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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Online Access: | http://dx.doi.org/10.1371/journal.pone.0290736 https://dx.plos.org/10.1371/journal.pone.0290736 |
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crplos:10.1371/journal.pone.0290736 2024-09-15T18:14:10+00:00 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. Paiva, Vitor Hugo Rodrigues University of Iceland Research Fund 2023 http://dx.doi.org/10.1371/journal.pone.0290736 https://dx.plos.org/10.1371/journal.pone.0290736 en eng Public Library of Science (PLoS) http://creativecommons.org/licenses/by/4.0/ PLOS ONE volume 18, issue 8, page e0290736 ISSN 1932-6203 journal-article 2023 crplos https://doi.org/10.1371/journal.pone.0290736 2024-07-09T04:08:35Z 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. Article in Journal/Newspaper Iceland PLOS PLOS ONE 18 8 e0290736 |
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English |
description |
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. |
author2 |
Paiva, Vitor Hugo Rodrigues University of Iceland Research Fund |
format |
Article in Journal/Newspaper |
author |
Ponsioen, Lieke Kapralova, Kalina H. Holm, Fredrik Hennig, Benjamin D. |
spellingShingle |
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 |
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 (PLoS) |
publishDate |
2023 |
url |
http://dx.doi.org/10.1371/journal.pone.0290736 https://dx.plos.org/10.1371/journal.pone.0290736 |
genre |
Iceland |
genre_facet |
Iceland |
op_source |
PLOS ONE volume 18, issue 8, page e0290736 ISSN 1932-6203 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1371/journal.pone.0290736 |
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PLOS ONE |
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18 |
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8 |
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e0290736 |
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