Surface composition of debris-covered glaciers across the Himalaya using spectral unmixing and multi-sensor imagery

Abstract

Abstract. The Hindu-Kush Himalaya mountain range is characterized by highly glacierized, complex, dynamic topography. The ablation area of these glaciers is often covered a highly heterogeneous debris cover mantle comprising ponds, steep and shallow slopes of various aspects, variable debris thickness and exposed ice cliffs. These surface elements are associated with differing ice ablation rates, and understanding the composition of the glacier surface is essential for a proper understanding of glacier hydrology and glacier-related hazards. Here we use high-resolution Pleiades (2 m) and RapidEye imagery (5 m) combined with Landsat Operational Land Imager (OLI) imagery (30 m) to estimate the composition of debris-covered glacier tongues across the Himalaya around the year 2015. We use linear spectral unmixing to map various types of debris, clean ice, supraglacial ponds and vegetation on debris-covered glaciers across the mountain range. We develop the spectral unmixing methods in the Khumbu region of eastern Nepal, and then apply them over the entire Himalaya (a glacier area of 2,254 km2). This allowed us to convert 30 m fractional maps into finer classification maps and to estimate the composition of debris-covered glaciers at various spatial scales. Debris-covered glaciers across the mountain range comprised 2.1 % supraglacial ponds, 12.8 % dark debris, 60.9 % light debris and 4.5 % supra glacial vegetation, with negligible amounts of clean ice and clouds and unclassified areas. Supraglacial ponds were more prevalent in the monsoon-influenced central-eastern Himalaya (up to 4 % of the debris cover area) compared to the monsoon-dry transition zone (only 0.3 %). The automated fractional supraglacial pond maps developed here serve to complement and improve the accuracy of existing regional lake datasets. They also provide a basis for exploring the turbidity of lakes and ponds as indicators of glacier change processes, and to monitor the evolution of ponds in the context of glacial hazards.

Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers

“…However, in high mountain areas, which experience frequent cloud activity and have a large amount of debris coverage, automatic and semi-automatic glacier mapping based on remote sensing images is challenging [1,11,20], and short-term studies such as these do not necessarily show stable changes over time. Debris-covered glaciers are a special type of glacier [21,22]. The surfaces of mountain glaciers are frequently covered in supraglacial debris, whose spectral characteristics are similar to those of rocks and soil at the foot of the mountain [21,23], making them hard to distinguish from rock glaciers and periglacial areas [24].…”

“…Debris-covered glaciers are a special type of glacier [21,22]. The surfaces of mountain glaciers are frequently covered in supraglacial debris, whose spectral characteristics are similar to those of rocks and soil at the foot of the mountain [21,23], making them hard to distinguish from rock glaciers and periglacial areas [24]. Particularly in the Tibetan Plateau, owing to the influence of the monsoon climate, frequent cloud activity occurs during the glacier melting period.…”

“…Seasonal snow cover and debris also increase the difficulty and uncertainty of glacier boundary identification [25,26]. These uncertain factors cause significant interference in research on glacier distribution, water storage, glacier changes, hydrological modeling, and information extraction on the local and regional scales, which is the focus [27] of current research on glaciers [21].…”

Comment on tc-2020-372

“…The Khumbu region has been well studied in the last decades in terms of glacier mass balance using the traditional glaciologic method (Wagnon et al, 2013), the geodetic method (Bolch et al, 2008;Nuimura et al, 2012;Brun et al, 2017;Bolch et al, 2011;Rieg et al, 2018), energy balance models (Rounce and McKinney, 2014;Rounce et al, 2015;Kayastha et al, 2000), debris cover characteristics (Iwata et al, 1980;Watanabe et al, 1986;Nakawo et al, 1999;Iwata et al, 2000;Casey et al, 2011;Yukari et al, 2000), surface velocity (Quincey et al, 2009) and more recently mapping of supraglacial lakes and ice cliffs at glacier scale (Watson et al, 2016(Watson et al, , 2017a. Rates of change of the debris-covered glacier tongues in this area vary from −0.12 ± 0.05 % per year from 1962 to 2005 (Bolch et al, 2008) to −0.27 ± 0.06 % per year from 1962 to 2011 (Thakuri et al, 2014). Watson et al (2017a) reported a pond coverage area of 1 -7 % of the glacierized area for some glaciers in the Khumbu based on high resolution Pleiades data.…”