Glacial lake extraction framework based on coupling of GEE and historical glacial lake position

• Published in: IEEE geoscience and remote sensing letters 2024-01, Vol.21, p.1-1
• Main Authors: Wu, Renzhe, Zhang, Rui, Cai, Jiaxin, Cai, Kun, Wu, Tingting, Lv, Jichao, Shi, Yueling, Liu, Guoxiang
• Format: Article
• Language: English
• Subjects: Adaptive optics; Adaptive Threshold; Algorithms; China; Climate change; Clouds; Clustering; Coupling; Data mining; Environmental monitoring; Floods; Glacial lake extraction; Glacial lakes; Glacial periods; Glaciers; Google Earth Engine (GEE); Lakes; Monitoring; Mountain regions; Mountainous areas; Mountains; Optical distortion; Optical imaging; Optical sensors; Radar imaging; Risk analysis; SAR (radar); Satellite images; Seasonal variation; Seasonal variations; Seasons; Synthetic aperture radar; Synthetic Aperture Radar (SAR); Thresholding (Imaging)
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2024.3370229

Monitoring changes in glacial lake (GL) area is of great significance for revealing climate change and analyzing the risk of GL outburst floods (GLOFs). However, in the case of Southeastern Tibet Plateau (SETP), the availability of optical images in high mountain areas is low, and Synthetic Aperture Radar (SAR) amplitude images have significant geometric distortions. Most GL extraction studies are mainly focused on autumn and winter seasons. In order to obtain accurate GL boundaries at any time and to capture seasonal changes of GLs in a wide area, this letter proposes a high mountain GL extraction framework based on the coupling of Google Earth Engine (GEE) and historical GL catalog data, which focuses on local GL extraction problems. GL extraction and validation were performed using various clustering and adaptive threshold segmentation methods, all of which showed strong stability and reliability of the proposed scheme. Finally, we employed a superpixel clustering algorithm to estimate the area of GLs as of August 1, 2019, and then compared the results with two widely used spatially referenced datasets. The results indicate that our method achieves a comprehensive Intersection over Union (IoU) of up to 95%. The proposed method can effectively support the extraction of wide-area summer GLs and the monitoring of seasonal changes in GL area, thus enabling the dynamic updating of GL information at a high temporal frequency.