Abstract
Debris-covered glaciers are common features on the eastern Pamir and serve as important indicators of climate change promptly. However, mapping of debris-covered glaciers in alpine regions is still challenging due to many factors including the spectral similarity between debris and the adjacent bedrock, shadows cast from mountains and clouds, and seasonal snow cover. Considering that few studies have added movement velocity features when extracting glacier boundaries, we innovatively developed an automatic algorithm consisting of rule-based image segmentation and Random Forest to extract information about debris-covered glaciers with Landsat-8 OLI/TIRS data for spectral, texture and temperature features, multi-digital elevation models (DEMs) for elevation and topographic features, and the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) for movement velocity features, and accuracy evaluation was performed to determine the optimal feature combination extraction of debris-covered glaciers. The study found that the overall accuracy of extracting debris-covered glaciers using combined movement velocity features is 97.60%, and the Kappa coefficient is 0.9624, which is better than the extraction results using other schemes. The high classification accuracy obtained using our method overcomes most of the above-mentioned challenges and can detect debris-covered glaciers, illustrating that this method can be executed efficiently, which will further help water resources management.
Highlights
Glaciers are important indicators of climate change and play an indispensable role in the global water cycle [1]
To show the difference of Land Surface Temperature (LST) in comparison with glaciers and debris‐covered glaciers, we demonstrated this difference using the LST map shown in Detailed texture features combined withwith the correlation coefficient matrixmatrix of the spectral index
digital elevation models (DEMs) accuracy was another key factor in this study that directly affected the topographical features of glaciers in the Random Forest classification process
Summary
Glaciers are important indicators of climate change and play an indispensable role in the global water cycle [1]. In the context of global warming, the global glaciers experienced large-scale glacier melting during 2003–2009, accounting for 29 ± 13% [2] of the global sea-level rise during the same period. Glacier changes in alpine areas of Asia contribute to sea-level rise [3], but most importantly, they affect the runoff patterns and sizes of dozens of rivers around alpine areas of Asia [4], which have the largest population density in the world. Some studies predict that in the few decades, glaciers in alpine areas of Asia will continue to decrease [6,7]. It is of great significance to study the characteristic of glaciers in alpine areas of Asia
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