| 摘 要： |
Studying the spatial and temporal water distribution in the Lake Baikal Basin, which hosts the freshwater lake with the most water storage in the world, is essential to understand the water resources and environment of the basin and its impact and influence in terms of climate change and disaster prevention and mitigation. The basin spans two countries, Russia and Mongolia, which, along with its vastness, makes it challenging to accurately automate the acquisition of large-scale and long-term series data. The Google Earth Engine (GEE) is capable of processing large amounts of remote sensing imagery but does not support the computation and application of deep learning models. This study uses a combination of local deep learning training and GEE cloud-based big data intelligent computing to empower GEE with deep learning computing power, enabling it to rapidly automate the deployment of deep learning models. Visible light, near infrared (NIR), modified normalized difference water index (MNDWI), short-wave infrared 1 (SWIR1), linear enhancement band (LEB), and digital elevation model (DEM), which are more sensitive to water bodies, were selected as input features, along with the optimized input features of the existing pixel-based convolutional neural network (CNN) model. This method corrects the initial water labels from the Landsat quality assessment bands to reduce the time cost of manually drawing the labels and improving the classification accuracy of the water bodies. On average, only 1-2 h are required to generate the results for each water body product for each period in Lake Baikal Basin. The extraction of water bodies from the Lake Baikal Basin was achieved for nine yearly periods between 2013 and 2021. The validation accuracy was 92.9 %, 92.7 %, and 92.4 % for the three years 2013, 2017 and 2021, respectively. The results showed that the mean area of water bodies in the basin was 37,500 km2 and that the area of water bodies in the basin fluctuated without significant change from 2013 to 2021. This study provides methodological support for the continuous monitoring and assessment of water body dynamics at more catchment scales and other large scenarios. |
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