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Geospatial Methods and Tools for Natural Risk Management and Communications

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

Deadline for manuscript submissions: closed (16 July 2018) | Viewed by 46664

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Special Issue Editors

Dr. Raffaele Albano

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Guest Editor

School of Engineering, University of Basilicata, Potenza, Italy
Interests: floods; water resource management; soil moisture; hydrology; artificial intelligence; GIS; geomorphic method; DEM; geospatial analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Aurelia Sole

E-Mail Website
Guest Editor

School of Engineering, University of Basilicata, 85100 Potenza, Italy
Interests: 1D/2D hydraulic modeling; flood prone areas; GIS and remote sensing; water drainage network; landslide susceptibility and mapping; geomorphic modeling for flood-prone area mapping; decision making; risk communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, there has been an increasing number of research projects focused on natural hazards (NHs) and climate change impacts, providing a variety of information to end users or to scientists working on related topics. The special issue aims at promoting new and innovative studies, experiences, and models to improve risk management and communication about natural hazards to different end users. End users such as decision and policy makers or the general public need information to be easily and quickly interpretable, properly contextualized, and therefore specifically tailored to their needs. On the other hand, scientists coming from different disciplines related to natural hazards and climate change (e.g., economists, sociologists) need more complete datasets to be integrated in their analysis. By facilitating data access and evaluation, as well as promoting open access to create a level playing field for non-funded scientists, data can be more readily used for scientific discovery and societal benefits. However, the new scientific advancements are not only represented by big/comprehensive datasets, geo-information and earth-observation architectures and services or new IT communication technologies (location-based tools, games, virtual and augmented reality technologies, and so on), but also by methods in order to communicate risk uncertainty as well as associated spatio-temporal dynamics, and involve stakeholders in risk management processes.

However, data and approaches are often fragmented across literature and among geospatial/natural hazard communities, with an evident lack of coherence. Furthermore, there is no unique approach of communicating information to the different audiences. Rather, several interdisciplinary techniques and efforts can be applied in order to simplify access to, evaluation of, and exploration of data.

This special issue encourages critical reflection on natural risk mitigation and communication practices, and provides an opportunity for geoscience communicators to share the best methods and tools in this field. Contributions are solicited that address these issues, and which have a clear objective and research methodology. Case studies and other experiences are also welcome, as long as they are rigorously presented and evaluated.

Dr. Raffaele Albano
Prof. Aurelia Sole
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. 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 1700 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

Geo-Web- and location-based services
distributed GIS
dynamic WebGIS
Earth Observation System
advanced ITC
data management and visualization
citizen participation
citizen science
multi-stakeholder collaboration and engagement
risk management
risk communication
uncertainty and sensitivity analysis
decision making

Published Papers (9 papers)

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Editorial

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9 pages, 235 KiB

Open AccessEditorial

Geospatial Methods and Tools for Natural Risk Management and Communications

by Raffaele Albano and Aurelia Sole

ISPRS Int. J. Geo-Inf. 2018, 7(12), 470; https://doi.org/10.3390/ijgi7120470 - 02 Dec 2018

Cited by 17 | Viewed by 3872

Abstract

In the last decade, real-time access to data and the use of high-resolution spatial information have provided scientists and engineers with valuable information to help them understand risk. At the same time, there has been a rapid growth of novel and cutting-edge information and communication technologies for the collection, analysis and dissemination of data, re-inventing the way in which risk management is carried out throughout its cycle (risk identification and reduction, preparedness, disaster relief and recovery). The applications of those geospatial technologies are expected to enable better mitigation of, and adaptation to, the disastrous impact of natural hazards. The description of risks may particularly benefit from the integrated use of new algorithms and monitoring techniques. The ability of new tools to carry out intensive analyses over huge datasets makes it possible to perform future risk assessments, keeping abreast of temporal and spatial changes in hazard, exposure, and vulnerability. The present special issue aims to describe the state-of-the-art of natural risk assessment, management, and communication using new geospatial models and Earth Observation (EO)architecture. More specifically, we have collected a number of contributions dealing with: (1) applications of EO data and machine learning techniques for hazard, vulnerability and risk mapping; (2) natural hazards monitoring and forecasting geospatial systems; (3) modeling of spatiotemporal resource optimization for emergency management in the post-disaster phase; and (4) development of tools and platforms for risk projection assessment and communication of inherent uncertainties. Full article

(This article belongs to the Special Issue Geospatial Methods and Tools for Natural Risk Management and Communications)

Research

Jump to: Editorial

21 pages, 2611 KiB

Open AccessArticle

Application of Industrial Risk Management Practices to Control Natural Hazards, Facilitating Risk Communication

by Jongook Lee and Dong Kun Lee

ISPRS Int. J. Geo-Inf. 2018, 7(9), 377; https://doi.org/10.3390/ijgi7090377 - 14 Sep 2018

