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

Publication: Canadian Journal of Fisheries and Aquatic Sciences
13 October 2021

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.

Résumé

Étant donné la demande croissante d’hydroélectricité, le nombre de réservoirs connaît une augmentation considérable partout sur terre. Ces nouveaux plans d’eau offrent la possibilité de développer de nouvelles activités de pêche. Cependant, ces réservoirs sont souvent aménagés dans des forêts non coupées, entraînant l’immersion de nombreux arbres. Ces arbres entravent les évaluations des stocks de poissons en compliquant l’échantillonnage acoustique et au filet maillant. Les arbres submergés peuvent facilement passer pour des bancs des poissons sur les échogrammes. Pour surmonter cette difficulté, nous avons mis au point une méthode permettant de distinguer les bancs de poissons des arbres submergés. Un algorithme de forêt aléatoire a été utilisé pour classer les écho-traces sur les échogrammes à 120 kHz et 200 kHz, enregistrées par un sondeur EK80 (SIMRAD) en modes à bande étroite (onde continue) et à large bande (modulé par la fréquence). Nous avons obtenu un bon taux de discrimination des arbres et des bancs, particulièrement en mode bande large (90 % de classements justes). Nous démontrons qu’il est possible de distinguer les bancs de poissons des arbres submergés et ainsi faciliter l’utilisation de relevés acoustiques des poissons dans les réservoirs. [Traduit par la Rédaction]

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cover image Canadian Journal of Fisheries and Aquatic Sciences
Canadian Journal of Fisheries and Aquatic Sciences
Volume 79Number 5May 2022
Pages: 738 - 748

History

Received: 12 April 2021
Accepted: 28 September 2021
Accepted manuscript online: 13 October 2021
Version of record online: 13 October 2021

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Arthur Blanluet [email protected]
Univ. Savoie Mont Blanc, INRAE, CARRTEL, 74200 Thonon-les-Bains, France.
Pôle R&D ECosystèmes LAcustres (ECLA) OFB–INRAE–USMB, 74200 Thonon-les-Bains, France.
The Mathematical Marine Ecology Lab School of Mathematics and Physics, Level 2, Physics Annexe, The University of Queensland, Saint Lucia, Brisbane, QLD, AUS 4072.
Sven Gastauer
Thünen-Institute of Sea Fisheries, 27572 Bremerhaven, Germany.
Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093, USA.
Franck Cattanéo
Hepia Geneva, Land, Nature, Environment Institute, University of Applied Sciences and Arts Western Switzerland, Route de Presinge 150, CH-1254, Jussy, Switzerland.
Chloé Goulon
Univ. Savoie Mont Blanc, INRAE, CARRTEL, 74200 Thonon-les-Bains, France.
Pôle R&D ECosystèmes LAcustres (ECLA) OFB–INRAE–USMB, 74200 Thonon-les-Bains, France.
David Grimardias
Hepia Geneva, Land, Nature, Environment Institute, University of Applied Sciences and Arts Western Switzerland, Route de Presinge 150, CH-1254, Jussy, Switzerland.
Jean Guillard
Univ. Savoie Mont Blanc, INRAE, CARRTEL, 74200 Thonon-les-Bains, France.
Pôle R&D ECosystèmes LAcustres (ECLA) OFB–INRAE–USMB, 74200 Thonon-les-Bains, France.

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