands, accompanied by a subtle peak in reflectance within the red band. Vegetation initially exhibits marked reflectance in the green band, absorption in the red, and a reflectance peak in the NIR. Sand/gravel bars show a high reflectance in the green band, a peak reflectance in the red band, and a lower reflectance in the NIR band. Finally, the RW shows a comparable trend to that observed in the sand/gravel bars, albeit to a lesser extent. This is evidenced by a peak reflectance in the red and a lower reflectance in the green and NIR.

Furthermore, the spectral trends observed in the four classes provide a rationale for the inclusion of additional indices, such as NDVI, BI, HOM and ENT. The NDVI is a useful indicator for differentiating between the vegetation class and the other classes. BI may be employed to differentiate between regions with high reflectivity, such as sand and gravel bars, and those with low reflectivity, such as water. Furthermore, BI may facilitate a more accurate differentiation between sand/gravel bars and recently deposited RW, contingent on the moisture content of the latter not being excessively low. The HOM serves to highlight textured areas, such as vegetated regions or areas where RW has been deposited, in comparison to smooth areas, such as those comprising water or sand/gravel bars. Ultimately, the ENT offers supplementary insight to the HOM, enabling the differentiation of areas with more pronounced textures, such as those of RW deposits, from their smooth counterparts.

A comparison of the TrS (Fig. 2) and TeS (Fig. 3) reveals some notable differences. In general, while the spectral trends are similar, the reflectance and absorption values for the TeS are lower than those for the TrS. To illustrate, the reflectance of RW for TrS in the green is 92, compared with 81 for TeS. Similarly, in the red there is a peak at 108 for TrS, compared with 94 for TeS. Furthermore, for NIR there is a reflectance of 89 for TrS, compared with 88 for TeS. A further distinction can be observed: in the green wavelength range, the RW exhibits a higher reflectance than the vegetation for the TrS, whereas this trend is reversed for the TeS. Notwithstanding these discrepancies, the TrS and TeS exhibit comparable spectral characteristics (Figs. 2 and 3).

Following a Shapiro-Wilk test demonstrating the non-normality of the distribution of TeS and TrS reflectance values for each band, a Mann-Whitney U test was performed to assess class differences for the green, red, and NIR bands.

Concerning TrS, the U test statistical values range from $1.1 \times 10^9$ to $1.35 \times 10^{11}$, while the p-values vary from 0 to $2.56 \times 10^{-5}$. The U-values, being high, indicate that in each class pair comparison, one has systematically larger values than the other. Furthermore, all p-values

being less than 0.05, we reject the null hypothesis of no significant difference between these classes. Thus, for TrS, each class is significantly different from the others for each spectral band.

For TeS, U-values range from $9.81 \times 10^6$ to $3.0 \times 10^9$, while p-values range from 0 to $1.77 \times 10^{-53}$. As with TrS, the high U-values and p-values all below 0.05 indicate significant differences between each class for each of the bands analyzed.

## 4.2. Supervised classification results

In this section, we will present the results of the supervised classification approaches and the different test we performed to map RW in rivers.

### 4.2.1. Models assessment

Four distinct models were processed, incorporating additional complexity into the exogenous indices to attain an acceptable level of prediction accuracy (Table 2). The initial model (Tables 2 & 3) incorporates solely the three bands accessible in the IRC orthophotographs, specifically green, red, and NIR. The overall accuracy (OA) is 86%. The classes with the highest levels of accuracy are vegetation and water. With regard to the classification of vegetation, the recall was 97% (i.e. 97% of pixels that should have been classified as vegetation were correctly identified as such), while the accuracy was 99% (i.e. 99% of pixels classified as vegetation were correctly classified as such), resulting in an F1-score of 98%. The recall of the water class is 85%, while the precision is 100%, resulting in an F1-score of 92%. The precision of sand/gravel bars is diminished, with a lower recall of 68% and a precision of 58%. This results in an F1-score of 63%. Consequently, the RW class exhibits the lowest precision, with a recall of 90% and a precision of 13%, resulting in an F1-score of 23%. An examination of the confusion matrices (Tables 4 to 7) reveals that the initial model under consideration exhibits the greatest degree of confusion between sand/gravel bars and RW. Specifically, 31.97% of the sand/gravel bank pixels were predicted as RW, which has the effect of reducing the percentage of correct predictions for this class to 67.52% true positives. A less pronounced instance of confusion can also be observed, with 11.04% of the pixels in the “Water” class being classified as RW. With regard to the RW class, 89.70% of the pixels were correctly classified, while 6.16% of the pixels in the class were predicted as sand/gravel bars.

