An insight into the surface velocity of Inylchek Glacier and its effect on Lake Merzbacher during 2006–2016 with Landsat time-series imagery

Yan, Song Y.; Li, Yi; Li, Zhiguo; Liu, Guang; Ruan, Zhixing; Li, Zian

doi:10.1007/s12665-018-7964-7

Cited by 7 publications

(5 citation statements)

References 29 publications

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“…At a distance of 7.5 km from the initial point along pro le AB, there is an extreme velocity was observed at the fork with high velocity along the middle line because of the friction from the surrounding mountains. Such velocity distribution shows a satisfying agreement with the result in the previous study (Neelmeijer et al 2014;Nobakht et al 2014;Mayer et al 2016;Yan et al 2018).…”

Section: Comparative Analysis Of Glacier Movementsupporting

confidence: 91%

“…As Figs. 4-6 show, there is an obvious discontinuity phenomenon in the upstream region of ice motion elds, which is mainly caused by the saturation associated with the high albedo of clean ice (Yan et al 2018). Although the large matching window was selected to perfect the Signal-to-Noise Ratio(SNR), the velocity information might be lost at the edge of the glacier at the same time.…”

Section: Comparative Analysis Of Glacier Movementmentioning

confidence: 99%

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The South Inylchek Glacier Activity Analysis over a Ten-year Interval in Surface Velocity with Multi-source Spaceborne Imagery

Zhang

et al. 2021

Preprint

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Ice movement is one of the most important characteristics in describing mountain glacier activity, which is a sensitive natural indicator of climate change. However, the short-term ice movement with a single data source could not precisely and sufficiently demonstrate the response of glaciers to climate change. In order to extract the reliable signal corresponding to climate change, the long-term monitoring of glacier movement should be widely exploited. This paper presents the ice motion distribution of the South Inylchek Glacier by improving pixel tracking algorithm with both synthetic aperture radar and optical imagery in 2007-2008 and 2017-2018. The analysis in spatiotemporal characteristics of the glacier velocity indicates that the South Inylchek Glacier remained almost stable in a ten-year interval with the average velocity of 32cm/d, which are computed with quasi-synchronization multi-source imagery. And the consistency of the velocity results was also verified with both optical and SAR imagery during the same period. Therefore, it would be valuable in expanding the research temporal cycle of glacier movement by uniting multi-source data. We also suggest that long-term ice motion should be collected for further analysis in mass balance prediction and assessing the climate change effects in the High Mountain Asia.

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Section: Comparative Analysis Of Glacier Movementsupporting

confidence: 91%

Section: Comparative Analysis Of Glacier Movementmentioning

confidence: 99%

The South Inylchek Glacier Activity Analysis over a Ten-year Interval in Surface Velocity with Multi-source Spaceborne Imagery

Zhang

et al. 2021

Preprint

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“…It can be identified that from the terminus to till 1 km, surface velocity was < 10 m/year which indicate the stagnancy in surface velocity (figure 4). The main reason behind stagnancy in surface velocity is presence of debris covered ice, which is common characteristic of debris covered glaciers (Quincey et al, 2009b;Yan et al, 2018). In debris covered glacier, in lower ablation zone and near the terminus, maximum ice thickness reduction and surface lowering occur; and as a result stagnant low sloping downstream lower ablation zone appears (Benn et al, 2012).…”

Section: Study Duration Surface Velocity (M/year)mentioning

confidence: 99%

“…Glacier surface velocity is one of important parameters to understand behaviour of glacier in absence of mass balance and long-term glacier area change information. Glacier surface velocity also helps to understand glacier retreat and advance, mass balance changes, ice thickness estimation, early warning of glacier lake outburst flood and change in strain rate (Dehecq et al, 2019;Gantayat et al, 2014;Paul et al, 2017;Yan et al, 2018). Therefore, it is necessary to study surface velocity of the glaciers to increase the knowledge in glacier dynamics.…”

Section: Introductionmentioning

confidence: 99%

Surface Velocity Dynamics of Samudra Tapu Glacier, India From 2013 to 2017 Using Landsat-8 Data

