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Remote Sensing for Landslide Monitoring, Mapping and Modeling

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 39529

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Special Issue Editors

Dr. Federica Bardi

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Guest Editor

Department of Earth Sciences, University of Firenze, Via La Pira 4, 50121 Firenze, Italy
Interests: landslide monitoring and modeling; geotechnical analysis; remote sensing; radar interferometry; landslide risk reduction; early warning systems; kinematic analysis

Dr. Pierluigi Confuorto

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Guest Editor

Department of Earth Sciences, University of Florence, Via La Pira, 4, 50121 Florence, Italy
Interests: landslides; engineering geology; monitoring; civil engineering; remote sensing; natural hazards; InSAR; satellite-based monitoring; GIS; subsidence; modelling of environmental processes
Special Issues, Collections and Topics in MDPI journals

Dr. Giulia Dotta

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Guest Editor

National Institute of Geophysics and Volcanology (INGV), Via Battisti 53, 56125 Pisa, Italy
Interests: landslide monitoring and modeling; rockmass characterization; remote sensing; laser scanning; rockfall analysis; GIS; GB-InSAR monitoring; spatial data analysis

Prof. Dr. Diego Di Martire

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Guest Editor

Department of Earth Sciences, Environment and Resources, University of Naples Federico II, 80126 Napoli, Italy
Interests: landslides, hazard and risk assessment; interferometry SAR; GIS
Special Issues, Collections and Topics in MDPI journals

Dr. Qingkai Meng

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Guest Editor

State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China
Interests: landslide monitoring; InSAR processing and application; remote sensing; UAV; landslide inventory mapping; landslide susceptibility assessment

Special Issue Information

Dear Colleagues,

Landslides are recurrent and widespread phenomena that can affect everyday life. In relation to their magnitude and velocity, they can cause human casualties and injuries as well as damage to private and public properties, with considerable loss of economic resources. In the current context of climate changes and land cover changes, their monitoring, mapping, and modeling assume a growing relevance in order to design the best risk reduction strategies. In this framework, remote sensing provides important support in landslide monitoring at relatively low costs, being able to offer a synoptic view and to acquire information at repeated and different time intervals. Indeed, remotely sensed data demonstrate excellent suitability for landslide mapping and modeling activities, at both local and regional scales.

Typical remote sensing for landslide observation includes optical, multi/hyper-spectral and radar satellite and airborne imagery, unmanned aerial vehicles (UAVs) systems equipped with various sensors, thermal cameras, laser scanner systems, LiDAR, etc.
Such data, especially when analyzed using Geographical Information Systems, may represent valuable tools for the modeling and assessment of landslide hazards.

This Special Issue therefore aims at spreading all novel contributions and advances in landslide studies by collecting original research, case studies, new approaches, and new theories.

Among the range of possible topics for this Special Issue, here are a few examples:

Characterization of rapid and slow-moving landslides;
Regional mapping of landslides;
Application of remotely sensed data to physically and statistically based hazard and risk models;
Post-processing chains for landslide applications;
Integration of multi-source data for landslide analyses;
Assessment of landslide intensity;
Development of early-warning systems.

Dr. Federica Bardi
Dr. Pierluigi Confuorto
Dr. Giulia Dotta
Dr. Diego Di Martire
Dr. Qingkai Meng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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.

Keywords

landslide monitoring
landslide modeling
landslide mapping
Earth Observation
GIS
risk reduction strategies
early warning
deformation time series

Published Papers (13 papers)

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31 pages, 26113 KiB

Open AccessArticle

Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping

by Rui Liu, Xin Yang, Chong Xu, Liangshuai Wei and Xiangqiang Zeng

Remote Sens. 2022, 14(2), 321; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020321 - 11 Jan 2022

Cited by 32 | Viewed by 4305

Abstract

Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced convolutional neural network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN-based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected Zhangzha Town in Sichuan Province, China, and Lantau Island in Hong Kong, China, as the study areas. Each landslide inventory and corresponding predisposing factors were stacked to form spatial datasets for LSM. The receiver operating characteristic analysis, area under the curve (AUC), and several statistical metrics, such as accuracy, root mean square error, Kappa coefficient, sensitivity, and specificity, were used to evaluate the performance of the models. Finally, the trained models were calculated, and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine learning-based models have a satisfactory performance. The CNN-based model exhibits an excellent prediction capability and achieves the highest performance but also significantly reduces the salt-of-pepper effect, which indicates its great potential for application to LSM. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Monitoring, Mapping and Modeling)