The second model tested (Table 3) includes the three bands, green, red and NIR, as well as NDVI. With an OA of 86%, the vegetation class and the water class also show the best accuracies. Vegetation had a recall of 96% and a precision of 99%, giving an F1-score of 98%. Water had a recall of 86% and a precision of 100%, giving an F1 score of 92%. The sand/gravel class shows better precision than the first model, with a recall of 67% and a precision of 75%, giving an F1-score of 71%. Finally, the RW class does not seem to show much difference from the previous model, as it has a recall of 90% and a precision of 12%, giving an F1-score of 21%. For this model, the confusion between the classes remains relatively similar (Tables 4 to 7). 32.70% of the sand/gravel pixels were classified as RW and 12.50% of the water pixels were also classified as RW. For the RW class, 6.07% of the pixels were classified as sand/gravel bars, but there were no other significant confusions.

The third model (Table 3) includes HOM as an additional index. This model has an improved OA of 89%. As in the previous models, vegetation and water are the classes with the highest precision. Vegetation has a recall of 97% and a precision of 99%, giving an F1-score of 98%. Water has a recall of 88% and an accuracy of 100%, giving an F1-score of 94%. The prediction of sand/gravel bars is further improved with a recall of 70% and an accuracy of 90%, giving an F1-score of 78%. Finally, the prediction of RW is also improved with a recall of 94% and an accuracy of 14%, giving an F1-score of 24%. This model also reduces confusion (Tables 4 to 7). Here 29.93% of the sand/gravel bars
<figure><img src="image_4.png" /><figcaption>Fig. 2. Spectral signatures for each class of the training set.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 3. Spectral signatures for each class of the test set.</figcaption></figure>

<table><caption>**Table 4**

Confusion Matrix for the model-test 1 combining Green + Red + NIR.</caption><thead><tr><th>Actual/Pred</th><th>River wood</th><th>Vegetation</th><th>Sand/Gravel</th><th>Water</th></tr></thead><tbody><tr><th>River wood</th><td>89.70</td><td>1.84</td><td>6.16</td><td>2.30</td></tr><tr><th>Vegetation</th><td>3.39</td><td>96.61</td><td>0.00</td><td>0.00</td></tr><tr><th>Sand/Gravel</th><td>31.97</td><td>0.22</td><td>67.52</td><td>0.29</td></tr><tr><th>Water</th><td>11.04</td><td>0.24</td><td>3.90</td><td>84.82</td></tr></tbody></table>

<table><caption>**Table 5**

Confusion Matrix for the model-test 2 combining Green + Red + NIR + NDVI.</caption><thead><tr><th>Actual/Pred</th><th>River wood</th><th>Vegetation</th><th>Sand/Gravel</th><th>Water</th></tr></thead><tbody><tr><th>River wood</th><td>89.74</td><td>1.73</td><td>6.07</td><td>2.46</td></tr><tr><th>Vegetation</th><td>3.51</td><td>96.49</td><td>0.00</td><td>0.00</td></tr><tr><th>Sand/Gravel</th><td>32.70</td><td>0.20</td><td>66.78</td><td>0.32</td></tr><tr><th>Water</th><td>12.50</td><td>0.13</td><td>1.76</td><td>85.61</td></tr></tbody></table>

were predicted as RW and 11.07% of the water was classified as RW. For RW, 3.01% of the pixels were classified as sand/gravel bars.

Finally, BI and ENT were added (Table 3). The precision for vegetation and water appears to be equivalent. Vegetation has a recall of 97% and a precision of 99%, giving an F1-score of 98%. Water has a recall of 95% and a precision of 99%, giving an F1-score of 97%. The accuracy of the sand/gravel class has improved significantly with a recall of 70% and an accuracy of 92%, giving an F1-score of 79%. Finally, the RW class has also seen an improvement in its prediction,

<table><caption>**Table 6**

Confusion Matrix for the model-test 3 combining Green + Red + NIR + NDVI + HOM.</caption><thead><tr><th>Actual/Pred</th><th>River wood</th><th>Vegetation</th><th>Sand/Gravel</th><th>Water</th></tr></thead><tbody><tr><th>River wood</th><td>94.43</td><td>1.47</td><td>3.01</td><td>1.09</td></tr><tr><th>Vegetation</th><td>3.17</td><td>96.83</td><td>0.00</td><td>0.00</td></tr><tr><th>Sand/Gravel</th><td>29.93</td><td>0.20</td><td>69.66</td><td>0.20</td></tr><tr><th>Water</th><td>11.07</td><td>0.14</td><td>0.62</td><td>88.17</td></tr></tbody></table>

