# Discrimination between schools and submerged trees in reservoirs: a preliminary approach using narrowband and broadband acoustics

Arthur Blanluet, Sven Gastauer, Franck Cattanéo, Chloé Goulon, David Grimardias, Jean Guillard

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Arthur Blanluet, Sven Gastauer, Franck Cattanéo, Chloé Goulon, David Grimardias, et al.. Discrimination between schools and submerged trees in reservoirs: a preliminary approach using narrowband and broadband acoustics. Canadian Journal of Fisheries and Aquatic Sciences, 2022, 79 (5), pp.738-748. 10.1139/cjfas-2021-0087. hal-03750344

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Discrimination between schools and submerged trees in reservoirs: A preliminary approach using narrowband and broadband acoustics

<table><tr><td>Journal:</td><td>Canadian Journal of Fisheries and Aquatic Sciences</td></tr><tr><td>Manuscript ID:</td><td>cjfas-2021-0087.R2</td></tr><tr><td>Manuscript Type:</td><td>Article</td></tr><tr><td>Date Submitted by the Author:</td><td>25-Sep-2021</td></tr><tr><td>Complete List of Authors:</td><td>Blanluet, Arthur; The University of Queensland, The Mathematical Marine Ecology Lab; INRAE, Univ. Savoie Mont Blanc, INRAE, CARRTEL, Pôle R&amp;D ECLA (ECosystèmes LAcustres) OFB - INRAE - USMB Gastauer, Sven; Thünen-Institute of Sea Fisheries; University of California San Diego Scripps Institution of Oceanography Cattanéo, Franck; University of Applied Sciences and Arts Western Switzerland, Hepia Geneva, Land, Nature, Environment Institute Goulon, Chloé; INRAE, Univ. Savoie Mont Blanc, INRAE, CARRTEL, Pôle R&amp;D ECLA (ECosystèmes LAcustres) OFB - INRAE - USMB Grimardias, David; University of Applied Sciences and Arts Western Switzerland, Hepia Geneva, Land, Nature, Environment Institute Guillard, Jean; INRAE, Univ. Savoie Mont Blanc, INRAE, CARRTEL, Pôle R&amp;D ECLA (ECosystèmes LAcustres) OFB - INRAE - USMB</td></tr><tr><td>Keyword:</td><td>Fisheries acoustics, Immersed trees, FRESHWATER &lt; Environment/Habitat, Broadband acoustic, Random forest</td></tr><tr><td>Is the invited manuscript for consideration in a Special Issue?:</td><td>Not applicable (regular submission)</td></tr></table>

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# Discrimination between schools and submerged trees in reservoirs: A preliminary approach using narrowband and broadband acoustics

Arthur Blanлюет¹,²,³* Sven Gastauer⁴,⁵ Franck Cattanéo⁶ Chloe Goulon¹,² David Grimardias⁶ Jean Guillard¹,²

¹ Univ. Savoie Mont Blanc, INRAE, CARRTEL, 74200 Thonon-les-Bains

² Pôle R&D ECLA (ECosystèmes LAcustres) OFB – INRAE – USMB

³ The University of Queensland, The Mathematical Marine Ecology Lab School of Mathematics and Physics, Level 2, Physics Annexe, The University of Queensland, Saint Lucia, Brisbane, QLD, AUS 4072

⁴ Thünen-Institute of Sea Fisheries, Bremerhaven, Germany

⁵ Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, United States

⁶ University of Applied Sciences and Arts Western Switzerland - Hepia Geneva, Land, Nature, Environment Institute, Route de Presinge 150, CH-1254, Jussy, Switzerland

<footnote>* Corresponding author: arthur.blanлюет0@gmail.com</footnote>
**Abstract**

With a growing demand for hydroelectric energy, the number of reservoirs is dramatically increasing worldwide. These new water bodies also present an opportunity for the development of fishing activities. However, these reservoirs are commonly impounded on uncut forests, resulting in many immersed trees. These trees hinder fish assessments by disrupting both gill netting and acoustic sampling. Immersed trees can easily be confused with fish schools on echograms. To overcome this issue, we developed a method to discriminate fish schools from immersed trees. A random forest algorithm was used to classify echo-traces at 120 and 200 kHz, recorded by an EK80 (SIMRAD) in narrowband (Continuous Wave) and in broadband mode (Frequency Modulated). We obtained a good discrimination rate between trees and schools, especially in broadband (90 % ratio of good classification). We demonstrate that it is possible to discriminate fish schools from immersed trees and thus facilitate the use of fisheries acoustics in reservoirs.

