Mapping and monitoring of glacier areal changes using multispectral and elevation data: A case study over Chhota-Shigri glacier

Patel, Akansha; Prajapati, Richa; Dharpure, Jaydeo K.; Mani, Sneh; Chauhan, Dhirendra

doi:10.1007/s12145-019-00388-x

Cited by 17 publications

(11 citation statements)

References 34 publications

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“…Glacier monitoring includes glacier area monitoring, height monitoring, volume monitoring, and mass balance monitoring. Among these, the monitoring of glacier elevation can reflect the volume change and elevation change of glaciers [ 8 ] and also provides support for the study of glacier mass balance (GMB).…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Spatiotemporal Difference in Glacier Elevation on Bogda Mountain from 2000 to 2017

Shi

et al. 2021

IJERPH

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The difference in glacier surface elevation is a sensitive indicator of climate change and is also important for disaster warning and water supply. In this paper, 25 glaciers on Bogda Mountain, in the eastern Tianshan Mountains, are selected as the study object as they are typical of glaciers in arid or semi-arid areas with importance for water supply. The Repeat Orbit Interferometry (ROI) method is used to survey the surface elevation of these glaciers using Sentinel-1A Radar data from 2017. Using data from the Shuttle Radar Topography Mission (SRTM) and a Digital Elevation Model (DEM), the difference in the glacier surface elevation between 2000 and 2017 is obtained. A scheme to evaluate the accuracy of estimated variations in glacier surface elevation is proposed in this article. By considering the surfaces of lakes in the study region as ideal horizontal planes, the average standard deviation (SD) value of the lake elevation is taken as the error caused by the radar sensor and observing conditions. The SD of the lake elevation is used as an index to evaluate the error in the estimated variation of the glacier surface elevation, and the obtained SD values indicate that the result obtained using the ROI method is reliable. Additionally, the glacier surface elevation variation pattern and a Logarithmic Fitting Model (LFM) are used to reduce the error in high-altitude glacial accumulation areas to improve the estimation of the difference in the glacier surface elevation obtained using ROI. The average SD of the elevation of the 12 lakes is ±2.87 m, which shows that the obtained glacier surface elevations are reliable. This article concludes that, between 2000 and 2017, the surface elevation of glaciers on Bogda Mountain decreased by an average of 11.6 ± 1.3 m, corresponding to an average decrease rate of 0.68 m/a, and glaciers volume decreased by an average of 0.504 km3. Meanwhile, the surface elevations of the lakes increased by an average of 8.16 m. The decrease of glacier surface elevation leads to the expansion of glacial lakes. From the north slope clockwise to the south slope, the glacier elevation variation showed a decreasing trend, and the elevation variation gradually increased from the south slope to the north slope. With the increase of glacier altitude, the variation of glacier surface elevation gradually changed from negative to positive. The findings of this article suggest that the rate of glacier retreat on Bogda Mountain increased from 2000 to 2017.

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Section: Introductionmentioning

confidence: 99%

Monitoring the Spatiotemporal Difference in Glacier Elevation on Bogda Mountain from 2000 to 2017

Shi

et al. 2021

IJERPH

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“…However, due to global warming, global glaciers are showing a trend of retreat and thinning. Recent studies on mass balance (Rowan et al, 2015;Lynch et al, 2016;Dehecq et al, 2018;Wu et al, 2018;Wang et al, 2019a;Wouters et al, 2019;Zemp et al, 2019), glacial area change (Paul et al, 2013;Patel et al, 2019;Reinthaler et al, 2019), surface velocity (Wang et al, 2018;Altena et al, 2019;Garg et al, 2019), glacio-hydrological modeling (Shrestha et al, 2015), and glaciers' response to climate change (Scherler et al, 2011;Rowan et al, 2015) have revealed that changes in glaciers are exerting an impact on socioeconomic development in their downstream areas through changes in glacial water resources in High Mountain Asia (Immerzeel et al, 2010). Climate change may force geomorphological processes on high mountain slopes (Tipper et al, 2012;Cook et al, 2020) by accelerating the disintegration of rocks and increasing the accumulation of debris on glaciers and mountain slopes.…”

Section: Introductionmentioning

confidence: 99%

“…These methods can robustly delineate clean ice or snow, but they cannot accurately and automatically classify debris-covered ice as distinct from clean ice and the surrounding land surface (Robson et al, 2015). This has stimulated studies on the use of other parameters, such as geomorphic parameters derived from digital elevation models (DEMs) (Paul et al, 2004;Frey and Paul, 2012;Patel et al, 2019) and thermal characteristics from the infrared band (Singh and Goyal, 2018), as well as utilizing the coherence change between two successive synthetic aperture radar (SAR) images (Janke et al, 2015;Robson et al, 2015;Yang et al, 2016;Lippl et al, 2018), and the recognition accuracy was improved. However, complex preprocessing and severe terrain noise from SAR data make large-scale applications difficult.…”

Section: Introductionmentioning

confidence: 99%

Upward Expansion of Supra-Glacial Debris Cover in the Hunza Valley, Karakoram, During 1990 ∼ 2019

Xie

Liu

et al. 2020

Front. Earth Sci.

