A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges

Abstract

Abstract. Knowledge about the coverage and characteristics of glaciers in High Mountain Asia (HMA) is still incomplete and heterogeneous. However, several applications, such as modelling of past or future glacier development, run-off, or glacier volume, rely on the existence and accessibility of complete datasets. In particular, precise outlines of glacier extent are required to spatially constrain glacier-specific calculations such as length, area, and volume changes or flow velocities. As a contribution to the Randolph Glacier Inventory (RGI) and the Global Land Ice Measurements from Space (GLIMS) glacier database, we have produced a homogeneous inventory of the Pamir and the Karakoram mountain ranges using 28 Landsat TM and ETM+ scenes acquired around the year 2000. We applied a standardized method of automated digital glacier mapping and manual correction using coherence images from the Advanced Land Observing Satellite 1 (ALOS-1) Phased Array type L-band Synthetic Aperture Radar 1 (PALSAR-1) as an additional source of information; we then (i) separated the glacier complexes into individual glaciers using drainage divides derived by watershed analysis from the ASTER global digital elevation model version 2 (GDEM2) and (ii) separately delineated all debris-covered areas. Assessment of uncertainties was performed for debris-covered and clean-ice glacier parts using the buffer method and independent multiple digitizing of three glaciers representing key challenges such as shadows and debris cover. Indeed, along with seasonal snow at high elevations, shadow and debris cover represent the largest uncertainties in our final dataset. In total, we mapped more than 27 800 glaciers >0.02 km2 covering an area of 35 520±1948 km2 and an elevation range from 2260 to 8600 m. Regional median glacier elevations vary from 4150 m (Pamir Alai) to almost 5400 m (Karakoram), which is largely due to differences in temperature and precipitation. Supraglacial debris covers an area of 3587±662 km2, i.e. 10 % of the total glacierized area. Larger glaciers have a higher share in debris-covered area (up to >20 %), making it an important factor to be considered in subsequent applications (https://doi.org/10.1594/PANGAEA.894707).

Global Assessment of Supraglacial Debris‐Cover Extents

“…Glacier areas that are neither ice nor snow are classified as debris cover. This procedure has previously been used for mapping supraglacial debris cover (e.g., Herreid et al, 2015;Mölg et al, 2018), recently also with GEE (Kraaijenbrink et al, 2017). For glacier areas, we rely on the Randolph Glacier Inventory (RGI) version 6.0, which provides digital polygons of all glaciers on Earth, except for the Greenland and Antarctic ice sheets (RGI Consortium, 2017).…”

“…Finally, we defined debris-covered ice surfaces based on the RATIO, NDSI, and FDC images and threshold values of RATIO > 2.0 (e.g., Rastner et al, 2017), NDSI<0.4 (e.g., Dozier, 1989), and FDC > 80%. To find a suitable FDC threshold and to evaluate how sensitive the debris-cover classification is to the chosen threshold values, we compared a subset of our global data set with existing debris-cover maps from parts of South and Central Asia (Mölg et al, 2018), hereafter called the M2018 data set. The M2018 data set was created based on manually selected Landsat images acquired around the year 2000 (±2 years).…”

“…The M2018 data set was created based on manually selected Landsat images acquired around the year 2000 (±2 years). Debris-cover extents were mapped manually in this study, using contrast-enhanced band composites and coherence images from ALOS1-PALSAR1 as a guide for identification (Mölg et al, 2018). We limited our comparison to~11,000 glaciers whose outlines are identical in the RGI60 and M2018 data sets.…”

“…These glaciers are situated in the Karakoram Mountains and have a wide range of sizes and debris-cover extents. We further restricted our comparison to areas situated below the median elevation of each glacier, because Mölg et al (2018) eliminated any potential debris cover above it. We evaluated the debris-cover classification for different threshold values based on the accuracy, which we defined as the sum of true positive (debris cover in both data sets) and true negative (no debris cover in both data sets) image pixels divided by the total number of image pixels.…”

“…The goal of this contribution is twofold. First, we present our new automatic mapping approach and evaluate it by comparison with a recently published data set of semiautomatically mapped supraglacial debris cover from the Karakoram and Pamir Mountains (Mölg et al, 2018). Second, we provide the first global assessment of the spatial distribution of supraglacial debris cover, excluding the Greenland ice sheet and Antarctica, which are mostly free of debris.…”