Jenna Lawson, Andre Farinha, Luca Romanello, Oscar Pang, Raphael Zufferey, Mirko Kovac
{"title":"利用无人驾驶航空水上飞行器在淡水生态系统中进行声学传感","authors":"Jenna Lawson, Andre Farinha, Luca Romanello, Oscar Pang, Raphael Zufferey, Mirko Kovac","doi":"10.1002/rse2.373","DOIUrl":null,"url":null,"abstract":"Freshwater ecosystems are endangered, underfunded and understudied, making new methods such as passive acoustic monitoring (PAM) essential for improving the efficiency and effectiveness of data collection. However, many challenges are still to be addressed with PAM: difficulty in accessing research sites, the logistics of implementing large-scale studies and the invasiveness of data collection. When combined with PAM and other sensing strategies, mobile robotics are a promising solution to directly address these challenges. In this paper, we integrate water surface and underwater acoustic monitoring equipment onto a prototype unmanned aerial-aquatic vehicle (UAAV) capable of sailing and flight (SailMAV). Twelve autonomous sailing missions were run on Lake Vrana, Croatia, during which acoustic data were collected, and the ability of the UAAV to facilitate the collection of acoustic data demonstrated. Data were simultaneously collected using standard recording methods on buoys and banksides to provide a comparative approach. Acoustic indices were used to analyse the soundscape of underwater acoustic data and BirdNET (a deep artificial neural network) was used on water surface datasets to determine bird species composition. Results show higher species richness and call abundance from UAAV surveys and high site dissimilarity owing to turnover between stationary and UAAV methods. This highlights the success of the UAAV in detecting biodiversity and the complementarity of these methods in providing a broad picture of the biodiversity of freshwater ecosystems. Increased bird diversity and underwater acoustic activity in protected areas demonstrate the benefits of protecting freshwater ecosystems; however, site dissimilarity driven by turnover highlights the importance of protecting the entire ecosystem. We show how, by integrating PAM and a UAAV, we can overcome some of the current challenges in freshwater biodiversity monitoring, improving accessibility, increasing spatial scale and coverage, and reducing invasiveness.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"70 1","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Use of an unmanned aerial-aquatic vehicle for acoustic sensing in freshwater ecosystems\",\"authors\":\"Jenna Lawson, Andre Farinha, Luca Romanello, Oscar Pang, Raphael Zufferey, Mirko Kovac\",\"doi\":\"10.1002/rse2.373\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Freshwater ecosystems are endangered, underfunded and understudied, making new methods such as passive acoustic monitoring (PAM) essential for improving the efficiency and effectiveness of data collection. However, many challenges are still to be addressed with PAM: difficulty in accessing research sites, the logistics of implementing large-scale studies and the invasiveness of data collection. When combined with PAM and other sensing strategies, mobile robotics are a promising solution to directly address these challenges. In this paper, we integrate water surface and underwater acoustic monitoring equipment onto a prototype unmanned aerial-aquatic vehicle (UAAV) capable of sailing and flight (SailMAV). Twelve autonomous sailing missions were run on Lake Vrana, Croatia, during which acoustic data were collected, and the ability of the UAAV to facilitate the collection of acoustic data demonstrated. Data were simultaneously collected using standard recording methods on buoys and banksides to provide a comparative approach. Acoustic indices were used to analyse the soundscape of underwater acoustic data and BirdNET (a deep artificial neural network) was used on water surface datasets to determine bird species composition. Results show higher species richness and call abundance from UAAV surveys and high site dissimilarity owing to turnover between stationary and UAAV methods. This highlights the success of the UAAV in detecting biodiversity and the complementarity of these methods in providing a broad picture of the biodiversity of freshwater ecosystems. Increased bird diversity and underwater acoustic activity in protected areas demonstrate the benefits of protecting freshwater ecosystems; however, site dissimilarity driven by turnover highlights the importance of protecting the entire ecosystem. 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Use of an unmanned aerial-aquatic vehicle for acoustic sensing in freshwater ecosystems
Freshwater ecosystems are endangered, underfunded and understudied, making new methods such as passive acoustic monitoring (PAM) essential for improving the efficiency and effectiveness of data collection. However, many challenges are still to be addressed with PAM: difficulty in accessing research sites, the logistics of implementing large-scale studies and the invasiveness of data collection. When combined with PAM and other sensing strategies, mobile robotics are a promising solution to directly address these challenges. In this paper, we integrate water surface and underwater acoustic monitoring equipment onto a prototype unmanned aerial-aquatic vehicle (UAAV) capable of sailing and flight (SailMAV). Twelve autonomous sailing missions were run on Lake Vrana, Croatia, during which acoustic data were collected, and the ability of the UAAV to facilitate the collection of acoustic data demonstrated. Data were simultaneously collected using standard recording methods on buoys and banksides to provide a comparative approach. Acoustic indices were used to analyse the soundscape of underwater acoustic data and BirdNET (a deep artificial neural network) was used on water surface datasets to determine bird species composition. Results show higher species richness and call abundance from UAAV surveys and high site dissimilarity owing to turnover between stationary and UAAV methods. This highlights the success of the UAAV in detecting biodiversity and the complementarity of these methods in providing a broad picture of the biodiversity of freshwater ecosystems. Increased bird diversity and underwater acoustic activity in protected areas demonstrate the benefits of protecting freshwater ecosystems; however, site dissimilarity driven by turnover highlights the importance of protecting the entire ecosystem. We show how, by integrating PAM and a UAAV, we can overcome some of the current challenges in freshwater biodiversity monitoring, improving accessibility, increasing spatial scale and coverage, and reducing invasiveness.
期刊介绍:
emote Sensing in Ecology and Conservation provides a forum for rapid, peer-reviewed publication of novel, multidisciplinary research at the interface between remote sensing science and ecology and conservation. The journal prioritizes findings that advance the scientific basis of ecology and conservation, promoting the development of remote-sensing based methods relevant to the management of land use and biological systems at all levels, from populations and species to ecosystems and biomes. The journal defines remote sensing in its broadest sense, including data acquisition by hand-held and fixed ground-based sensors, such as camera traps and acoustic recorders, and sensors on airplanes and satellites. The intended journal’s audience includes ecologists, conservation scientists, policy makers, managers of terrestrial and aquatic systems, remote sensing scientists, and students.
Remote Sensing in Ecology and Conservation is a fully open access journal from Wiley and the Zoological Society of London. Remote sensing has enormous potential as to provide information on the state of, and pressures on, biological diversity and ecosystem services, at multiple spatial and temporal scales. This new publication provides a forum for multidisciplinary research in remote sensing science, ecological research and conservation science.