Special Issue "Geospatial Approaches to Landslide Mapping and Monitoring"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: 31 October 2019.

Special Issue Editors

Guest Editor
Dr. Veronica Tofani Website E-Mail
Department of Earth Sciences, Università degli Studi di Firenze, Florence, Italy
Interests: landslide analysis; engineering geology; landslide hazard assessment; geomatics; risk management
Guest Editor
Dr. William Frodella Website E-Mail
Department of Earth Sciences, University of Firenze, Via La Pira 4, Firenze, Italy
Interests: landslide remote sensing; infrared thermography; geomorphological mapping; radar interferometric data interpretation; cultural heritage protection

Special Issue Information

Dear Colleagues,

Landslides play an important role in the evolution of landscapes and still represent a major threat to urban areas and anthropogenic activities, to infrastructures, cultural and environmental heritage.

In the last decades, advanced remote sensing techniques have undergone a significant increase of usage as effective tools for landslide mapping, inventory and monitoring at various scales (these include but are not limited to Radar inteferometry, Lidar, Digital photogrammetry, Optical and Infrared imaging).

The landslide scientific community, practitioners and end-users, have greatly benefited from both the technological development of these techniques, in terms of spatial resolution, accuracy, fast measurement and processing times, and from the data availability and cost-effectiveness. In this perspective, a key issue is still represented by data management and processing tools which, in turn, can lead to a proper and accurate comprehensive view and interpretation of the slope instability processes, with the final aim of implementing mitigation measures for landslide risk management.

The goal of this Special Issue is to gather high-quality original research articles and reviews on innovative geospatial approaches, synergistic use of remote sensing techniques, and case studies applications for landslide detection, mapping and monitoring, from spaceborne, ground-based to UAV platforms.

Dr. Veronica Tofani
Dr. William Frodella
Guest Editors

Manuscript Submission Information

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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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1000 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 mapping
  • remote sensing
  • geomatics
  • InSAR
  • Lidar
  • digital photogrammetry infrared thermography
  • landslide hazard
  • mitigation measures
  • risk management

Published Papers (5 papers)

