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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ftdoajarticles:oai:doaj.org/article:95945ec9863948fab23127db55d0b064 2023-10-09T21:52:46+02:00 Remote sensing of salmonid spawning sites in freshwater ecosystems: The potential of low-cost UAV data. Lieke Ponsioen Kalina H Kapralova Fredrik Holm Benjamin D Hennig 2023-01-01T00:00:00Z https://doi.org/10.1371/journal.pone.0290736 https://doaj.org/article/95945ec9863948fab23127db55d0b064 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pone.0290736 https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0290736 https://doaj.org/article/95945ec9863948fab23127db55d0b064 PLoS ONE, Vol 18, Iss 8, p e0290736 (2023) Medicine R Science Q article 2023 ftdoajarticles https://doi.org/10.1371/journal.pone.0290736 2023-09-10T00:35:54Z 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 Directory of Open Access Journals: DOAJ Articles PLOS ONE 18 8 e0290736 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Medicine R Science Q |
spellingShingle |
Medicine R Science Q Lieke Ponsioen Kalina H Kapralova Fredrik Holm Benjamin D Hennig Remote sensing of salmonid spawning sites in freshwater ecosystems: The potential of low-cost UAV data. |
topic_facet |
Medicine R Science Q |
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. |
format |
Article in Journal/Newspaper |
author |
Lieke Ponsioen Kalina H Kapralova Fredrik Holm Benjamin D Hennig |
author_facet |
Lieke Ponsioen Kalina H Kapralova Fredrik Holm Benjamin D Hennig |
author_sort |
Lieke Ponsioen |
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 |
https://doi.org/10.1371/journal.pone.0290736 https://doaj.org/article/95945ec9863948fab23127db55d0b064 |
genre |
Iceland |
genre_facet |
Iceland |
op_source |
PLoS ONE, Vol 18, Iss 8, p e0290736 (2023) |
op_relation |
https://doi.org/10.1371/journal.pone.0290736 https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0290736 https://doaj.org/article/95945ec9863948fab23127db55d0b064 |
op_doi |
https://doi.org/10.1371/journal.pone.0290736 |
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PLOS ONE |
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