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27 pages, 14367 KiB

Open AccessArticle

Evaluation of Conditioning Factors of Slope Instability and Continuous Change Maps in the Generation of Landslide Inventory Maps Using Machine Learning (ML) Algorithms

by Rocío N. Ramos-Bernal, René Vázquez-Jiménez, Claudia A. Cantú-Ramírez, Antonio Alarcón-Paredes, Gustavo A. Alonso-Silverio, Adrián G. Bruzón, Fátima Arrogante-Funes, Fidel Martín-González, Carlos J. Novillo and Patricia Arrogante-Funes

Remote Sens. 2021, 13(22), 4515; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224515 - 10 Nov 2021

Cited by 9 | Viewed by 2411

Abstract

Landslides are recognized as high-impact natural hazards in different regions around the world; therefore, they are extensively researched by experts. Landslide inventories are essential to identify areas that are likely to be affected in the future, thereby enabling interventions to prevent loss of life. Today, through combined approaches, such as remote sensing and machine learning techniques, it is possible to apply algorithms that use data derived from satellite images to produce landslide inventories. This work presents the performance of five machine learning methods—k-nearest neighbor (KNN), stochastic gradient descendent (SGD), support vector machine radial basis function (SVM RBF Kernel), support vector machine (SVM linear kernel), and AdaBoost—in landslide detection in a zone of the state of Guerrero in southern Mexico, using continuous change maps and primary landslide factors, such as slope angle, terrain orientation (aspect), and lithology, as inputs. The models were trained with 2/3 of ground truth samples of 671 slidden/non-slidden polygons. The obtained inventory maps were evaluated with the remaining 1/3 of ground truth samples by generating a confusion matrix and applying the Kappa concordance coefficient, accuracy, precision, recall, and F1 score as evaluation metrics, as well as omission and commission errors. According to the results, the AdaBoost classifier reached greater spatial and statistical coherence than the other implemented methods. The best input layer combination for detection was the continuous change maps obtained by the linear regression and image differencing detection methods, together with the slope angle, aspect, and lithology conditioning factors. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Monitoring, Mapping and Modeling)

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29 pages, 10321 KiB

Open AccessArticle

Landslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results

by Laura Coco, Debora Macrini, Tommaso Piacentini and Marcello Buccolini

Remote Sens. 2021, 13(21), 4280; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214280 - 25 Oct 2021

Cited by 6 | Viewed by 2744

Abstract

Landslide susceptibility is one of the main topics of geomorphological risk studies. Unfortunately, many of these studies applied an exclusively statistical approach with little coherence with the geomorphodynamic models, resulting in susceptibility maps that are difficult to read. Even if many different models have been developed, those based on statistical techniques applied to slope units (SUs) are among the most promising. SU segmentation divides terrain into homogenous domains and approximates the morphodynamic response of the slope to landslides. This paper presents a landslide susceptibility (LS) analysis at the catchment scale for a key area based on the comparison of two GIS-based bivariate statistical methods using the landslide index (LI) approach. A new simple and reproducible method for delineating SUs is defined with an original GIS-based terrain segmentation based on hydrography. For the first time, the morphometric slope index (MSI) was tested as a predisposing factor for landslides. Beyond the purely statistic values, the susceptibility maps obtained have strong geomorphological significance and highlight the areas with the greatest propensity to landslides. We demonstrate the efficiency of the SU segmentation method and the potential of the proposed statistical methods to perform landslide susceptibility mapping (LSM). Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Monitoring, Mapping and Modeling)

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23 pages, 15994 KiB

Open AccessArticle

Assessment of Shoreline Transformation Rates and Landslide Monitoring on the Bank of Kuibyshev Reservoir (Russia) Using Multi-Source Data

by Oleg Yermolaev, Bulat Usmanov, Artur Gafurov, Jean Poesen, Evgeniya Vedeneeva, Fedor Lisetskii and Ionut Cristi Nicu

Remote Sens. 2021, 13(21), 4214; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214214 - 21 Oct 2021

