Snowline Change Monitoring in the Galongla Mountain Based on the DeepLabV3+ Model and Multi-Temporal Remote Sensing Data

Résumé fourni par la source

Studying snowline elevation changes is crucial for understanding the cryosphere's response to climate change and provides important data for water resource management in snow-dominated regions. This paper presents a snowline monitoring method based on GF-1 and Sentinel-2 remote sensing imagery, utilizing the DeepLabV3+ model to identify snow cover and cloud in images. A multi-source, multi-temporal data fusion strategy is proposed, where snowline elevations are derived through the integration of DEM data. This method effectively addresses the challenges of cloud cover and data gaps in mountainous regions, enabling precise detection of the snowline at a fine scale. The Galongla Mountain region was selected as the study area. The results indicated that from 2013 to 2023, the snowline elevation of Galongla Mountain fluctuated between 4200 and 4700 meters, with an overall upward trend. On the south slope, snowline elevation showed more significant variation due to factors such as water vapor and topography, while the north slope exhibited smaller fluctuations and a more consistent distribution. Analysis of seasonal snowline changes from 2018 to 2023 revealed that the snowline increased from April to September and decreased from November to April.

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