Abstract
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 is an indicator of the equilibrium line altitude 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 band ratios combined with topographic criteria based on two versions of the Shuttle Radar Topography Mission elevation dataset. Snowline altitudes were extracted on a pixel-by-pixel basis using a “buffer” method adapted for each elevation dataset. Over the period studied (2000 to 2016), end-of-the-ablation season annual ELAs fluctuated from 4,917 m a.s.l. to 5,336 m a.s.l. for the Hunza, with a 16-year average of 5,177 ±108 m, and 5,395 m a.s.l. to 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 Sentinel-2 data (5 days temporal resolution) as well as daily PlanetScope to derive sub-monthly snowlines.
Highlights
Identifying various surfaces on glaciers and extracting glacier snowlines are needed for glacier mass balance calibration and validation, as demonstrated in a growing body of literature (Rabatel et al, 2005, 2008, 2017; Gardelle et al, 2013; Huss et al, 2013; Kienholz et al, 2017; Barandun et al, 2018)
In the Trishuli, the total glacierized area exhibited an apparent decline by about 200 km2 in exposed glacier ice from spring to winter in 2013; this was due to a large number of NoData values due to clouds or shadows obscuring the glacier surface during winter season in this area, and was not a “true” glacier ice loss
Manual digitization of snow and ice on glaciers and subsequent extraction of snowline altitude (SLA) is generally a time-consuming process and is difficult to apply over large areas, especially when time series of the snowlines are needed
Summary
Identifying various surfaces on glaciers (fresh snow, clean glacier ice, and supra-glacial debris cover) and extracting glacier snowlines are needed for glacier mass balance calibration and validation, as demonstrated in a growing body of literature (Rabatel et al, 2005, 2008, 2017; Gardelle et al, 2013; Huss et al, 2013; Kienholz et al, 2017; Barandun et al, 2018). In remote areas of High Mountain Asia (HMA), access to glaciers, especially to their accumulation areas, is limited by rugged terrain and difficult logistics. Such measurements are sparse in global glacier databases such as the Randolph Glacier Inventory (RGI) (Pfeffer et al, 2014) or the Global Land Ice Monitoring from Space (GLIMS) database (Raup et al, 2007). The relationship between ELA and the end-of-summer SLA can be applied to infer annual mass balance from remotely sensed snowlines in such areas with limited field-based measurements as well as for missing years (Rabatel et al, 2012). Late in the ablation season, under climate warming conditions, the snowline may retreat beyond the firnline, but optical satellite imagery in this case detects the firnline rather than the snowline
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