The DOI matches its Crossref record.
An automated scheme for glacial lake dynamics mapping using Landsat imagery and digital elevation models: a case study in the Himalayas
Abstract
Glacial lakes in alpine regions are sensitive to climate change. Mapping and monitoring these lakes would improve our understanding of regional climate change and glacier-related hazards. However, glacial lake mapping over large areas using remote sensing remains a challenge because of various disturbing factors in glacial and periglacial environments. This article presents an automated mapping algorithm based on hierarchical image segmentation and terrain analysis to delineate glacial lake extents. In this algorithm, each glacial lake is delineated with a local segmentation value, and the topographic features derived from digital elevation models (DEMs) are also used to separate mountain shadows from glacial lakes. About 100 scenes of Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images from circa 1990, circa 2000 and 2009 were used to map the glacial lakes and their changes over the entire Himalayas. The results show that the algorithm can map the glacial lakes effectively and efficiently. Mountain shadows or melting glaciers can be differentiated from glacial lakes automatically, and those lakes with mountain shadows can also be identified. Area changes of more than 1000 glacial lakes show that the glacial lakes in the Himalayas have experienced mixed directions of change, while the overall lake areas are expanding at an accelerated rate in the past two decades, indicating great changes to the glacial lakes in the Himalayas.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Landsat-8: Science and product vision for terrestrial global change research
Roy, D. P., Wulder, M. A., Loveland, T. R., C.E., W., Allen, R. G., Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., et al. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.02.001 · Cited by 2,659
Self-citationNie, Y., Sheng, Y., Liu, Q., Liu, L., Liu, S., Zhang, Y., & Song, C. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.11.008 · Cited by 469
Self-citationZhang, G., Yao, T., Chen, W., Zheng, G., Shum, C. K., Yang, K., Piao, S., Sheng, Y., Yi, S., Li, J., et al. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.11.038 · Cited by 455
Self-citationZhang, G., Yao, T., Piao, S., Bolch, T., Xie, H., Chen, D., Gao, Y., O'Reilly, C. M., Shum, C. K., Yang, K., et al. (2017) · Geophysical Research Letters · doi:10.1002/2016gl072033 · Cited by 323
An approach for global monitoring of surface water extent variations in reservoirs using MODIS data
Khandelwal, A., Karpatne, A., Marlier, M. E., Kim, J., Lettenmaier, D. P., & Kumar, V. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.05.039 · Cited by 197
Metadata
- Authors
- Li, J., & Sheng, Y.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2012-02-21
- Volume
- 33
- Issue
- 16
- Pages
- 5194-5213
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2024578125
- Cited by
- 252
- References
- 0
- FWCI
- 3.27
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An automated scheme for glacial lake dynamics mapping using Landsat imagery and digital elevation models: a case study in the Himalayas. https://app.cochat.ai/papers/10.1080/01431161.2012.657370
- The paper
- Li, J., & Sheng, Y. (2012). An automated scheme for glacial lake dynamics mapping using Landsat imagery and digital elevation models: a case study in the Himalayas. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2012.657370
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.