**keywords** Fisheries acoustics, immersed trees, freshwater, broadband acoustic, random forest
# 1 Introduction

Fisheries acoustics are now a standard method to estimate fish abundances in lakes and reservoirs and to describe their relationships with ecosystems [Rudstam et al., 2003, Parker-Stetter et al., 2009, Pollom and Rose, 2016, Draštík et al., 2017]. Fisheries acoustics can provide a non-intrusive and reliable estimate of fish biomass, size distribution and spatial distributions in a relatively short period [Rudstam et al., 2012]. Despite these advantages, fisheries acoustics remains underused in reservoirs, notably in tropical environments [Tessier et al., 2016]. Recently, the number of artificial lakes and associated dams significantly increased globally, and is expected to continue to increase in the coming decades [Zarfl et al., 2015]. This increase is mainly caused by an increase in demand for energy and as a response to the transition towards sustainable energy sources. Besides the benefits in terms of energy generation, these artificial aquatic ecosystems can present an important source of animal proteins for the local population through fishing, most notably in Asia [Tessier et al., 2016, 2020].

The widely distributed presence of immersed trees in reservoirs could heavily impair the monitoring of fish stock [Coll et al., 2007, Frouzová et al., 2008], especially in tropical areas, where the forests were generally not cut down before the flooding. Immersed trees largely limit navigation, constrain sampling to daylight hours for security reasons [Tessier et al., 2020], hinder the use of gill nets to sample fish (entanglement risks), and make acoustic sampling challenging: immersed trees appear similar to fish schools on echograms (Figure 1) and could easily be confused with them [Cardenas et al., 1988, Coll et al., 2007]. This similarity between trees and schools dramatically increase the needed time to clean and process echograms during the data processing [Coll et al., 2007, Frouzová et al., 2008]; as well as being an important source of uncertainty in stock estimate within reservoirs [Tessier et al., 2020]. Finding a way to discriminate immersed trees from fish schools on acoustic data is critical to develop acoustic in reservoir lakes and improve the fish stock assessment precision.
One of the oldest applications of fisheries acoustics is the classification of different types of “echo-traces” (coherent acoustic objects on an echogram, ICES, 2000, Fernandes, 2009), e.g., extracting fish schools from planktonic layers [Madureira et al., 1993, Jurvelius et al., 2008] or distinguishing between different fish species [Scalabrin et al., 1996, Getabu et al., 2003, Logerwell and Wilson, 2004, Horne, 2000]. Despite the technological evolution of fisheries acoustic, this classification of acoustic targets remains challenging [Korneliussen et al., 2018].

Despite the development of several echo-traces discriminant methods [Korneliussen et al., 2018], few studies have aimed to discriminate fish from trees in reservoirs [Coll and Lebourges Dhaussy, 2005, Josse and Le Squin, 2013]. These studies, based on single frequency acoustics school parameters [Reid et al., 2000], did not yield satisfactory results [Josse and Le Squin, 2013]. Josse and Le Squin [2013], concluding that the processing of at least two frequencies might improve the discrimination. Trees and fish are expected to present different scattering properties with varying frequency responses based on their differences in material proprieties (acoustic impedance) (gaseous for fish, fluid like or rigid for trees [Stanton et al., 1994]).

Additionally, the recent development of broadband echo-sounders (Frequency Modulated signal, Demer et al. [2017]) presents an opportunity to use the broadband spectrum to discriminate between different targets [Bassett et al., 2018, Blanluet et al., 2019] and to develop new broadband school parameters to classify echo-traces.

Our study investigated how to discriminate between immersed trees and schools in lakes using bi-frequency and broadband parameters. To do so, we constructed two Random Forest models [Breiman, 2001] to discriminate fish schools from trees, using the energetic parameters of echo-traces at 120 and 200 kHz center frequencies. The first model used parameters extracted from Continuous Wave
<figure><img src="image_4.png" /><figcaption>Figure 1: 120 kHz Frequency Modulated (FM) echograms of (a) schools in Lake Geneva and of (b) immersed trees in Bimont reservoir. The corrected lakebed detection is represented by a thick black line.</figcaption></figure>
(CW) acoustic data, and a second model used parameters extracted from Frequency Modulated (FM) acoustic data. The success of the CW and FM models are discussed, and the main parameters contributing to the discrimination power were identified.

Three main hypotheses were tested as discriminant factors between echo-traces: (i) Do immersed trees show a higher variance of the returned energy, expressed as the Volume backscattering strength ($S_v$, dB re 1m⁻¹, MacLennan et al., 2002), than fish schools due to the high contrast between the energetic response of the trunk compared to the branches? (ii) Do immersed trees and fish schools present different broadband acoustics spectra due to differences in their material properties [Stanton et al., 1994]? (iii) Do the amplitude Probability Density Function (PDF, i.e. the echo's energy distribution within an echo-trace) of fish schools will be closer to Rayleigh distribution [Scalabrin et al., 1996, Chu and Stanton, 2010] than trees one? To answer these questions, we sampled a series of lakes to gather echo-traces of trees and fish schools in different environments.
# 2 Materials and Methods

## 2.1 Study sites

Data were acquired at daylight (when fish are expected to be schooling, while being more dispersed at night [Girard et al., 2020]) in a selection of peri-alpine and reservoir lakes (Table 1). The peri-alpine lakes were sampled to record fish school echo-traces in zones known to be rich in fish schools [Yule et al., 2013, Anneville et al., 2017] and without any immersed trees. Reservoirs were sampled to obtain tree echo-traces in areas known to be rich in immersed trees.

