Monitoring and Analyzing Mountain Glacier Surface Movement Using SAR Data and a Terrestrial Laser Scanner: A Case Study of the Himalayas North Slope Glacier Area

Jiang, Fan; Wang, Qun; Liu, Guang; Zhang, Lu; Guo, Zhancheng; Liqiang, Tong; Peng, Junhuan; Yuan, Weilin; Zhou, Wei; Jin, Yan; Perski, Zbigniew; Sousa, Joaquim J.

doi:10.3390/rs11060625

Cited by 19 publications

(23 citation statements)

References 38 publications

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“…In the last decade, massive glacier retreat, especially of large valley glaciers, has resulted in an increased formation of glacial lakes [57]. Due to natural hazards related to glacial lakes such as glacier lake outburst floods (GLOFs), monitoring these lakes has become a crucial task in cryosphere research [58,59]. Since GLOFs are not among the most frequent hazards in the Alps, monitoring the evolution of glacier lakes using TLS was limited to work on Brenva Glacier [54].…”

Section: Terrestrial Laserscanningmentioning

confidence: 99%

The Status of Earth Observation Techniques in Monitoring High Mountain Environments at the Example of Pasterze Glacier, Austria: Data, Methods, Accuracies, Processes, and Scales

et al. 2020

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Earth observation offers a variety of techniques for monitoring and characterizing geomorphic processes in high mountain environments. Terrestrial laserscanning and unmanned aerial vehicles provide very high resolution data with high accuracy. Automatic cameras have become a valuable source of information—mostly in a qualitative manner—in recent years. The availability of satellite data with very high revisiting time has gained momentum through the European Space Agency’s Sentinel missions, offering new application potential for Earth observation. This paper reviews the status of recent techniques such as terrestrial laserscanning, remote sensed imagery, and synthetic aperture radar in monitoring high mountain environments with a particular focus on the impact of new platforms such as Sentinel-1 and -2 as well as unmanned aerial vehicles. The study area comprises the high mountain glacial environment at the Pasterze Glacier, Austria. The area is characterized by a highly dynamic geomorphological evolution and by being subject to intensive scientific research as well as long-term monitoring. We primarily evaluate landform classification and process characterization for: (i) the proglacial lake; (ii) icebergs; (iii) the glacier river; (iv) valley-bottom processes; (v) slope processes; and (vi) rock wall processes. We focus on assessing the potential of every single method both in spatial and temporal resolution in characterizing different geomorphic processes. Examples of the individual techniques are evaluated qualitatively and quantitatively in the context of: (i) morphometric analysis; (ii) applicability in high alpine regions; and (iii) comparability of the methods among themselves. The final frame of this article includes considerations on scale dependent process detectability and characterization potentials of these Earth observation methods, along with strengths and limitations in applying these methods in high alpine regions.

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Section: Terrestrial Laserscanningmentioning

confidence: 99%

The Status of Earth Observation Techniques in Monitoring High Mountain Environments at the Example of Pasterze Glacier, Austria: Data, Methods, Accuracies, Processes, and Scales

et al. 2020

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“…China has the largest number of mountain glaciers in the world, and most of its glaciers are mountain glaciers [ 5 ]. In addition, mountain glaciers have irreplaceable ecological service functions for the population, resource environment, and economic development of agriculture and animal husbandry in arid and semi-arid regions for their special spatial and temporal distribution and evolutionary processes [ 6 , 7 ]. Mountain glaciers are in a state of rapid melting in the context of global warming [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Prediction of Glacier Deformation in the Meili Snow Mountain Based on InSAR Technology and GA-BP Neural Network Algorithm

Yang

et al. 2022

Sensors

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The morphological changes in mountain glaciers are effective in indicating the environmental climate change in the alpine ice sheet. Aiming at the problems of single monitoring index and low prediction accuracy of mountain glacier deformation at present, this study takes Meili Mountain glacier in western China as the research object and uses InSAR technology to construct the mountain glacier deformation time series and 3D deformation field from January 2020 to December 2021. The relationship between glacier deformation and elevation, slope, aspect, glacier albedo, surface organic carbon content, and rainfall was revealed by grey correlation analysis. The GA-BP neural network prediction model is established from the perspective of multiple factors to predict the deformation of Meili Mountain glacier. The results showed that: The deformation of Meili Mountain glacier has obvious characteristics of spatio-temporal differentiation; the cumulative maximum deformation quantity of glaciers in the study period is −212.16 mm. After three-dimensional decomposition, the maximum deformation quantity of glaciers in vertical direction, north–south direction and east–west direction is −125.63 mm, −77.03 mm, and 107.98 mm, respectively. The average annual deformation rate is between −94.62 and 75.96 mm/year. The deformation of Meili Mountain glacier has a gradient effect, the absolute value of deformation quantity is larger when the elevation is below 4500 m, and the absolute value of deformation quantity is smaller when it is above 4500 m. The R2, MAPE, and RMSE of the GA-BP neural network to predict the deformation of Meili glacier are 0.86, 1.12%, and 10.38 mm, respectively. Compared with the standard BP algorithm, the prediction accuracy of the GA-BP neural network is significantly improved, and it can be used to predict the deformation of mountain glaciers.

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“…This technique is widely used to obtain glacier surface velocities (yan et al 2013, Strozzi et al 2017, Fang et al 2020, Neckel et al 2020 but also displacement values in mining areas (Huang et al 2016, 2020, Zhao et al 2021 or even in the areas of landslides (Cai et al 2017;Sun et al 2017) or sand dunes (Mahmoud et al 2020). The accuracy of this method is determined as 1/10-1/30 of the pixel size (Fan et al 2019). There are also other algorithms for glacier velocity calculations.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Velocity Changes of the Jakobshavn Glacier Based on SAR Imagery

Łukosz

Hejmanowski

Witkowski

2022

Quaestiones Geographicae

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The study analyzes the changes in dynamics of the Jakobshavn Glacier in summer and winter in 2017 and 2021. Satellite radar observations and the available database were used for this. Moreover, the influence of the time baseline between SAR images on the quality of the results was also investigated. The velocities computed from Sentinel-1 images and the offset-tracking technique were compared with the MEaSUREs database information. The results showed that Jakobshavn Glacier accelerated in 2021 up to 39.0 m d−1. However, this value may be underestimated due to the resolution of Sentinel-1 data. The results therefore confirm the acceleration of the glacier melting process, which may be a result of the observed climate changes on our planet.

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Monitoring and Analyzing Mountain Glacier Surface Movement Using SAR Data and a Terrestrial Laser Scanner: A Case Study of the Himalayas North Slope Glacier Area

Cited by 19 publications

References 38 publications

The Status of Earth Observation Techniques in Monitoring High Mountain Environments at the Example of Pasterze Glacier, Austria: Data, Methods, Accuracies, Processes, and Scales

The Status of Earth Observation Techniques in Monitoring High Mountain Environments at the Example of Pasterze Glacier, Austria: Data, Methods, Accuracies, Processes, and Scales

Monitoring and Prediction of Glacier Deformation in the Meili Snow Mountain Based on InSAR Technology and GA-BP Neural Network Algorithm

Analysis of the Velocity Changes of the Jakobshavn Glacier Based on SAR Imagery

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