Use of Landsat Imagery Time-Series and Random Forests Classifier to Reconstruct Eelgrass Bed Distribution Maps in Eeyou Istchee

The eastern coastline of James Bay is known to have been home to sizeable eelgrass beds ( Zostera marina L.) which thrived in the bay’s shallow, subarctic waters. The region was subjected to substantial hydroelectric dams, large fires, and other human activities in the past half-century. To assess t...

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Published in:Remote Sensing
Main Authors: Kevin Clyne, Armand LaRocque, Brigitte Leblon, Maycira Costa
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2024
Subjects:
Q
Online Access:https://doi.org/10.3390/rs16152717
https://doaj.org/article/5c444305c8ed4f42844048f75c063719
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spelling ftdoajarticles:oai:doaj.org/article:5c444305c8ed4f42844048f75c063719 2024-09-15T18:38:04+00:00 Use of Landsat Imagery Time-Series and Random Forests Classifier to Reconstruct Eelgrass Bed Distribution Maps in Eeyou Istchee Kevin Clyne Armand LaRocque Brigitte Leblon Maycira Costa 2024-07-01T00:00:00Z https://doi.org/10.3390/rs16152717 https://doaj.org/article/5c444305c8ed4f42844048f75c063719 EN eng MDPI AG https://www.mdpi.com/2072-4292/16/15/2717 https://doaj.org/toc/2072-4292 doi:10.3390/rs16152717 2072-4292 https://doaj.org/article/5c444305c8ed4f42844048f75c063719 Remote Sensing, Vol 16, Iss 15, p 2717 (2024) eelgrass Cree remote sensing temporal monitoring ecological monitoring James Bay Science Q article 2024 ftdoajarticles https://doi.org/10.3390/rs16152717 2024-08-12T15:24:03Z The eastern coastline of James Bay is known to have been home to sizeable eelgrass beds ( Zostera marina L.) which thrived in the bay’s shallow, subarctic waters. The region was subjected to substantial hydroelectric dams, large fires, and other human activities in the past half-century. To assess the impact of these factors on eelgrass beds, a historical reconstruction of eelgrass bed distribution was performed from images acquired by Landsat-5 Thematic Mapper (TM) in 1988, 1991, and 1996 and images of the Landsat-8 Operational Land Imager (OLI) in 2019. All the images were classified using the Random Forests classifier (RF) and assessed for accuracy each year on a bay-wide scale using an independent field validation dataset. The validation data were extracted from an eelgrass bed map established using aerial photos and field surveys in 1986, 1991, and 1995 and from a field survey in 2019. The overall validation accuracy of the classified images (between 72% and 85%) showed good agreement with the other datasets for most locations, providing reassurance about the reliability of the research. This makes it possible to use satellite imagery to detect past changes to eelgrass distribution within a bay. The classified images of 1988 and 1996 were also compared to aerial photos taken in years close to each other at ten sites to determine their ability to assess small eelgrass beds’ shape and presence. Such a comparison revealed that the classified images accurately portrayed eelgrass distribution even at finer scales. Article in Journal/Newspaper Subarctic James Bay Directory of Open Access Journals: DOAJ Articles Remote Sensing 16 15 2717
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic eelgrass
Cree
remote sensing
temporal monitoring
ecological monitoring
James Bay
Science
Q
spellingShingle eelgrass
Cree
remote sensing
temporal monitoring
ecological monitoring
James Bay
Science
Q
Kevin Clyne
Armand LaRocque
Brigitte Leblon
Maycira Costa
Use of Landsat Imagery Time-Series and Random Forests Classifier to Reconstruct Eelgrass Bed Distribution Maps in Eeyou Istchee
topic_facet eelgrass
Cree
remote sensing
temporal monitoring
ecological monitoring
James Bay
Science
Q
description The eastern coastline of James Bay is known to have been home to sizeable eelgrass beds ( Zostera marina L.) which thrived in the bay’s shallow, subarctic waters. The region was subjected to substantial hydroelectric dams, large fires, and other human activities in the past half-century. To assess the impact of these factors on eelgrass beds, a historical reconstruction of eelgrass bed distribution was performed from images acquired by Landsat-5 Thematic Mapper (TM) in 1988, 1991, and 1996 and images of the Landsat-8 Operational Land Imager (OLI) in 2019. All the images were classified using the Random Forests classifier (RF) and assessed for accuracy each year on a bay-wide scale using an independent field validation dataset. The validation data were extracted from an eelgrass bed map established using aerial photos and field surveys in 1986, 1991, and 1995 and from a field survey in 2019. The overall validation accuracy of the classified images (between 72% and 85%) showed good agreement with the other datasets for most locations, providing reassurance about the reliability of the research. This makes it possible to use satellite imagery to detect past changes to eelgrass distribution within a bay. The classified images of 1988 and 1996 were also compared to aerial photos taken in years close to each other at ten sites to determine their ability to assess small eelgrass beds’ shape and presence. Such a comparison revealed that the classified images accurately portrayed eelgrass distribution even at finer scales.
format Article in Journal/Newspaper
author Kevin Clyne
Armand LaRocque
Brigitte Leblon
Maycira Costa
author_facet Kevin Clyne
Armand LaRocque
Brigitte Leblon
Maycira Costa
author_sort Kevin Clyne
title Use of Landsat Imagery Time-Series and Random Forests Classifier to Reconstruct Eelgrass Bed Distribution Maps in Eeyou Istchee
title_short Use of Landsat Imagery Time-Series and Random Forests Classifier to Reconstruct Eelgrass Bed Distribution Maps in Eeyou Istchee
title_full Use of Landsat Imagery Time-Series and Random Forests Classifier to Reconstruct Eelgrass Bed Distribution Maps in Eeyou Istchee
title_fullStr Use of Landsat Imagery Time-Series and Random Forests Classifier to Reconstruct Eelgrass Bed Distribution Maps in Eeyou Istchee
title_full_unstemmed Use of Landsat Imagery Time-Series and Random Forests Classifier to Reconstruct Eelgrass Bed Distribution Maps in Eeyou Istchee
title_sort use of landsat imagery time-series and random forests classifier to reconstruct eelgrass bed distribution maps in eeyou istchee
publisher MDPI AG
publishDate 2024
url https://doi.org/10.3390/rs16152717
https://doaj.org/article/5c444305c8ed4f42844048f75c063719
genre Subarctic
James Bay
genre_facet Subarctic
James Bay
op_source Remote Sensing, Vol 16, Iss 15, p 2717 (2024)
op_relation https://www.mdpi.com/2072-4292/16/15/2717
https://doaj.org/toc/2072-4292
doi:10.3390/rs16152717
2072-4292
https://doaj.org/article/5c444305c8ed4f42844048f75c063719
op_doi https://doi.org/10.3390/rs16152717
container_title Remote Sensing
container_volume 16
container_issue 15
container_start_page 2717
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