<table><caption>Table 1: Lake and reservoir characteristics (area, mean depth, max depth) and sampling date.</caption><thead><tr><th>Lakes</th><th>surface (km²)</th><th>mean depth (m)</th><th>max depth (m)</th><th>Sampling date</th></tr></thead><tbody><tr><td colspan="5" style="text-align: center;">Hydropower reservoirs</td></tr><tr><td>Chancy-Pougny</td><td>0.264</td><td>6.35</td><td>9.9</td><td>2019-09-26</td></tr><tr><td>Bimont</td><td>0.73</td><td>27</td><td>55</td><td>2020-01-28</td></tr><tr><td>Génissiat</td><td>0.4</td><td>5</td><td>20</td><td>2020-03-04</td></tr><tr><td colspan="5" style="text-align: center;">Peri-alpine lakes</td></tr><tr><td>Lake Geneva</td><td>580</td><td>154</td><td>310</td><td>2019-10-11 & 2020-06-24</td></tr><tr><td>Lake Bourget</td><td>44.5</td><td>85</td><td>145</td><td>2019-12-04</td></tr></tbody></table>

The fish school data were acquired in two French peri-alpine lakes, Lakes Geneva and Bourget, regularly monitored in the Observatory of LAkes (OLA, http://www6.inra.fr/soere-ola ©OLA-IS, AnaEE-France, INRAE Thonon-les-Bains, developed by Eco-Informatics ORE INRAE Team), with well known fish population [Rimet et al., 2020]. The fish schools in the alpine lakes are dominated by juvenile perch (*Perca fluviatilis*) with the occasional roach (*Rutilus rutilus*) [Guillard et al., 2006, 2011]. No specific biological sampling has been carried out within the present study, but being part of a long term observatory project (OLA; Rimet et al., 2020), these ecosystems are well studied and regularly assessed.

Due to their abundance and our knowledge of these lakes' fish populations, we assumed that the observed fish schools were mainly composed of young-of-the-
year (YoY) perch. The perch sampled in October in Lake Geneva and in December in Lake Bourget have reached their asymptotic size [Guillard et al., 2006], around a mean total length of 95 and 85 mm, respectively for Lake Geneva and Bourget [Frantz et al., 2018]. In the same way, the June fish school acquisitions in Lake Geneva were assumed to be mainly composed of YoY perch, smaller in this period of the year [Guillard et al., 2006], with an expected mean total length of 40 mm.

The immersed trees were sampled in two run-of-river hydropower reservoirs on the Rhône River: the Chancy-Pougny and the Génissiat Reservoirs, and on the Cause River: the Bimont Reservoir. The position and the condition of the immersed trees on the Rhône River reservoirs are known from other surveys [Grimardias et al., 2017, Cattanéo et al., 2021] and have been inspected visually for the presence of branches reaching the surface.

The trees in the studied reservoirs on the Rhône River resulted from recent landslides, presenting a great variety of orientation, condition and species (mainly ash, oak, poplar and willow). The targeted acoustic sampling was performed on trees visible from the surface, on immobile echo-traces with a high backscattering. Only a few fish species, mainly young Cyprinids, are known to school in the Rhône Reservoirs [Grimardias et al., 2017]. As the position of the trees were spotted by the landslide mark and by the emerging branches, we assumed that a misidentification of a fish school as a tree's echo-traces was unlikely.

In the Bimont Reservoir, the position and the condition of the immersed trees were identified from a 2018 satellite photo of the empty lake (T. Tormos, personal communication). The reservoir was flooded in 1952, and the immersed pines are old and stand upright, with few branches. As the Bimont Reservoir was emptied from 2017 to May 2019 for dam renovations, very few fish remained inside. Therefore, based on similar clues related to the Rhône, it was assumed that a misidentification of trees and fish schools echo-traces was unlikely.
## 2.2 Data acquisition

