Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Int J Environ Res Public Health. 2020 Feb 8;17(3):1072. doi: 10.3390/ijerph17031072.
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.
冰川湖是青藏高原冰冻圈的重要组成部分。由于气候变暖，冰川湖在短时间内通过突发洪水对下游的生命、生态环境和公共基础设施构成威胁。尽管已经投入了大量精力利用遥感图像提取冰川湖轮廓并检测其变化，但冰川湖数据集的时间频率和空间分辨率通常不够精细，无法反映冰川湖动态的详细过程，特别是对于那些具有高频变化的潜在危险冰川湖。本研究利用一年的全时序列 Sentinel-1A/1B 图像，提出了一种新的系统方法，以提取在青藏高原东南部快速变化的冰川湖轮廓，时间间隔为六天。我们的方法基于水平集分割，结合合成孔径雷达（SAR）后向散射系数的中值像素组合作为正则化项，以在观测时间范围内稳健地估计湖的范围。将制图结果与从高分二号全色多光谱（GF-2 PMS）图像手动数字化的湖轮廓进行了验证，总体精度和kappa 系数分别为 96.54%和 0.95。与经典的监督支持向量机（SVM）和无监督迭代自组织数据分析技术算法（ISODATA）方法的结果相比，所提出的方法在检测具有与周围环境相似后向散射的不规则边界的冰川湖方面更加稳健和有效。本研究还证明了时间序列 Sentinel-1A/1B SAR 数据在连续监测冰川湖轮廓动态方面的可行性。
