Glacial lake inventory of high-mountain Asia in 1990 and 2018 derived from Landsat images

Wang, Xin; Guo, Xiexian; Yang, Chengde; Liu, Qionghuan; Jian, Wei; Zhang, Yong; Liu, Shiyin; Zhang, Yanlin; Jiang, Zongli; Tang, Zilong

doi:10.5194/essd-12-2169-2020

Cited by 144 publications

(258 citation statements)

References 46 publications

(89 reference statements)

Supporting

Mentioning

202

Contrasting

Order By: Relevance

“…Figure 11. Comparison of the mapped supraglacial lakes with the glacial lake inventory of HMA [36], Hi-MAG database [61] and glacial lakes inventory of CPEC [62]. Surprisingly, the Hi-MAG and CPEC inventories have not detected supraglacial lakes on the Baltoro glacier.…”

Section: Mapping Supraglacial Lakes On the Baltoro Glacier And Comparmentioning

confidence: 95%

“…The sizes of the supraglacial lakes on the Baltoro glacier mapped using PlanetScope imagery of different dates are given in Table 3. We then used the following inventories for comparison: (1) glacial lakes inventory of High Mountain Asia [36], (2) A dataset of glacial lakes in High Mountain Asia (Hi-MAG) from 2008 to 2017 [61] and (3) glacial lakes in the China-Pakistan Economic Corridor (CPEC) [62]. These three inventories have been derived from Landsat-8 imagery (recent years).…”

Section: Mapping Supraglacial Lakes On the Baltoro Glacier And Comparmentioning

confidence: 99%

“…The minimum mapping area of (1) is 0.0054 km 2 (2) is 0.0081 km 2 and (3) is 0.01 km 2 . These inventories have relied on manual [36] or semi-automatic [61,62] techniques for delineation of glacial lakes using Landsat imagery. The comparison of the supraglacial lakes is presented in Figure 11 and Table 4, which shows that several lakes have been missed in the inventory [36] and the inventories [61,62] have not detected lakes on the Baltoro glacier even though the data in Table 3 suggest that a significant number of lakes have an area greater than the minimum mapping area of these inventories.…”

Section: Mapping Supraglacial Lakes On the Baltoro Glacier And Comparmentioning

confidence: 99%

“…We thank Tobias Bolch and Sonam Wangchuk from University of St Andrews, UK for sharing the GLakeMap results and for their feedback and comments. We would also like to thank the authors of the glacial lake inventories [36,61,62] and Adam Emmer, University of Graz, Austria for sharing their datasets. We thank Akiko Sakai from Ngoya University, Japan for providing the GAMDAM inventory data.…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…Due to difficulties in the automated mapping of the glacial lakes, there exists no continuously-updated glacial lake inventory for this region. The previous glacial lake inventories have been developed using manual digitization or require significant human intervention making frequent updates difficult [29,[33][34][35][36] The Top of Atmosphere (TOA) reflectance is computed by multiplying the coefficients given in the metadata file with the 16-bit radiance value. The atmospheric effects are not compensated in TOA reflectance.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

Qayyum

Ghuffar

Ahmad

et al. 2020

IJGI

View full text Add to dashboard Cite

Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability of imaging the whole Earth landmass everyday at 3–4 m spatial resolution. The higher spatial, as well as temporal resolution of PlanetScope imagery in comparison with Landsat-8 and Sentinel-2, makes it a valuable data source for monitoring the glacial lakes. Therefore, this paper explores the potential of the PlanetScope imagery for glacial lakes mapping with a focus on the Hindu Kush, Karakoram and Himalaya (HKKH) region. Though the revisit time of the PlanetScope imagery is short, courtesy of 130+ small satellites, this imagery contains only four bands and the imaging sensors in these small satellites exhibit varying spectral responses as well as lower dynamic range. Furthermore, the presence of cast shadows in the mountainous regions and varying spectral signature of the water pixels due to differences in composition, turbidity and depth makes it challenging to automatically and reliably extract surface water in PlanetScope imagery. Keeping in view these challenges, this work uses state of the art deep learning models for pixel-wise classification of PlanetScope imagery into the water and background pixels and compares the results with Random Forest and Support Vector Machine classifiers. The deep learning model is based on the popular U-Net architecture. We evaluate U-Net architecture similar to the original U-Net as well as a U-Net with a pre-trained EfficientNet backbone. In order to train the deep neural network, ground truth data are generated by manual digitization of the surface water in PlanetScope imagery with the aid of Very High Resolution Satellite (VHRS) imagery. The created dataset consists of more than 5000 water bodies having an area of approx. 71km2 in eight different sites in the HKKH region. The evaluation of the test data show that the U-Net with EfficientNet backbone achieved the highest F1 Score of 0.936. A visual comparison with the existing glacial lake inventories is then performed over the Baltoro glacier in the Karakoram range. The results show that the deep learning model detected significantly more lakes than the existing inventories, which have been derived from Landsat OLI imagery. The trained model is further evaluated on the time series PlanetScope imagery of two glacial lakes, which have resulted in an outburst flood. The output of the U-Net is also compared with the GLakeMap data. The results show that the higher spatial and temporal resolution of PlanetScope imagery is a significant advantage in the context of glacial lakes mapping and monitoring.

show abstract

Section: Mapping Supraglacial Lakes On the Baltoro Glacier And Comparmentioning

confidence: 95%

Section: Mapping Supraglacial Lakes On the Baltoro Glacier And Comparmentioning

confidence: 99%

Section: Mapping Supraglacial Lakes On the Baltoro Glacier And Comparmentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

Qayyum

Ghuffar

Ahmad

et al. 2020

IJGI

View full text Add to dashboard Cite

show abstract

Glaciers and Glacier Lake Outburst Floods in the Himalaya

Andermann

Nepal

Wagnon

et al. 2023

Himalaya, Dynamics of a Giant 3

View full text Add to dashboard Cite

Glacier structure influence on Himalayan ice‐front morphology

Peacey

Reynolds

Holt

et al. 2023

Earth Surf Processes Landf

View full text Add to dashboard Cite

We investigate the role of glacier structures in controlling ice-front morphology and dynamics of four Himalayan lake-terminating glaciers over a 20-year period.At Imja, Trakarding, Lumdin and Dang Pu glaciers, lake area was mapped between 2000 and 2020 using Landsat 5/7/8 and Sentinel-2 imagery. Discrete glacier flow units were identified, with glacier structures (e.g., open crevasses, transverse structures, longitudinal structures) digitised using the finest resolution panchromatic bands in each year (30, 15, or 10 m). Mapping revealed a distinct pattern of transverse structures towards the terminus of each glacier that influence ice-front position and morphology and are then exploited via iceberg calving events. Our structural analysis also illustrates the role of subsurface conduits in calving events.During subsurface conduit collapse, glacier recession is enhanced, leading to calving along adjacent transverse structures. Furthermore, our analysis shows that ice-front morphology influences the pattern of glacier recession. Ice fronts with distinct ice aprons undergo slower periods of recession than ice fronts with ice cliffs. We conclude that glacier structures are important in determining ice-front morphologies at lake-terminating Himalayan glaciers, and therefore, structural analysis is vital when assessing future ice-front positions and behaviour, as well as rates of glacier recession.

show abstract

Glacial lake inventory of high-mountain Asia in 1990 and 2018 derived from Landsat images

Cited by 144 publications

References 46 publications

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

Glaciers and Glacier Lake Outburst Floods in the Himalaya

Glacier structure influence on Himalayan ice‐front morphology

Contact Info

Product

Resources

About