A regionally resolved inventory of High Mountain Asia surge-type glaciers, derived from a multi-factor remote sensing approach

Guillet, Grégoire; King, Owen; Lv, Mingyang; Ghuffar, Sajid; Benn, Douglas I.; Quincey, Duncan J.; Bolch, Tobias

doi:10.5194/tc-2021-303

Cited by 6 publications

(10 citation statements)

References 72 publications

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“…Surge type glaciers inventory from Guillet et al (2022) The code to reproduce the results can be found on github (https://github.com/pohleric/barandun_pohl_tsl-correl). The required main input files, containing the morphological and meteorological data are deposited on a zenodo repository (https://doi.org/10.5281/zenodo.6631963).…”

Section: Discussionmentioning

confidence: 99%

“…We use glacier outlines and areas from the RGIv6 (RGI, 2017), which were kept unchanged over time, and the freely available, void-filled Shuttle Radar Topography Mission (SRTM, Jarvis et al, 2008) digital elevation model to derive glacier topography, aspect and slope as morphological parameters. We use further the information provided in Scherler et al (2018) on percentage of debris-cover on individual glaciers for the Tien Shan and Pamir and the data on surge activity from (Guillet et al, 2022).…”

Section: Glacier Morphological Characteristicsmentioning

confidence: 99%

“…For aspect, we use the sine and cosine of the original values given in the range between 0 and 360 • to have consistent value ranges in zonal and meridional directions. We showcase, additionally, any possible relationship with surge activity using separate histograms and boxplots where we simplify the complex information on surge activity given in Guillet et al (2022) as either having experienced surge activity (1) or not (0) during the 2000 to 2018 study period.…”

Section: Spatial Correlationmentioning

confidence: 99%

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Central Asia's spatiotemporal glacier response ambiguity due to data inconsistencies and regional simplifications

Barandun¹,

Pohl²

2022

Preprint

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Abstract. Glacier evolution across Tien Shan and Pamir is heterogeneous in space and time. This heterogeneity is believed to be mainly driven by contrasting climatic settings and changing atmospheric conditions. However, a systematic and consistent region-wide analysis of the climatic and static morphological drivers remains limited to date. Meteorological reanalysis and remote sensing products, and novel approaches to derive region-wide annual mass balance time series, all provide the basis to investigate the drivers behind the observed heterogeneous glacier response. Here, we investigate the consistency of interpretations derived from available datasets through correlation analyses between climatic and static drivers with mass balance estimates in Tien Shan and Pamir. Our results show that even supposedly similar datasets lead to different and partly contradicting assumptions on dominant drivers of mass balance variability. Only when considering all glaciers in the Pamir and Tien Shan together, we find a similar picture of dominant meteorological drivers over space. Using either existing mountain subdivisions or glacier subdivisions based on mass balance variability, no consistencies can be found. Within different mass balance and meteorological datasets the results suggest very different drivers. This conclusion is even more prominent in the temporal correlation analysis where contradicting patterns of dominant drivers result from presumably similar meteorological datasets. Clear non-climatic drivers could not be identified. Even with newly available mass balance and meteorological data, a knowledge gap about the main mechanisms behind the heterogeneous glacier response in Central Asia remains. The results highlight that apparent but false consistencies across studies using a single dataset might largely relate to the chosen dataset rather than to the processes or involved environmental variables. As long as no glaciological, meteorological, or hydrological in situ observation network provides data for direct calibration and validation of extensive datasets, we cannot predict a realistic improvement in our understanding of the changing cryosphere at regional scale for Tien Shan and Pamir.