Cited by 15 | Viewed by 2698

Abstract

This study focuses on the Kuibyshev reservoir (Volga River basin, Russia)—the largest in Eurasia and the third in the world by area (6150 km²). The objective of this paper is to quantitatively assess the dynamics of reservoir bank landslides and shoreline abrasion at active zones based on the integrated use of modern instrumental methods (i.e., terrestrial laser scanning—TLS, unmanned aerial vehicle—UAV, and a global navigation satellite system—GNSS) and GIS analysis of historical imagery. A methodology for the application of different methods of instrumental assessment of abrasion and landslide processes is developed. Different approaches are used to assess the intensity of landslide and abrasion processes: the specific volume and material loss index, the planar displacement of the bank scarp, and the planar-altitude analysis of displaced soil material based on the analysis of slope profiles. Historical shoreline position (1958, 1985, and 1987) was obtained from archival aerial photo data, whereas data for 1975, 1993, 2010, 2011, and 2012 were obtained from high-resolution satellite image interpretation. Field surveys of the geomorphic processes from 2002, 2003, 2005, 2006, 2014 were carried out using Trimble M3 and Trimble VX total stations; in 2012–2014 and 2019 TLS and UAV surveys were made, respectively. The monitoring of landslide processes showed that the rate of volumetric changes at Site 1 remained rather stable during the measurement period with net material losses of 0.03–0.04 m⁻³ m⁻² yr⁻¹. The most significant contribution to the average annual value of the material loss was snowmelt runoff. The landslide scarp retreat rate at Site 2 showed a steady decreasing trend, due to partial overgrowth of the landslide accumulation zone resulting in its relative stabilization. The average long-term landslide scarp retreat rate is—2.3 m yr⁻¹. In 2019 earthworks for landscaping at this site have reduced the landslide intensity by more than 2.5 times to—0.84 m yr⁻¹. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Monitoring, Mapping and Modeling)

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32 pages, 14198 KiB

Open AccessArticle

Multitemporal Landslide Inventory and Activity Analysis by Means of Aerial Photogrammetry and LiDAR Techniques in an Area of Southern Spain

by Tomás Fernández, José L. Pérez-García, José M. Gómez-López, Javier Cardenal, Francisco Moya and Jorge Delgado

Remote Sens. 2021, 13(11), 2110; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112110 - 27 May 2021

Cited by 13 | Viewed by 2753

Abstract

This paper deals with the use of aerial photogrammetry and LiDAR techniques to analyze landslide activity over a long time span—just over 32 years. The data correspond to several aerial surveys (1984, 1996, 2001, 2005, 2009, 2010, 2011, 2013 and 2016) covering an area of about 50 km² along highway A-44, near Jaén (Southern Spain). An ad hoc combined photogrammetric and LiDAR aerial survey of 2010 was established as the reference flight. This flight was processed by means of direct orientation methods and iterative adjustments between both data sets. Meanwhile, historical flights available in public geographical data servers were oriented by transferring ground control points from the reference flight. Then, digital surface models (DSMs) and orthophotographs were generated, as well as the corresponding differential models (DoDs), which, after the application of filters and taking into account the estimated uncertainty of ± 1 m, allowed us to identify true changes on the ground surface. This analysis, complemented by photointerpretation, led us to obtain a landslide multitemporal inventory in the study area that was analyzed in order to characterize the landslide type, morphology and activity. Three basic typologies were identified: rock falls–collapses, slides and flows. These types present different morphometric properties (area, perimeter and height interval) and are associated with different conditions (height, slope, orientation and lithology). Moreover, a set of monitoring areas, common for the different flights, was also used to analyze the activity throughout the study period. Thus, some more active periods were identified (2009–2010, 2010–2011, 2011–2013 and 1996–2001) among other less active ones (1984–1996, 2001–2005, 2005–2009 and 2013–2016), which are related to rainy events and dry years, respectively. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Monitoring, Mapping and Modeling)

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19 pages, 8398 KiB

Open AccessArticle

Ambient Seismic Noise and Microseismicity Monitoring of a Prone-To-Fall Quartzite Tower (Ormea, NW Italy)

by Chiara Colombero, Alberto Godio and Denis Jongmans

Remote Sens. 2021, 13(9), 1664; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091664 - 24 Apr 2021

