Special Issue "Landslide Hazard and Environment Risk Assessment"

A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: closed (30 September 2021).

Special Issue Editors

Prof. Dr. Enrico Miccadei
E-Mail Website
Guest Editor
Department of Engineering and Geology (InGeo), “G. d’Annunzio” University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti Scalo (CH), Italy
Interests: geological and geomorphological mapping; Quaternary geology; tectonic geomorphology; geomorphology and climate change; landslide hazard; hazard and risk assessment; geotourism; engineering geology
Dr. Cristiano Carabella
E-Mail Website
Guest Editor
Department of Engineering and Geology (InGeo), “G. d’Annunzio” University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti Scalo (CH), Italy
Interests: geological and geomorphological mapping; geographic information systems; tectonic geomorphology; hazard assessment; landslides; floods; early warning systems
Dr. Giorgio Paglia
E-Mail Website
Guest Editor
Department of Engineering and Geology (InGeo), “G. d’Annunzio” University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti Scalo (CH), Italy
Interests: geological and geomorphological mapping; tectonic geomorphology; geomorphology and climate change; geographic information systems; landslides; hazard assessment

Special Issue Information

Dear Colleagues,

Landslides are global geomorphological phenomena occurring in all geographic regions in response to a wide range of triggering factors. Landsliding is linked to the combination of geological, geomorphological, and climatic factors in response to trigger mechanisms mostly represented by heavy rainfall events, seismicity, or human action. Landslides directly and indirectly impact a territory, causing fatalities and huge socioeconomic losses also due to environmental degradation and rapid population growth, which requires correct land use policies and best practices for long-term risk mitigation and reduction. In this context, geomorphological field activities, satellite remote sensing, and numerical modeling offer effective support for mapping and monitoring the activity of landslides at both the local and regional scales.

We would like to invite you to participate in this Special Issue, which will focus primarily on such innovative methods to accurately investigate landslide phenomena providing useful products to characterize landslide risk management. All landslide types are considered, from fast rockfalls to debris flows and from slow-moving slides to very rapid rock avalanches. All climatic and geographical scales are considered, from the local to the global, including individual and multiple slope failures.

Contribution to this Special Issue will provide scientific tools for the preparation and optimal use of landslide maps, landside prediction models, landslide susceptibility maps, and the design of risk mitigation measures.

Submissions are encouraged to cover a broad range of topics on the various applications of different techniques, which may include, but are not limited to, the following topics: (i) analysis of landslide conditioning and triggering factors, (ii) geomorphological analysis and GIS mapping, (iii) landslide hazard mapping, (iv) landslide susceptibility mapping, (v) remote sensing techniques for the definition of vulnerability and characterization of elements at risk, (vi) investigation and monitoring of landslide dynamics, (vii) implementation of sustainable territorial planning, and (viii) design and implementation of smart adaptation measures to reduce risks, such as early warning systems.


Prof. Dr. Enrico Miccadei
Dr. Cristiano Carabella
Dr. Giorgio Paglia
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 papers will be 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. Land 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 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.

Keywords

  • landslide dynamics, mechanisms, and processes
  • landslide hazard mapping
  • geomorphological mapping
  • geomorphology and climate change
  • remote sensing and GIS analysis
  • landslide susceptibility map
  • landslide monitoring
  • risk assessment
  • remedial or preventive measures
  • territorial planning

Published Papers (10 papers)

