Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation

Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset

Time series analysis of very slow landslides in the three Gorges region through small baseline SAR offset tracking

Sub-pixel offset tracking has been used in various applications, including measurements of glacier movement, earthquakes, landslides, etc., as a complementary method to time series InSAR. In this work, we explore the use of a small baseline subset (SBAS) Offset Tracking approach to monitor very slow landslides with centimetre-level annual displacement rate, and in challenging areas characterized by high humidity, dense vegetation cover, and steep slopes. This approach, herein referred to as SBAS Offset Tracking, is used to minimize temporal and spatial de -correlation in offset pairs, in order to achieve high density of reliable measurements. This approach is applied to a case study of the Tanjiahe landslide in the Three Gorges Region. Using the TerraSAR-X Staring Spotlight (TSX-ST) data, with sufficient density of observations, we estimate the precision of the SBAS offset tracking approach to be 2-3 cm on average. The results demonstrated accord well with corresponding GPS measurements

Evaluation of the Use of Sub-Pixel Offset Tracking Techniques to Monitor Landslides in Densely Vegetated Steeply Sloped Areas

Sub-Pixel Offset Tracking (sPOT) is applied to derive high-resolution centimetre-level landslide rates in the Three Gorges Region of China using TerraSAR-X Hi-resolution Spotlight (TSX HS) space-borne SAR images. These results contrast sharply with previous use of conventional differential Interferometric Synthetic Aperture Radar (DInSAR) techniques in areas with steep slopes, dense vegetation and large variability in water vapour which indicated around 12% phase coherent coverage. By contrast, sPOT is capable of measuring two dimensional deformation of large gradient over steeply sloped areas covered in dense vegetation. Previous applications of sPOT in this region relies on corner reflectors (CRs), (high coherence features) to obtain reliable measurements. However, CRs are expensive and difficult to install, especially in remote areas; and other potential high coherence features comparable with CRs are very few and outside the landslide boundary. The resultant sub-pixel level deformation field can be statistically analysed to yield multi-modal maps of deformation regions. This approach is shown to have a significant impact when compared with previous offset tracking measurements of landslide deformation, as it is demonstrated that sPOT can be applied even in densely vegetated terrain without relying on high-contrast surface features or requiring any de-noising process

Time-variable 3D ground displacements from High-Resolution Synthetic Aperture Radar (SAR). Application to La Valette landslide (South French Alps).

International audienceWe apply an image correlation technique to multi-orbit and multi-temporal High-Resolution (HR) SAR data. Image correlation technique has the advantage of providing displacement maps in two directions; e.g. the Line of Sight direction (LoS) and the Azimuth direction. This information, derived from the two modes of data acquisition (ascending and descending), can be combined routinely to infer the three dimensional surface displacement field at different epochs. In this study, a methodology is developed to characterize the displacement pattern of the large La Valette landslide (South French Alps) using TerraSAR-X images acquired in 2010. The results allow mapping the dynamics of different units of the La Valette landslide at high spatial resolution. The study demonstrates the potential of this new application of High Resolution SAR image correlation technique for landslide ground surface deformation monitoring

Quantitative monitoring of surface movements on active landslides by multi-temporal, high-resolution X-Band SAR amplitude information: Preliminary results

Multi-temporal image cross-correlation is a method for tracking moving features and can there-fore be used for quantitative assessments of surface displacements. Accuracies of up to 1/8th of the original image geometric resolution can be achieved. We present the results of an analysis car- ried out on Corvara landslide located in the Italian Dolomites. Image offset-tracking was applied to CosmoSky-Med amplitude images acquired between October 2013 and August 2015. The presence of a validation dataset consisting of periodical GPS surveys carried out on 16 benchmarks represents an ideal opportunity to test the applicability of SAR-based image cross-correlation for landslide moni- toring. Despite the relative low accuracy of the results amplitude-based offset-tracking proved to be beneficial due to the ability of this method to capture large displacements. In particular the results evidence its complementarity with respect to multi-temporal interferometry that is confined to slow displacements along E-W directions

Temporal development of the displacement field of the Ponzano landslide in February 2017

The conducted research determined the temporal evolution of the displacement field for the Ponzano landslide case study. The offset-tracking method, so far used mainly for the relatively rapid but uniform displacement of glaciers, was tested for the 2017 study of the Ponzano landslide in Italy. The suitability of the method for high-resolution TerraSAR-X and medium-resolution Sentinel-1 imagery was investigated. The results proved the applicability of the OT method for studying processes with high and variable displacement dynamics. However, for such purposes, high-resolution radar data are crucial. With an uncertainty in the determination of residual displacements of about ±1 m, it was shown that the values of residual displacements occurring up to several days after the main phase of landslide movements are within the range of uncertainty but are determinable. The research conducted in the paper filled a gap in the analysis of the phenomenon just after the main movement phase. It allowed determination of the time and speed of extinction of landslide movements

