TY - JOUR
T1 - An Automated Approach for Estimating Snowline Altitudes in the Karakoram and Eastern Himalaya From Remote Sensing
AU - Racoviteanu, Adina
AU - Rittger, Karl
AU - Armstrong, Richard
N1 - Funding Information:
We acknowledge the CHARIS project; the USGS EROS Data Center for providing free access to the Landsat imagery; the Randolph Consortium for providing the glacier outlines; the Planet Labs Inc., for providing free access to the PlanetScope imagery through their API program; and Antoine Rabatel for providing his feedback and suggestions on the snowline mapping. Funding. AR, KR, and RA were primarily supported by the Contribution to High Asian Runoff from Ice and Snow (CHARIS) project, funded by the United States Agency for International Development (USAID, Cooperative Agreement No. AID-0AA-A-11-00045). Additional support for AR was from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No 663830. KR was also supported by the NASA Applied Science Grant 80NSSC18K0427 and the NASA HiMAT project.
Publisher Copyright:
© Copyright © 2019 Racoviteanu, Rittger and Armstrong.
PY - 2019/9/20
Y1 - 2019/9/20
N2 - The separation of fresh snow, exposed glacier ice and debris covered ice on glacier surfaces is needed for hydrologic applications and for understanding the response of glaciers to climate variability. The end-of-season snowline altitude (SLA) is an indicator of the equilibrium line altitude (ELA) of a glacier and is often used to infer the mass balance of a glacier. Regional snowline estimates are generally missing from glacier inventories for remote, high-altitude glacierized areas such as High Mountain Asia. In this study, we present an automated, decision-based image classification algorithm implemented in Python to separate snow, ice and debris surfaces on glaciers and to extract glacier snowlines at monthly and annual time steps and regional scales. The method was applied in the Hunza basin in the Karakoram and the Trishuli basin in eastern Himalaya. We automatically partitioned the various types of surfaces on glaciers at each time step using image band ratios combined with topographic criteria based on two versions of the Shuttle Radar Topography Mission elevation dataset. SLAs were extracted on a pixel-by-pixel basis using a “buffer” method adapted for each elevation dataset. Over the period studied (2000–2016), end-of-the-ablation season annual ELAs fluctuated from 4,917 to 5,336 m a.s.l. for the Hunza, with a 16-year average of 5,177 ± 108 m a.s.l., and 5,395–5,565 m a.s.l. for the Trishuli, with an average of 5,444 ± 63 m a.s.l. Snowlines were sensitive to the manual corrections of the partition, the topographic slope, the elevation dataset and the band ratio thresholds particularly during the spring and winter months, and were not sensitive to the size of the buffer used to extract the snowlines. With further refinement and calibration with field measurements, this method can be easily applied to higher resolution Sentinel-2 data (5 days temporal resolution) as well as daily PlanetScope to derive sub-monthly snowlines.
AB - The separation of fresh snow, exposed glacier ice and debris covered ice on glacier surfaces is needed for hydrologic applications and for understanding the response of glaciers to climate variability. The end-of-season snowline altitude (SLA) is an indicator of the equilibrium line altitude (ELA) of a glacier and is often used to infer the mass balance of a glacier. Regional snowline estimates are generally missing from glacier inventories for remote, high-altitude glacierized areas such as High Mountain Asia. In this study, we present an automated, decision-based image classification algorithm implemented in Python to separate snow, ice and debris surfaces on glaciers and to extract glacier snowlines at monthly and annual time steps and regional scales. The method was applied in the Hunza basin in the Karakoram and the Trishuli basin in eastern Himalaya. We automatically partitioned the various types of surfaces on glaciers at each time step using image band ratios combined with topographic criteria based on two versions of the Shuttle Radar Topography Mission elevation dataset. SLAs were extracted on a pixel-by-pixel basis using a “buffer” method adapted for each elevation dataset. Over the period studied (2000–2016), end-of-the-ablation season annual ELAs fluctuated from 4,917 to 5,336 m a.s.l. for the Hunza, with a 16-year average of 5,177 ± 108 m a.s.l., and 5,395–5,565 m a.s.l. for the Trishuli, with an average of 5,444 ± 63 m a.s.l. Snowlines were sensitive to the manual corrections of the partition, the topographic slope, the elevation dataset and the band ratio thresholds particularly during the spring and winter months, and were not sensitive to the size of the buffer used to extract the snowlines. With further refinement and calibration with field measurements, this method can be easily applied to higher resolution Sentinel-2 data (5 days temporal resolution) as well as daily PlanetScope to derive sub-monthly snowlines.
KW - snowlines
KW - ELA
KW - landsat
KW - Karakoram and Himalaya (HKH)
KW - remote sensing
KW - Karakoram
KW - classification
KW - Himalaya
UR - http://www.scopus.com/inward/record.url?scp=85072978398&partnerID=8YFLogxK
U2 - 10.3389/feart.2019.00220
DO - 10.3389/feart.2019.00220
M3 - Article
SN - 2296-6463
VL - 7
JO - Frontiers in Earth Science
JF - Frontiers in Earth Science
M1 - 220
ER -