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Research

Article
Evaluation of the Effect of Hydroseeded Vegetation for Slope Reinforcement
Land 2021, 10(10), 995; https://doi.org/10.3390/land10100995 - 22 Sep 2021
Viewed by 511
Abstract
A landslide is a significant environmental hazard that results in an enormous loss of lives and properties. Studies have revealed that rainfall, soil characteristics, and human errors, such as deforestation, are the leading causes of landslides, reducing soil water infiltration and increasing the [...] Read more.
A landslide is a significant environmental hazard that results in an enormous loss of lives and properties. Studies have revealed that rainfall, soil characteristics, and human errors, such as deforestation, are the leading causes of landslides, reducing soil water infiltration and increasing the water runoff of a slope. This paper introduces vegetation establishment as a low-cost, practical measure for slope reinforcement through the ground cover and the root of the vegetation. This study reveals the level of complexity of the terrain with regards to the evaluation of high and low stability areas and has produced a landslide susceptibility map. For this purpose, 12 conditioning factors, namely slope, aspect, elevation, curvature, hill shade, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distances to roads, distance to lakes, distance to trees, and build-up, were used through the analytic hierarchy process (AHP) model to produce landslide susceptibility map. Receiver operating characteristics (ROC) was used for validation of the results. The area under the curve (AUC) values obtained from the ROC method for the AHP model was 0.865. Four seed samples, namely ryegrass, rye corn, signal grass, and couch, were hydroseeded to determine the vegetation root and ground cover’s effectiveness on stabilization and reinforcement on a high-risk susceptible 65° slope between August and December 2020. The observed monthly vegetation root of couch grass gave the most acceptable result. With a spreading and creeping vegetation ground cover characteristic, ryegrass showed the most acceptable monthly result for vegetation ground cover effectiveness. The findings suggest that the selection of couch species over other species is justified based on landslide control benefits. Full article
(This article belongs to the Special Issue Landslide Hazard and Environment Risk Assessment)
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Article
Factors Affecting Landslide Susceptibility Mapping: Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting
Land 2021, 10(9), 989; https://doi.org/10.3390/land10090989 - 19 Sep 2021
Viewed by 405
Abstract
Data driven methods are widely used for the development of Landslide Susceptibility Mapping (LSM). The results of these methods are sensitive to different factors, such as the quality of input data, choice of algorithm, sampling strategies, and data splitting ratios. In this study, [...] Read more.
Data driven methods are widely used for the development of Landslide Susceptibility Mapping (LSM). The results of these methods are sensitive to different factors, such as the quality of input data, choice of algorithm, sampling strategies, and data splitting ratios. In this study, five different Machine Learning (ML) algorithms are used for LSM for the Wayanad district in Kerala, India, using two different sampling strategies and nine different train to test ratios in cross validation. The results show that Random Forest (RF), K Nearest Neighbors (KNN), and Support Vector Machine (SVM) algorithms provide better results than Naïve Bayes (NB) and Logistic Regression (LR) for the study area. NB and LR algorithms are less sensitive to the sampling strategy and data splitting, while the performance of the other three algorithms is considerably influenced by the sampling strategy. From the results, both the choice of algorithm and sampling strategy are critical in obtaining the best suited landslide susceptibility map for a region. The accuracies of KNN, RF, and SVM algorithms have increased by 10.51%, 10.02%, and 4.98% with the use of polygon landslide inventory data, while for NB and LR algorithms, the performance was slightly reduced with the use of polygon data. Thus, the sampling strategy and data splitting ratio are less consequential with NB and algorithms, while more data points provide better results for KNN, RF, and SVM algorithms. Full article
(This article belongs to the Special Issue Landslide Hazard and Environment Risk Assessment)
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Article
Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece
Land 2021, 10(9), 973; https://doi.org/10.3390/land10090973 - 15 Sep 2021
Viewed by 572
Abstract
Over the last few years, landslides have occurred more and more frequently worldwide, causing severe effects on both natural and human environments. Given that landslide susceptibility (LS) assessments and mapping can spatially determine the potential for landslides in a region, it constitutes a [...] Read more.