<table><caption>**Table 7**

Confusion Matrix for the model-test 4 combining Green + Red + NIR + NDVI + HOM + BI + ENT.</caption><thead><tr><th>Actual/Pred</th><th>River wood</th><th>Vegetation</th><th>Sand/Gravel</th><th>Water</th></tr></thead><tbody><tr><th>River wood</th><td>92.18</td><td>1.74</td><td>4.24</td><td>1.84</td></tr><tr><th>Vegetation</th><td>3.39</td><td>96.61</td><td>0.00</td><td>0.00</td></tr><tr><th>Sand/Gravel</th><td>28.95</td><td>0.08</td><td>69.80</td><td>1.17</td></tr><tr><th>Water</th><td>3.44</td><td>0.35</td><td>1.58</td><td>94.62</td></tr></tbody></table>

with a precision of 92% and a recall of 39%, giving this class an F1-score of 54%. In addition, this model has drastically reduced confusion (Tables 4 to 7). This is particularly true for water, where only 3.44% of pixels were predicted as RW. Confusion with sand/gravel bars was slightly reduced, with 28.95% of pixels classified as RW. Within the RW class, 4.24% of pixels were classified as sand/gravel bars.
<figure><img src="image_6.png" /><figcaption>Fig. 4. Classification map obtained from the model-test 4 on the Meurthe river ZOI n°1.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 5. Classification map obtained from the model-test 4 on the Meurthe river ZOI n°2.</figcaption></figure>

### 4.2.2. Spatial classification from the model and cartographic assessment

Four zones of interest (ZOI) (Figs. 4–7) were selected to apply the model to the Meurthe River to limit the treatment loading time. The spatialization of the model confirms the results described above. Regarding the detection of RW, the model detects RW well within the water channel (Fig. 5, A & B; Fig. 6, A), but also on the sand/gravel

bars (Fig. 5, B). Detection of the other classes, i.e. vegetation, water and sand/gravel bars, appears to be satisfactory.

With regard to the confusion highlighted above, there is confusion between RW and sand/gravel bars, to a greater or lesser extent depending on the nature of the latter (Fig. 7, thumbnails B & C). This is even more pronounced when the sand/gravel bars is covered by pioneer
<figure><img src="image_8.png" /><figcaption>Fig. 6. Classification map obtained from the model-test 4 on the Meurthe river ZOI n°3.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 7. Classification map obtained from the model-test 4 on the Meurthe river ZOI n°4.</figcaption></figure>

vegetation (Fig. 5, B). There is also confusion between the RW and the edges of the plant beds (Fig. 4, A & C; Fig. 5, thumbnail 2). Finally, there is some confusion between the RW and water areas, for example when the latter have aquatic vegetation (Fig. 5, C) or a relatively low water level (Fig. 7, A & C).

4.3. Extending the model to other rivers

The generalization of the model by spatial inference to other water-courses highlights its well ability to predict RW on the three various fluvial system (Fig. 8).
<figure><img src="image_10.png" /><figcaption>Fig. 8. Well detection areas by the model-test 4 on the Loire, Doubs and Buëch rivers.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 9. Confusion areas by the model-test 4 on the Loire, Doubs and Buëch rivers.</figcaption></figure>

In order to calculate the RW detection rate for the other rivers, the model's measurements were compared with those of the digitized measurements, which were used as the reference data. The model detects 75.41%, 80.00% and 86.57% of the RW for the Buëch, the Loire and the Doubs, respectively.

The classification of sections of the Doubs highlighted the good prediction of small IT features within the channel (Fig. 8.1). The model also gives good predictions for larger IT features, as in the Loire (Fig. 8.2), but also for LJ, as in the Doubs (Fig. 8.1).

Although the model has a good capacity to detect RW in rivers, it also shares certain difficulties already observed in the Meurthe. Taking the Doubs as an example (Fig. 9.1), there is some confusion between the edges of under-water plants and RW. The model also has difficulties with pioneer vegetation on sand/gravel bars. The Loire (Fig. 9.2) shows confusion between RW and eddy water zones. The moisture content of the RW deposited on the sand/gravel bars also influences its detection. As in the Loire example, long deposited and therefore dry ice jams are difficult to detect. Finally, the Buëch (Fig. 9.3) shows confusion

between shaded areas and water. In addition, as in the case of the Doubs, there is confusion between pioneer vegetation and RW.

