Changes over the Last 35 Years in Alaska’s Glaciated Landscape: A Novel Deep Learning Approach to Mapping Glaciers at Fine Temporal Granularity
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 st...
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ftdoajarticles:oai:doaj.org/article:d52bf24dc2ee4240bcc25f09172e3d9f 2023-05-15T15:46:59+02:00 Changes over the Last 35 Years in Alaska’s Glaciated Landscape: A Novel Deep Learning Approach to Mapping Glaciers at Fine Temporal Granularity Ben M. Roberts-Pierel Peter B. Kirchner John B. Kilbride Robert E. Kennedy 2022-09-01T00:00:00Z https://doi.org/10.3390/rs14184582 https://doaj.org/article/d52bf24dc2ee4240bcc25f09172e3d9f EN eng MDPI AG https://www.mdpi.com/2072-4292/14/18/4582 https://doaj.org/toc/2072-4292 doi:10.3390/rs14184582 2072-4292 https://doaj.org/article/d52bf24dc2ee4240bcc25f09172e3d9f Remote Sensing, Vol 14, Iss 4582, p 4582 (2022) glacier change glacier inventory deep learning neural network remote sensing Landsat Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14184582 2022-12-30T22:02:17Z 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 km 2 (−13%) between 1985 and 2020, and supraglacial debris expanded by 2799 km 2 (64%) during the same period across the state of Alaska. Article in Journal/Newspaper Brooks Range glacier glaciers Alaska Directory of Open Access Journals: DOAJ Articles Remote Sensing 14 18 4582 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
glacier change glacier inventory deep learning neural network remote sensing Landsat Science Q |
spellingShingle |
glacier change glacier inventory deep learning neural network remote sensing Landsat Science Q Ben M. Roberts-Pierel Peter B. Kirchner John B. Kilbride Robert E. Kennedy Changes over the Last 35 Years in Alaska’s Glaciated Landscape: A Novel Deep Learning Approach to Mapping Glaciers at Fine Temporal Granularity |
topic_facet |
glacier change glacier inventory deep learning neural network remote sensing Landsat Science Q |
description |
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 km 2 (−13%) between 1985 and 2020, and supraglacial debris expanded by 2799 km 2 (64%) during the same period across the state of Alaska. |
format |
Article in Journal/Newspaper |
author |
Ben M. Roberts-Pierel Peter B. Kirchner John B. Kilbride Robert E. Kennedy |
author_facet |
Ben M. Roberts-Pierel Peter B. Kirchner John B. Kilbride Robert E. Kennedy |
author_sort |
Ben M. Roberts-Pierel |
title |
Changes over the Last 35 Years in Alaska’s Glaciated Landscape: A Novel Deep Learning Approach to Mapping Glaciers at Fine Temporal Granularity |
title_short |
Changes over the Last 35 Years in Alaska’s Glaciated Landscape: A Novel Deep Learning Approach to Mapping Glaciers at Fine Temporal Granularity |
title_full |
Changes over the Last 35 Years in Alaska’s Glaciated Landscape: A Novel Deep Learning Approach to Mapping Glaciers at Fine Temporal Granularity |
title_fullStr |
Changes over the Last 35 Years in Alaska’s Glaciated Landscape: A Novel Deep Learning Approach to Mapping Glaciers at Fine Temporal Granularity |
title_full_unstemmed |
Changes over the Last 35 Years in Alaska’s Glaciated Landscape: A Novel Deep Learning Approach to Mapping Glaciers at Fine Temporal Granularity |
title_sort |
changes over the last 35 years in alaska’s glaciated landscape: a novel deep learning approach to mapping glaciers at fine temporal granularity |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14184582 https://doaj.org/article/d52bf24dc2ee4240bcc25f09172e3d9f |
genre |
Brooks Range glacier glaciers Alaska |
genre_facet |
Brooks Range glacier glaciers Alaska |
op_source |
Remote Sensing, Vol 14, Iss 4582, p 4582 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/18/4582 https://doaj.org/toc/2072-4292 doi:10.3390/rs14184582 2072-4292 https://doaj.org/article/d52bf24dc2ee4240bcc25f09172e3d9f |
op_doi |
https://doi.org/10.3390/rs14184582 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
18 |
container_start_page |
4582 |
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1766381798189694976 |