Use of Landsat imagery time-series and random forests classifier to reconstruct eelgrass bed distribution maps in Eeyou Istchee

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dc.contributor.authorClyne, Kevin
dc.contributor.authorLaRocque, Armand
dc.contributor.authorLeblon, Brigitte
dc.contributor.authorCosta, Maycira
dc.date.accessioned2024-10-10T17:23:08Z
dc.date.available2024-10-10T17:23:08Z
dc.date.issued2024
dc.description.abstractThe 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.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.description.sponsorshipThis study is part of an ongoing multidisciplinary research project entitled the “Eeyou Istchee Coastal Habitat Comprehensive Research Project” (https://www.eeyoucoastalhabitat.ca) (accessed on 2 March 2024), conducted in the James Bay region of northern Québec. The project is co-funded by the Cree Nation Government, Niskamoon Corporation, and Hydro-Québec and administered through Niskamoon Corporation, a Cree-run organization. Kevin Clyne was funded by an MITACS grant awarded to Dr. Leblon and sponsored by Niskamoon Corporation.
dc.identifier.citationClyne, K., LaRocque, A., Leblon, B., & Costa, M. (2024). Use of Landsat imagery time-series and random forests classifier to reconstruct eelgrass bed distribution maps in Eeyou Istchee. Remote Sensing, 16(15), Article 15. https://doi.org/10.3390/rs16152717
dc.identifier.urihttps://doi.org/10.3390/rs16152717
dc.identifier.urihttps://hdl.handle.net/1828/20516
dc.language.isoen
dc.publisherRemote Sensing
dc.rightsAttribution CC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCree
dc.subjectecological monitoring
dc.subjecteelgrass
dc.subjectEeyou Istchee
dc.subjectJames Bay
dc.subjectremote sensing
dc.subjecttemporal monitoring
dc.subject.departmentDepartment of Geography
dc.titleUse of Landsat imagery time-series and random forests classifier to reconstruct eelgrass bed distribution maps in Eeyou Istchee
dc.typeArticle

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