Long-Term Satellite Monitoring of the Slumgullion Landslide Using Space-Borne Synthetic Aperture Radar Sub-Pixel Offset Tracking

Abstract

Kinematic characterization of a landslide at large, small, and detailed scale is today still rare and challenging, especially for long periods, due to the difficulty in implementing demanding ground surveys with adequate spatiotemporal coverage. In this work, the suitability of space-borne synthetic aperture radar sub-pixel offset tracking for the long-term monitoring of the Slumgullion landslide in Colorado (US) is investigated. This landslide is classified as a debris slide and has so far been monitored through ground surveys and, more recently, airborne remote sensing, while satellite images are scarcely exploited. The peculiarity of this landslide is that it is subject to displacements of several meters per year. Therefore, it cannot be monitored with traditional synthetic aperture radar differential interferometry, as this technique has limitations related to the loss of interferometric coherence and to the maximum observable displacement gradient/rate. In order to overcome these limitations, space-borne synthetic aperture radar sub-pixel offset tracking is applied to pairs of images acquired with a time span of one year between August 2011 and August 2013. The obtained results are compared with those available in the literature, both at landslide scale, retrieved through field surveys, and at point scale, using airborne synthetic aperture radar imaging and GPS. The comparison showed full congruence with the past literature. A consistency check covering the full observation period is also implemented to confirm the reliability of the technique, which results in a cheap and effective methodology for the long-term monitoring of large landslide-induced movements.

Digital Image Correlation of Google Earth Images for Earth’s Surface Displacement Estimation

“…The authors of [33] indicate that, over the next century, the landslide movement rate is expected to decrease due to a change in regional moisture balance conditions. This seems to be consistent with estimations completed by the authors of [16,34], indicating a maximum velocity from 4 to 4.4 m/year in the period 2011-2013.…”

“…Remote Sens. 2020, 12, x FOR PEER REVIEW 3 of 14 estimations completed by the authors of [16,34], indicating a maximum velocity from 4 to 4.4 m/year in the period 2011-2013.…”

“…However, this approach is not always suitable due to limitations related to the maximum detectable velocity [11] and potential high cost, especially in the context of high-resolution analyses (e.g., persistent scatterer approaches, [12,13]). This limitation has been partially overcome through the implementation of specific low-resolution approaches [14][15][16]. The use of optical images to track Earth's surface displacement is a potential alternative to SAR interferometry that has no limitations in terms of maximum detectable velocity and has the potential to be used in a number of deformation scenarios [17][18][19][20][21].…”

Glacier Monitoring Using Frequency Domain Offset Tracking Applied to Sentinel-1 Images: A Product Performance Comparison

“…These parameters are interconnected. Higher resolution images tend to maximize correlation at finer scale and should be preferred when dealing, as an example, with landslides [30,56]. Spatial decorrelation can be also due to orbital issues, i.e., differences in the acquisition geometry and/or large spatial baseline that, changing speckle patterns or the response of rugged terrains [35], can affect the amplitude and the sharpness of the correlation peak [59].…”

“…intensity tracking methods, originally developed for multispectral data, started being developed to deal with large displacements. OT methods have been successfully applied to map movements of terrains due to natural phenomena such as landslides, earthquakes [29][30][31], human activities (e.g., mining) [32,33] and glaciers [8][9][10][34][35][36][37]. In this paper, the technique is applied to pairs of S1 images acquired in different modalities to assess the performance of the different product classes and determine the best in terms of accuracy of the estimated displacements and computational demand.…”