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Supra-glacial debris cover is key to glacier ablation through increasing (thin debris layer) or decreasing (thick debris layer) melt rates, thereby regulating the mass balance of a glacier and its meltwater runoff. The thickening or lateral expansion of supra-glacial debris cover correlates with a reduction of glacier ablation and, consequently, runoff generation, which is also considered to be an influential factor on the rheology and dynamics of a glacierized system. Studies on supra-glacial debris cover have recently attracted wide attention especially for glaciers in the Himalayas and Karakoram, where the glaciers have heterogeneously responded to climate change. In this study, we used 32 images from the Landsat Thematic Mapper, Enhanced Thematic Mapper Plus, and Operational Land Imager archive, going back to 1990, which are available on the Google Earth Engine cloud-computing platform, to map the supra-glacial debris cover in the Hunza Valley, Karakoram, Pakistan, based on a band ratio segmentation method (normalized difference snow index [NDSI] < 0.4), Otsu thresholding, and machine learning algorithms. Compared with manual digitization, the random forest (RF) model was found to have the greatest accuracy in identifying supra-glacial debris, with a Kappa coefficient of 0.94 ± 0.01 and an overall accuracy of 95.5 ± 0.9%. Overall, the supraglacial debris cover in the study area showed an increasing trend, and the total area expanded by 8.1-21.3% for various glaciers from 1990 to 2019. The other two methods (Otsu thresholding and NDSI < 0.4) generally overestimated the supra-glacial debris covered area, by 36.3 and 18.8%, respectively, compared to that of the RF model. The supra-glacial debris cover has migrated upward on the glaciers, with intensive variation near the equilibrium-line altitude zone (4,500-5,500 m a.s.l.). The increase in ice or snow avalanche activity at high altitudes may be responsible for this upward expansion of supra-glacial debris cover in the Hunza Valley, which is attributed to the combined effect of temperature decrease and precipitation increase in the study area.

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“…Glaciers, also known as 'alpine solid reservoirs', are not only a promising natural freshwater resource but also a sensitive indicator of global climate change (Kaab et al, 2012;Yang, 1995;Zemp et al, 2019;). Recent studies have been conducted on mass balance (Dehecq et al, 2018;Zemp et al, 2019), glacial area change (Patel et al, 2019;Paul et al, 2013), surface velocity (Altena et al, 2019;Garg et al, 2019), glacialhydrological modeling (Shrestha et al, 2015), and glaciers' response to climate change (Rowan et al, 2015;Scherler et al, 2011). However, accurate glacier boundary outlines are always fundamental for change detection and model validation.…”

Section: Introductionmentioning

confidence: 99%

“…These methods can robustly delineate clean ice or snow, but they cannot accurately and automatically classify debris-covered glacier ice as distinct from clean ice and the surrounding land surface (Robson et al, 2015). This has stimulated studies on the use of other parameters such as geomorphic parameters derived from a digital elevation model (DEM) (Patel et al, 2019;Paul et al, 2004) and thermal characteristics from the infrared band (Singh and Goyal, 2018), as well as utilizing the coherence change between two successive synthetic aperture radar (SAR) images (Janke et al, 2015;Robson et al, 2015). However, complex pre-processing and severe terrain noise of SAR data make large-scale applications difficult.…”

Section: Introductionmentioning

confidence: 99%

Derivation of Supraglacial Debris Cover by Machine Learning Algorithms on the Gee Platform: A Case Study of Glaciers in the Hunza Valley

Xie

Liu

Gao

et al. 2020

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

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Abstract. Calculating the spatial-temporal distribution of supraglacial debris cover on glaciers is essential for understanding mass balance processes, glacier lake outburst floods, hydrological predictions, and glacier fluctuations that have attracted attention in recent years. However, due to the reflectance of supraglacial debris is similar to that of non-glacier slopes, mapping supraglacial debris cover based on optical remote sensing remains challenging. In this paper, we used NDSI and machine learning algorithm to extract debris cover on glaciers in Hunza Valley, Pakistan. Our result showed that the RF model has the best classification accuracy with kappa coefficient of 0.94 and overall accuracy of 96%. The debris-covered area increased by 21.31% from 1990 to 2019 (394.76 km2 – 478.88 km2) in the study area. Results and the method are of significance in the assessment of meltwater modeling for glaciers with debris cover.

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Mapping and monitoring of glacier areal changes using multispectral and elevation data: A case study over Chhota-Shigri glacier

Cited by 17 publications

References 34 publications

Monitoring the Spatiotemporal Difference in Glacier Elevation on Bogda Mountain from 2000 to 2017

Monitoring the Spatiotemporal Difference in Glacier Elevation on Bogda Mountain from 2000 to 2017

Upward Expansion of Supra-Glacial Debris Cover in the Hunza Valley, Karakoram, During 1990 ∼ 2019

Derivation of Supraglacial Debris Cover by Machine Learning Algorithms on the Gee Platform: A Case Study of Glaciers in the Hunza Valley

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