Chen Jiaqing, Gao Hong, Han Le, Yu Ruilin, Mei Gang
School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China.
Engineering and Technology Innovation Center for Risk Prevention and Control of Major Project Geosafety, Ministry of Natural Resources, Beijing 100083, China.
Sensors (Basel). 2023 Jul 22;23(14):6608. doi: 10.3390/s23146608.
Glacial debris flow is a common natural disaster, and its frequency has been increasing in recent years due to the continuous retreat of glaciers caused by global warming. To reduce the damage caused by glacial debris flows to human and physical properties, glacier susceptibility assessment analysis is needed. Most research efforts consider the effect of existing glacier area and ignore the effect of glacier ablation volume change. In this paper, we consider the impact of glacier ablation volume change to investigate the susceptibility of glacial debris flow. The susceptibility to mudslide was evaluated by taking the glacial mudslide-prone ditch of G318 Linzhi section of Sichuan-Tibet Highway as the research object. First, by using a simple band ratio method with manual correction, we produced a glacial mudslide remote sensing image dataset, and second, we proposed a deep-learning-based approach using a weight-optimized glacial mudslide semantic segmentation model for accurately and automatically mapping the boundaries of complex glacial mudslide-covered remote sensing images. Then, we calculated the ablation volume by the change in glacier elevation and ablation area from 2015 to 2020. Finally, glacial debris flow susceptibility was evaluated based on the entropy weight method and Topsis method with glacial melt volume in different watersheds as the main factor. The research results of this paper show that most of the evaluation indices of the model are above 90%, indicating that the model is reasonable for glacier boundary extraction, and remote sensing images and deep learning techniques can effectively assess the glacial debris flow susceptibility and provide support for future glacial debris flow disaster prevention.
冰川泥石流是一种常见的自然灾害，近年来，由于全球变暖导致冰川持续退缩，其发生频率不断增加。为减少冰川泥石流对人类和物质财产造成的损害，需要进行冰川易发性评估分析。大多数研究工作考虑了现有冰川面积的影响，而忽略了冰川消融量变化的影响。在本文中，我们考虑冰川消融量变化的影响来研究冰川泥石流的易发性。以川藏公路G318林芝段的冰川泥石流易发沟为研究对象，对泥石流易发性进行了评估。首先，通过采用带手动校正的简单波段比值法，生成了一个冰川泥石流遥感影像数据集；其次，我们提出了一种基于深度学习的方法，使用权重优化的冰川泥石流语义分割模型，对复杂的冰川泥石流覆盖的遥感影像边界进行准确自动映射。然后，根据2015年至2020年冰川高程和消融面积的变化计算消融量。最后，以不同流域的冰川融化量为主要因素，基于熵权法和Topsis法对冰川泥石流易发性进行了评估。本文的研究结果表明，该模型的大多数评价指标在90%以上，表明该模型对冰川边界提取是合理的，遥感影像和深度学习技术能够有效地评估冰川泥石流的易发性，为今后的冰川泥石流灾害防治提供支持。
