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SAR Imagery for Landslide Detection and Prediction

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 34531

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Dr. Massimiliano Bordoni

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Guest Editor

Department of Earth and Environmental Sciences, University of Pavia, Pavia, Italy
Interests: landslides monitoring; landslides modeling; soil hydrology; remote sensing
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Prof. Dr. Claudia Meisina

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Guest Editor

Department of Earth and Environmental Sciences, University of Pavia, Via Adolfo Ferrata 1, 27100 Pavia, Italy
Interests: engineering geological mapping; hazard assessment (subsidence, landslide, liquefaction); geological interpretation of satellite interferometric data
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Dr. Roberta Bonì

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Guest Editor

Department of Pure and Applied Sciences, University of Urbino “Carlo Bo”, Urbino, Italy
Interests: engineering geology; remote sensing; landslides; land subsidence; InSAR; monitoring; modelling
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Special Issue Information

Dear Colleagues,

In recent decades, satellite remote sensing has been established to measure surface displacements due to natural and human-induced processes. This tool has been improved with the synthetic aperture radar (SAR) technique. Thanks to this technique, millimetric–centimetric ground deformations can be measured, furnishing a fundamental tool for detecting and monitoring ground surface deformations related to landslides and for studying the trends of evolution of these phenomena.

SAR-based techniques have also been developed for the identification of landslides triggered in consequence of a particular event, allowing to create inventories and databases, overcoming the intrinsic limitation of the traditionally used optical images due to the cloud cover.

In this context, the launch in April 2014 of the first Sentinel-1 satellite and the other missions scheduled in the following years opened new frontiers for SAR image exploitation, increasing the temporal and spatial resolutions of deformation time-series and the ground response in terms of radar coherence for detecting new events and monitoring the evolution of slope instabilities at both regional and local scales.

This Special Issue aims at collecting new developments and methodologies, best practices, and applications of SAR imagery for the detection of landslides, the characterization of landslide displacements, and the prediction of new landslides triggering or of the evolution of displacement trends.

Without constraining the range of topics that are potentially suitable for inclusion in the Special Issue, we provide the following as examples:

- Methodologies to process SAR products for landslides detection, characterization, and prediction;

- Methods established for the detection of new landslides and for the creation of inventories and databases exploiting SAR images;

- Use of displacement time-series obtained through SAR techniques for the characterization of landslide kinematic behaviors;

- Models of prediction of landslides triggering using SAR images;

- Estimation of landslides state of activity using multitemporal SAR data;

- Prediction of the evolution in landslide displacements since the analyses of deformation time-series obtained through SAR;

- Identification of precursors and early detection of the triggering moment of catastrophic slope failures;

- Hazard assessment and early-warning strategies implemented through SAR data.

Dr. Massimiliano Bordoni
Prof. Claudia Meisina
Dr. Roberta Bonì
Guest Editors

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Keywords

SAR imagery
Differential interferometric SAR
Landslides monitoring
Landslides modeling and prediction
Displacement time-series
Landslides state of activity
Change detection
Landslides identification
Landslide inventory mapping
Early detection

Published Papers (8 papers)

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Research

26 pages, 26388 KiB

Open AccessArticle

Assessing Urban Landslide Dynamics through Multi-Temporal InSAR Techniques and Slope Numerical Modeling

by Nicușor Necula, Mihai Niculiță, Simone Fiaschi, Rinaldo Genevois, Paolo Riccardi and Mario Floris

Remote Sens. 2021, 13(19), 3862; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193862 - 27 Sep 2021

Cited by 10 | Viewed by 3650

Abstract

Landslides threaten more than before the urbanized areas and are a worldwide growing problem for the already affected communities and the local authorities committed to landslide risk management and mitigation. For this reason, it is essential to analyze landslide dynamics and environmental conditioning factors. Various techniques and instruments exist for landslide investigation and monitoring. Out of these, Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) techniques have been widely used in the last decades. Their capabilities are enhanced by the availability of the active Sentinel-1 mission, whose 6-day revisiting time enables near real-time monitoring of landslides. Interferometric results, coupled with ground measurements or other approaches such as numerical modeling, significantly improve the knowledge of the investigated surface processes. In this work, we processed the C-band SAR images of the available European Space Agency (ESA) satellite missions, using MT-InSAR methods to identify the surface deformations related to landslides affecting the Iași Municipality (Eastern Romania). The results (i.e., velocity maps) point out the most active landslides with velocities of up to 20 mm/year measured along the satellite Line of Sight (LOS). Following, we focused on the most problematic landslide that affects the Țicău neighborhood and is well-known for its significant implications that it had. To better understand its behavior and the sensitivity of the displacements to the environmental factors (i.e., rainfall), we carried out 2D numerical modeling using a finite difference code. The simulated displacement field is consistent with the InSAR displacements and reveals the most active sectors of the landslide and insights about its mechanism. Full article

