Combined use of multi-source satellite imagery and deep learning for automated mapping of glacial lakes in the Bhutan Himalaya

Abstract

Himalayan glacial lakes have been rapidly developing and expanding in recent decades under climate change and glacier mass loss. These growing glacial lakes can produce glacial lake outburst floods (GLOFs) events with far-reaching and devastating consequences. However, the latest spatial distribution and temporal evolution of the Himalayan glacial lakes is not timely updated due to the inaccessibility of high mountain areas and the lack of an effective automated mapping method that can leverage the availability of wide-ranging remote sensing data. To frequently update glacial lake inventory in GLOF-vulnerable regions, we developed the state-of-the-art glacial lake mapping approaches based on deep learning technique and multi-source remote sensing imagery. DeepLabv3+, an advanced semantic segmentation algorithm, was trained to delineate glacial lakes with areas larger than 0.005 km² from multi-source imagery and their derivatives, including PlanetScope red-green-blue (RGB), PlanetScope-derived Normalized Difference Water Index (NDWI), Sentinel-2 RGB, Sentinel-2-derived NDWI, Sentinel-1 Synthetic Aperture Radar (SAR), and Landsat-8 RGB images. The well-trained deep learning models achieved high mapping accuracy in the northern Bhutan test region, with the F1 score varying from 0.74 (Sentinel-1) to 0.91 (Planet-RGB) among the six types of images. We applied the well-trained models to automatically map the glacial lakes from multi-source satellite imagery. After manually cataloging the mapping results, we compiled a glacial lake inventory for the Bhutan Himalaya in 2021 that includes 2563 glacial lakes with a total area of 153.85 ± 9.33 km². Our results demonstrated the mapping capability of deep learning on multiple satellite imagery, the key roles of PlanetScope optical images for accurate glacial lake mapping, and the essential supplementary usage of SAR images and NDWI images to complement the glacial lake inventory over Bhutan Himalaya. This study provides an advanced and transferable workflow for inventorying glacial lakes from multi-source satellite imagery, as well as provides a high-quality and comprehensive glacial lake inventory for outburst flood studies.