The acoustic recordings were performed from small boats equipped with a SIM-RAD (Simrad Kongsberg Maritime AS, Horten, Norway) EK80 WBT-Mini and two multiplexed split-beam transducers (ES120-7C and ES200-7CD) mounted on a pole fixed on the boat side, oriented vertically down and kept at a depth of 0.5 m below the waterline. We will refer to the ensemble WBT-Mini/ES120-7C as the “120 kHz channel” and the ensemble WBT-Mini/ES200-7CD as the “200 kHz channel”. The two channels were set to ping sequentially at 10 Hz, with an acquisition threshold of -80 dB. The 3-dB beamwidth, pulse length, transmitting power and bandwidth limits for the CW of FM pulses are presented in Table 2. The boat speed varied between 0.5 and 5 m.s⁻¹, due to navigational constraints. At each sampling site, the first round of acquisitions was carried out in CW mode, followed by a second acquisition in FM mode covering roughly the same area. Standard sphere calibrations using a 22 mm and a 38.1 mm tungsten carbide (WC) with 6% cobalt binder spheres, according to the standard protocol of Demer et al. [2015] and the manufacturer’s manual, were performed in November 2019 in Lake Geneva and in March 2020 in Lake Annecy.

<table><caption>Table 2: Echosounders settings.</caption><thead><tr><th>Transducer</th><th>ES120-7C</th><th>ES120-7C</th><th>ES200-7CD</th><th>ES200-7CD</th></tr></thead><tbody><tr><td>Signal</td><td>CW</td><td>FM</td><td>CW</td><td>FM</td></tr><tr><td>Ramping<sup>a</sup></td><td>NA</td><td>Fast</td><td>NA</td><td>Fast</td></tr><tr><td>Frequency range (kHz)</td><td>120</td><td>95-150</td><td>200</td><td>170-260</td></tr><tr><td>Beamwidth (°)</td><td>7</td><td>8.8° at 95 kHz,<br>5.6° at 150 kHz<sup>c</sup></td><td>7</td><td>8.3° at 170 kHz,<br>5.4° at 260 kHz<sup>c</sup></td></tr><tr><td>Pulse duration (ms)</td><td>0.256<sup>b</sup></td><td>1.024<sup>c</sup></td><td>0.256<sup>b</sup></td><td>1.024<sup>c</sup></td></tr><tr><td>Transmit power (W)</td><td>75</td><td>75</td><td>75</td><td>75</td></tr><tr><td>Sphere diameter (mm)<sup>d</sup></td><td>22</td><td>22</td><td>38.1</td><td>22</td></tr></tbody></table>

<footnote>a see Demer et al. [2017] for the ramping definition</footnote>

<footnote>b Godlewska et al. [2011]</footnote>

<footnote>c Demer et al. [2017]</footnote>

<footnote>d the 22-mm sphere was used for the FM calibrations and the 120-kHz CW mode as the nulls in the spectrum were less constraining than the 38.1-mm sphere for the FM calibration. The 38.1-mm sphere was used for the 200-kHz CW mode because of the null in the spectrum at 200 kHz with the 22-mm sphere.</footnote>
## 2.3 Data post-processing

After the conversion of the acoustic data from the “.RAW” format to the “.HAC” format [ICES, 2005] using the HERMES software [Trenkel et al., 2009, Doray et al., 2018], acoustic data were pre-processed through the MOVIES3D software [Trenkel et al., 2009]. A Fast Fourier Transform (FFT) [Demer et al., 2017] with a 5-kHz window size was applied to the FM data (resulting in an effective temporal resolution of 0.15 m). This spectral resolution in FM mode was chosen as a trade-off between a stable broadband spectrum and a good spatial resolution [Blanluet et al., 2019]. Before working at the echo-trace scale, the acoustic data was echo-integrated (EI) into 0.5 m of depth by two horizontal ping bins, in FM and CW mode. This echo-integration aimed to compensate for the small shift between the two channels due to the sequential pinging and to reduce the backscattering variability while keeping a good resolution. Lakebed detections were obtained automatically and corrected manually to improve the separation between trees or schools and the lake bed (black lines in Figure 1).

## 2.4 Echo-traces extraction

Echo-traces were defined as structures observed within 12 or more contiguous EI cells above an $S_v$ threshold of -62 dB on the 120 kHz and 200 kHz channels. These thresholds were chosen to better distinguish the echo-traces from the background (Figure 2) and were based on our empirical knowledge of the expected trees [Coll et al., 2007] and fish schools [Guillard et al., 2006, Grimardias et al., 2017] backscattering properties. Each identified echo-trace was labeled manually as immersed trees or fish schools in their corresponding data set.

