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dc.contributor.author Racoviteanu, Adina
dc.contributor.author Rittger, Karl
dc.contributor.author Armstrong, Richard
dc.date.accessioned 2020-02-23T01:03:14Z
dc.date.available 2020-02-23T01:03:14Z
dc.date.issued 2019-09-20
dc.identifier.citation Racoviteanu , A , Rittger , K & Armstrong , R 2019 , ' A automated approach for estimating snowline altitudes in the Karakoram and eastern Himalaya from remote sensing ' Frontiers in Earth Science , vol. 7 , 220 . https://doi.org/10.3389/feart.2019.00220 en
dc.identifier.issn 2296-6463
dc.identifier.other PURE: 27251304
dc.identifier.other PURE UUID: 002becf9-ccfa-4419-8d34-c26ec97f2410
dc.identifier.other ORCID: /0000-0003-4954-1871/work/62286128
dc.identifier.other Scopus: 85072978398
dc.identifier.other handle.net: http://hdl.handle.net/2160/002becf9-ccfa-4419-8d34-c26ec97f2410
dc.identifier.uri http://hdl.handle.net/2160/47155
dc.description.abstract The separation of fresh snow, exposed glacier ice and debris-covered ice on glacier surfaces is needed for hydrologic applications and for understanding glacier response to climate variability. The end-of-season snowline altitude (SLA) is an indicator of the equilibrium line altitude (ELA) of a glacier and thus it is used to infer the mass balance of a glacier. Regional SLA estimates are generally missing from glacier inventories for remote, high-altitude glacierized areas such as High Asia. In this study, we present a semi-automated, decision based image classification algorithm to separate snow, ice and debris surfaces on glaciers and to extract SLAs of glaciers at regional scales. The algorithm was implemented in Python using Landsat satellite imagery combined with terrain information from two versions of the Shuttle Radar Topography Mission (SRTM), and was applied to subsets of the Hunza basin in the Karakoram range and the Trishuli basin in the eastern Himalaya. For each region, the semi-automated method was used to separate surfaces on the glaciers, to extract the SLAs to find the approximate date of the end of ablation season in each study area (date of maximum exposed glacier ice and maximum SLA). To infer regional fluctuations in ELAs, we increased the analysis domain to the full extent of Landsat scenes, and performed various sensitivity analyses to assess the robustness and the representativeness of the method at large scales. For the Hunza (Shimshal Valley), SLAs ranged from 4,727 m in the spring months (May) to 5,171 m in the fall months (October) in 2013. For the Trishuli (Langtang valley), SLAs ranged from 5,261 m in the early summer months (June) to a maximum of 5,500 m in late fall/winter (December) in 2016. Average regional accumulation-to-ablation area ratio (AAR) values were 0.77 for the Shimshal valley in 2013 and 0.67 for the Langtang valley in 2016, indicating relatively stable conditions for these two years. In the Hunza, end-of-the-ablation season annual SLAs (2000 to 2015) ranged from 4,917m to 5,336 m, with no significant trends for this period en
dc.language.iso eng
dc.relation.ispartof Frontiers in Earth Science en
dc.rights en
dc.subject snowlines en
dc.subject ELA en
dc.subject landsat en
dc.subject Karakoram and Himalaya (HKH) en
dc.subject remote sensing en
dc.title A automated approach for estimating snowline altitudes in the Karakoram and eastern Himalaya from remote sensing en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.description.version publishersversion en
dc.identifier.doi https://doi.org/10.3389/feart.2019.00220
dc.contributor.institution Department of Geography and Earth Sciences en
dc.description.status Peer reviewed en


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