{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T02:41:54Z","timestamp":1768531314036,"version":"3.49.0"},"reference-count":99,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T00:00:00Z","timestamp":1663113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007516","name":"National Park Service, Inventory &amp; Monitoring Program, Southwest Alaska Network and Focused Condition Funds","doi-asserted-by":"publisher","award":["P20AC00176"],"award-info":[{"award-number":["P20AC00176"]}],"id":[{"id":"10.13039\/100007516","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007516","name":"National Park Service, Inventory &amp; Monitoring Program, Southwest Alaska Network and Focused Condition Funds","doi-asserted-by":"publisher","award":["NA17OAR4320101"],"award-info":[{"award-number":["NA17OAR4320101"]}],"id":[{"id":"10.13039\/100007516","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NOAA Cooperative Agreement with CIRES","award":["P20AC00176"],"award-info":[{"award-number":["P20AC00176"]}]},{"name":"NOAA Cooperative Agreement with CIRES","award":["NA17OAR4320101"],"award-info":[{"award-number":["NA17OAR4320101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Glaciers are important sentinels of a changing climate, crucial components of the global cryosphere and integral to their local landscapes. However, many of the commonly used methods for mapping glacier change are labor-intensive and limit the temporal and spatial scope of existing research. This study addresses some of the limitations of prior approaches by developing a novel deep-learning-based method called GlacierCoverNet. GlacierCoverNet is a deep neural network that relies on an extensive, purpose-built training dataset. Using this model, we created a record of over three decades long at a fine temporal cadence (every two years) for the state of Alaska. We conducted a robust error analysis of this dataset and then used the dataset to characterize changes in debris-free glaciers and supraglacial debris over the last ~35 years. We found that our deep learning model could produce maps comparable to existing approaches in the capture of areal extent, but without manual editing required. The model captured the area covered with glaciers that was ~97% of the Randolph Glacier Inventory 6.0 with ~6% and ~9% omission and commission rates in the southern portion of Alaska, respectively. The overall model area capture was lower and omission and commission rates were significantly higher in the northern Brooks Range. Overall, the glacier-covered area retreated by 8425 km2 (\u221213%) between 1985 and 2020, and supraglacial debris expanded by 2799 km2 (64%) during the same period across the state of Alaska.<\/jats:p>","DOI":"10.3390\/rs14184582","type":"journal-article","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T23:16:36Z","timestamp":1663197396000},"page":"4582","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Changes over the Last 35 Years in Alaska\u2019s Glaciated Landscape: A Novel Deep Learning Approach to Mapping Glaciers at Fine Temporal Granularity"],"prefix":"10.3390","volume":"14","author":[{"given":"Ben M.","family":"Roberts-Pierel","sequence":"first","affiliation":[{"name":"Geography Program, College of Earth, Ocean and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9770-5530","authenticated-orcid":false,"given":"Peter B.","family":"Kirchner","sequence":"additional","affiliation":[{"name":"Southwest Alaska Monitoring Network, National Park Service, Anchorage, AK 99501, USA"},{"name":"Department of Ecosystem and Conservation Science Franke College of Forestry and Conservation, University of Montana Forestry, Missoula, MT 59812, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9421-6480","authenticated-orcid":false,"given":"John B.","family":"Kilbride","sequence":"additional","affiliation":[{"name":"Geography Program, College of Earth, Ocean and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA"}]},{"given":"Robert E.","family":"Kennedy","sequence":"additional","affiliation":[{"name":"Geography Program, College of Earth, Ocean and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1038\/s41522-017-0019-0","article-title":"The Microbiome of Glaciers and Ice Sheets","volume":"3","author":"Anesio","year":"2017","journal-title":"Npj Biofilms Microbiomes"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"360","DOI":"10.3389\/feart.2019.00360","article-title":"High Resolution Mapping of Ice Mass Loss in the Gulf of Alaska From Constrained Forward Modeling of GRACE Data","volume":"7","author":"Doumbia","year":"2020","journal-title":"Front. 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