(This article belongs to the Special Issue SAR Imagery for Landslide Detection and Prediction)

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31 pages, 11143 KiB

Open AccessEditor’s ChoiceArticle

The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings

by Serena Moretto, Francesca Bozzano and Paolo Mazzanti

Remote Sens. 2021, 13(18), 3735; https://doi.org/10.3390/rs13183735 - 17 Sep 2021

Cited by 33 | Viewed by 4556

Abstract

The paper explores the potential of the satellite advanced differential synthetic aperture radar interferometry (A-DInSAR) technique for the identification of impending slope failure. The advantages and limitations of satellite InSAR in monitoring pre-failure landslide behaviour are addressed in five different case histories back-analysed using data acquired by different satellite missions: Montescaglioso landslide (2013, Italy), Scillato landslide (2015, Italy), Bingham Canyon Mine landslide (2013, UT, USA), Big Sur landslide (2017, CA, USA) and Xinmo landslide (2017, China). This paper aimed at providing a contribution to improve the knowledge within the subject area of landslide forecasting using monitoring data, in particular exploring the suitability of satellite InSAR for spatial and temporal prediction of large landslides. The study confirmed that satellite InSAR can be successful in the early detection of slopes prone to collapse; its limitations due to phase aliasing and low sampling frequency are also underlined. According to the results, we propose a novel landslide predictability classification discerning five different levels of predictability by satellite InSAR. Finally, the big step forward made for landslide forecasting applications since the beginning of the first SAR systems (ERS and Envisat) is shown, highlighting that future perspectives are encouraging thanks to the expected improvement of upcoming satellite missions that could highly increase the capability to monitor landslides’ pre-failure behaviour. Full article

(This article belongs to the Special Issue SAR Imagery for Landslide Detection and Prediction)

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20 pages, 15254 KiB

Open AccessArticle

Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

by Jinmin Zhang, Wu Zhu, Yiqing Cheng and Zhenhong Li

Remote Sens. 2021, 13(18), 3566; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183566 - 08 Sep 2021

Cited by 31 | Viewed by 3721

Abstract

Construction of the 998.64-km Linzhi–Ya’an section of the Sichuan–Tibet Railway has been influenced by landslide disasters, threatening the safety of Sichuan–Tibet railway projects. Landslide identification and deformation analysis in this area are urgently needed. In this context, it was the first time that 164 advanced land-observing satellite-2 (ALOS-2) phased array type L-band synthetic aperture radar-2 (PALSAR-2) images were collected to detect landslide disasters along the entire Linzhi–Ya’an section. Interferogram stacking and small baseline interferometry methods were used to derive the deformation rate and time-series deformation from 2014–2020. After that, the hot spot analysis method was introduced to conduct spatial clustering analysis of the annual deformation rate, and the effective deformation area was quickly extracted. Finally, 517 landslide disasters along the Linzhi–Ya’an route were detected by integrating observed deformation, Google Earth optical images, and external geological data. The main factors controlling the spatial landslide distribution were analyzed. In the vertical direction, the spatial landslide distribution was mainly concentrated in the elevation range of 3000–5000 m, the slope range of 10–40°, and the aspect of northeast and east. In the horizontal direction, landslides were concentrated near rivers, and were also closely related to earthquake-prone areas, fault zones, and high-precipitation areas. In short, rainfall, freeze–thaw weathering, seismic activity, and fault zones are the main factors inducing landslides along this route. This research provides scientific support for the construction and operation of the Linzhi–Ya’an section of the Sichuan–Tibet Railway. Full article

(This article belongs to the Special Issue SAR Imagery for Landslide Detection and Prediction)

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21 pages, 11289 KiB

Open AccessArticle

Study on the Creep-Sliding Mechanism of the Giant Xiongba Ancient Landslide Based on the SBAS-InSAR Method, Tibetan Plateau, China

by Changbao Guo, Yiqiu Yan, Yongshuang Zhang, Xujiao Zhang, Yueze Zheng, Xue Li, Zhihua Yang and Ruian Wu

Remote Sens. 2021, 13(17), 3365; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173365 - 25 Aug 2021