Cited by 7 | Viewed by 5235

Abstract

Establishing a comprehensive management framework to manage the risk from natural hazards is challenging because of the extensive affected areas, uncertainty in predictions of natural disasters, and the involvement of various stakeholders. Applying risk management practices proven in the industrial sector can assist systematic hazard identification and quantitative risk assessment for natural hazards, thereby promoting interactive risk communication to the public. The objective of this study is to introduce methods of studying risk commonly used in the process industry, and to suggest how such methods can be applied to manage natural disasters. In particular, the application of Hazard and Operability (HAZOP), Safety Integrated Level (SIL), and Quantitative Risk Analysis (QRA) was investigated, as these methods are used to conduct key studies in industry. We present case studies of the application of HAZOP to identify climate-related natural hazards, and of SIL and QRA studies that were performed to provide quantitative risk indices for landslide risk management. The analyses presented in this study can provide a useful framework for improving the risk management of natural hazards through establishing a more systematic context and facilitating risk communication. Full article

(This article belongs to the Special Issue Geospatial Methods and Tools for Natural Risk Management and Communications)

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11 pages, 4172 KiB

Open AccessArticle

Influences of the Shadow Inventory on a Landslide Susceptibility Model

by Cheng-Chien Liu, Wei Luo, Hsiao-Wei Chung, Hsiao-Yuan Yin and Ke-Wei Yan

ISPRS Int. J. Geo-Inf. 2018, 7(9), 374; https://doi.org/10.3390/ijgi7090374 - 09 Sep 2018

Cited by 4 | Viewed by 3638

Abstract

A landslide inventory serves as the basis for assessing landslide susceptibility, hazard, and risk. It is generally prepared from optical imagery acquired from spaceborne or airborne platforms, in which shadows are inevitably found in mountainous areas. The influences of shadow inventory on a landslide susceptibility model (LSM), however, have not been investigated systematically. This paper employs both the shadow and landslide inventories prepared from eleven Formosat-2 annual images from the I-Lan area in Taiwan acquired from 2005 to 2016, using a semiautomatic expert system. A standard LSM based on the geometric mean of multivariables was used to evaluate the possible errors incurred by neglecting the shadow inventory. The results show that the LSM performance was significantly improved by 49.21% for the top 1% of the most highly susceptible area and that the performance decreased gradually by 15.25% for the top 10% most highly susceptible areas and 9.71% for the top 20% most highly susceptible areas. Excluding the shadow inventory from the calculation of landslide susceptibility index reveals the real contribution of each factor. They are crucial in optimizing the coefficients of a nondeterministic geometric mean LSM, as well as in deriving the threshold of a landslide hazard early warning system. Full article

(This article belongs to the Special Issue Geospatial Methods and Tools for Natural Risk Management and Communications)

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Graphical abstract

15 pages, 4040 KiB

Open AccessArticle

Susceptibility to Translational Slide-Type Landslides: Applicability of the Main Scarp Upper Edge as a Dependent Variable Representation by Reduced Chi-Square Analysis

by Marco Capitani, Adriano Ribolini and Monica Bini

ISPRS Int. J. Geo-Inf. 2018, 7(9), 336; https://doi.org/10.3390/ijgi7090336 - 22 Aug 2018

Cited by 5 | Viewed by 2814

Abstract

The applicability of main scarp upper edge (MSUE) as dependent variable representation was performed in a translational slide susceptibility zonation of the Milia and Roglio basins, Italy. Two landslide inventories were built thanks to detailed geomorphological mapping and aerial photograph analysis. The landslides were used to create the models before 1975, while those after 1975 were employed to validate the predictive power of the model. Possible landslide-related factors were chosen from a geomorphological survey. The inventory landslide maps and the landslide-related factor maps were processed by conditional analysis, producing landslide susceptibility maps with five susceptibility classes. A comparison between the distribution of landslides after 1975 and those derived from models provided the predictive power of each model, which in turn was used to define the best predictive model. Reduced chi-square analysis allowed to define the efficiency of MSUE as dependent variable representation. MSUE can be applied as dependent variable representation to landslide susceptibility zonation with appreciable results. In the Roglio basin, slope angle, distance from streams, and from tectonic lineaments proved to be the main controlling factors of translational slides, whereas in the Milia basin, lithology and slope angle gave more satisfactory results as landslide-predisposing factors. Full article

(This article belongs to the Special Issue Geospatial Methods and Tools for Natural Risk Management and Communications)

