Abstract
Glacier retreat is a common phenomenon in the Qinghai-Tibetan Plateau (QTP) with global warming during the past several decades, except for several mountains, such as the glaciers in the Karakoram and the western Kunlun Mountains. The dynamic nature of glaciers significantly influences the hydrologic, geologic, and ecological systems in the mountain regions. The sensitivity and dynamic response to climate change make glaciers excellent indicators of regional and global climate change, such as glacier melting and retreat with the rise of local air temperature. Long-term monitoring of glacier change is important to understand and assess past, current, and possible future climate environments. Some glacier surfaces are safe and accessible by foot, and are monitored using mass balance stakes and snow pits. Meanwhile, some glaciers with inaccessible termini may be surveyed using satellite remote images and Unmanned Aerial Vehicles (UAVs). Those inaccessible glaciers are generally covered by debris in the southeast QTP, which is hardly accessible due to the wide distribution of crevasses and cliffs. In this paper, we used the UAV to monitor the dynamic features of mass balance and velocity of the debris-covered region of Baishui River Glacier No. 1 (BRG1) on the Yulong Snow Mountain (YSM), Southeast QTP. We obtained the Orthomosaic and DEM with a high resolution of 0.10 m on 20 May and 22 September 2018, respectively. The comparison showed that the elevation of the debris-covered region of the BRG1 decreased by 6.58 m ± 3.70 m on average, and the mean mass balance was −5.92 m w.e. ± 3.33 m w.e. during the summer, correspondingly. The mean displacement of debris-covered glacier surface was 18.30 m ± 6.27 m, that is, the mean daily velocity was 0.14 m/d ± 0.05 m/d during the summer. In addition, the UAV images not only revealed the different patterns of glacier melting and displacement but also captured the phenomena of mass loss due to ice avalanches at the glacier front and the development of large crevasses. This study provides a feasible method for understanding the dynamic features of global debris-covered glaciers which are inaccessible and unobservable by other means.
Highlights
Large amounts of glacier and snow resources are stored in the Qinghai-Tibetan Plateau (QTP), which supply important freshwater resources from glacier- and snow-melting feeds for more than 1.4 billion people of surrounding river basins, e.g., the Indus, Ganges, Brahmaputra, Yangtze, and Yellow rivers [1,2,3,4,5]
The melting is heterogeneous in space, for example, glacier mass balance ranged from −0.30 ± 0.12 m w.e./a to −0.11 ± 0.14 m w.e./a in the southeastern QTP, which was stronger than that in the northwestern QTP, with glacier mass balance ranging from −0.11 ± 0.13 m w.e./a to 0.02 ± 0.10 m w.e./a [7]
The total Root Mean Square Error (RMSE) of Ground Validation Points (GVPs) in the sXuYrvdeiyresc,taionnds wera1s8locwme.r Tthhaenre1f4orcem, tinhebogtehnseurarvteedysD, aEnMdsthaentdotOalrRtoMmSoEsawicass slohwowered18gcomo.dTahcecruerfoarcey,thwehgicehnemraettedthDeEcMonsdaintidonOsrtoofmcoosmaipcus tsihnogwtehde gimooadgeascc(uer.gac.,y,gwlahcicehr msuertfathce vcoelnodcitiyonasnodf ecloemvaptuiotinngchthanegimesa).geIns.gInimadagdeitsioin, dthifefecrendtibtilmityesoifsrdeseuscltrsibfreodmincothmepdairsicnugsismioangesescitniodniff. erent times is described in the discussion section
Summary
Large amounts of glacier and snow resources are stored in the Qinghai-Tibetan Plateau (QTP), which supply important freshwater resources from glacier- and snow-melting feeds for more than 1.4 billion people of surrounding river basins, e.g., the Indus, Ganges, Brahmaputra, Yangtze, and Yellow rivers [1,2,3,4,5]. The geodetic methods of glacier mass balance and surface velocity have been widely applied using remotely sensed datasets, either spaceborne or airborne, as they provide a relatively easy and inexpensive method to obtain frequent data for large, possibly inaccessible areas [8]. For the geodetic measurements of glacier surface velocity, manual or automated cross-correlation feature tracking is employed to optically sensed imagery, such as the images from Landsat Thematic Mapper (TM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Satellite Pour l’Observation de la Terre-5 (SPOT5), Envisat Advanced Synthetic Aperture Radar (ASAR), and Moderate Resolution Imaging Spectroradiometer (MODIS) [8,15,16,17]. Shadows are difficult to detect with respect to these artifacts in the high sky These products considerably reduce the accuracy of the surface velocity or elevation change at specific locations [9]
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