Cited by 18 | Viewed by 2685

Abstract

The geohazards associated with strongly deformed and reactivated large-scale ancient landslide are analyzed through a study of the Xiongba ancient landslide. The SBAS-InSAR method, combined with remote sensing interpretation, was used to obtain the Xiongba ancient landslide surface deformation characteristics, on the western bank of the Jinsha River, during the period from October 2017 to June 2020. Two large strong deformation zones were discovered in this study, H₁ and H₂, which were located at the front edge of the Xiongba landslide. The maximum cumulative deformation in the H₁ deformation zone was approximately 204 mm, and the deformation in the H₂ deformation zone was approximately 302 mm. Influenced by the Jinsha River erosion, the Baige landslide-dammed lake-dam breakage-debris (LDLDB) flow/flood hazard chains, which occurred 75 km upstream reaches in October and November 2018, and the erosion of the foot of the Xiongba ancient landslide foot resulted in notably enhanced deformation. The creep rate in the H₁ deformation zone was 14~16 times that before the Baige landslide hazard chains occurred, and the hazard chains caused sliding in the H₂ zone. The Xiongba ancient landslide is undergoing retrogressive reactivation. The Xiongba ancient landslide is currently experiencing continuously creep-sliding, and the deformation rate in some areas is accelerating, which may induce a large-scale reactivation of the Xiongba ancient landslide and an LDLDB hazard chain. Full article

(This article belongs to the Special Issue SAR Imagery for Landslide Detection and Prediction)

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21 pages, 15471 KiB

Open AccessArticle

A Large Old Landslide in Sichuan Province, China: Surface Displacement Monitoring and Potential Instability Assessment

by Siyuan Ma, Chong Xu, Xiaoyi Shao, Xiwei Xu and Aichun Liu

Remote Sens. 2021, 13(13), 2552; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132552 - 29 Jun 2021

Cited by 12 | Viewed by 2996

Abstract

Using advanced Differential Interferometric Synthetic Aperture Radar (InSAR) with small baseline subsets (SBAS) and Permanent Scatter Interferometry (PSI) techniques and C-band Sentinel-1A data, this research monitored the surface displacement of a large old landslide at Xuecheng town, Lixian County, Sichuan Province, China. Based on the MassMov2D model, the effect of the dynamic process and deposit thickness of the potentially unstable rock mass (deformation rate < −70 mm/year) on this landslide body were numerically simulated. Combined with terrain data and images generated by an Unmanned Aerial Vehicle (UAV), the driving factors of this old landslide were analyzed. The InSAR results show that the motion rate in the middle part of the landslide body is the largest, with a range of −55 to −80 mm/year on average, whereas those of the upper part and toe area were small, with a range of −5 to −20 mm/year. Our research suggests that there is a correlation between the LOS (line of sight) deformation rate and rainfall. In rainy seasons, particularly from May to July, the deformation rate is relatively high. In addition, the analysis suggests that SBAS can provide smoother displacement time series, even in areas with vegetation and the steepest sectors of the landslide. The simulation results show that the unstable rock mass may collapse and form a barrier dam with a maximum thickness of about 16 m at the Zagunao river in the future. This study demonstrates that combining temporal UAV measurements and InSAR techniques from Sentinel-1A SAR data allows early recognition and deformation monitoring of old landslide reactivation in complex mountainous areas. In addition, the information provided by InSAR can increase understanding of the deformation process of old landslides in this area, which would enhance urban safety and assist in disaster mitigation. Full article

(This article belongs to the Special Issue SAR Imagery for Landslide Detection and Prediction)

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22 pages, 23620 KiB

Open AccessArticle

Processing Framework for Landslide Detection Based on Synthetic Aperture Radar (SAR) Intensity-Image Analysis

by Shih-Yuan Lin, Cheng-Wei Lin and Stephan van Gasselt

Remote Sens. 2021, 13(4), 644; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040644 - 10 Feb 2021

Cited by 9 | Viewed by 3115

Abstract

We present an object-based image analysis (OBIA) approach to identify temporal changes in radar-intensity images and to locate land-cover changes caused by mass-wasting processes at small to large scales, such as landslides. Our approach is based upon change detection in SAR intensity images that remain in their original imaging coordinate system rather than being georeferenced and map-projected, in order to reduce accumulation of filtering artifacts and other unwanted effects that would deteriorate the detection efficiency. Intensity images in their native slant-range coordinate frame allow for a consistent level of detection of land-cover changes. By analyzing intensity images, a much faster response can be achieved and images can be processed as soon as they are made publicly available. In this study, OBIA was introduced to systematically and semiautomatically detect landslides in image pairs with an overall accuracy of at least 60% when compared to in-situ landslide inventory data. In this process, the OBIA feature extraction component was supported by derived data from a polarimetric decomposition as well as by texture indices derived from the original image data. The results shown here indicate that most of the landslide events could be detected when compared to a closer visual inspection and to established inventories, and that the method could therefore be considered as a robust detection tool. Significant deviations are caused by the limited geometric resolution when compared to field data and by an additional detection of stream-related sediment redeposition in our approach. This overdetection, however, turns out to be potentially beneficial for assessing the risk situation after landslide events. Full article

