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Fish stock monitoring is an important element for the sustainable management of inland water resources. A scarcity of data and the lack of systematic monitoring for Lake Trichonis precludes an up-to-date assessment. To assess the current status of pelagic fish stock, a hydroacousting survey was conducted for the first time in Lake Trichonis, Greece. In October 2019, the lake was acoustically surveyed with two, horizontally and vertically mounted, 120 kHz transducers during day and night. A decreasing gradient in pelagic fish density from the western to the eastern shores of the lake was observed. Fish density was significantly higher in the intermediate layers of the water column, in the eastern region, compared to the western region. The lake appears to host primarily communities of small-sized fish (TL: 0–5 cm), whereas larger fish (TL: 5–50 cm) are a small minority of the total fish stock. The overall average estimated fish length was approximately 2.4 cm. The adoption of routine inland fish stock monitoring through hydroacoustic methods could be a promising step in the effort to improve the understanding of unique inland water ecosystems with minimum impact on endemic species, as well as to mitigate human impact and achieve long-term sustainable management.

In this study, a remote sensing-based method of mapping and predicting fish spatial distribution in inland waters is developed. A combination of Earth Observation data, in-situ measurements, and hydroacoustics is used to relate fish biomass distribution and water-quality parameters along the longitudinal transect of theŘímov Reservoir (Czech Republic) using statistical and machine learning techniques. Parameter variations and biomass distribution are estimated and validated, and apparent trends are explored and discussed, together with potential limitations and weaknesses. Water-quality parameters exhibit longitudinal gradients along the reservoir, while calculations reveal a distinct fish assemblage pattern observed as a patchy overall biomass distribution. Although the proposed methodology has a great potential for sustainable water management, careful planning is needed to ensure the simultaneous acquisition of remote sensing and in-situ data to maximize calibration accuracy.

DIDSON acoustic cameras provide a way to collect temporally dense, high-resolution imaging data, similar to videos. Detection of fish targets on those videos takes place in a manual or semi-automated manner, typically assisted by specialised software. Exploiting the visual nature of the recordings, tools and techniques from the field of computer vision can be applied in order to facilitate the relatively involved workflows. Furthermore, machine learning techniques can be used to minimise user intervention and optimise for specific detection and tracking scenarios. This study explored the feasibility of combining optical flow with a genetic algorithm, with the aim of automating motion detection and optimising target-to-background segmentation (masking) under custom criteria, expressed in terms of the result. A 1000-frame video sequence sample with sparse, smoothly moving targets, reconstructed from a 125 s DIDSON recording, was analysed under two distinct scenarios, and an elementary detection method was used to assess and compare the resulting foreground (target) masks. The results indicate a high sensitivity to motion, as well as to the visual characteristics of targets, with the resulting foreground masks generally capturing fish targets on the majority of frames, potentially with small gaps of undetected targets, lasting for no more than a few frames. Despite the high computational overhead, implementation refinements could increase computational feasibility, while an extension of the algorithms, in order to include the steps of target detection and tracking, could further improve automation and potentially provide an efficient tool for the automated preliminary assessment of voluminous DIDSON data recordings.

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