### 4.4. River wood metrics

For the measurement of RW metrics, i.e. length, diameter, area and volume, a distinction is made between isolated trunks (IT) and logjams (LJ) (an accumulation of at least two trunks).

#### 4.4.1. User filter

Following the application of the model to the various reaches of the Meurthe, a visual analysis was conducted to distinguish the RW features identified by the model from those potentially affected by the various possible confusions.

In order to achieve this, all of the predicted RW features that not corresponded to a genuine RW were removed from the classification. Subsequently, RW exhibiting metrics that were incongruent with those of the references were excluded from the dataset. Following the
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Digitized value according to the estimated value by the model for the volume (A), area (C), diameter (E) and length (G) for isolated trunks. Digitized value according to the corrected value estimated by the model after calibration for the volume (B), area (D), diameter (F) and length (H).</figcaption></figure>

filter process, 28.57% of all RW entities were identified as being non-compliant and were subsequently removed. Finally, 66.66% of IT and 63.63% of LJ remain.

### 4.4.2. Model accuracy to estimate shape, area and volume of isolated trunks

For the IT, all metrics were well predicted by the model. Strong statistically power significant relationships were found between the values estimated by the model and by digitizing process. The $R^2$ equals to 0.79, 0.86, 0.90 and 0.97 for the diameter, volume, the area and the length, respectively (Fig. 10).

Starting with length (Fig. 10G), the trend line approximates the $y = x$ distribution, highlighting the ability of the model to predict lengths that are close to reality, with globally a slight underestimation by the model. Before correction, the MAE is 0.84 m, meaning that on average the predicted values differ by 0.84 m from the actual values. The RMSE is 1.14 m. In other words, the predicted values differ by 1.14 m from the actual values when the extreme values are better

taken into account. After correction (Fig. 10H), the overall accuracy changes slightly, the RMSE is reduced to 1.10 m, while the MAE is 0.83 m. Looking at the boxplot before correction (Fig. 11G), the range of residuals (for the four rivers) is between 2 m and -3 m (especially with outliers for the Meurthe). On the one hand, the residual medians for Meurthe and Buëch are around -0.5 m and the quartiles range from -0.1 m to -0.75 m for Meurthe and from 0.1 m to -1 m for Buëch. Secondly, the Loire and Doubs show a median equivalent to about 0.5 m and quartiles between 0.3 m and 1 m for the Loire and 0.25 m and 0.75 m for the Doubs. After the correction, the range of residuals is slightly reduced between 1.5 m and -2 m (with an outlier for the Meurthe). For the Meurthe, the median remains at -0.5 m and the quartiles between -0.25 m and -0.75 m. The Loire has a median of 0.25 m and quartiles between 0.1 m and 0.9 m. The Buëch has a median of 0.5 m and quartiles between 1 and -0.75 m. Finally, the Doubs has a median around 0 m and quartiles between 0.5 m and -0.25 m. Thus, after correction, a slight improvement in the prediction of the metrics can be observed, particularly for the Loire and the Doubs.
<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Residual errors without correction for the volume (A), area (C), diameter (E) and length (G) and residual errors after the power law correction for the volume (B), area (D), diameter (F) and length (H) for isolated trunks.</figcaption></figure>

For area, the metric is well estimated and globally overestimated compared to the reference values. The RMSE is 6.81 m² while the MAE is 4.63 m² (Fig. 10 C). After applying the correction (Fig. 10D), the values are refocused around the y = x distribution, with a decrease in both the RMSE (5.34 m) and the MAE (3.21 m). Before the correction, the residuals ranged from 5 to -20 m² (Fig. 11C) (with an outlier for the Buëch). The Meurthe has a median of around -2 m², with quartiles ranging from -1 m² to -7 m². The Buëch has a median of -3 m² and quartiles ranging from -1 m² to -6 m². The Loire has a median of 2 m² and quartiles ranging from 0 to 4 m². Finally, the Doubs has a median close to 0 m² and quartiles between 1 and -2 m². After the correction (Fig. 11D), the improvement in accuracy is confirmed by a reduction in the amplitude of the residuals. On the one hand, the range is between 25 and -5 m² (with the appearance of four outliers for the Meurthe and the retention of an extreme value for the Buëch). For the Meurthe and the Doubs, the medians are concentrated around 0 m², with a reduction in the range of the quartiles. For the Buëch, the median has been re-centered around 0 m² but the range of its quartiles does not seem to have been drastically reduced (between 4 and -2 m²). Finally, the residuals for the Loire do not seem to be much affected by the correction, as the median and quartiles remain equivalent.

