Remote sensing as a tool to detect sudden glacier detachment events

冰川脱落事件，即一大块冰突然从冰川上脱落，有可能在山区环境中造成灾难性的破坏。近年来最值得注意的是，2021年2月印度北阿坎德发生的一次大型冰岩雪崩造成200多人死亡（Shugar等人，2020年）和2022年7月从意大利马尔莫拉达冰川出发的一支分队造成11人死亡。此外，在世界各地的山地冰川中也观察到了这种沉积物，其危害可能比最初想象的更为频繁（Kääb等人，2021年）。然而，由于其发生的罕见性，人们对导致突然脱离的过程知之甚少，如果被发现，可以用来警告即将发生的事件。由于气候变化加速了山区冰冻圈的下降，因此在利用观测和建模方法有力地跟踪这些变化方面存在挑战。分析导致冰川崩塌的事件的研究表明，有许多可观察到的前兆，例如裂缝扩大（Leinss等人，2021），冰川运动的地震信号变化（Roux等人，2010年），和浪涌般的速度加速（Kääb等人，2018年）。许多这些前兆在崩溃前几天甚至几周就出现了。将这些信号从正常的冰川过程中分离出来是将它们纳入可能即将崩溃的警告系统的关键部分。2021年）可能能够探测和预测即使在世界上最偏远地区的大型冰川。通过扩展的分离事件数据集，可以训练机器学习算法来分离指示崩塌的独特表面信号和指示正常冰川行为的独特表面信号（如在冰盖上应用的; Zhao等人，2022年）。此外，低成本的现场传感器可以自由地部署在高风险地区，以收集重要的数据到地面上的变化在冰川崩溃之前，这个项目的目的是约束可能的前兆冰川崩溃事件与遥感。将使用广泛的遥感技术，包括利用谷歌地球引擎等云计算环境中的全球卫星档案进行全球范围的分析。还将使用非常高分辨率的光学和合成孔径雷达图像自动探测表明即将发生坍塌的关键地面特征。机器学习还可以用于在更广泛的空间尺度上自动检测前兆。还将有机会与区域利益攸关方和决策者合作，在高风险地区部署低成本延时摄像机和地震仪等实地工具。最终的现场地点将根据对坍塌风险的评估、监督团队与地面合作伙伴的事先联系以及与成功候选人的讨论进行选择。潜在的地点包括阿尔卑斯山、喜马拉雅山和秘鲁的安第斯山脉。目的：1.根据未来冰川崩溃的脆弱性，绘制全球范围的山区冰川风险图;2.利用甚高分辨率遥感和机器学习技术，根据历史事件探测突发性崩塌的前兆;3.在脆弱地区部署低成本的实地传感器，以检测可能表明冰川突然崩塌的地面变化。"

Glacier detachment events, where a large section of ice suddenly detaches from a glacier, have the potential to cause catastrophic damage in mountainous environments. Most notably in recent years, a large ice-rock avalanche in Uttarakhand, India in February 2021 killed more than 200 people (Shugar et al., 2020) and a detachment from the Marmolada glacier in Italy in July 2022 killed 11 people. Further such detachments have been observed in mountain glaciers worldwide and may be a more frequent hazard than originally thought (Kääb et al., 2021). However, given the rarity of their occurrence, little is known about the processes that lead up to sudden detachment, which if detected, could be used to warn of an imminent event. As climate change accelerates the decline of the mountain cryosphere, a challenge exists in being able to robustly track these changes using observation and modelling methods. Research which analyses the events leading up to glacier collapses have shown that there are a number of observable precursors, such as expanding crevasses (Leinss et al., 2021), changes in the seismic signals from glacier movement (Roux et al., 2010), and surge-like acceleration in velocity (Kääb et al., 2018). Many of these precursors are exhibited days, or even weeks, prior to collapse. Disentangling these signals from normal glaciological processes is a key part of incorporating them within a warning system that imminent collapse is likely.A new generation of remote sensing techniques for the mountain cryosphere (Taylor et al., 2021) may be able to detect, and predict, large glacier detachments even in the world's remotest region. With an expanded dataset of detachment events, machine learning algorithms can be trained to separate the unique surface signals which indicate collapse and those that indicate normal glacier behaviour (as is being applied over ice sheets; Zhao et al., 2022). In addition, low-cost field sensors can be deployed liberally across high risk regions to gather vital data into on-the-ground changes in a glacier prior to collapse.The aim of this project is to constrain the likely precursors of glacier collapse events with remote sensing. A broad range of remote sensing techniques will be used, including harnessing global satellite archives in cloud computing environments such as Google Earth Engine to conduct global-scale analyses. Very high resolution optical and SAR images will also be used to automatically detect key surface-based features which indicate imminent collapse. Machine learning may also be used to automate the detection of precursors on a wider spatial scale. There will also be an opportunity to deploy field-based tools, such as low-cost time-lapse cameras and seismometers, in high risk areas, in partnership with regional stakeholders and policymakers. The final field site(s) will be chosen based on the assessment of risk of collapse, prior connections within the supervisory team to on-the-ground partners, and in discussion with the successful candidate. Potential sites include the Alps, Himalaya, and Peruvian Andes. Objectives:1. Produce a global-scale risk map of mountain glaciers based on their vulnerability towards future collapse;2. Use very high resolution remote sensing and machine learning techniques to detect the precursors to sudden collapse based on historical events;3. Deploy low-cost field-based sensors in vulnerable areas to detect on-the-ground changes in glaciers which may indicate sudden collapse."