Detecting glacial lake water quality indicators from RGB surveillance images via deep learning

• Synchronized surveillance and in-situ water quality monitoring at a high-altitude glacial lake on the Qinghai-Tibet Plateau. • Various deep learning methods used to extract lake bodies and model water quality indicators. • RGB imagery effectively estimates glacial lake turbidity. • The color of glacial lakes is recognized as the primary feature identified by deep learning models. • A cost-effective method for real-time monitoring of rapidly changing high-altitude glacial lakes. Global warming has accelerated glacier retreat, subsequently leading to the formation of glacial lakes in high-altitude mountainous regions. These lakes represent emerging ecological water systems and could potentially pose significant hazards. Observations of these systems are constrained by their remote locations and the lack of cost-effective monitoring methods, resulting in limited understanding of their dynamics. In this study, we synchronized surveillance monitoring with in-situ water quality measurements at a typical high-altitude glacial lake on the Qinghai-Tibet Plateau. We aim to use images from surveillance cameras to estimate the turbidity parameter, a key indicator of changes in the water environment and the impacts of climate change on high-altitude ecosystems. We segmented RGB images and applied regression modeling with field-measured water turbidity data, and then used deep learning models to accurately estimates turbidity levels and their changes. Our study demonstrates the potential of RGB imagery and deep learning for the long-term, continuous, and high-resolution monitoring of water quality in remote glacial lakes. It presents a novel and cost-effective approach for monitoring these newly emerged and swiftly changing water systems at high altitudes, offering a significant advancement in tracking environmental changes in these critical high mountain regions.