Cited by 8 | Viewed by 2294

Abstract

Remote sensing techniques are leading methodologies for landslide characterization and monitoring. However, they may be limited in highly vegetated areas and do not allow for continuously tracking the evolution to failure in an early warning perspective. Alternative or complementary methods should be designed for potentially unstable sites in these environments. The results of a six-month passive seismic monitoring experiment on a prone-to-fall quartzite tower are here presented. Ambient seismic noise and microseismicity analyses were carried out on the continuously recorded seismic traces to characterize site stability and monitor its possible irreversible and reversible modifications driven by meteorological factors, in comparison with displacement measured on site. No irreversible modifications in the measured seismic parameters (i.e., natural resonance frequencies of the tower, seismic velocity changes, rupture-related microseismic signals) were detected in the monitored period, and no permanent displacement was observed at the tower top. Results highlighted, however, a strong temperature control on these parameters and unusual preferential vibration directions with respect to the literature case studies on nearly 2D rock columns, likely due the tower geometric constraints, as confirmed by 3D numerical modeling. A clear correlation with the tower displacement rate was found in the results, supporting the suitability of passive seismic monitoring systems for site characterization and early waning purposes. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Monitoring, Mapping and Modeling)

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16 pages, 9920 KiB

Open AccessArticle

High-Quality Pixel Selection Applied for Natural Scenes in GB-SAR Interferometry

by Yunkai Deng, Weiming Tian, Ting Xiao, Cheng Hu and Hong Yang

Remote Sens. 2021, 13(9), 1617; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091617 - 21 Apr 2021

Cited by 8 | Viewed by 1889

Abstract

Phase analysis based on high-quality pixel (HQP) is crucial to ensure the measurement accuracy of ground-based SAR (GB-SAR). The amplitude dispersion (ADI) criterion has been widely applied to identify pixels with high amplitude stability, i.e., permanent scatterers (PSs), which typically are point-wise scatterers such as stones or man-made structures. However, the PS number in natural scenes is few and limits the GB-SAR applications. This paper proposes an improved method to take HQP selection applied for natural scenes in GB-SAR interferometry. In order to increase the spatial density of HQP for phase measurement, three types of HQPs including PS, quasi-permanent scatter (QPS), and distributed scatter (DS), are selected with different criteria. The ADI method is firstly utilized to take PS selection. To select those pixels with high phase stability but moderate amplitude stability, the temporal phase coherence (TPC) is defined. Those pixels with moderate ADI values and high TPC are selected as QPSs. Then the feasibility of the DS technique is explored. To validate the feasibility of the proposed method, 2370 GB-SAR images of a natural slope are processed. Experimental results prove that the HQP number could be significantly increased while slightly sacrificing phase quality. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Monitoring, Mapping and Modeling)

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15 pages, 22844 KiB

Open AccessArticle

Long-Term and Emergency Monitoring of Zhongbao Landslide Using Space-Borne and Ground-Based InSAR

by Ting Xiao, Wei Huang, Yunkai Deng, Weiming Tian and Yonglian Sha

Remote Sens. 2021, 13(8), 1578; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081578 - 19 Apr 2021

Cited by 12 | Viewed by 2553

Abstract

This work presents the ideal combination of space-borne and ground-based (GB) Interferometric Synthetic Aperture Radar (InSAR) applications. In the absence of early investigation reporting and specialized monitoring, the Zhongbao landslide unexpectedly occurred on 25 July 2020, forming a barrier lake that caused an emergency. As an emergency measure, the GB-InSAR system was installed 1.8 km opposite the landslide to assess real-time cumulative deformation with a monitoring frequency of 3 min. A zone of strong deformation was detected, with 178 mm deformation accumulated within 15 h, and then a successful emergency warning was issued to evacuate on-site personnel. Post-event InSAR analysis of 19 images acquired by the ESA Sentinel-1 from December 2019 to August 2020 revealed that the landslide started in March 2020. However, the deformation time series obtained from satellite InSAR did not show any signs that the landslide had occurred. The results suggest that satellite InSAR is effective for mapping unstable areas but is not qualified for rapid landslide monitoring and timely warning. The GB-InSAR system performs well in monitoring and providing early warning, even with dense vegetation on the landslide. The results show the shortcomings of satellite InSAR and GB-InSAR and a clearer understanding of the necessity of combining multiple monitoring methods. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Monitoring, Mapping and Modeling)

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30 pages, 63151 KiB

Open AccessArticle

Window-Based Morphometric Indices as Predictive Variables for Landslide Susceptibility Models

by Natalie Barbosa, Louis Andreani, Richard Gloaguen and Lothar Ratschbacher

Remote Sens. 2021, 13(3), 451; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030451 - 28 Jan 2021