Over the last few years, landslides have occurred more and more frequently worldwide, causing severe effects on both natural and human environments. Given that landslide susceptibility (LS) assessments and mapping can spatially determine the potential for landslides in a region, it constitutes a basic step in effective risk management and disaster response. Nowadays, several LS models are available, with each one having its advantages and disadvantages. In order to enhance the benefits and overcome the weaknesses of individual modeling, the present study proposes a hybrid LS model based on the integration of two different statistical analysis models, the multivariate Geographical Detector (GeoDetector) and the bivariate information value (IV). In a GIS-based framework, the hybrid model named GeoDIV was tested to generate a reliable LS map for the vicinity of the Pinios artificial lake (Ilia, Greece), a Greek wetland. A landslide inventory of 60 past landslides and 14 conditioning (morphological, hydro-lithological and anthropogenic) factors was prepared to compose the spatial database. An LS map was derived from the GeoDIV model, presenting the different zones of potential landslides (probability) for the study area. This map was then validated by success and prediction rates—which translate to the accuracy and prediction ability of the model, respectively. The findings confirmed that hybrid modeling can outperform individual modeling, as the proposed GeoDIV model presented better validation results than the IV model. Full article
(This article belongs to the Special Issue Landslide Hazard and Environment Risk Assessment)
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Article
Landslide Hazard Assessment in a Monoclinal Setting (Central Italy): Numerical vs. Geomorphological Approach
Land 2021, 10(6), 624; https://doi.org/10.3390/land10060624 - 11 Jun 2021
Viewed by 548
Abstract
A correct landslide hazard assessment (LHA) is fundamental for any purpose of territorial planning. In Italy, the methods currently in use to achieve this objective alternate between those based on mainly qualitative (geomorphological) and quantitative (statistical–numerical) approaches. The present study contributes to the [...] Read more.
A correct landslide hazard assessment (LHA) is fundamental for any purpose of territorial planning. In Italy, the methods currently in use to achieve this objective alternate between those based on mainly qualitative (geomorphological) and quantitative (statistical–numerical) approaches. The present study contributes to the evaluation of the best procedure to be implemented for LHA, comparing the results obtained using two different approaches (geomorphological and numerical) in a territorial context characterized by conditioning and triggering factors, favorable to the instability of the slopes. The results obtained, although preliminary, evidence the respective limitations of the methods and demonstrate how a combined approach can certainly provide mutual advantages, by addressing the choice of the best numerical model through direct observations and surveys. Full article
(This article belongs to the Special Issue Landslide Hazard and Environment Risk Assessment)
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Article
Definition of Environmental Indicators for a Fast Estimation of Landslide Risk at National Scale
Land 2021, 10(6), 621; https://doi.org/10.3390/land10060621 - 09 Jun 2021
Cited by 1 | Viewed by 1180
Abstract
The purpose of this paper is to propose a new set of environmental indicators for the fast estimation of landslide risk over very wide areas. Using Italy (301,340 km2) as a test case, landslide susceptibility maps and soil sealing/land consumption maps [...] Read more.
The purpose of this paper is to propose a new set of environmental indicators for the fast estimation of landslide risk over very wide areas. Using Italy (301,340 km2) as a test case, landslide susceptibility maps and soil sealing/land consumption maps were combined to derive a spatially distributed indicator (LRI—landslide risk index), then an aggregation was performed using Italian municipalities as basic spatial units. Two indicators were defined, namely ALR (averaged landslide risk) and TLR (total landslide risk). All data were processed using GIS programs. Conceptually, landslide susceptibility maps account for landslide hazard while soil sealing maps account for the spatial distribution of anthropic elements exposed to risk (including buildings, infrastructure, and services). The indexes quantify how much the two issues overlap, producing a relevant risk and can be used to evaluate how each municipality has been prudent in planning sustainable urban growth to cope with landslide risk. The proposed indexes are indicators that are simple to understand, can be adapted to various contexts and at various scales, and could be periodically updated, with very low effort, making use of the products of ongoing governmental monitoring programs of Italian environment. Of course, the indicators represent an oversimplification of the complexity of landslide risk, but this is the first time that a landslide risk indicator has been defined in Italy at the national scale, starting from landslide susceptibility maps (although Italy is one of the European countries most affected by hydro-geological hazards) and, more in general, the first time that land consumption maps are integrated into a landslide risk assessment. Full article
(This article belongs to the Special Issue Landslide Hazard and Environment Risk Assessment)
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Article
Geomorphological Hazard in Active Tectonics Area: Study Cases from Sibillini Mountains Thrust System (Central Apennines)
Land 2021, 10(5), 510; https://doi.org/10.3390/land10050510 - 11 May 2021
Viewed by 566
Abstract
