Improvement of glacial lakes detection under shadow environment using ASTER data in Himalayas, Nepal

Chen, Wenbo; Fukui, Hiromichi; Doko, Tomoko; Gu, Xingfa

doi:10.1007/s11769-012-0584-3

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…The Landsat data analysed included several scenes that were affected by cloud cover, deep shadows (Chen and others, 2013) and seasonal snowcover, all of which required masking. The Fmask algorithm, version 3.2.1 (Zhu and others, 2015), was applied to detect clouds spanning several spectral classes semi-automatically (Step 2 in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…At sites with very steep terrain, persistent shadows can be problematic for automated classification routines based on thresholds (Chen and others, 2013). For this study, we detected shadows in the scene based on the Fmask results, 6S-corrected TM/ETM+ band 1 and band 5 reflectance values and terrain slope.…”

Section: Methodsmentioning

confidence: 99%

“…Prior efforts to identify supraglacial ponds on debris-covered glaciers have used band metrics (Huggel and others, 2002; Wessels and others, 2002; Gardelle and others, 2011; Chen and others, 2013) or image morphological operations (Panday and others, 2012; Liu and others, 2015; Watson and others, 2016), while studies of debris-covered glaciers in general also use values of thermal band derived brightness temperature (BT) to classify glacier facies (e.g. Mihalcea and others, 2008).…”

Section: Methodsmentioning

confidence: 99%

“…Ponds are known to form only in areas of low surface gradient (<10°), but studies differ in the critical slope threshold used to determine the area of a debris-covered glacier conducive to ponding (Reynolds, 2000; Quincey and others, 2007; Gardelle and others, 2011; Sakai, 2012; Chen and others, 2013). Due to the coarse spatial resolution of the SRTM DEM and the rugged surface of the debris-covered glaciers in the study area, which is especially pronounced near supraglacial ponds, we did not use a slope filter to restrict pond classification (Fig.…”

Section: Methodsmentioning

confidence: 99%

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Spatial, seasonal and interannual variability of supraglacial ponds in the Langtang Valley of Nepal, 1999–2013

Miles¹,

Willis²,

Arnold³

et al. 2016

J. Glaciol.

120

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ABSTRACT. Supraglacial ponds play a key role in absorbing atmospheric energy and directing it to the ice of debris-covered glaciers, but the spatial and temporal distribution of these features is not well documented. We analyse 172 Landsat TM/ETM+ scenes for the period 1999-2013 to identify thawed supraglacial ponds for the debris-covered tongues of five glaciers in the Langtang Valley of Nepal. We apply an advanced atmospheric correction routine (Landcor/6S) and use band ratio and image morphological techniques to identify ponds and validate our results with 2.5 m Cartosat-1 observations. We then characterize the spatial, seasonal and interannual patterns of ponds. We find high variability in pond incidence between glaciers (May-October means of 0.08-1.69% of debris area), with ponds most frequent in zones of low surface gradient and velocity. The ponds show pronounced seasonality, appearing in the pre-monsoon as snow melts, peaking at the monsoon onset at 2% of debris-covered area, then declining in the post-monsoon as ponds drain or freeze. Ponds are highly recurrent and persistent, with 40.5% of pond locations occurring for multiple years. Rather than a trend in pond cover over the study period, we find high interannual variability for each glacier after controlling for seasonality.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Spatial, seasonal and interannual variability of supraglacial ponds in the Langtang Valley of Nepal, 1999–2013

Miles¹,

Willis²,

Arnold³

et al. 2016

J. Glaciol.

120

View full text Add to dashboard Cite

show abstract

“…The boundaries of two glacial lakes were identified by integrating the NDWI and angle of inclination criteria. If the satellite imageries are covered by shadow, DSGL method (Chen et al 2013b) is effective to detect glacial lakes. However, as the satellite imageries we used are fortunately not covered by shadow, the DSGL method was not used in this study.…”

Section: Identification Of Glacial Lakesmentioning

confidence: 99%

Changes in Rongbuk lake and Imja lake in the Everest region of Himalaya

Chen

Doko

Liu

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The Himalaya holds the world record in terms of range and elevation. It is one of the most extensively glacierized regions in the world except the Polar Regions. The Himalaya is a region sensitive to climate change. Changes in the glacial regime are indicators of global climate changes. Since the second half of the last century, most Himalayan glaciers have melted due to climate change. These changes directly affected the changes of glacial lakes in the Himalayan region due to the glacier retreat. New glacial lakes are formed, and a number of them have expanded in the Everest region of the Himalayas. This paper focuses on the two glacial lakes which are Imja Lake, located at the southern slope, and Rongbuk Lake, located at the northern slope in the Mt. Everest region, Himalaya to present the spatio-temporal changes from 1976 to 2008. Topographical conditions between two lakes were different (Kruskal-Wallis test, p < 0.05). Rongbuk Lake was located at 623 m higher than Imja Lake, and radiation of Rongbuk Lake was higher than the Imja Lake. Although size of Imja Lake was larger than the Rongbuk Lake in 2008, the growth speed of Rongbuk Lake was accelerating since 2000 and exceeds Imja Lake in 2000-2008. This trend of expansion of Rongbuk Lake is anticipated to be continued in the 21st century. Rongbuk Lake would be the biggest potential risk of glacial lake outburst flood (GLOF) at the Everest region of Himalaya in the future.

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Can a dam type of an alpine lake be derived from lake geometry? A negative result

Emmer

Cuřín

2021

J. Mt. Sci.

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Glacial lake outburst floods (GLOFs) represent one of the most serious hazard and risk in deglaciating high mountain regions worldwide and the need for GLOF hazard and risk assessment is apparent. As a consequence, numerous region- and nation-wide GLOF assessment studies have been published recently. These studies cover large areas and consider hundreds to thousands of lakes, prioritizing the hazard posed by them. Clearly, certain simplification is required for executing such studies, often resulting in neglecting qualitative characteristics which would need manual assignment. Different lake dam types (e.g., bedrock-dammed, moraine-dammed) are often not distinguished, despite they control GLOF mechanism (dam overtopping/dam breach) and thus GLOF magnitude. In this study, we explore the potential of easily measurable quantitative characteristics and four ratios to approximate the lake dam type. Our dataset of 851 lakes of the Cordillera Blanca suggests that while variances and means of these characteristics of individual lake types differ significantly (F-test, t-test), value distribution of different geometrical properties can’t be used for the originally proposed purpose along the spectra. The only promising results are obtained for extreme values (selected bins) of the ratios. For instance, the low width to length ratio indicates likely moraine-dammed lake while the high value of ratio indicating round-shape of the lake indicates increased likelihood of bedrock-dammed lake. Overall, we report a negative result of our experiment since there are negligible differences of relative frequencies in most of the bins along the spectra.

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Improvement of glacial lakes detection under shadow environment using ASTER data in Himalayas, Nepal

Cited by 7 publications

References 17 publications

Spatial, seasonal and interannual variability of supraglacial ponds in the Langtang Valley of Nepal, 1999–2013

Spatial, seasonal and interannual variability of supraglacial ponds in the Langtang Valley of Nepal, 1999–2013

Changes in Rongbuk lake and Imja lake in the Everest region of Himalaya

Can a dam type of an alpine lake be derived from lake geometry? A negative result

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