Annual 30 m dataset for glacial lakes in High Mountain Asia from 2008 to 2017

Chen, Fang; Zhang, Meimei; Guo, Huadong; Allen, Simon; Kargel, Jeffrey S.; Haritashya, U. K.; Watson, C. Scott

doi:10.5194/essd-13-741-2021

Cited by 133 publications

(166 citation statements)

References 62 publications

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“…To comprehensively evaluate the extraction results of supra-glacial lakes, two newly released data sets for High Mountain Asia (HMA) were chosen for comparison: those from Wang et al (2020) and Chen et al (2021) for the closest period (2018 for Wang et al and 2017 for Chen et al) over the spatial extent of our region [68,69]. Landsat TM, ETM+, and OLI images were used to produce the annual glacial lake inventory over the entire HMA region.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Comparing Methods for Segmenting Supra-Glacial Lakes and Surface Features in the Mount Everest Region of the Himalayas Using Chinese GaoFen-3 SAR Images

Chen

2021

Remote Sensing

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Glaciers and numerous glacial lakes that are produced by glacier melting are key indicators of climate change. Often overlooked, supra-glacial lakes develop in the melting area in the low-lying part of a glacier and appear to be highly variable in their size, shape, and location. The lifespan of these lakes is thought to be quite transient, since the lakes may be completely filled by water and burst out within several weeks. Changes in supra-glacial lake outlines and other surface features such as supra-glacial rivers and crevasses on the glaciers are useful indicators for the direct monitoring of glacier changes. Synthetic aperture radar (SAR) is not affected by weather and climate, and is an effective tool for study of glaciated areas. The development of the Chinese GaoFen-3 (GF-3) SAR, which has high spatial and temporal resolution and high-precision observation performance, has made it possible to obtain dynamic information about glaciers in more detail. In this paper, the classical Canny operator, the variational B-spline level-set method, and U-Net-based deep-learning model were applied and compared to extract glacial lake outlines and other surface features using different modes and Chinese GF-3 SAR imagery in the Mount Everest Region of the Himalayas. Particularly, the U-Net-based deep-learning method, which was independent of auxiliary data and had a high degree of automation, was used for the first time in this context. The experimental results showed that the U-Net-based deep-learning model worked best in the segmentation of supra-glacial lakes in terms of accuracy (Precision = 98.45% and Recall = 95.82%) and segmentation efficiency, and was good at detecting small, elongated, and ice-covered supra-glacial lakes. We also found that it was useful for accurately identifying the location of supra-glacial streams and ice crevasses on glaciers, and quantifying their width. Finally, based on the time series of the mapping results, the spatial characteristics and temporal evolution of these features over the glaciers were comprehensively analyzed. Overall, this study presents a novel approach to improve the detection accuracy of glacier elements that could be leveraged for dynamic monitoring in future research.

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Section: Accuracy Assessmentmentioning

confidence: 99%

Comparing Methods for Segmenting Supra-Glacial Lakes and Surface Features in the Mount Everest Region of the Himalayas Using Chinese GaoFen-3 SAR Images

Chen

2021

Remote Sensing

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“…Growing lake depths increase the hydrostatic pressure acting on moraine dams, thus raising the potential of failure (Iribarren Anacona et al, 2014;Rounce et al, 2016). In the past decades, lake areas have grown largest in the Central Himalayas (+23 % in 1990-2015; Nie et al, 2017) and Nyainqentanglha Mountains but lowest in the northwestern Himalayas (Chen et al, 2021;Nie et al, 2017), and many studies have emphasised the role of growing lakes on GLOF susceptibility (e.g. GAPHAZ, 2017;Prakash and Nagarajan, 2017;Rounce et al, 2016).…”

Section: Study Area and Datamentioning

confidence: 99%

Controls of outbursts of moraine-dammed lakes in the greater Himalayan region

et al. 2021

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Abstract. Glacial lakes in the Hindu Kush–Karakoram–Himalayas–Nyainqentanglha (HKKHN) region have grown rapidly in number and area in past decades, and some dozens have drained in catastrophic glacial lake outburst floods (GLOFs). Estimating regional susceptibility of glacial lakes has largely relied on qualitative assessments by experts, thus motivating a more systematic and quantitative appraisal. Before the backdrop of current climate-change projections and the potential of elevation-dependent warming, an objective and regionally consistent assessment is urgently needed. We use an inventory of 3390 moraine-dammed lakes and their documented outburst history in the past four decades to test whether elevation, lake area and its rate of change, glacier-mass balance, and monsoonality are useful inputs to a probabilistic classification model. We implement these candidate predictors in four Bayesian multi-level logistic regression models to estimate the posterior susceptibility to GLOFs. We find that mostly larger lakes have been more prone to GLOFs in the past four decades regardless of the elevation band in which they occurred. We also find that including the regional average glacier-mass balance improves the model classification. In contrast, changes in lake area and monsoonality play ambiguous roles. Our study provides first quantitative evidence that GLOF susceptibility in the HKKHN scales with lake area, though less so with its dynamics. Our probabilistic prognoses offer improvement compared to a random classification based on average GLOF frequency. Yet they also reveal some major uncertainties that have remained largely unquantified previously and that challenge the applicability of single models. Ensembles of multiple models could be a viable alternative for more accurately classifying the susceptibility of moraine-dammed lakes to GLOFs.

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“…Big Earth Data, a type of big data associated with the Earth sciences derived from but not limited to Earth observation, is becoming a new frontier in contributing to the advancement of Earth science and significant scientific discoveries [4,5]. Satellite based spatial data and technologies, especially Big Earth Data approaches, are an essential tool for improving our understanding of disaster risks and for coordinated efforts to reduce climate change related disasters and sustainable development [6].…”

Section: Editorial On the Research Topic Big Earth Data For Disaster Risk Reductionmentioning

confidence: 99%