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Research

Open AccessArticle
Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling
ISPRS Int. J. Geo-Inf. 2019, 8(9), 397; https://doi.org/10.3390/ijgi8090397 - 05 Sep 2019
Abstract
This study explores two modeling issues that may cause uncertainty in landslide susceptibility assessments when different sampling strategies are employed. The first issue is that extracted attributes within a landslide inventory polygon can vary if the sample is obtained from different locations with [...] Read more.
This study explores two modeling issues that may cause uncertainty in landslide susceptibility assessments when different sampling strategies are employed. The first issue is that extracted attributes within a landslide inventory polygon can vary if the sample is obtained from different locations with diverse topographic conditions. The second issue is the mixing problem of landslide inventory that the detection of landslide areas from remotely-sensed data generally includes source and run-out features unless the run-out portion can be removed manually with auxiliary data. To this end, different statistical sampling strategies and the run-out influence on random forests (RF)-based landslide susceptibility modeling are explored for Typhoon Morakot in 2009 in southern Taiwan. To address the construction of models with an extremely high false alarm error or missing error, this study integrated cost-sensitive analysis with RF to adjust the decision boundary to achieve improvements. Experimental results indicate that, compared with a logistic regression model, RF with the hybrid sample strategy generally performs better, achieving over 80% and 0.7 for the overall accuracy and kappa coefficient, respectively, and higher accuracies can be obtained when the run-out is treated as an independent class or combined with a non-landslide class. Cost-sensitive analysis significantly improved the prediction accuracy from 5% to 10%. Therefore, run-out should be separated from the landslide source and labeled as an individual class when preparing a landslide inventory. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment
ISPRS Int. J. Geo-Inf. 2019, 8(8), 332; https://doi.org/10.3390/ijgi8080332 - 27 Jul 2019
Abstract
Logistic regression methods have been widely used for landslide research. However, previous studies have seldom paid attention to the frequent occurrence of spatial autocorrelated residuals in regression models, which indicate a model misspecification problem and unreliable results. This study accounts for spatial autocorrelation [...] Read more.
Logistic regression methods have been widely used for landslide research. However, previous studies have seldom paid attention to the frequent occurrence of spatial autocorrelated residuals in regression models, which indicate a model misspecification problem and unreliable results. This study accounts for spatial autocorrelation by implementing eigenvector spatial filtering (ESF) into logistic regression for landslide susceptibility assessment. Based on a landslide inventory map and 11 landslide predisposing factors, we developed the eigenvector spatial filtering-based logistic regression (ESFLR) model, as well as a conventional logistic regression (LR) model and an autologistic regression (ALR) model for comparison. The three models were evaluated and compared in terms of their prediction capability and model fit. The ESFLR model performed better than the other two models. The overall predictive accuracy of the ESFLR model was 90.53%, followed by the ALR model (76.21%) and the LR model (74.76%), and the areas under the ROC curves for the ESFLR, ALR and LR models were 0.957, 0.828 and 0.818, respectively. The ESFLR model adequately addressed the spatial autocorrelation of residuals by reducing the Moran’s I value of the residuals to 0.0270. In conclusion, the ESFLR model is an effective and flexible method for landslide analysis. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Multi-Aspect Analysis of Object-Oriented Landslide Detection Based on an Extended Set of LiDAR-Derived Terrain Features
ISPRS Int. J. Geo-Inf. 2019, 8(8), 321; https://doi.org/10.3390/ijgi8080321 - 24 Jul 2019
Abstract
Landslide identification is a fundamental step enabling the assessment of landslide susceptibility and determining the associated risks. Landslide identification by conventional methods is often time-consuming, therefore alternative techniques, including automatic approaches based on remote sensing data, have captured the interest among researchers in [...] Read more.
Landslide identification is a fundamental step enabling the assessment of landslide susceptibility and determining the associated risks. Landslide identification by conventional methods is often time-consuming, therefore alternative techniques, including automatic approaches based on remote sensing data, have captured the interest among researchers in recent decades. By providing a highly detailed digital elevation model (DEM), airborne laser scanning (LiDAR) allows effective landslide identification, especially in forested areas. In the present study, object-based image analysis (OBIA) was applied to landslide detection by utilizing LiDAR-derived data. In contrast to previous investigations, our analysis was performed on forested and agricultural areas, where cultivation pressure has degraded specific landslide geomorphology. A diverse variety of aspects that influence OBIA accuracy in landslide detection have been considered: DEM resolution, segmentation scale, and feature selection. Finally, using DEM delivered layers and OBIA, landslide was identified with an overall accuracy (OA) of 85% and a kappa index (KIA) equal to 0.60, which illustrates the effectiveness of the proposed approach. In the end, a field investigation was performed in order to evaluate the results achieved by applying an automatic OBIA approach. The advantages and challenges of automatic approaches for landslide identification for various land use were also discussed. Final remarks underline that effective landslide detection in forested areas could be achieved while this is still challenging in agricultural areas. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Monitoring Ground Instabilities Using SAR Satellite Data: A Practical Approach
ISPRS Int. J. Geo-Inf. 2019, 8(7), 307; https://doi.org/10.3390/ijgi8070307 - 17 Jul 2019
Abstract
Satellite interferometric data are widely exploited for ground motion monitoring thanks to their wide area coverage, cost efficiency and non-invasiveness. The launch of the Sentinel-1 constellation opened new horizons for interferometric applications, allowing the scientists to rethink the way in which these data [...] Read more.
Satellite interferometric data are widely exploited for ground motion monitoring thanks to their wide area coverage, cost efficiency and non-invasiveness. The launch of the Sentinel-1 constellation opened new horizons for interferometric applications, allowing the scientists to rethink the way in which these data are delivered, passing from a static view of the territory to a continuous streaming of ground motion measurements from space. Tuscany Region is the first worldwide example of a regional scale monitoring system based on satellite interferometric data. The processing chain here exploited combines a multi-interferometric approach with a time-series data mining algorithm aimed at recognizing benchmarks with significant trend variations. The system is capable of detecting the temporal changes of a wide variety of phenomena such as slow-moving landslides and subsidence, producing a high amount of data to be interpreted in a short time. Bulletins and reports are derived to the hydrogeological risk management actors at regional scale. The final output of the project is a list of potentially hazardous and accelerating phenomena that are verified on site by field campaign by completing a sheet survey in order to qualitatively estimate the risk and to suggest short-term actions to be taken by local entities. Two case studies, one related to landslides and one to subsidence, are proposed to highlight the potential of the monitoring system to early detect anomalous ground changes. Both examples represent a successful implementation of satellite interferometric data as monitoring and risk management tools, raising the awareness of local and regional authorities to geohazards. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Shallow Landslide Susceptibility Mapping in Sochi Ski-Jump Area Using GIS and Numerical Modelling
ISPRS Int. J. Geo-Inf. 2019, 8(3), 148; https://doi.org/10.3390/ijgi8030148 - 19 Mar 2019
Abstract
The mountainous region of Greater Sochi, including the Olympic ski-jump complex area, located in the northern Caucasus, is always subjected to landslides. The weathered mudstone of low strength and potential high-intensity earthquakes are considered as the crucial factors causing slope instability in the [...] Read more.
The mountainous region of Greater Sochi, including the Olympic ski-jump complex area, located in the northern Caucasus, is always subjected to landslides. The weathered mudstone of low strength and potential high-intensity earthquakes are considered as the crucial factors causing slope instability in the ski-jump complex area. This study aims to conduct a seismic slope instability map of the area. A slope map was derived from a digital elevation model (DEM) and calculated using ArcGIS. The numerical modelling of slope stability with various slope angles was conducted using Geostudio. The Spencer method was applied to calculate the slope safety factors (Fs). The pseudostatic analysis was used to compute Fs considering seismic effect. A good correlation between Fs and slope angle was found. Combining these data, sets slope instability maps were achieved. Newmark displacement maps were also drawn according to empirical regression equations. The result shows that the static safety factor map corresponds to the existing slope instability locations in a shallow landslide inventory map. The seismic safety factor maps and Newmark displacement maps may be applied to predict potential landslides of the study area in the case of earthquake occurrence. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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