## 2.5 Data analysis

According to Josse and Le Squin [2013], morphological and positional parameters were not sufficiently informative to discriminate trees from schools. Thus, based on fish school parameters available [Reid et al., 2000, Gastauer et al., 2017], we
<figure><img src="image_5.png" /><figcaption>Figure 2: Echo-integration and echo-traces extraction of a Lake Bourget FM dataset subset. (a): 120 and 200 kHz frequencies MOVIES3D echograms with -62 dB threshold; (b): 120 and 200 kHz frequencies echo-integrated echograms (0.5 m height and 2 pings width cells); (c): echo-traces extraction from the background.</figcaption></figure>
selected a set of six (CW mode) or seven (FM mode) schools energetic parame-
ters [Reid et al., 2000], for both frequencies 120 and 200 kHz.

We classed these energetic parameters in three categories, that each cor-
responding at one of the tested hypotheses. The mean backscattering volume
(120/200 kHz mean S<sub>v</sub>, dB re 1m<sup>-1</sup>), the difference between maximum and min-
imum backscattering volume (120/200 kHz max S<sub>v</sub> - min S<sub>v</sub>, dB re 1m<sup>-1</sup>) and
the backscattering volume standard deviation (120/200 kHz S<sub>v</sub> standard devia-
tion, dB re 1m<sup>-1</sup>), were selected to investigate the variance difference hypothe-
sis between tree and fish school echo-traces. The mean difference between 120
kHz and 200 kHz backscattering volume (ΔMVBS, dB re 1m<sup>-1</sup>) and the median
frequency spectrum slope parameters (120/200 kHz median frequency spectrum
slope, s<sub>m120kHz</sub> and s<sub>m200kHz</sub>, calculated in FM mode only) were selected to in-
vestigate the difference in frequency response between tree and fish school echo-
traces. The skewness, an indicator of the S<sub>v</sub> distribution symmetry [Lawson, 2001,
Gastauer et al., 2017] (120/200 kHz S<sub>v</sub> skewness), and the kurtosis, an indicator
of the S<sub>v</sub> distribution tail length [Lawson, 2001] (120/200 kHz S<sub>v</sub> kurtosis), were
selected to investigate if the amplitude PDF (Probability Density Function) of fish
school and tree echo-traces could be differentiated. As the selected parameters
were designed for CW mode, we calculated them in FM mode on an average in nat-
ural scale over the 120 or 200 kHz bandwidth [Blanluet et al., 2019]. The median
frequency spectrum slope parameters (120/200 kHz median frequency spectrum
slope, s<sub>m120kHz</sub> and s<sub>m200kHz</sub>) were calculated in FM mode only, through a linear
regression on the echo-traces EI cells median frequency spectrum at 120 and 200
kHz.

## 2.6 Statistical analyses

Various statistical classification methods have been used to discriminate between
echo-traces [Korneliussen et al., 2018], as classification trees [Fernandes et al.,
2006, Fernandes, 2009, Escobar-Flores et al., 2019] or random forest D'Elia et al.
[2014], Fallon et al. [2016], Proud et al. [2020]. The use of ensemble tree methods
(repetition of regression/classification tree method), as Random Forest or boosted trees [Breiman, 2001], obtains high classification rates on fisheries acoustics data and are increasingly used [Fernandes, 2009, D'Elia et al., 2014, Fallon et al., 2016, Proud et al., 2020], notably because this method is recognized for its ability to deal with small sample sizes and high-dimensional feature spaces [Cutler et al., 2007, Biau and Scornet, 2016].

Random Forest (RF) models [Breiman, 2001, Cutler et al., 2007] were used to build the classification algorithms. A "forest" of classification/regression trees, each using a random (training) subset of the available parameters to classify the dataset, was built. The resulting classifier used the mean values of this forest of decision trees to perform a class prediction on the test data subset, composed of the data not contained within the training subset.

A separate RF model was built for both CW and FM data. Following the standard RF protocol [Breiman, 2001], the respective datasets were randomly split into a training dataset (75% of the echo-traces) and a test dataset (25% of the echo-traces). The R [R Core Team, 2020] packages "caret" and "ranger" [Wright and Ziegler, 2017, Kuhn, 2019] were used to build the RF models. Two tuning parameters were set for each RF: the number of parameters selected randomly was at each tree node (mtry), and the number of trees to build (ntrees). These tuning parameters were set independently for each RF model by cross-validation, using the accuracy metric (% of good classification) [Breiman, 2001]. A multiple (three times) tenfold cross-validation [Stone, 1974, Breiman, 2001, Proud et al., 2020] has been performed after selection of the tuning parameters to assess the models training accuracy and kappa statistic (proportion of the classification results beyond the results expected to obtain by chance alone, Biau and Scornet, 2016). The contribution of each parameter to the models' powers was evaluated by the "Mean Decrease Impurity" value [Biau and Scornet, 2016]. This indicator estimates the total (averaged over all the trees) decrease in impurity obtained by splitting the respective tree on the given variable.
# 3 Results

## 3.1 Echo-traces extraction

A total of 104 echo-traces were isolated in CW (43 trees and 61 schools) and 117 echo-traces in FM (53 trees and 64 schools). Details of the number of echo-traces by lakes are presented in the Table 3.

