Elia Letizia, Castellaro Silvia, Dahal Ashok, Lombardo Luigi
Department of Physics and Astronomy, Alma Mater Studiorum University of Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy.
University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), PO Box 217, Enschede AE 7500, Netherlands.
Sci Total Environ. 2023 Nov 10;898:165289. doi: 10.1016/j.scitotenv.2023.165289. Epub 2023 Jul 7.
Classifying a given landscape on the basis of its susceptibility to surface processes is a standard procedure in low to mid-latitudes. Conversely, these procedures have hardly been explored in periglacial regions. However, global warming is radically changing this situation and will change it even more in the future. For this reason, understanding the spatial and temporal dynamics of geomorphological processes in peri-arctic environments can be crucial to make informed decisions in such unstable environments and shed light on what changes may follow at lower latitudes. For this reason, here we explored the use of data-driven models capable of recognizing locations prone to develop retrogressive thaw slumps (RTSs) and/or active layer detachments (ALDs). These are cryospheric hazards induced by permafrost degradation, and their development can negatively affect human settlements or infrastructure, change the sediment budget and release greenhouse gases. Specifically, we test a binomial Generalized Additive Modeling structure to estimate the probability of RST and ALD occurrences in the North sector of the Alaskan territory. The results we obtain show that our binary classifiers can accurately recognize locations prone to RTS and ALD, in a number of goodness-of-fit (AUC = 0.83; AUC = 0.86), random cross-validation (mean AUC = 0.82; mean AUC = 0.86), and spatial cross-validation (mean AUC = 0.74; mean AUC = 0.80) routines. Overall, our analytical protocol has been implemented to build an open-source tool scripted in Python where all the operational steps are automatized for anyone to replicate the same experiment. Our protocol allows one to access cloud-stored information, pre-process it, and download it locally to be integrated for spatial predictive purposes.
根据给定景观对地表过程的敏感性进行分类，是低纬度到中纬度地区的标准程序。相反，这些程序在冰缘地区几乎没有得到探索。然而，全球变暖正在从根本上改变这种状况，并且未来还会有更大的变化。因此，了解北极周边环境中地貌过程的时空动态，对于在这种不稳定环境中做出明智决策以及揭示低纬度地区可能随之发生的变化至关重要。因此，我们在此探索了使用数据驱动模型来识别容易发生溯源融冻滑塌（RTSs）和/或活动层分离（ALDs）的位置。这些是由多年冻土退化引发的冰冻圈灾害，它们的发展会对人类住区或基础设施产生负面影响，改变沉积物收支并释放温室气体。具体而言，我们测试了一种二项广义相加模型结构，以估计阿拉斯加地区北部发生RST和ALD的概率。我们获得的结果表明，我们的二元分类器能够在多种拟合优度（AUC = 0.83；AUC = 0.86）、随机交叉验证（平均AUC = 0.82；平均AUC = 0.86）和空间交叉验证（平均AUC = 0.74；平均AUC = 0.80）例程中准确识别容易发生RTS和ALD的位置。总体而言，我们的分析方案已被实施，以构建一个用Python编写的开源工具脚本，其中所有操作步骤都是自动化的，任何人都可以复制相同的实验。我们的方案允许用户访问云存储信息、对其进行预处理，并将其本地下载以进行集成，用于空间预测目的。
