Remote sensing of salmonid spawning sites in freshwater ecosystems: The potential of low-cost UAV data
Salmonids are especially vulnerable during their embryonic development, but monitoring of their spawning grounds is rare and often relies on manual counting of their nests (redds). This method, however, is prone to sampling errors resulting in over- or underestimations of redd counts. Salmonid spawn...
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ftpubmed:oai:pubmedcentral.nih.gov:10464957 2023-10-01T03:56:57+02:00 Remote sensing of salmonid spawning sites in freshwater ecosystems: The potential of low-cost UAV data Ponsioen, Lieke Kapralova, Kalina H. Holm, Fredrik Hennig, Benjamin D. 2023-08-29 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464957/ http://www.ncbi.nlm.nih.gov/pubmed/37643193 https://doi.org/10.1371/journal.pone.0290736 en eng Public Library of Science http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464957/ http://www.ncbi.nlm.nih.gov/pubmed/37643193 http://dx.doi.org/10.1371/journal.pone.0290736 © 2023 Ponsioen et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PLoS One Research Article Text 2023 ftpubmed https://doi.org/10.1371/journal.pone.0290736 2023-09-03T01:22:53Z Salmonids are especially vulnerable during their embryonic development, but monitoring of their spawning grounds is rare and often relies on manual counting of their nests (redds). This method, however, is prone to sampling errors resulting in over- or underestimations of redd counts. Salmonid spawning habitat in shallow water areas can be distinguished by their visible reflection which makes the use of standard unmanned aerial vehicles (UAV) a viable option for their mapping. Here, we aimed to develop a standardised approach to detect salmonid spawning habitat that is easy and low-cost. We used a semi-automated approach by applying supervised classification techniques to UAV derived RGB imagery from two contrasting lakes in Iceland. For both lakes six endmember classes were obtained with high accuracies. Most importantly, producer’s and user’s accuracy for classifying spawning redds was >90% after applying post-classification improvements for both study areas. What we are proposing here is an entirely new approach for monitoring spawning habitats which will address some the major shortcomings of the widely used redd count method e.g. collecting and analysing large amounts of data cost and time efficiently, limiting observer bias, and allowing for precise quantification over different temporal and spatial scales. Text Iceland PubMed Central (PMC) PLOS ONE 18 8 e0290736 |
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Research Article Ponsioen, Lieke Kapralova, Kalina H. Holm, Fredrik Hennig, Benjamin D. Remote sensing of salmonid spawning sites in freshwater ecosystems: The potential of low-cost UAV data |
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Salmonids are especially vulnerable during their embryonic development, but monitoring of their spawning grounds is rare and often relies on manual counting of their nests (redds). This method, however, is prone to sampling errors resulting in over- or underestimations of redd counts. Salmonid spawning habitat in shallow water areas can be distinguished by their visible reflection which makes the use of standard unmanned aerial vehicles (UAV) a viable option for their mapping. Here, we aimed to develop a standardised approach to detect salmonid spawning habitat that is easy and low-cost. We used a semi-automated approach by applying supervised classification techniques to UAV derived RGB imagery from two contrasting lakes in Iceland. For both lakes six endmember classes were obtained with high accuracies. Most importantly, producer’s and user’s accuracy for classifying spawning redds was >90% after applying post-classification improvements for both study areas. What we are proposing here is an entirely new approach for monitoring spawning habitats which will address some the major shortcomings of the widely used redd count method e.g. collecting and analysing large amounts of data cost and time efficiently, limiting observer bias, and allowing for precise quantification over different temporal and spatial scales. |
format |
Text |
author |
Ponsioen, Lieke Kapralova, Kalina H. Holm, Fredrik Hennig, Benjamin D. |
author_facet |
Ponsioen, Lieke Kapralova, Kalina H. Holm, Fredrik Hennig, Benjamin D. |
author_sort |
Ponsioen, Lieke |
title |
Remote sensing of salmonid spawning sites in freshwater ecosystems: The potential of low-cost UAV data |
title_short |
Remote sensing of salmonid spawning sites in freshwater ecosystems: The potential of low-cost UAV data |
title_full |
Remote sensing of salmonid spawning sites in freshwater ecosystems: The potential of low-cost UAV data |
title_fullStr |
Remote sensing of salmonid spawning sites in freshwater ecosystems: The potential of low-cost UAV data |
title_full_unstemmed |
Remote sensing of salmonid spawning sites in freshwater ecosystems: The potential of low-cost UAV data |
title_sort |
remote sensing of salmonid spawning sites in freshwater ecosystems: the potential of low-cost uav data |
publisher |
Public Library of Science |
publishDate |
2023 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464957/ http://www.ncbi.nlm.nih.gov/pubmed/37643193 https://doi.org/10.1371/journal.pone.0290736 |
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Iceland |
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Iceland |
op_source |
PLoS One |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464957/ http://www.ncbi.nlm.nih.gov/pubmed/37643193 http://dx.doi.org/10.1371/journal.pone.0290736 |
op_rights |
© 2023 Ponsioen et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
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https://doi.org/10.1371/journal.pone.0290736 |
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