The diameter is also well estimated by the model (Fig. 10E). As area and volume, the diameter values are overestimated compared to

the real values, but this overestimation tends to decrease with larger measurements. The RMSE is 1.22 m and the MAE is 1.02 m. After correction (Fig. 10F), the RMSE and MAE are reduced to 0.74 m and 0.54 m respectively, although the values retain some volatility around the y = X distribution. Looking at the boxplot (Fig. 11E), the Meurthe has a median of around -1.25 m, with interquartiles ranging from around -1 to -1.6 m. The Loire has a median of -0.5 m, with interquartiles between -0.25 and -0.9 m. The Buëch has a median of -1 m and quartiles between -0.75 and -1.25 m. In addition, an outlier can be observed at around 1 m. The Doubs has a median of -0.4 m and weak quartiles between -0.35 and -0.55 m (an outlier can also be observed at around 0.5 m). After correction (Fig. 11F), all medians are refocused around 0 m and the quartiles are also reduced, except for the Buëch, where the quartiles are between 1.1 and -0.25 m.

For the estimation of IT volume, as same as the prediction of the area, all predicted values are overestimated compared to the reference values. Before correction (Fig. 10A), the RMSE is 57.64 m³ and the MAE is 28.06 m³. After correction (Fig. 10B), the values are centered around the y = X distribution and the accuracy is also improved, with an RMSE of 27.40 m³ and an MAE of 13.04 m³. The rivers with the largest residuals are the Meurthe and the Buëch (Fig. 11A). The Meurthe has a median of about -0.15 m³ and quartiles ranging from -10 to -50 m³,
<figure><img src="image_14.png" /><figcaption>**Fig. 12.** Digitized value according to the estimated value by the model for the volume (A), area (C), diameter (E) and length (G) for logjams. Digitized value according to the corrected value estimated by the model after calibration for the volume (B), area (D), diameter (F) and length (H).</figcaption></figure>

with an outlier of -175 m³. The Buëch has a median of -20 m³, with interquartile ranges from -10 to -45 m³ and an outlier of around -135 m³. In contrast, both the Loire and the Doubs show very low medians and quartiles. After correction (Fig. 11B), the medians of all rivers are centered around 0 m³ and the ranges of the quartiles are also refocused. However, it is worth noting the appearance of outliers for the Meurthe, Buëch and Doubs.

### 4.4.3. Model accuracy to estimate shape, area and volume of logjams

For LJ, all metrics were also well predicted by the model. Strong statistically significant relationships were found between the values estimated by the model and by digitizing process. The R² equals to 0.93, 0.93, 0.95 and 0.95 for the area, the volume, the length and the diameter, respectively (Fig. 12). For all metrics the small extremes are underestimated while the large extremes are overestimated. The amplitudes may vary from one metric to another, but they remain relatively similar.

Starting with the area, before correction (Fig. 12C), the RMSE is 11.60 m² and the MAE is 7.85 m². After the correction (Fig. 12D), although the values are refocused around the y = x law, the RMSE and MAE have increased slightly. The RMSE is now 13.95 m² and the MAE is 8.33. Looking at the boxplot (Fig. 13C), the residuals are initially between 15 and -35 m². The Meurthe, the Buëch and the Doubs have relatively similar medians, close to 0 m². The Meurthe has quartiles ranging from 0 m² to about 12 m², the Buëch from 8 to -5 m² and the Doubs from 1 to -3 m². The Loire has a median of around 4 m² and quartiles between 8 and 1 m². After correction (Fig. 13D), the range of residuals is from 60 to -20 m². All the medians are refocused around 0 m² and the quartiles are also reduced. The Buëch still has quartiles between 12 and -2 m². There are also outliers such as the Meurthe (60 m²), the Loire (20 m²) and the Buëch (40 m²).