Cited by 5 | Viewed by 2600

Abstract

The identification of areas that are prone to landslides is essential in mitigating associated risks. This is usually achieved using landslide susceptibility models, which estimate landslide likelihood given local terrain conditions and the location of known past events. Detailed databases covering different conditioning factors are paramount in producing reliable susceptibility maps. However, thematic data from developing countries are scarce. As a result, susceptibility models often rely on morphometric parameters that are derived from widely-available digital elevation models. In most cases, simple parameters, such as slope, aspect, and curvature, computed using a moving window of 3 × 3 pixels, are used. Recently, the use of window-based morphometric indices as an additional input has increased. These rely on a user-defined observation window size. In this contribution, we examine the influence of observation window size when using window-based morphometric indices as core predictive variables for landslide susceptibility assessment. We computed a variety of models that include morphometric indices that are calculated with different window sizes, and compared the predictive capabilities and reliability of the resulting predictions. All of the models are based on the random forest algorithm. The results improved significantly when each window-based morphometric index was calculated with a different and meaningful observation window (AUC-ROC of 0.89 and AUC-PR of 0.87). The sensitivity analysis highlights both the highly-informative observation windows and the impact of their selection on the model performance. We also stress the importance of evaluating landslide susceptibility results while using different adapted metrics for predictive performance and reliability. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Monitoring, Mapping and Modeling)

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22 pages, 14477 KiB

Open AccessArticle

Characterizing the Development Pattern of a Colluvial Landslide Based on Long-Term Monitoring in the Three Gorges Reservoir

by Xin Liang, Lei Gui, Wei Wang, Juan Du, Fei Ma and Kunlong Yin

Remote Sens. 2021, 13(2), 224; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020224 - 11 Jan 2021

Cited by 20 | Viewed by 2652

Abstract

Since the impoundment of the Three Gorges Reservoir (TGR) in June 2003, the fluctuation of the reservoir water level coupled with rainfall has resulted in more than 2500 landslides in this region. Among these instability problems, most colluvial landslides exhibit slow-moving patterns and pose a significant threat to local people and channel navigation. Advanced monitoring techniques are therefore implemented to investigate landslide deformation and provide insights for the subsequent countermeasures. In this study, the development pattern of a large colluvial landslide, locally named the Ganjingzi landslide, is analyzed on the basis of long-term monitoring. To understand the kinematic characteristics of the landslide, an integrated analysis based on real-time and multi-source monitoring, including the global navigation satellite system (GNSS), crackmeters, inclinometers, and piezometers, was conducted. The results indicate that the Ganjingzi landslide exhibits a time-variable response to the reservoir water fluctuation and rainfall. According to the supplement of community-based monitoring, the evolution of the landslide consists of three stages, namely the stable stage before reservoir impoundment, the initial movement stage of retrogressive failure, and the shallow movement stage with stepwise acceleration. The latter two stages are sensitive to the drawdown of reservoir water level and rainfall infiltration, respectively. All of the monitoring approaches used in this study are significant for understanding the time-variable pattern of colluvial landslides and are essential for landslide mechanism analysis and early warning for risk mitigation. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Monitoring, Mapping and Modeling)

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28 pages, 20805 KiB

Open AccessArticle

Application of Unmanned Aerial Vehicle Data and Discrete Fracture Network Models for Improved Rockfall Simulations

by Mirko Francioni, Federico Antonaci, Nicola Sciarra, Carlo Robiati, John Coggan, Doug Stead and Fernando Calamita

Remote Sens. 2020, 12(12), 2053; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122053 - 25 Jun 2020

Cited by 30 | Viewed by 3734

Abstract

In this research, we present a new approach to define the distribution of block volumes during rockfall simulations. Unmanned aerial vehicles (UAVs) are utilized to generate high-accuracy 3D models of the inaccessible SW flank of the Mount Rava (Italy), to provide improved definition of data gathered from conventional geomechanical surveys and to also denote important changes in the fracture intensity. These changes are likely related to the variation of the bedding thickness and to the presence of fracture corridors in fault damage zones in some areas of the slope. The dataset obtained integrating UAV and conventional surveys is then utilized to create and validate two accurate 3D discrete fracture network models, representative of high and low fracture intensity areas, respectively. From these, the ranges of block volumes characterizing the in situ rock mass are extracted, providing important input for rockfall simulations. Initially, rockfall simulations were performed assuming a uniform block volume variation for each release cell. However, subsequent simulations used a more realistic nonuniform distribution of block volumes, based on the relative block volume frequency extracted from discrete fracture network (DFN) models. The results of the simulations were validated against recent rockfall events and show that it is possible to integrate into rockfall simulations a more realistic relative frequency distribution of block volumes using the results of DFN analyses. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Monitoring, Mapping and Modeling)

