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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/nhess-2019-219
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-219
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Jul 2019

15 Jul 2019

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This preprint has been withdrawn by the authors.

High-frequency glacial lake mapping using time series of Sentinel-1A/1B SAR imagery: An assessment for southeastern Tibetan Plateau

Meimei Zhang1, Fang Chen1,2,3, Bangsen Tian1, Dong Liang1,2, and Aqiang Yang1 Meimei Zhang et al.
  • 1Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Hainan Key Laboratory of Earth Observation, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Sanya 572029, China

Abstract. Glacial lakes are 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 in a short time. Although most of the efforts have been made to extract 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 process of glacial lake dynamics, especially for potentially dangerous glacial lakes with high-frequency variability. By using a full time-series Sentinel-1A/1B imagery during a year, this study presents a new systematic method to extract the glacial lake outlines with fast variability in southeastern Tibetan Plateau at the time interval of six days. Our approach was based on the level-set segmentation, combined with a median pixel compositing of SAR backscattering coefficients stacks as regularization term, to robustly estimate the lake extent across the observed time range. The mapping results were validated against with manually digitized lake outlines derived from GF-2 PMS imagery, with the overall accuracy and Kappa coefficient of 96.54 % and 0.95, respectively. In comparison with results from classical supervised SVM and unsupervised ISODATA methods, the proposed method proves to be much more robust and effective to detect glacial lakes with irregular boundaries and that have similar backscattering with surroundings. This study also demonstrates the feasibility of time-series Sentinel-1A/1B SAR data in continuous monitoring of glacial lake outline dynamics.

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Meimei Zhang et al.

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Meimei Zhang et al.

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