Patch-Like Reduction (PLR): A SAR offset tracking amplitude filter for deformation monitoring

As complementary to Synthetic Aperture Radar (SAR) Differential Interferometry (DInSAR), SAR Offset Tracking (OT) is an efficient tool for large ground deformation monitoring in situations when DInSAR cannot work. However, SAR images are affected by speckle noise and some strong point-like scatters which can cause what is known as Patch Like (PL), a kind of errors that can be seen as homogeneous patches of almost constant deformation in the results. These errors are clearly visible in the results as non-consistent deformations along time, but they are difficult to detect with the traditional metrics that evaluate the cross-correlation results, like the Signal to Noise Ratio (SNR). This paper addresses this problem and proposes a simple amplitude filter to reduce PL named as Patch Like Reduction (PLR). The main idea is to find a sensor and scene independent threshold to remove the high amplitude pixels prone to cause PL. Five different SAR data sets and in-field GPS measurements are used to determine the optimal threshold and evaluate the performance of the proposed method. The results show that PL effects can be reduced with the proposed amplitude filter. The processing parameters of the improved OT processing chain are optimized as well to preserve the results resolution as much as possible.This work has been financially supported by China Scholarship Council (Grant No. 201806420035), the Spanish Ministry of Science and Innovation (MCINN) and the State Research Agency (AEI) project PID2020-117303GB-C21 MCIN/AEI/10.13039/501100011033. This work has also been financially supported by the Natural Science Foundation of China (Grant No. 42004011), China Postdoctoral Science Foundation (Grant No. 2020M671646), Centro para el Desarrollo Tecnológico Industrial and Ministry of Science and Technology of the People’s Republic of China (Spanish-Chinese CHINEKA project No. 2022YFE0102600), and the Ministry of Education of the People’s Republic of China (Construction Program of Space-Air-Ground-Well Cooperative Awareness Spatial Information Project B20046)Peer ReviewedPostprint (published version

A robust sar speckle tracking workflow for measuring and interpreting the 3d surface displacement of landslides

We present a workflow for investigating large, slow‐moving landslides which combines the synthetic aperture radar (SAR) technique, GIS post‐processing, and airborne laser scanning (ALS), and apply it to Fels landslide in Alaska, US. First, we exploit a speckle tracking (ST) approach to derive the easting, northing, and vertical components of the displacement vectors across the rock slope for two five‐year windows, 2010–2015 and 2015–2020. Then, we perform post‐processing in a GIS environment to derive displacement magnitude, trend, and plunge maps of the landslide area. Finally, we compare the ST‐derived displacement data with structural lineament maps and profiles extracted from the ALS dataset. Relying on remotely sensed data, we estimate that the thickness of the slide mass is more than 100 m and displacements occur through a combination of slumping at the toe and planar sliding in the central and upper slope. Our approach provides information and interpretations that can assist in optimizing and planning fieldwork activities and site investigations at landslides in remote locations

A Robust SAR Speckle Tracking Workflow for Measuring and Interpreting the 3D Surface Displacement of Landslides

We present a workflow for investigating large, slow-moving landslides which combines the synthetic aperture radar (SAR) technique, GIS post-processing, and airborne laser scanning (ALS), and apply it to Fels landslide in Alaska, US. First, we exploit a speckle tracking (ST) approach to derive the easting, northing, and vertical components of the displacement vectors across the rock slope for two five-year windows, 2010–2015 and 2015–2020. Then, we perform post-processing in a GIS environment to derive displacement magnitude, trend, and plunge maps of the landslide area. Finally, we compare the ST-derived displacement data with structural lineament maps and profiles extracted from the ALS dataset. Relying on remotely sensed data, we estimate that the thickness of the slide mass is more than 100 m and displacements occur through a combination of slumping at the toe and planar sliding in the central and upper slope. Our approach provides information and interpretations that can assist in optimizing and planning fieldwork activities and site investigations at landslides in remote locations

Brief Communication: Rapid mapping of landslide events: the 3 December 2013 Montescaglioso landslide, Italy

We present an approach to measure 3-D surface deformations caused by large, rapid-moving landslides using the amplitude information of high-resolution, X-band synthetic aperture radar (SAR) images. We exploit SAR data captured by the COSMO-SkyMed satellites to measure the deformation produced by the 3 December 2013 Montescaglioso landslide, southern Italy. The deformation produced by the deep-seated landslide exceeded 10 m and caused the disruption of a main road, a few homes and commercial buildings. The results open up the possibility of obtaining 3-D surface deformation maps shortly after the occurrence of large, rapid-moving landslides using high-resolution SAR data