Automated prediction of tailings areas at historic gold mine districts in Nova Scotia using multispectral images and a random forest classifier

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dc.contributor.advisor Campbell, Linda M., 1970-
dc.coverage.spatial Nova Scotia
dc.creator Jewell, Daniel A.
dc.date.accessioned 2024-04-30T14:10:31Z
dc.date.available 2024-04-30T14:10:31Z
dc.date.issued 2023-09-06
dc.identifier.uri http://library2.smu.ca/xmlui/handle/01/31906
dc.description 1 online resource (vii, 24, 107, 10 pages) : maps (some colour), graphs (some colour)
dc.description Includes abstract and appendices.
dc.description Includes bibliographical references (pages 15-24, 62-71, 103-107).
dc.description.abstract Satellite imagery can be analyzed to offer a preliminary regional assessment of mine tailings indicators, enabling identification before performing in-depth fieldwork. Nova Scotia, Canada, still retains mine tailings produced in the 1860s to the 1940s in 64 historic gold districts, which exceed soil guidelines for arsenic (As) and mercury (Hg) levels. Tailings data often relies on historical maps, which may not accurately depict the current extent due to wind and rain transportation. This study employs historical data to train a classifier, enabling the classification of multispectral satellite images from Sentinel-2. Both pixel-wise and object-based methods were evaluated, yielding a median F1-score above 0.7 for most tested methods at two case study sites. Accuracy varied at other sites, particularly those with significant proportions of wetland areas. Lastly, we investigate false positives and propose future research to create a more resilient classifier. en_CA
dc.description.provenance Submitted by Greg Hilliard (greg.hilliard@smu.ca) on 2024-04-30T14:10:31Z No. of bitstreams: 1 Jewell_Daniel_MASTERS_2023.pdf: 5350978 bytes, checksum: e74b22eff549f5f930ffefbaa9297d11 (MD5) en
dc.description.provenance Made available in DSpace on 2024-04-30T14:10:31Z (GMT). No. of bitstreams: 1 Jewell_Daniel_MASTERS_2023.pdf: 5350978 bytes, checksum: e74b22eff549f5f930ffefbaa9297d11 (MD5) Previous issue date: 2023-09-06 en
dc.language.iso en en_CA
dc.publisher Halifax, N.S. : Saint Mary's University
dc.subject.lcsh Tailings (Metallurgy) -- Environmental aspects -- Nova Scotia
dc.subject.lcsh Gold mines and mining -- Environmental aspects -- Nova Scotia
dc.subject.lcsh Soils -- Arsenic content -- Nova Scotia
dc.subject.lcsh Soils -- Mercury content -- Nova Scotia
dc.subject.lcsh Multispectral imaging -- Nova Scotia
dc.title Automated prediction of tailings areas at historic gold mine districts in Nova Scotia using multispectral images and a random forest classifier en_CA
dc.type Text en_CA
thesis.degree.name Master of Science in Applied Science
thesis.degree.level Masters
thesis.degree.discipline Environmental Science
thesis.degree.grantor Saint Mary's University (Halifax, N.S.)
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