Sahu

Gupta

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In glacier dynamics, surface velocity of glacier is an important parameter to understand the behaviour of glacier in absence of mass balance and long-term glacier area change information. In present study, surface velocity of Samudra Tapu Glacier, India is estimated using freely available Landsat-8 OLI (PAN) images during 2013–2017. To estimate surface velocity, open source COSI-Corr tool is used which is based on cross-correlation algorithm. Maximum annual surface velocity estimated is 55.68 ± 4.01 m/year during 2015–2016 while the minimum surface velocity being 44.99 ± 4.67 m/year in 2016–2017. The average annual velocity during 2013–2017 was 50.51 ± 4.49 m/year which is higher than other glaciers in Chandra basin. The variation in annual surface velocity is analysed which not only depends on mass loss but also on temperature, pressure and internal drainage. Further, as one moves opposite to glacier terminus, the surface velocity increases with the increase in glacier elevation and slope. The higher surface velocity can be attributed to the fact that Samudra Tapu is a top-heavy glacier based on HI index analysis having larger accumulation area along with high glacier ice-thickness.

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“…Recent literature shows that Landsat time series have been used in studies on glacier dynamics, detecting glacial lakes, their expansion, and associated floods in different mountain regions such as HKH, Central Asia, and European Alps (Huang et al 2018;Ma et al 2021;Veh et al 2018;Yan et al 2018;Zheng et al 2019). In addition, changes in a forest in wetland areas of China were investigated, and flood impact on Swiss floodplains was also analyzed (Milani et al 2020;Wu et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Detecting abrupt change in land cover in the eastern Hindu Kush region using Landsat time series (1988–2020)

et al. 2022

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Land cover change in the semi-arid environment of the eastern Hindu Kush region is driven by anthropogenic activities and environmental change impacts. Natural hazards, such as floods presumably influenced by climatic change, cause abrupt change of land cover. So far, little research has been conducted to investigate the spatiotemporal aspects of this abrupt change in the valleys. In order to explore the abrupt change in land cover and floods as its possible drivers in the eastern Hindu Kush, a semi-arid mountain region characterized by complex terrain, vegetation variation, and precipitation seasonality, we analyzed long-term Landsat image time series from 1988 to 2020 using Breaks For Additive Seasonal and Trend (BFAST). Overall, BFAST effectively detected abrupt change by using Landsat-derived Modified Soil Adjusted Vegetation Index (MSAVI). The results of our study indicate that approximately 95% of the study area experienced at least one abrupt change during 1988–2020. The years 1991, 1995, 1998, 2007, and 2016 were detected as the peak years, with the peaks occurring in different seasons. The annual trend of abrupt change is decreasing for the study area. The seasonality of abrupt change at the catchment level shows an increasing trend in the spring season for the southern catchments of Panjkora and Swat. The spatial distribution patterns show that abrupt change is primarily concentrated in the floodplains indicating that flooding is the primary driver of the land cover change in the region. We also demonstrated the accurate detection of past flood events (2015) based on the two case examples of Ayun, Rumbur, and Kalash valleys. The detection of the flood events was verified by fieldwork and historical high-resolution Google Earth imagery. Finally, our study provides an example of applying Landsat time series in a dry mountain region to detect abrupt changes in land cover and analyze impact of natural hazards such as floods.

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An insight into the surface velocity of Inylchek Glacier and its effect on Lake Merzbacher during 2006–2016 with Landsat time-series imagery

Cited by 7 publications

References 29 publications

The South Inylchek Glacier Activity Analysis over a Ten-year Interval in Surface Velocity with Multi-source Spaceborne Imagery

The South Inylchek Glacier Activity Analysis over a Ten-year Interval in Surface Velocity with Multi-source Spaceborne Imagery

Surface Velocity Dynamics of Samudra Tapu Glacier, India From 2013 to 2017 Using Landsat-8 Data

Detecting abrupt change in land cover in the eastern Hindu Kush region using Landsat time series (1988–2020)

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