Glacier Velocity Changes in the Himalayas in Relation to Ice Mass Balance

Abstract

Glacier evolution with time provides important information about climate variability. Here, we investigated glacier velocity changes in the Himalayas and analysed the patterns of glacier flow. We collected 220 scenes of Landsat-7 panchromatic images between 1999 and 2000, and Sentinel-2 panchromatic images between 2017 and 2018, to calculate surface velocities of 36,722 glaciers during these two periods. We then derived velocity changes between 1999 and 2018 for the early winter period, based on which we performed a detailed analysis of motion of each individual glacier, and noted that the changes are spatially heterogeneous. Of all the glaciers, 32% have sped up, 24.5% have slowed down, and the rest 43.5% have remained stable. The amplitude of glacier slowdown, as a result of glacier mass loss, is significantly larger than that of speedup. At regional scales, we found that glacier surface velocity in winter has uniformly decreased in the western part of the Himalayas between 1999 and 2018, while increased in the eastern part; this contrasting difference may be associated with decadal changes in accumulation and/or melting under different climatic regimes. We also found that the overall trend of surface velocity exhibits seasonal variability: summer velocity changes are positively correlated with mass loss, i.e., velocity increases with increasing mass loss, whereas winter velocity changes show a negative correlation. Our study suggests that glacier velocity changes in the Himalayas are spatially and temporally heterogeneous, in agreement with studies that previously highlighted this trend, emphasising complex interactions between glacier dynamics and environmental forcing.

The Spatio-Temporal Patterns of Glacier Activities in the Eastern Pamir Plateau Investigated by Time Series Sub-Pixel Offsets From Sentinel-2 Optical Images

“…The revisit period is 10 days for one satellite and 5 days for two satellites constellation (https://sentinels.copernicus.eu/web/sentinel/userguides/sentinel-2-msi). Sentinel-2 images, with their high temporal-spatial resolution, large-scale coverage, and open access data policy, have become an ideal data source for detecting time-series changes in land [36] in comparison to other optical remote sensing images [37][38][39]. Given the radiometric characteristics of different bands of Sentinel-2 image for various surface targets, band 8 has been suggested to be more suitable for surface deformation monitoring with the pixel offset method than other bands [38,40].…”

“…In this study, the glacier movement was estimated by using the pixel offset tracking (POT) method with the Co-registration of Optically Sensed Images and Correlation (COSI-Corr) package. The COSI-Corr package was developed for coseismic ground displacement extraction with optical images [39] and integrated as a module in ENVI software platform (http://www.tectonics.caltech.edu), and it has been widely used to monitor the earth's surface changes [35,[40][41][42][43] and glacier monitoring [15,26,33,[44][45][46][47]. The output files of the COSI-Corr package include the horizontal ground displacement of the east-west (EW) and north-south (NS) components, as well as the relative signal-to-noise ratio (SNR) in each pair of images.…”

“…It should be used as a priority when correlating optical images. However, as this correlator is more sensitive to noise, it is recommended for high-quality optical images [39]. In this work, the initial and final window size was set as 32 × 32 pixels, with a moving step length of 8, and a robustness iteration of 2 [26,41,43].…”

Assessing the Variation of Glaciers Velocity in Hunza Basin, Gilgit Baltistan Using Advanced Geospatial Techniques

“…For the correlation of optical images, different techniques have been applied and developed like normalized cross-correlation [48][49], phase correlation [50], least-squares [51], and orientation correlation (CCF-O) [52]. It has been stated that phase correlation [53] with some add-on advantages like pre and post-processing [54].…”

Mid-Term Monitoring of Glacier’s Variations with UAVs: The Example of the Belvedere Glacier

“…Thanks to the global coverage, the long time-series of data available and the short revisiting period, satellite optical imageries are nowadays widely used to derive planimetric glacier displacements and ice flow velocities [9][10][11][12][13]. Satellite SAR data can be used to measure 3D motion of glaciers [14][15][16].…”