For the diameter, the RMSE is 1.50 m and the MAE is 1.10 m before correction (Fig. 12E). After correction (Fig. 12F), both indices
<figure><img src="image_15.png" /><figcaption>**Fig. 13.** Residual errors without correction for the volume (A), area (C), diameter (E) and length (G) and residual errors after the power law correction for the volume (B), area (D), diameter (F) and length (H) for logjams.</figcaption></figure>

are reduced, with an RMSE of 0.93 and an MAE of 0.69. As for the boxplot (Fig. 13E), the residuals before correction range from 3 to -4 m. Both the Meurthe and the Buëch have medians close to -1 m. The Meurthe has interquartiles between -0.9 and -2 m and the Buëch between 0.8 and -1 m. Both the Loire and the Doubs have medians close to 0 m. The Loire has interquartiles between 0.5 and -0.4 m and the Doubs between 0 and -0.5 m. After the correction, the residuals are on a finer scale, between 3 and -2 m. The median for the Meurthe has fallen to -0.5 m, while that for the Buëch has risen to 1 m. The interquartiles for the Meurthe have also fallen to between -0.2 and -0.7 m, while those of the Buëch are between 1.6 and 0 m. Both the Loire and the Doubs have medians close to 0 m. The Loire has quartiles between 0.3 and -0.1 m and the Doubs between 0.1 and -0.3 m.

For the length, the RMSE before correction (Fig. 12G) is 1.63 and the MAE is 1.13. Like the IT length, the LJ length shows a slight improvement in accuracy as the RMSE drops to 1.54 and the MAE to 1.18 after correction (Fig. 12H). The residuals in the boxplot (Fig. 13G) lie between about 4 and -5 m. The Meurthe has a median at -1 m and the Loire at 1 m. Both the Buëch and the Doubs have a median close to 0 m. The Meurthe has quartiles between -0.5 and -1.75 m and the Loire

between 0 and 1.75 m. The Buëch has the smallest quartiles, between 0 and -0.75 m, and the Doubs between 0.25 and -1 m. After correction (Fig. 13H), the residuals are between 4 and -3 m. The medians remain relatively unchanged, except for the Loire, where they have fallen to around 0 m. The quartiles for the Meurthe are between 0.25 and -1.5 m. Those for the Loire have risen sharply and are now between 2.25 and -0.25 m. The Meurthe is relatively similar to the Doubs.

Finally, for volume, before the correction (Fig. 12A), the RMSE is 291.34 and the MAE is 122.35. After correction (Fig. 12B) the RMSE drops to 171.88 and the MAE to 74.50. The volume residuals (Fig. 13A) initially range from about 200 to -1200 m³. Although the medians for the four rivers are relatively centered around 0 m³, the Meurthe and the Buëch show the largest quartiles. Moreover, this metric is largely characterized by the presence of numerous outliers for all four rivers. After correction (Fig. 13B), the medians remain equivalent. The quartiles for the Loire and the Doubs are similar to those before correction. Those for the Meurthe have been reduced as for the Buëch. The application of the correction has also reduced the number of outliers, particularly for the Meurthe.
<table><caption>**Table 8**

Comparative the capacity and accuracy of methods to automatically detect river RW.</caption><thead><tr><th>Study</th><th>River</th><th>Data</th><th>Methods</th><th>OA</th><th>Recall</th><th>Precision</th><th>Metrics</th></tr></thead><tbody><tr><td>Smikrud and Prakash (2006)</td><td>Uniik River (USA)</td><td>Aerial photos</td><td>Low-pass filter</td><td>89</td><td>95.4</td><td>82</td><td>Area</td></tr><tr><td>Marcus et al. (2002)</td><td>Greater Yellowstone Ecosystem (USA)</td><td>Field surveys and aerial photos</td><td>Supervised classification</td><td>NA</td><td>17.3</td><td>45</td><td>NA</td></tr><tr><td>Kuiper et al. (2023)</td><td>Artlish and Nahmint River (Canada)</td><td>Airborne Laser Scanning</td><td>Classification</td><td>63</td><td>Better detection for logjams (81%) than isolated trunks (49%)</td><td>NA</td><td>Diameter, Length and Depth</td></tr><tr><td>Benacchio et al. (2017)</td><td>SE France rivers</td><td>Ground imagery</td><td>Random Forest classification</td><td>NA</td><td>98</td><td>97</td><td>Area and Weight</td></tr><tr><td>This study</td><td>Meurthe, Doubs, Loire and Buëch River (France)</td><td>IGN BD Ortho IRC</td><td>Random Forest classification</td><td>92</td><td>92</td><td>39</td><td>Length, diameter, area, and volume</td></tr></tbody></table>

# 5. Discussion

## 5.1. Model accuracy to detect automatically in-stream DW on the Meurthe river

The results of this study show that it possible to well detect the in-stream RW using a RF algorithm using IGN's BD Ortho® IRC. The best model developed on the Meurthe River has a precision of 92% which shows that the model well detects the RW features in the river corridor. This recall value is much higher than that reported in some previous studies (Marcus et al., 2003). It is also important to highlight the fact that this recall value remains in the same order of magnitude as those observed in other studies (Smikrud and Prakash, 2006; Benacchio et al., 2017). This underlines the consistency of the results in comparison with studies carried out on the same subject, while at the same time pointing to an improvement in recall in certain cases. On the one hand, the OA of 92% demonstrates the model's ability to detect and distinguish between classes. Our model well detect more than 70% of in-stream RW after visual filtration.