<table><caption>Table 3: Number of echo-traces by lake and by acquisition mode (Continuous Wave (CW) and Frequency Modulated (FM))</caption><thead><tr><th></th><th>CW echo-traces</th><th>FM echo-traces</th></tr></thead><tbody><tr><td>Hydropower reservoirs (trees)</td><td></td><td></td></tr><tr><td>Chancy-Pougny</td><td>16</td><td>16</td></tr><tr><td>Bimont</td><td>19</td><td>23</td></tr><tr><td>Génissiat</td><td>8</td><td>14</td></tr><tr><td>Peri-alpine lakes (schools)</td><td></td><td></td></tr><tr><td>Lake Geneva (September)</td><td>15</td><td>5</td></tr><tr><td>Lake Bourget</td><td>33</td><td>45</td></tr><tr><td>Lake Geneva (June)</td><td>13</td><td>14</td></tr></tbody></table>

## 3.2 Parameter distributions

The selected parameter boxplots of echo-traces for each lake are presented in Figures 3 and 4 for the CW and FM modes, respectively. The mean depth (a) and the echo-trace height (b) are also presented in the Figures 3 and 4 in an indicative manner. All parameters present a high variability from one lake to another lake, even for the same echo-trace types (immersed trees or fish schools).

In CW mode, no clear tendency can be shown, and thus no single parameter was able to discriminate directly between trees and schools. Mean dB differences at 200 and 120 kHz were low for school echo-traces ($\Delta MVBS_{school}$: 0.63 dB, with a 2.5 dB s.d., at the exception of the September Geneva's school echo-traces, with a $\Delta MVBS$ of -4.1 dB, s.d.: 0.8 dB); when dB differences were on average greater for tree echo-traces ($\Delta MVBS_{tree}$: -3.6 dB with a 1.7 dB s.d.), Figure 3 (k). Note that these values are expressed in dB, a logarithmic scale. 0.6 dB equates
<figure><img src="image_6.png" /><figcaption>Figure 3: Each echo-traces parameter boxplots by lake in CW mode. Boxes show the median and interquartile ranges; whiskers show the maximum and minimum values (excluding outliers); points represent outliers; diamonds represent means.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 4: Each echo-traces parameter boxplots by lake in FM mode. Boxes show the median and interquartile ranges; whiskers show the maximum and minimum values (excluding outliers); points represent outliers; diamonds represent means.</figcaption></figure>
approximately 1.1 times the backscatter volume in a linear scale and -3.6 equates to 2.3 times less acoustic energy scattered at the given frequency.

The Bimont reservoir tree echo-trace parameters generally yielded results closer to the fish school echo-trace parameters (Figure 3 (a) to (f)). In FM mode, the Bimont reservoir tree echo-trace parameters were also generally closer to fish schools echo-trace parameters (Figure 4, (a) to (f)). The $\Delta MVBS$ parameter revealed less separating power than in CW mode ($\Delta MVBS_{school}$: -4.8 dB and $\Delta MVBS_{tree}$: -4.3 dB, Figure 4 (m)).

The extracted broadband spectra for each FM echo-traces were normalized by Z-score and were presented in Figure 5. No obvious discriminant features could be extracted from the spectral noise for trees or schools. At 120 kHz, the median spectrum for trees presented a slightly decreasing slope ($s_{m120kHz}$ schools : -0.075, s.d. 0.076), while this slope was slightly flatter for schools ($s_{m120kHz}$ schools : 0.022, s.d. 0.048).

## 3.3 Random forest models

For the CW model as for the FM one, the default values of mtry (4) and ntrees (500) gave the best model fit, evaluated by model accuracy. The other mtry and ntrees parameter values tested (mtry: 2–6 and ntrees: 200–2500) showed no improvement in accuracy (Figure 6).

The random forest model tenfold cross-validation for the CW and FM model resulted in accuracy values of 0.86 and 0.87 with kappa values of 0.73 and 0.74, respectively.
<figure><img src="image_8.png" /><figcaption>Figure 5: Echo-traces (schools in (a) and trees in (b)) normalized $S_v(f)$ median spectrum in FM mode at 120 kHz (left) and 200 kHz (right). The mean spectrum for each lake are represented by the thicker line of the corresponding color.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure 6: Model accuracy of the random forest in function of the number of trees (ntrees) and the number of parameters selected at each tree nodes (mtry), for the CW modes (a) and the FM mode (b).</figcaption></figure>
## 3.4 Random forest predictions

The application of the CW RF model on the CW test dataset gave a 65.38 % classification ratio, while the application of the FM RF model on the FM test dataset gave a 90 % classification ratio.

