Estimating Dynamic Parameters of Bara Shigri Glacier and Derivation of Mass Balance from Velocity

Nela, Bala Raju; Singh, Gulab; Bandyopadhyay, Debmita; Patil, Akshay; Mohanty, Shradha; Musthafa, Mohamed; Dasondhi, Girjesh

doi:10.1109/igarss39084.2020.9323152

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…[42,43]. As the largest glacier in Himachal Pradesh (Indian State), it provides valuable information about climate change studies [44]. Various studies also explored the retreating of the Bara Shigri glacier with an average terminus retreat of 1100.2 ± 32.1 m (22.5 ± 0.7 m per year) from 1965 to 2014 [45,46].…”

Section: Study Area and Datamentioning

confidence: 99%

Glacier Boundary Mapping Using Deep Learning Classification over Bara Shigri Glacier in Western Himalayas

et al. 2022

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Glacier, snow, and ice are the essential components of the Himalayan cryosphere and provide a sustainable water source for different applications. Continuous and accurate monitoring of glaciers allows the forecasting analysis of natural hazards and water resource management. In past literature, different methodologies such as spectral unmixing, object-based detection, and a combination of various spectral indices are commonly utilized for mapping snow, ice, and glaciers. Most of these methods require human intervention in feature extraction, training of the models, and validation procedures, which may create bias in the implementation approaches. In this study, the deep learning classifier based on ENVINet5 (U-Net) architecture is demonstrated in the delineation of glacier boundaries along with snow/ice over the Bara Shigri glacier (Western Himalayas), Himachal Pradesh, India. Glacier monitoring with Landsat data takes the advantage of a long coverage period and finer spectral/spatial resolution with wide coverage on a larger scale. Moreover, deep learning utilizes the semantic segmentation network to extract glacier boundaries. Experimental outcomes confirm the effectiveness of deep learning (overall accuracy, 91.89% and Cohen’s kappa coefficient, 0.8778) compared to the existing artificial neural network (ANN) model (overall accuracy, 88.38% and kappa coefficient, 0.8241) in generating accurate classified maps. This study is vital in the study of the cryosphere, hydrology, agriculture, climatology, and land-use/land-cover analysis.

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Section: Study Area and Datamentioning

confidence: 99%

Glacier Boundary Mapping Using Deep Learning Classification over Bara Shigri Glacier in Western Himalayas

et al. 2022

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“…Previous studies in the region have also observed a decline in glacier area. Garg et al, (2017) and Sahu and Gupta, (2020) 610 can be attributed to the exclusion of flanks on either side of the glacier which contribute to the glacier flux and have been included in several studies (Chand et al, 2017;Yellala et al, 2019;Nela et al, 2020). The distinctions in glacier boundary defined by different studies contributes further to the challenge of statistical intercomparison and necessitates field surveys and visual inspection in order to ensure accuracy.…”

Section: Comparison With Previous Studies In the Regionmentioning

confidence: 99%

A comprehensive multidecadal glacier inventory dataset for the Chandra-Bhaga Basin, Western Himalaya, India

Vatsal

Bhardwaj

Azam

et al. 2022

Preprint

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Abstract. Delineation of Glaciers is a challenging task in the Himalaya due to its complex topography, cloud cover, seasonal snow cover, hillshade, debris cover. Glacio-hydrological studies including mass balance, run-off, and dynamic modelling rely on the availability of consistent and reliable glacier inventory datasets. This article on data set presents a homogenous, multidecadal inventory of glaciers in the Chandra-Bhaga Basin (CB Basin), western Himalaya, for 1993, 2000, 2010, and 2019. Landsat Thematic Mapper (TM), Enhanced Thematic mapper (ETM+), and Operational Land Imager (OLI) imageries, with minimum snow and cloud cover have been used for enhanced accuracy and consistency. Uncertainty assessment for the generated glacier inventory was performed, following various approaches such as buffer method, standard error estimation, and manual digitisation error and the maximum uncertainty has been quantified. We have identified and manually mapped a total of 251 glaciers with an area > 0.5 km2, and in order to minimise the uncertainty, field surveys were carried out on 6 glaciers in the basin. Out of these 251 glaciers, 217 are clean ice and 35 were debris-covered glaciers. The estimated total glacier area was 996 ± 62 km2 in 1993 that decreased to 973 ± 70 km2 in 2019. Apart from quantifying temporal changes in glacier area, this inventory further allows the estimation of supraglacial debris cover and glacier volume. The supraglacial debris cover area has increased by 14.1 ± 2.54 km2 (15.2 %) during 1993–2019. Accuracy of the debris cover dataset estimated using ground surveys is 82 % with a kappa coefficient of 0.87. Moreover, a glacier ice volume dataset was also generated by incorporating the inventory into Glacier Bed Topography Version 2 (GlabTop2) model and shows a total of 112.5 ± 41 km3 of ice volume stored in the CB Basin glaciers. For accuracy assessment of the DEMs generated using ASTER Stereopairs images, DGPS surveys were carried out on (28 GCPs) and off (6 GCPs) the glaciers. Glacier volume uncertainty with respect to the generated DEMs and model bias is 5.3 km3 and 35.5 km3 respectively. Overestimation of glacier volume due to over deepening is estimated to be 1.2 km3. The impact of climate change on the Himalayan glaciers is a matter of serious concern and such holistic multitemporal inventory datasets can help quantify that impact with improved certainty.

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Ice thickness distribution of Himalayan glaciers inferred from DInSAR-based glacier surface velocity

Nela

Singh

Kulkarni

2022

Environ Monit Assess

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Estimating Dynamic Parameters of Bara Shigri Glacier and Derivation of Mass Balance from Velocity

Cited by 7 publications

References 9 publications

Glacier Boundary Mapping Using Deep Learning Classification over Bara Shigri Glacier in Western Himalayas

Glacier Boundary Mapping Using Deep Learning Classification over Bara Shigri Glacier in Western Himalayas

A comprehensive multidecadal glacier inventory dataset for the Chandra-Bhaga Basin, Western Himalaya, India

Ice thickness distribution of Himalayan glaciers inferred from DInSAR-based glacier surface velocity

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