In many areas of the Umbria-Marche Apennines, evident traces of huge landslides have been recognized; these probably occurred in the Upper Pleistocene and are conditioned by the tectonic-structural setting of the involved Meso-Cenozoic formations, in a sector of the Sibillini Mountains (central Italy). [...] Read more.
In many areas of the Umbria-Marche Apennines, evident traces of huge landslides have been recognized; these probably occurred in the Upper Pleistocene and are conditioned by the tectonic-structural setting of the involved Meso-Cenozoic formations, in a sector of the Sibillini Mountains (central Italy). The present work aimed to focus on a geomorphological hazard in the tectonic-structural setting of a complex area that is the basis of several gravitational occurrences in different types and mechanisms, but nonetheless with very considerable extension and total destabilized volume. An aerophoto-geological analysis and geomorphological survey allowed verification of how the main predisposing factor of these phenomena is connected with the presence in depth of an important tectonic-structural element: the plane of the Sibillini Mountains thrust, which brings the pre-evaporitic member of the Laga Formation in contact with the Cretaceous-Eocene limestone lithotypes (from the Maiolica to the Scaglia Rosata Formations) of the Umbria-Marche sedimentary sequence. Another important element for the mass movements activation is the presence of an important and vast water table and related aquifer, confined prevalently by the different structural elements and in particular by the thrust plane, which has acted and has continued to act, weakening the rocky masses and the overlaying terrains. Full article
(This article belongs to the Special Issue Landslide Hazard and Environment Risk Assessment)
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Article
Combining Site Characterization, Monitoring and Hydromechanical Modeling for Assessing Slope Stability
Land 2021, 10(4), 423; https://doi.org/10.3390/land10040423 - 15 Apr 2021
Viewed by 599
Abstract
Rainfall-induced landslides are a disastrous natural hazard causing loss of life and significant damage to infrastructure, farmland and housing. Hydromechanical models are one way to assess the slope stability and to predict critical combinations of groundwater levels, soil water content and precipitation. However, [...] Read more.
Rainfall-induced landslides are a disastrous natural hazard causing loss of life and significant damage to infrastructure, farmland and housing. Hydromechanical models are one way to assess the slope stability and to predict critical combinations of groundwater levels, soil water content and precipitation. However, hydromechanical models for slope stability evaluation require knowledge about mechanical and hydraulic parameters of the soils, lithostratigraphy and morphology. In this work, we present a multi-method approach of site characterization and investigation in combination with a hydromechanical model for a landslide-prone hillslope near Bonn, Germany. The field investigation was used to construct a three-dimensional slope model with major geological units derived from drilling and refraction seismic surveys. Mechanical and hydraulic soil parameters were obtained from previously published values for the study site based on laboratory analysis. Water dynamics were monitored through geoelectrical monitoring, a soil water content sensor network and groundwater stations. Historical data were used for calibration and validation of the hydromechanical model. The well-constrained model was then used to calculate potentially hazardous precipitation events to derive critical thresholds for monitored variables, such as soil water content and precipitation. This work introduces a potential workflow to improve numerical slope stability analysis through multiple data sources from field investigations and outlines the usage of such a system with respect to a site-specific early-warning system. Full article
(This article belongs to the Special Issue Landslide Hazard and Environment Risk Assessment)
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Article
Relationships between Morphostructural/Geological Framework and Landslide Types: Historical Landslides in the Hilly Piedmont Area of Abruzzo Region (Central Italy)
Land 2021, 10(3), 287; https://doi.org/10.3390/land10030287 - 11 Mar 2021
Cited by 3 | Viewed by 780
Abstract
Landslides are a widespread natural phenomenon that play an important role in landscape evolution and are responsible for several casualties and damages. The Abruzzo Region (Central Italy) is largely affected by different types of landslides from mountainous to coastal areas. In particular, the [...] Read more.