The prediction confusion matrices of the two models are presented in Table 4. The two models presented an acceptable prediction error for the schools (respectively 100% for CW and 77% for FM), the CW model displayed a poor prediction for trees (respectively 55% for CW and 95% for FM).

<table><caption>Table 4: Results of the confusion matrix of the random forest predictive models in CW and FM mode on the test data sets.</caption><thead><tr><th rowspan="2"></th><th colspan="2">CW</th><th colspan="2">FM</th></tr><tr><th>Predicted tree</th><th>Predicted school</th><th>Predicted tree</th><th>Predicted school</th></tr></thead><tbody><tr><td>Labeled tree</td><td>11 (55%)</td><td>9 (45%)</td><td>20 (95%)</td><td>1 (5%)</td></tr><tr><td>Labeled school</td><td>0 (0%)</td><td>6 (100%)</td><td>2 (22%)</td><td>7 (78%)</td></tr></tbody></table>

## 3.5 Parameters importance

The evaluation of the importance [Breiman, 2001, Biau and Scornet, 2016] of each school parameter (Figure 7) revealed a different importance pattern for the FM and the CW model. $\Delta MVBS$ is the clear key parameter (100% relative variable importance) in the CW model, with a small contribution attributed to the other parameters. In FM, key variables were the $s_{m120kHz}$ and the $\Delta MVBS$ parameters (82% and 100% relative variable importance, respectively). Four other parameters contributed over 50%: the 120 kHz skewness (53%), the $s_{m200kHz}$ (52.7%), and the 120/200 kHz $S_v$ standard deviation (respectively 51% for the 120 kHz and 48% for the 200 kHz).
<figure><img src="image_10.png" /><figcaption>Figure 7: Relative variable importance (conditional variable importance normalized between 0 and 1) of the echo-traces parameters used to build the CW (red) and FM (blue) Random Forest models.</figcaption></figure>
# 4 Discussion

This paper aimed to test a method to select parameters to discriminate echo-traces of trees and fish schools in reservoir lakes, using random forest models. These echo-traces were obtained by standard echosounders operated in FM and CW modes, with center frequencies at 120 and 200 kHz. Such a discrimination is not possible using a single frequency sounder [Josse and Le Squin, 2013], as demonstrated in previous studies. We tested two approaches using CW and FM sound datasets.

The CW model's classification result was not sufficient (65.38 %) to efficiently discriminate trees from schools, but the FM model displayed a good classification ratio on the test dataset (90 %). These results demonstrate, for the first time at the best of our knowledge, the possibility to discriminate trees from fish schools in reservoir lakes using broadband acoustics.

The results highlight the benefits of the new parameters added by using broadband acoustics. It shows that the choice of parameters was critical to obtain good classification results. Acoustic parameters are classed in three categories, morphological, positional and energetic Reid et al. [2000]. Following the conclusion of Josse and Le Squin [2013], the morphological and positional parameters were not considered enough informative to discriminate trees from schools and to be included in our analysis. Due to the high diversity of shapes and positions of trees and schools, these parameters are likely to be difficult to generalize [Horne, 2000, Coll and Lebourges Dhaussy, 2005]. However, the distance from the lake bottom to echo-traces could be seen as a good parameter, as it is expected that trees are associated with the lake bottom. Nevertheless, schools could also be regularly associated with the lake bottom and immersed trees regularly show no connection with the bottom due to horizontal branches, and/or when the tree center is not in the beam [Coll and Lebourges Dhaussy, 2005].
Therefore we only considered energetic parameters in this work, selected to test our three hypotheses on how to discriminate trees from fish schools. The mean $S_v$, $S_v$ standard deviation and min $S_v$ - max $S_v$ were selected on the hypothesis that variance of the backscattering echoes were higher in trees. However, even if trees present a higher $S_v$ variance than schools (Figure 4 (e) and (f)) and that the 120 and 200 kHz $S_v$ standard deviation parameters substantially participate to the FM model (respectively 51 and 48%), these parameters are not discriminant by their own. The skewness and kurtosis parameters were selected on the hypothesis that the PDFs of fish schools will be closer to a Rayleigh function [Scalabrin et al., 1996, Chu and Stanton, 2010] than trees. The 120 kHz skewness participates substantially to the FM model (53%), but like the standard deviation parameters, it is not discriminant by their own.

