Pinpointing Early Signs of Impending Slope Failures From Space. Issue 2 (1st February 2022)

• Record Type: Journal Article
• Authors: Zhou, Shuo; Tordesillas, Antoinette; Intrieri, Emanuele; Di Traglia, Federico; Qian, Guoqi; Catani, Filippo
• Is Part Of: Journal of geophysical research. Volume 127:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-01
• Subjects: Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1029/2021JB022957
• Languages: English
• ISSNs: 2169-9313

Abstract

Abstract: A promised potential of spaceborne interferometric synthetic aperture radars (InSAR) is a capability for regularly monitoring ground deformation with millimeter accuracy, for timely forecasting of impending natural hazards such as landslides. The main limitation in InSAR being actually capable of unleashing this potential for hazard prediction is that key precursory ground displacements are, in the majority of cases, a very small subset of the entire big data set provided by the method over large regions. Consequently, pinpointing a single impending failure may become very difficult or impossible. We develop a data‐driven framework that can handle such imbalanced spatiotemporal data based on the concept of outlying aspects mining, to find a subset of features out of a collection of potential features, which best distinguishes the landslide source area from the others. We show that the identified feature subspace can be used to find anomalous areas across multiple spatial scales, such that Sentinel‐1 satellite monitoring points which persistently lie in these areas can accurately detect the location of the Xinmo landslide (China) almost 1 year in advance—without false alarms. In a second case study, we identify the area affected by rockfalls on Stromboli volcano, a task that is generally infeasible with traditional methods applied to InSAR data. With continuing improvements in the spatial and temporal resolution from the new generation of satellites, such as Sentinel‐1, this approach opens the door to reliable and early prediction of failure over a broad range of slope instabilities. Plain Language Summary: The utility of Earth observation satellite data is well recognized in early warning for natural hazards such as landslides. With the revisiting times now down to 6 days, sufficient information can be extracted to predict the location of an impending landslide. However, one still needs to know where to focus the analysis at first, especially when the target area is very small relative to the size of the monitoring domain and lies in among other unstable high‐risk slopes. We address this problem by learning the most unique characteristics related to the landslide source area directly from the displacement data captured by Sentinel‐1 satellites, and building a new prediction method to find the temporally persistent outlying areas over multiple spatial scales as the final prediction. The results from two case studies—2017 Xinmo landslide and Stromboli volcano rockfalls in 2015–2016—show that the location of the impending failure can be accurately identified well before the time of failure, by tracking the abnormality in a simple feature: the third quartile in the ground displacement of small regions. This approach provides an important advance for timely risk assessment from satellite data, especially for remote areas that are difficult, if not impossible, to access. Key Points: Ground deformation data from Sentinel‐1 encode rich information and can be used to predict the location of impending landslides In imbalanced satellite data, point‐level clustering dynamics diminish but outlying motions at grid cell level become distinctive The third quartile of ground displacement in a cell accurately identifies the location of the 2017 Xinmo landslide almost 1 year in advance