Landslides are a widespread natural phenomenon that play an important role in landscape evolution and are responsible for several casualties and damages. The Abruzzo Region (Central Italy) is largely affected by different types of landslides from mountainous to coastal areas. In particular, the hilly piedmont area is characterized by active geomorphological processes, mostly represented by slope instabilities related to mechanisms and factors that control their evolution in different physiographic and geological–structural conditions. This paper focuses on the detailed analysis of three selected case studies to highlight the multitemporal geomorphological evolution of landslide phenomena. An analysis of historical landslides was performed through an integrated approach combining literature data and landslide inventory analysis, relationships between landslide types and lithological units, detailed photogeological analysis, and geomorphological field mapping. This analysis highlights the role of morphostructural features on landslide occurrence and distribution and their interplay with the geomorphological evolution. This work gives a contribution to the location, abundance, activity, and frequency of landslides for the understanding of the spatial interrelationship of landslide types, morphostructural setting, and climate regime in the study area. Finally, it represents a scientific tool in geomorphological studies for landslide hazard assessment at different spatial scales, readily available to interested stakeholders to support sustainable territorial planning. Full article
(This article belongs to the Special Issue Landslide Hazard and Environment Risk Assessment)
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Article
Development of the Landslide Susceptibility Map of Attica Region, Greece, Based on the Method of Rock Engineering System
Land 2021, 10(2), 148; https://doi.org/10.3390/land10020148 - 03 Feb 2021
Viewed by 1082
Abstract
The triggering of slope failures can cause a significant impact on human settlements and infrastructure in cities, coasts, islands and mountains. Therefore, a reliable evaluation of the landslide hazard would help mitigate the effects of such landslides and decrease the relevant risk. The [...] Read more.
The triggering of slope failures can cause a significant impact on human settlements and infrastructure in cities, coasts, islands and mountains. Therefore, a reliable evaluation of the landslide hazard would help mitigate the effects of such landslides and decrease the relevant risk. The goal of this paper is to develop, for the first time on a regional scale (1:100,000), a landslide susceptibility map for the entire area of the Attica region in Greece. In order to achieve this, a database of slope failures triggered in the Attica Region from 1961 to 2020 was developed and a semi-quantitative heuristic methodology called Rock Engineering System (RES) was applied through an interaction matrix, where ten parameters, selected as controlling factors for the landslide occurrence, were statistically correlated with the spatial distribution of slope failures. The generated model was validated by using historical landslide data, field-verified slope failures and a methodology developed by the Oregon Department of Geology and Mineral Industries, showing a satisfactory correlation between the expected and existing landslide susceptibility level. Having compiled the landslide susceptibility map, studies focusing on landslide risk assessment can be realized in the Attica Region. Full article
(This article belongs to the Special Issue Landslide Hazard and Environment Risk Assessment)
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Article
Nationwide Susceptibility Mapping of Landslides in Kenya Using the Fuzzy Analytic Hierarchy Process Model
Land 2020, 9(12), 535; https://doi.org/10.3390/land9120535 - 21 Dec 2020
Cited by 3 | Viewed by 841
Abstract
Landslide susceptibility mapping (LSM) is a cost-effective tool for landslide hazard mitigation. To date, no nationwide landslide susceptibility maps have been produced for the entire Kenyan territory. Hence, this work aimed to develop a landslide susceptibility map at the national level in Kenya [...] Read more.
Landslide susceptibility mapping (LSM) is a cost-effective tool for landslide hazard mitigation. To date, no nationwide landslide susceptibility maps have been produced for the entire Kenyan territory. Hence, this work aimed to develop a landslide susceptibility map at the national level in Kenya using the fuzzy analytic hierarchy process method. First, a hierarchical evaluation index system containing 10 landslide contributing factors and their subclasses was established to produce a susceptibility map. Then, the weights of these indexes were determined through pairwise comparisons, in which triangular fuzzy numbers (TFNs) were employed to scale the relative importance based on the opinions of experts. Ultimately, these weights were merged in a hierarchical order to obtain the final landslide susceptibility map. The entire Kenyan territory was divided into five susceptibility levels. Areas with very low susceptibility covered 5.53% of the Kenyan territory, areas with low susceptibility covered 20.58%, areas with the moderate susceptibility covered 29.29%, areas with high susceptibility covered 29.16%, and areas with extremely high susceptibility covered 15.44% of Kenya. The resulting map was validated using an inventory of 425 historical landslides in Kenya. The results indicated that the TFN-AHP model showed a significantly improved performance (AUC = 0.86) compared with the conventional AHP (AUC = 0.72) in LSM for the study area. In total, 31.53% and 29.88% of known landslides occurred within the “extremely high” and “high” susceptibility zones, respectively. Only 8.24% and 1.65% of known landslides fell within the “low” and “very low” susceptibility zones, respectively. The map obtained as a result of this study is beneficial to inform planning and land resource management in Kenya. Full article
(This article belongs to the Special Issue Landslide Hazard and Environment Risk Assessment)
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