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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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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1810482397001547776 |