Special Issue "Remote Sensing of Landslides II"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editors

Dr. Zhong Lu
E-Mail Website
Guest Editor
Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75275-0395, USA
Tel. 214-768-0101
Interests: SAR; InSAR; time-series InSAR; geophysical modeling; volcanoes; landslides; geohazards
Special Issues and Collections in MDPI journals
Dr. Chaoying Zhao
E-Mail Website
Guest Editor
School of Geology Engineering and Geomatics, Chang’an University, No.126, Yanta road, Xi’an, 710054, China
Tel. +86-29-82339251
Interests: SAR; InSAR; geodetic measurement; geohazards; landslide; land subsidence; mining
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Triggered by precipitation, water-level fluctuation, freeze thaw, irrigation, earthquakes, anthropogenic activities and other factors, landslide has become a severe geohazard worldwide. In recent years, multiple remote sensing techniques that use synthetic aperture radar (SAR), light detection and ranging (LiDAR), optical, and photogrammetric measurements from spaceborne, airborne, mobile-vehicle, and ground-based platforms have been widely applied for landslide classification, detection, digital elevation model reconstruction, surface deformation monitoring, volume/thickness inversion, and stability and mechanism analysis. In addition, landslide susceptibility zonation, hazard assessment, and risk evaluation can be further analyzed by the synergic fusion of multiple remote sensing data and other observations with the aid of GIS and statistical tools. This Special Issue invites innovative remote sensing methods, inversion techniques, and stability and mechanism analyses on landslide studies.

Some of the topics we would suggest that the papers should focus on are:

  • Landslide detection and inventory mapping with remote sensing data (SAR, optical, LiDAR, photogrammetric, and others);
  • Multiple sensor data assimilation and synergy for landslide analysis;
  • Landslide time-series deformation monitoring, validation and calibration;
  • Landslide modeling and volume estimation;
  • Landslide trigger factor analysis and mechanism inversion;
  • Landslide susceptibility, stability analysis, hazard assessment, and risk evaluation;
  • Landslide early-warning system design and implementation;
  • Big data and deep learning on landslide analysis.

Dr. Zhong Lu
Dr. Chaoying Zhao
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