The recall of 39% of the model for the RW class highlights some confusions between the RW and the others classes. Unfortunately, this accuracy value is well below the accuracies observed in some studies (Smikrud and Prakash, 2006; Benacchio et al., 2017; Marcus et al., 2003) (Table 8). Strong confusions (i.e. salt and pepper effect Hirayama et al., 2019) were observed between the RW and the sand/gravel bars, which has also been observed in other studies (Leckie et al., 2005). Some authors put forward the idea of taking better account of the variability of the terrain and elements that can cause confusion, such as aquatic vegetation. This requires the creation of a more refined typology (Leckie et al., 2005). Difficulties in classifying certain stretches have been noted, particularly those of narrow width, which are subject to shadows that can cause variations in spectral information and therefore lead to confusion (Tsunetaka et al., 2021). To overcome these difficulties, masks could be used to deduce areas of shadow and/or areas of dense vegetation. There are various precise typologies, such as those that take into account the structural variability of the sand (Arora et al., 2019) or those the different characteristics of the water zones (Leckie et al., 2005). In order to avoid salt and pepper effect (Hirayama et al., 2019), recent Deep Learning (DL) approaches such as semantic segmentation (Kemker et al., 2018), even if resource consuming, can lead to a better identification of RW. These methods take into account the spatial context of each image (i.e. patch), which is really interesting as RW location depends on several geomorphic factors such as the stream geometry, presence of sand/gravel bars or vegetated islands, etc. (Piegay and Gurnell, 1997). In addition, one of the advantages of using DL is that it does not require the calculation of exogenous indices, as it performs non-linear interactions between each feature (i.e. spectral bands in this case) of the datasets.

This study highlights that it necessary to combine orthophotos with exogenous indices in order to create a comprehensive study element. Effectively, the use of spectral information alone would not be sufficient

to achieve satisfactory accuracy (Table 3). Indeed, the incorporation of exogenous indices proved beneficial in enhancing the overall accuracy of the tested models (Table 3). The use of the NDVI index did not result in a significant enhancement in the overall accuracy of the model, however it did facilitate a more precise differentiation between sand/gravel bars, vegetation and water. The incorporation of textural indices (HOM and ENT) improve significantly the RW detection by the model and the other class types (Table 3; Table 7). In this study, the aforementioned indices were calculated on the basis of the most discriminating spectral band, namely red, as opposed to being calculated on the basis of NDVI (Richardson and Moskal, 2016). The final index to be incorporated was the BI. In particular, this enabled a more accurate distinction to be made between sand/gravel bars, deep water and shallow water. However, adding additional indices to the model requires empirical expertise beforehand and is highly dependent on the object of study.

## 5.2. Testing the ability of the model to automatically detect in-stream RW on various fluvial systems

The results of this study highlights that it possible to reliable detect in-stream RW in a large variety of fluvial systems using our model build on the Meurthe river. Whether it is a braided style (the Buëch), a long meandering style (the Loire) or a meandering style combined with anastomosis (the Doubs), more than 70% of in-stream RW were well detected by the model on the three rivers. The classifications produced by the model highlight sites in the watercourses that are conducive to the deposition and accumulation of RW. Taking the example of the Meurthe and Loire, RW tends to be deposited inside meanders or on sand/gravel bars, helping to stabilize them (Massé, 2014). For the Doubs, as for the Meurthe and the Loire, the RW seems to be deposited inside the meanders and on the sand/gravel bars, but also within the anastomosis (narrow channels), which can lead to the obstruction of the latter and the raising of the bed (Piegay and Gurnell, 1997). Finally, for the Buëch, generally adopted by mountain streams, the RW is not very present in the water channel. In fact, for a watercourse such as the Buëch, with high floods and a high capacity for reworking of fluvial forms (Gautier, 1994), most of the RW is deposited on bars.