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Section: Discussionmentioning

confidence: 99%

Section: Glacier Morphological Characteristicsmentioning

confidence: 99%

Section: Spatial Correlationmentioning

confidence: 99%

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Central Asia's spatiotemporal glacier response ambiguity due to data inconsistencies and regional simplifications

Barandun¹,

Pohl²

2022

Preprint

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“…However, the velocity data used in Guillet et al. (2022) is annual scale data extracted from optical images. The elevation data also has the problem of a too long time interval.…”

Section: Introductionmentioning

confidence: 99%

“…Guillet et al. (2022) identify surge‐type glaciers by using multiple factors, including surface velocity, elevation and feature changes in High Mountain Asia. However, the velocity data used in Guillet et al.…”

Section: Introductionmentioning

confidence: 99%

Identification of Unstable Glacier Flow in the Western Tibetan Plateau and Karakoram Using Machine Learning

Zhu

et al. 2022

JGR Earth Surface

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Glacier surge represents a strong glacier flow instability mode that is expressed as a velocity increase of more than one order of magnitude and a mass transfer from the accumulation area to the ablation area during the active phase. The active phase is usually maintained for months to years before the glacier enters the quiescent phase. Surge-type glaciers tend to cluster within particular regions, such as High Mountain Asia, Arctic Canada, Russian Arctic, Yukon, Greenland, Svalbard and Alaska. Besides glacier surges, glacier pulses are another type of unstable glacier flow and have smaller temporal and spatial scales than surges (Herreid & Truffer, 2016;Turrin et al., 2014). In time, pulse events usually last about one month and span two or three 12-day time intervals. In space, pulse events usually occur in local areas of glaciers and do not affect the whole glacier (Zhu et al., 2021). Pulse events can be observed during the quiescent phase of surge-type glaciers (Nolan, 2003), during a longterm surge (Frappé & Clarke, 2007

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A Pipeline for Automated Processing of Declassified Corona KH-4 (1962–1972) Stereo Imagery

Ghuffar

Bolch

Rupnik

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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The Corona KH-4 reconnaissance satellite missions acquired panoramic stereo imagery with high spatial resolution of 1.8-7.5 m from 1962-1972. The potential of 800,000+ declassified Corona images has not been leveraged due to the complexities arising from handling of panoramic imaging geometry, film distortions and limited availability of the metadata required for georeferencing of the Corona imagery. This paper presents the Corona Stereo Pipeline (CoSP): A pipeline for processing of Corona KH-4 stereo panoramic imagery. CoSP utilizes deep learning based feature matcher SuperGlue to automatically match features point between Corona KH-4 images and recent satellite imagery to generate Ground Control Points (GCPs). To model the imaging geometry and the scanning motion of the panoramic KH-4 cameras, a rigorous camera model consisting of modified collinearity equations with time-dependent exterior orientation parameters is employed. Using the entire frame of the Corona image, bundle adjustment with well-distributed GCPs results in an average standard deviation or σ0 of less than two pixels. We evaluate fiducial marks on the Corona films and show that preprocessing the Corona images to compensate for film bending improves the 3D reconstruction accuracy. The distortion pattern of image residuals of GCPs and y-parallax in epipolar resampled images suggest that film distortions due to long-term storage likely cause systematic deviations of up to six pixels. Compared to the SRTM DEM, the Corona DEM computed using CoSP achieved a Normalized Median Absolute Deviation of elevation differences of ≈ 4 m over an area of approx. 4000 km 2 after a tilebased fine coregistration of the DEMs. We further assess CoSP on complex scenes involving high relief and glacierized terrain and show that the resulting DEMs can be used to compute long-term glacier elevation changes over large areas.

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A regionally resolved inventory of High Mountain Asia surge-type glaciers, derived from a multi-factor remote sensing approach

Cited by 6 publications

References 72 publications

Central Asia's spatiotemporal glacier response ambiguity due to data inconsistencies and regional simplifications

Central Asia's spatiotemporal glacier response ambiguity due to data inconsistencies and regional simplifications

Identification of Unstable Glacier Flow in the Western Tibetan Plateau and Karakoram Using Machine Learning

A Pipeline for Automated Processing of Declassified Corona KH-4 (1962–1972) Stereo Imagery

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