The high importance of the Δ*MVBS* for the CW and the FM model, and the *s*<sub>m120kHz</sub> parameter for the FM model, confirm that frequency spectrum differences between trees and fish schools are the key parameters to discriminate trees from schools in our models. Even if the slope difference between trees' and schools' 120 kHz median spectrum is small (around 0.05), it is the most important parameter in the FM random forest models. These differences in the slope between tree and school 120 kHz spectrum is probably due to the non-random distribution of echoes from trees resulting from the material properties and the complex shape of the trees (L. Berger, personal communication): the dense trunk will result in a higher backscattering amplitude, when the surrounding smaller and less dense branches, will result in less pronounced backscatter. Furthermore, trees are motionless from one ping to another, compared to moving schools with a random distribution of echoes from one ping to another [Scalabrin et al., 1996]. In broadband, higher frequencies present a smaller beam than the lower frequencies for a given band [Demer et al., 2017], implying that the probability of missing the center of the tree will be higher for the higher part of the band. The detected backscattering differences detected between trees and fish schools seem, at these frequencies, likely not primarily driven by their respective material properties and
acoustic impedance, as first hypothesized, but by their spatial organization (the distribution of the elementary particles of each object in the water column).

Most considered parameters significantly contributed to the power of the FM model. This fact strengthens the relevance of ensemble tree methods, such as random forest, to discriminate echo-traces. Confirming observations by D'Elia et al. [2014] and Proud et al. [2020], identifying different fish species groups.

Immobile trees are expected to show a coherent phase response, while mobile fish schools are expected to show an incoherent phase response (L. Berger, personal communication). If the spectrum slope was a way to highlight the spatial organization discriminant characteristic, phase responses [?] could be, in a future work, an interesting way to discriminate trees from schools, in FM as well in CW mode.

Simultaneous use of side-scan sonars [Bollinger and Kline, 2017] with echo-sounders could be another interesting way to discriminate trees from schools. Portable recreational side-scans could probably deliver enough resolution to highlight the details of the tree branch structure and identify trees. Thus, side-scan sonars could be used as “decision support tools” [Kaeser and Litts, 2010] to remove trees from the acoustic dataset during the data cleaning step before a stock assessment. However, the recorded volume being different, the user will have to manually set the echo-traces, which can be time-consuming.

We observed high inter-lake variability in the echotrace parameters (Figures 3 and 4). Building a reference dataset with a variety of different lakes (with different fish and tree species) will be necessary steps to ensure the efficiency of the model in a variety of conditions. Additionally, this method could be tested in tropical reservoir lakes that present immersed trees and fish schools simultaneously, as Manantali (Mali, Coll et al., 2007) or Nam Theun 2 (Lao PDR, Tessier et al., 2020).
Horizontally-oriented echosounders are frequently used in reservoirs, as these lakes are often shallow and/or the fish are commonly present near the surface [Kubecka and Wittingerova, 1998, Goulon et al., 2018]. Our method could potentially be adapted to horizontal pinging, but this type of survey mainly use single echo detection of individual fish to assess fish stock, and not fish school echos [Kubecka and Wittingerova, 1998, Knudsen and Sægrov, 2002]. In addition, we will need to take account of the high directivity of fish acoustic backscattering in horizontal acquisition [Tušer et al., 2009].

In lakes, 120 and 200 kHz are the ones of the main commonly used frequencies [Draštík et al., 2017] and are convenient due to the small size of the transducer. However, using a lower frequency (as the 70 kHz) could potentially help to obtain a better discrimination power as the small branches will possibly be in the Rayleigh scattering regime [Simmonds and MacLennan, 2005]. However, the larger size of low frequency transducers (18/38 kHz) [Mouget et al., 2019] could hinder their use in lakes or in reservoirs, where small craft are more commonly used [Tessier et al., 2016].

The development of an automatic method to discriminate submerged trees and fish schools, notably using broadband acoustics, is a new subject explored in this article. We showed that it is possible to obtain an improved classification rate when using broadband acoustics. Larger datasets, including tropical reservoirs, are necessary to obtain a robust discrimination tool that could be used routinely. In this way, hydroacoustics, a non-intrusive tool already used in numerous lakes can be used to monitor fish populations in reservoirs with more confidence.
Acknowledgements

The authors want to thank the IRD staff from the UMR LEMAR and especially
Jeremie Habasque for the lending of an ES200-7CD transducer, as well as Tonny
Algrøy from SIMRAD Kongsberg for the lending of the corresponding license for
WBT. We additionally want to thank Julien Dublon and Thierry Tormos for their
knowledge of the Bimont Reservoir and their help during the field survey, as well as
Laurent Berger for his help concerning the use of MOVIES3D software on broad-
band data. Finally, we gratefully thank Jean-Christophe Hustache for his priceless
help in the sampling preparation and the fieldwork, without whom nothing would
have been possible. This work was supported by Pole ECLA (OFB - INRAE -
USMB) and had support from AnaEE-France and Observatory of LAkes (OLA),
boat and technical facilities. This work is associated to Bi-O-Rhône project, sup-
ported and co-financed by the FEDER fund (Fonds Européens de Développement
Régional) Interreg France-Switzerland.
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