Although at first sight the generalization of the model seems competent on various fluvial context, confusion occurs between the water and the sand/gravel bars as well as the RW and the vegetation developing on these same bars was observed, particularly on the Buëch river characterized by low water level (Fig. 9). The same observations have been made, notably that IT are more difficult to detect on sand/gravel banks than those grouped together (Sendrowski and Wohl, 2021). In addition, these confusions may be due to spectral similarities between the two classes (Sendrowski and Wohl, 2021), which has been observed in the spectral signatures of TrS and TeS (Figs. 2 and 3), but also to variations in surface reflectance (Sendrowski and Wohl, 2021). To limit these confusions, a more ambitious model performed on a wide range
of fluvial systems would make it possible to further limit the confusions observed in this study.

The well detection of sand/gravel bars, water channels and vegetation areas (Table 3; Table 7) will make it possible to quantify the spatio-temporal changes of aquatic and riparian habitats and the intensity of geomorphic processes (e.g. lateral migration) with high accuracy over time along the four rivers studied.

### 5.3. Testing the ability of the model to estimate the in-stream RW shapes and volumes

The results of this study show that our model well characterizes shapes, areas, and volumes of RW after power regressions correction (Fig. 10; 11; 12; 13). Although the application of the user filter shows slightly better detection of IT than LJ, the model seems to predict the latter metrics better. For LJ, length and diameter are the two best predicted metrics. This is reflected by the RMSE and MAE values, which are relatively low, indicating good accuracy (Fig. 12). For IT, length and diameter also show low values of RMSE and MAE 10. With regard to area and volume, these two metrics, for both LJ and IT, show higher RMSEs and MAEs, but the accuracy of the predictions of these metrics is very good (Figs. 10 and 12).

As with the spatial distribution of RW, the measurement of metrics makes it possible to study the structural characteristics of the wood debris. In terms of volume of RW, the correction of the metrics highlights that the Buëch and the Meurthe as the rivers (of the four) where the largest volumes of RW were deposited the studied reaches. On the one hand, the case of the Buëch can be explained, as previously stated, by its large floods and channel slope with an equally large carrying capacity (Gautier, 1994). As for the Meurthe, with its numerous free meanders with high lateral migration, low channel width and it vegetated floodplain are explain factors of the important accumulation of in-stream RW. For the Loire or the Doubs, which despite the presence of obstacles such as bars vegetated islands or meanders, has a wider channel and therefore a greater capacity for leaching RW (Massé, 2014).

The well detection results of our model and the availability of IGN's BD Ortho® IRC at the national scale opens up the prospect of developing a tool to detect automatically in-stream wood deposition areas in medium to large rivers at the national scale allowing to perform spatio-temporal studies and helping to making-decision support for river practitioners to manage river wood in critical areas (in-stream hydraulic structures, flood risk areas, etc.).

## 6. Conclusion

This study demonstrates that an accurate automatic detection of river wood (RW) on the Meurthe River is now feasible using a machine learning model (Random Forest) trained on IGN's BD Ortho® IRC data. Furthermore, the model generalizes well across different river systems in France, successfully detecting in-stream RW deposited in three additional rivers through spatial inference. Additionally, this study highlights the potential for automated shape characterization of in-stream RW and LJ, along with the estimation of their areas and deposited volumes at the reach scale.

The RiverDetectWood tool developed in this work is fully optimized for most computers and has been parallelized to enhance processing efficiency and reproducibility. However, processing large-scale datasets remains computationally demanding. Future improvements could focus on enhancing scalability, for instance, by optimizing memory usage, leveraging cloud computing, or implementing more efficient deep learning architectures such as UNet or Vision Transformers (ViTs). Additionally, incorporating higher-resolution imagery, LiDAR data, or alternative spectral bands could further refine detection and characterization accuracy.

Ultimately, this approach represents a first step towards the development of an open-access tool to support decision-making for river managers in high-stakes areas, facilitating more efficient and data-driven river wood monitoring.

## CRediT authorship contribution statement

**Gauthier Grimmer**: Writing – review & editing, Writing – original draft, Software, Methodology, Investigation, Data curation, Conceptualization. **Romain Wenger**: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Formal analysis, Conceptualization. **Germain Forestier**: Writing – review & editing, Formal analysis. **Valentin Chardon**: Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization.

## 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.

## Acknowledgments

This work has been achieved within the framework of the *IDEX University of Strasbourg and *Investissements d'Avenir*. We also would like to thank Pierre-Alexis Herrault, Lecturer at the University of Strasbourg, for the constructive feedbacks and suggestions of this work.

## Data availability

The test set and the Python script are available on Github (https://github.com/g-grimmer/RiverDetectWood).

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