Taylor, LS orcid.org/0000-0001-7916-0856, Quincey, DJ orcid.org/0000-0002-7602-7926, Smith, MW orcid.org/0000-0003-4361-9527 et al. (3 more authors) (2021) Remote sensing of the mountain cryosphere: Current capabilities and future opportunities for research. Progress in Physical Geography: Earth and Environment, 45 (6). pp. 931-964. ISSN 0309-1333
Abstract
Remote sensing technologies are integral to monitoring the mountain cryosphere in a warming world. Satellite missions and field-based platforms have transformed understanding of the processes driving changes in mountain glacier dynamics, snow cover, lake evolution, and the associated emergence of hazards (e.g. avalanches, floods, landslides). Sensors and platforms are becoming more bespoke, with innovation being driven by the commercial sector, and image repositories are more frequently open access, leading to the democratisation of data analysis and interpretation. Cloud computing, artificial intelligence, and machine learning are rapidly transforming our ability to handle this exponential increase in data. This review therefore provides a timely opportunity to synthesise current capabilities in remote sensing of the mountain cryosphere. Scientific and commercial applications were critically examined, recognising the technologies that have most advanced the discipline. Low-cost sensors can also be deployed in the field, using microprocessors and telecommunications equipment to connect mountain glaciers to stakeholders for real-time monitoring. The potential for novel automated pipelines that can process vast volumes of data is also discussed, from reimagining historical aerial imagery to produce elevation models, to automatically delineating glacier boundaries. Finally, the applications of these emerging techniques that will benefit scientific research avenues and real-world societal programmes are discussed.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2021. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | Artificial Intelligence; Earth Observation; Glacier; Satellite; Snow; Technology |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > River Basin Processes & Management (Leeds) |
Funding Information: | Funder Grant number NERC DTP NE/L002574/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Jul 2021 11:15 |
Last Modified: | 25 Jun 2023 22:42 |
Status: | Published |
Publisher: | SAGE Publications |
Identification Number: | 10.1177/03091333211023690 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:175852 |