Preprints
https://doi.org/10.5194/nhess-2019-219
https://doi.org/10.5194/nhess-2019-219
15 Jul 2019
 | 15 Jul 2019
Status: this preprint has been withdrawn by the authors.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 Zhang, Fang Chen, Bangsen Tian, Dong Liang, and Aqiang Yang

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, Fang Chen, Bangsen Tian, Dong Liang, and Aqiang Yang

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Meimei Zhang, Fang Chen, Bangsen Tian, Dong Liang, and Aqiang Yang
Meimei Zhang, Fang Chen, Bangsen Tian, Dong Liang, and Aqiang Yang

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