Open AccessArticle
Characterizing Seasonally Rainfall-Driven Movement of a Translational Landslide using SAR Imagery and SMAP Soil Moisture
Remote Sens. 2019, 11(20), 2347; https://doi.org/10.3390/rs11202347 - 10 Oct 2019
Abstract
Precipitation infiltrates into basal shearing zones, triggering seasonal landslide motion by increasing pore-pressure and reducing shear resistance. This process is jointly controlled by basal depth, rainfall intensity, soil moisture, and hydraulic conductivity/diffusivity. Using interferometric synthetic aperture radar (InSAR), we detected and mapped a [...] Read more.
Precipitation infiltrates into basal shearing zones, triggering seasonal landslide motion by increasing pore-pressure and reducing shear resistance. This process is jointly controlled by basal depth, rainfall intensity, soil moisture, and hydraulic conductivity/diffusivity. Using interferometric synthetic aperture radar (InSAR), we detected and mapped a slow-moving slide in the southwestern Oregon. Its basal depths are estimated using InSAR-derived surface velocity fields based on the mass conservation approach by assuming a power-law rheology. The estimated maximum thickness over the central region of the landslide is 6.9 ± 2.6 m. This result is further confirmed by an independent limit equilibrium analysis that solely relies on soil mechanical properties. By incorporating satellites-captured time lags of 27–49 days between the onset of wet seasons and the initiation of landslide motions, the averaged characteristic hydraulic conductivity and diffusivity of the landslide material is estimated as 1.2 × 10−5 m/s and 1.9 × 10−4 m2/s, respectively. Our investigation layouts a framework for using InSAR and satellite-sensed soil moisture to infer landslide basal geometry and estimate corresponding hydraulic parameters. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides II)
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Open AccessArticle
A Synergetic Analysis of Sentinel-1 and -2 for Mapping Historical Landslides Using Object-Oriented Random Forest in the Hyrcanian Forests
Remote Sens. 2019, 11(19), 2300; https://doi.org/10.3390/rs11192300 - 02 Oct 2019
Abstract
Despite increasing efforts in the mapping of landslides using Sentinel-1 and -2, research on their combination for discerning historical landslides in forest areas is still lacking, particularly using object-oriented machine learning approaches. This study was accomplished to test the efficiency of Sentinel-derived features [...] Read more.
Despite increasing efforts in the mapping of landslides using Sentinel-1 and -2, research on their combination for discerning historical landslides in forest areas is still lacking, particularly using object-oriented machine learning approaches. This study was accomplished to test the efficiency of Sentinel-derived features and digital elevation model (DEM) derivatives for mapping old and new landslides, using object-oriented random forest. Two forest subsets were selected including a protected and non-protected forest in northeast Iran. Landslide samples were obtained from CORONA images and aerial photos (old landslides), and also field mensuration and high-resolution images (new landslides). Segment objects were generated from a set combination of Sentinel-1A, Sentinel-2A, and some topographic-derived indices using multiresolution segmentation algorithm. Various object features were derived from the main channels of Sentinel images and DEM derivatives in the seven main groups, including spectral layers, spectral indices, geometric, contextual, textural, topographic, and hydrologic features. A single database was created, including landslide samples and Sentinel- and DEM-derived object features. Roughly 20% of landslide-affected objects and non-landslide-affected objects were randomly selected as an input for training the random forest classifier. Two-thirds of the selected objects were assigned as learning samples for classification, and the remainder were used for testing the accuracy of landslide and non-landslide classification. Results indicated that: (1) The sensitivity of mapping historical landslides was 86.6% and 80.3% in the protected and non-protected forests, respectively; (2) the object features of Sentinel-2A and DEM obtained the highest importance with the total scores of 55.6% and 32%, respectively in the protected forests, and 65.4% and 21% respectively in the non-protected forests; (3) the features derived from the combination of Sentinel-1 and -2A demonstrated a total importance of 10% for mapping new landslides; and (4) textural features were obtained in approximately two-thirds of the total scores for mapping new landslides, however a combination of topographic, spectral, textural, and contextual features were the effective predictors for mapping old landslides. This research proposes applying a synergetic analysis of Sentinel- and DEM-derived features for mapping historical landslides; however, there are no uniformly pre-defined influential variables for mapping historical landslides in different forest areas. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides II)
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Open AccessFeature PaperArticle
Diagnosis of Xinmo (China) Landslide Based on Interferometric Synthetic Aperture Radar Observation and Modeling
Remote Sens. 2019, 11(16), 1846; https://doi.org/10.3390/rs11161846 - 08 Aug 2019
Abstract
The Xinmo landslide occurred on 24 June 2017 and caused huge casualties and property losses. As characteristics of spatiotemporal pre-collapse deformation are a prerequisite for further understanding the collapse mechanism, in this study we applied the interferometric synthetic aperture radar (InSAR) technique to [...] Read more.
