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High-Frequency Glacial Lake Mapping Using Time Series of Sentinel-1A/1B SAR Imagery

An Assessment for the Southeastern Tibetan Plateau

Bibliographic Data

ID15511123
AuthorsMeimei Zhang (0000-0001-9621-4879, Chinese Academy of Sciences), Fang Chen (0000-0002-5216-1522, Chinese Academy of Sciences, corresponding author), Bangsen Tian (0000-0001-9449-0941, Chinese Academy of Sciences), Dong Liang (0009-0004-5119-3014, Chinese Academy of Sciences), Aqiang Yang (0000-0002-2650-9521, Chinese Academy of Sciences)
Year2020
Volume17
Issue3
Pages1072-1072
Publication date2020-02-08
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph17031072
PMID32046245
OpenAlexW3005101161
LanguageEN
Citations received2
References cited36

Glacial lakes are an important component of the cryosphere in the Tibetan Plateau. In response to climate warming, they threaten the downstream lives, ecological environment, and public infrastructures through outburst floods within a short time. Although most of the efforts have been made toward extracting glacial lake outlines and detect their changes with remotely sensed images, the temporal frequency and spatial resolution of glacial lake datasets are generally not fine enough to reflect the detailed processes of glacial lake dynamics, especially for potentially dangerous glacial lakes with high-frequency variability. By using full time-series Sentinel-1A/1B imagery over a year, this study presents a new systematic method to extract the glacial lake outlines that have a fast variability in the southeastern Tibetan Plateau with a time interval of six days. Our approach was based on a level-set segmentation, combined with a median pixel composition of synthetic aperture radar (SAR) backscattering coefficients stacked as a regularization term, to robustly estimate the lake extent across the observed time range. The mapping results were validated against manually digitized lake outlines derived from Gaofen-2 panchromatic multi-spectral (GF-2 PMS) imagery, with an overall accuracy and kappa coefficient of 96.54% and 0.95, respectively. In comparison with results from classical supervised support vector machine (SVM) and unsupervised Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) methods, the proposed method proved to be much more robust and effective at detecting glacial lakes with irregular boundaries that have similar backscattering as the surroundings. This study also demonstrated the feasibility of time-series Sentinel-1A/1B SAR data in the continuous monitoring of glacial lake outline dynamics

Geography · Geomorphology · Glacial lake · Glacial period · Glacier · Multispectral image · Multispectral pattern recognition · Panchromatic film · Physical geography · Plateau (mathematics · Remote sensing · Synthetic aperture radar · Arctic and Antarctic ice dynamics · Climate change and permafrost · Cryospheric studies and observations · Geology

  • Multi-temporal analysis of forest canopy cover in Ngel Nyaki Forest Reserve using the Sentinel-1 and Sentinel-2 data

    Ahmed Onimisi Abdulrahaman, Hazel Chapman et al.•African Geographical Review•2025

  • The response of glaciers and glacial lakes to climate change in the Southeastern Tibetan Plateau over the past three decades

    Open Access•Xiangyang Dou, Xuanmei Fan et al.•Land Degradation and Development•2023

  • The relationship between air temperature fluctuation and Glacial Lake Outburst Floods in Tibet, China

    Jing-Jing Liu, Zun-Lan Cheng et al.•Quaternary International•2014

Citations per year0.67
Citation span2023 - 2025 (3)
Highly citedNo

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