(This article belongs to the Special Issue SAR Imagery for Landslide Detection and Prediction)

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23 pages, 25485 KiB

Open AccessArticle

Regional Recognition and Classification of Active Loess Landslides Using Two-Dimensional Deformation Derived from Sentinel-1 Interferometric Radar Data

by Qingkai Meng, Pierluigi Confuorto, Ying Peng, Federico Raspini, Silvia Bianchini, Shuai Han, Haocheng Liu and Nicola Casagli

Remote Sens. 2020, 12(10), 1541; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12101541 - 12 May 2020

Cited by 21 | Viewed by 3907

Abstract

Identification and classification of landslides is a preliminary and crucial work for landslide risk assessment and hazard mitigation. The exploitation of surface deformation velocity derived from satellite synthetic aperture radar interferometry (InSAR) is a consolidated and suitable procedure for the recognition of active landslides over wide areas. However, the calculated displacement velocity from InSAR is one-dimensional motion along the satellite line of sight (LOS), representing a major hurdle for landslide type and failure mechanism classification. In this paper, different velocity datasets derived from both ascending and descending Sentinel-1 data are employed to analyze the surface ground movement of the Huangshui region (Northwestern China). With global warming, precipitation in the Huangshui region, geologically belonging to the loess basin in the eastern edge of Qing-Tibet Plateau, has been increasing, often triggering a large number of landslides, posing a potential threat to local citizens and natural and anthropic environments. After processing both SAR data geometries, the surface motion was decomposed to obtain the two-dimensional displacements (vertical and horizontal E–W). Thus, a classification criterion of the loess landslide types and failure mode is proposed, according to the analysis of deformation direction, velocities, texture, and topographic characteristics. With the support of high-resolution images acquired by remote sensing and unmanned aerial vehicle (UAV), 14 translational slides, seven rotational slides, and 10 loess flows were recognized in the study area. The derived results may provide solid support for stakeholders to comprehend the hazard of unstable slopes and to undertake specific precautions for moderate and slow slope movements. Full article

(This article belongs to the Special Issue SAR Imagery for Landslide Detection and Prediction)

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22 pages, 5660 KiB

Open AccessArticle

Landslide Mapping and Monitoring Using Persistent Scatterer Interferometry (PSI) Technique in the French Alps

by Gokhan Aslan, Michael Foumelis, Daniel Raucoules, Marcello De Michele, Severine Bernardie and Ziyadin Cakir

Remote Sens. 2020, 12(8), 1305; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081305 - 20 Apr 2020

Cited by 89 | Viewed by 6729

Abstract

Continuous geodetic measurements in landslide prone regions are necessary to avoid disasters and better understand the spatiotemporal and kinematic evolution of landslides. The detection and characterization of landslides in high alpine environments remains a challenge associated with difficult accessibility, extensive coverage, limitations of available techniques, and the complex nature of landslide process. Recent studies using space-based observations and especially Persistent Scatterer Interferometry (PSI) techniques with the integration of in-situ monitoring instrumentation are providing vital information for an actual landslide monitoring. In the present study, the Stanford Method for Persistent Scatterers InSAR package (StaMPS) is employed to process the series of Sentinel 1-A and 1-B Synthetic Aperture Radar (SAR) images acquired between 2015 and 2019 along ascending and descending orbits for the selected area in the French Alps. We applied the proposed approach, based on extraction of Active Deformation Areas (ADA), to automatically detect and assess the state of activity and the intensity of the suspected slow-moving landslides in the study area. We illustrated the potential of Sentinel-1 data with the aim of detecting regions of relatively low motion rates that be can attributed to activate landslide and updated pre-existing national landslide inventory maps on a regional scale in terms of slow moving landslides. Our results are compared to pre-existing landslide inventories. More than 100 unknown slow-moving landslides, their spatial pattern, deformation rate, state of activity, as well as orientation are successfully identified over an area of 4000 km² located in the French Alps. We also address the current limitations due the nature of PSI and geometric characteristic of InSAR data for measuring slope movements in mountainous environments like Alps. Full article

(This article belongs to the Special Issue SAR Imagery for Landslide Detection and Prediction)

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