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24 pages, 14429 KiB

Open AccessArticle

Different Approaches to Use Morphometric Attributes in Landslide Susceptibility Mapping Based on Meso-Scale Spatial Units: A Case Study in Rio de Janeiro (Brazil)

by Vanessa Canavesi, Samuele Segoni, Ascanio Rosi, Xiao Ting, Tulius Nery, Filippo Catani and Nicola Casagli

Remote Sens. 2020, 12(11), 1826; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111826 - 05 Jun 2020

Cited by 26 | Viewed by 3701

Abstract

Landslide susceptibility maps are widely used in landslide hazard management. Although many models have been proposed, mapping unit definition is a matter that still needs to be fully examined. In the literature, the most reported mapping units are pixels and slope units, while in this work, developed in the Rio de Janeiro region (Brazil), the use of drainage basins as a mapping unit is examined; even if their use leads to the definition of maps with a coarser spatial resolution than pixels-based maps, they convey information that can be easily and rapidly handled by civil defense organizations. However, for the morphometrical characterization of entire basins, a standardized procedure does not exist, and the susceptibility results may be sensitive to the approach used. To investigate this issue, a random forest model was used to assess landslide susceptibility, using 12 independent variables: four categorical (land use, soil type, lithology and slope orientation) and eight numerical variables (slope gradient, elevation, slope curvature, profile curvature, planar curvature, flow accumulation, topographic wetness index, stream power index). For each basin, the numerical variables were aggregated according to different approaches, which, in turn, were used to set up four different model configurations: i) maximum values, ii) mean values, iii) standard deviation values, iv) joint use of all the above. The resulting maps showed noticeable differences and a quantitative validation procedure showed that the best configurations were the ones based on mean values of independent variables, and the one based on the combination of all the values of the numerical variables. The main outcomes of this work consist of a landslide susceptibility map of the study area, to be used in operational procedures of risk management and in some insights on the best approaches to aggregate raster cell data into wider spatial units. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Monitoring, Mapping and Modeling)

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16 pages, 8718 KiB

Open AccessTechnical Note

Integration of Satellite Interferometric Data in Civil Protection Strategies for Landslide Studies at a Regional Scale

by Silvia Bianchini, Lorenzo Solari, Davide Bertolo, Patrick Thuegaz and Filippo Catani

Remote Sens. 2021, 13(10), 1881; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101881 - 11 May 2021

Cited by 8 | Viewed by 2528

Abstract

Multi-Temporal Satellite Interferometry (MTInSAR) is gradually evolving from being a tool developed by the scientific community exclusively for research purposes to a real operational technique that can meet the needs of different users involved in geohazard mitigation. This work aims at showing the innovative operational use of satellite radar interferometric products in Civil Protection Authority (CPA) practices for monitoring slow-moving landslides. We present the example of the successful ongoing monitoring system in the Valle D’Aosta Region (VAR-Northern Italy). This system exploits well-combined MTInSAR products and ground-based instruments for landslide management and mitigation strategies over the whole regional territory. Due to the critical intrinsic constraints of MTInSAR data, a robust regional satellite monitoring integrated into CPA practices requires the support of both in situ measurements and remotely sensed systems to guarantee the completeness and reliability of information. The monitoring network comprises three levels of analysis: Knowledge monitoring, Control monitoring, and Emergency monitoring. At the first monitoring level, MTInSAR data are used for the preliminary evaluation of the deformation scenario at a regional scale. At the second monitoring level, MTInSAR products support the prompt detection of trend variations of radar benchmarks displacements with bi-weekly temporal frequency to identify active critical situations where follow-up studies must be carried out. In the third monitoring level, MTInSAR data integrated with ground-based data are exploited to confirm active slow-moving deformations detected by on-site instruments. At this level, MTInSAR data are also used to carry out back analysis that cannot be performed by any other tool. From the example of the Valle D’Aosta Region integrated monitoring network, which is one of the few examples of this kind around Europe, it is evident that MTInSAR provides a great opportunity to improve monitoring capabilities within CPA activities. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Monitoring, Mapping and Modeling)

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