The Xinmo landslide occurred on 24 June 2017 and caused huge casualties and property losses. As characteristics of spatiotemporal pre-collapse deformation are a prerequisite for further understanding the collapse mechanism, in this study we applied the interferometric synthetic aperture radar (InSAR) technique to recover the pre-collapse deformation, which was further modeled to reveal the mechanism of the Xinmo landslide. Archived SAR data, including 44 Sentinel-1 A/B data and 20 Envisat/ASAR data, were used to acquire the pre-collapse deformation of the Xinmo landslide. Our results indicated that the deformation of the source area occurred as early as 10 years before the landslide collapsed. The deformation rate of source area accelerated about a month before the collapse, and the deformation rate in the week before the collapse reached 40 times the average before the acceleration. Furthermore, the pre-collapse deformation was modeled with a distributed set of rectangular dislocation sources. The characteristics of the pre-collapse movement of the slip surface were acquired, which further confirmed that a locked section formed at the bottom of the slope. In addition, the spatial-temporal characteristics of the deformation was found to have changed significantly with the development of the landslide. We suggested that this phenomenon indicated the expansion of the slip surface and cracks of the landslide. Due to the expansion of the slip surface, the locked section became a key area that held the stability of the slope. The locked section sheared at the last stage of the development, which triggered the final run-out. Our study has provided new insights into the mechanism of the Xinmo landslide. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides II)
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Open AccessArticle
A Multi-Disciplinary Approach to the Study of Large Rock Avalanches Combining Remote Sensing, GIS and Field Surveys: The Case of the Scanno Landslide, Italy
Remote Sens. 2019, 11(13), 1570; https://doi.org/10.3390/rs11131570 - 02 Jul 2019
Abstract
This research aims to highlight the importance of adopting a multi-disciplinary approach to understanding the factors controlling large rock avalanches using the Scanno landslide, Italy, as a case study. The study area is the Mount Genzana, Abruzzi Central Apennines, characterized by the regional [...] Read more.
This research aims to highlight the importance of adopting a multi-disciplinary approach to understanding the factors controlling large rock avalanches using the Scanno landslide, Italy, as a case study. The study area is the Mount Genzana, Abruzzi Central Apennines, characterized by the regional Difesa-Mount Genzana-Vallone delle Masserie fault zone. The Scanno landslide is famous for its role in the formation of the Scanno Lake. The landslide is characterized by a wide exposed scar, which was interpreted in previous studies as the intersection of high-angle joints and an outcropping bedding plane on which the landslide failed sometime between the Upper Pleistocene and the Holocene. In this study, the Scanno landslide was investigated through the integration of geological, geomechanical and geomorphological surveys. Remote sensing techniques were used to enrich the conventionally gathered datasets, while Geographic Information Systems (GIS) were used to integrate, manage and investigate the data. The results of the authors investigation show that the outcropping landslide scar can be interpreted as a low-angle fault, associated with the Difesa-Mount Genzana-Vallone delle Masserie fault zone, which differs from previous investigations and interpretations of the area. The low-angle fault provides the basal failure surface of the landslide, with two systematic high-angle joint sets acting as lateral release and back scarp surfaces, respectively. In light of these new findings, pre- and post-failure models of the area have been created. The models were generated in GIS by combining LiDAR (Light Detection and Ranging) and geophysics data acquired on the landslide body and through bathymetric survey data of the Scanno Lake. Using the pre- and post-failure models it was possible to estimate the approximate volume of the landslide. Finally, back-analyses using static and dynamic limit equilibrium methods is also used to show the possible influence of medium-to-high magnitude seismic events in triggering the Scanno landslide. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides II)
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Open AccessArticle
Landslide-Induced Damage Probability Estimation Coupling InSAR and Field Survey Data by Fragility Curves
Remote Sens. 2019, 11(12), 1486; https://doi.org/10.3390/rs11121486 - 22 Jun 2019
Cited by 1
Abstract
Landslides are considered to be one of the main natural geohazards causing relevant economic damages and social effects worldwide. Italy is one of the countries worldwide most affected by landslides; in the Region of Tuscany alone, more than 100,000 phenomena are known and [...] Read more.
Landslides are considered to be one of the main natural geohazards causing relevant economic damages and social effects worldwide. Italy is one of the countries worldwide most affected by landslides; in the Region of Tuscany alone, more than 100,000 phenomena are known and mapped. The possibility to recognize, investigate, and monitor these phenomena play a key role to avoid further occurrences and consequences. The number of applications of Advanced Differential Interferometric Synthetic Aperture Radar (A-DInSAR) analysis for landslides monitoring and mapping greatly increased in the last decades thanks to the technological advances and the development of advanced processing algorithms. In this work, landslide-induced damage on structures recognized and classified by field survey and velocity of displacement re-projected along the steepest slope were combined in order to extract fragility curves for the hamlets of Patigno and Coloretta, in the Zeri municipality (Tuscany, northern Italy). Images using ERS1/2, ENVISAT, COSMO-SkyMed (CSK) and Sentinel-1 SAR (Synthetic Aperture Radar) were employed to investigate an approximate 25 years of deformation affecting both hamlets. Three field surveys were conducted for recognizing, identifying, and classifying the landslide-induced damage on structures and infrastructures. At the end, the damage probability maps were designed by means of the use of the fragility curves between Sentinel-1 velocities and recorded levels of damage. The results were conceived to be useful for the local authorities and civil protection authorities to improve the land managing and, more generally, for planning mitigation strategies. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides II)
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