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30 pages, 17877 KiB

Open AccessArticle

Landslide Susceptibility Assessment at Mila Basin (Algeria): A Comparative Assessment of Prediction Capability of Advanced Machine Learning Methods

by Abdelaziz Merghadi, Boumezbeur Abderrahmane and Dieu Tien Bui

ISPRS Int. J. Geo-Inf. 2018, 7(7), 268; https://doi.org/10.3390/ijgi7070268 - 10 Jul 2018

Cited by 99 | Viewed by 7908

Abstract

Landslide risk prevention requires the delineation of landslide-prone areas as accurately as possible. Therefore, selecting a method or a technique that is capable of providing the highest landslide prediction capability is highly important. The main objective of this study is to assess and compare the prediction capability of advanced machine learning methods for landslide susceptibility mapping in the Mila Basin (Algeria). First, a geospatial database was constructed from various sources. The database contains 1156 landslide polygons and 16 conditioning factors (altitude, slope, aspect, topographic wetness index (TWI), landforms, rainfall, lithology, stratigraphy, soil type, soil texture, landuse, depth to bedrock, bulk density, distance to faults, distance to hydrographic network, and distance to road networks). Subsequently, the database was randomly resampled into training sets and validation sets using 5 times repeated 10 k-folds cross-validations. Using the training and validation sets, five landslide susceptibility models were constructed, assessed, and compared using Random Forest (RF), Gradient Boosting Machine (GBM), Logistic Regression (LR), Artificial Neural Network (NNET), and Support Vector Machine (SVM). The prediction capability of the five landslide models was assessed and compared using the receiver operating characteristic (ROC) curve, the area under the ROC curves (AUC), overall accuracy (Acc), and kappa index. Additionally, Wilcoxon signed-rank tests were performed to confirm statistical significance in the differences among the five machine learning models employed in this study. The result showed that the GBM model has the highest prediction capability (AUC = 0.8967), followed by the RF model (AUC = 0.8957), the NNET model (AUC = 0.8882), the SVM model (AUC = 0.8818), and the LR model (AUC = 0.8575). Therefore, we concluded that GBM and RF are the most suitable for this study area and should be used to produce landslide susceptibility maps. These maps as a technical framework are used to develop countermeasures and regulatory policies to minimize landslide damages in the Mila Basin. This research demonstrated the benefit of selecting the best-advanced machine learning method for landslide susceptibility assessment. Full article

(This article belongs to the Special Issue Geospatial Methods and Tools for Natural Risk Management and Communications)

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18 pages, 6374 KiB

Open AccessArticle

Research on a 3D Geological Disaster Monitoring Platform Based on REST Service

by Xiaopeng Leng, Dunlong Liu, Junsong Luo and Zhanyong Mei

ISPRS Int. J. Geo-Inf. 2018, 7(6), 226; https://doi.org/10.3390/ijgi7060226 - 19 Jun 2018

Cited by 12 | Viewed by 4008

Abstract

Representational state transfer (REST) is a resource-based service architectural style. It abstracts data and services as resources and accesses them through a unique Uniform Resource Identifier (URI). Compared with traditional Simple Object Access Protocol (SOAP) methods, REST is more concise. It takes full advantage of HyperText Transfer Protocol (HTTP) and has better scalability and extensibility. Based on REST services, this article integrates geographic information, real-time disaster monitoring data, and warning services in a three-dimensional (3D) digital Earth infrastructure and establishes a three-dimensional geological disaster monitoring GIS platform with good service compatibility and extensibility. The platform visually displays geographical and geological information and real-time monitoring data in a three-dimensional Earth, accesses warning model services to implement disaster warnings, and realizes comprehensive information management, monitoring, and warnings of multiple types of geological disasters. This can provide decision support for disaster prevention and relief and improve the informatization of geological disaster prevention and control. Full article

(This article belongs to the Special Issue Geospatial Methods and Tools for Natural Risk Management and Communications)

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24 pages, 16036 KiB

Open AccessFeature PaperArticle

Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment

by Santina Scarpino, Raffaele Albano, Andrea Cantisani, Leonardo Mancusi, Aurelia Sole and Giovanni Milillo

ISPRS Int. J. Geo-Inf. 2018, 7(3), 105; https://doi.org/10.3390/ijgi7030105 - 14 Mar 2018

Cited by 25 | Viewed by 4363

Abstract

The increasing number of floods and the severity of their consequences, which is caused by phenomena, such as climate change and uncontrolled urbanization, create a growing need to develop operational procedures and tools for accurate and timely flood mapping and management. Synthetic Aperture Radar (SAR), with its day, night, and cloud-penetrating capacity, has proven to be a very useful source of information during calibration of hydrodynamic models considered indispensable tools for near real-time flood forecasting and monitoring. The paper begins with the analysis of radar signatures of temporal series of SAR data, by exploiting the short revisit time of the images that are provided by the Cosmo-SkyMed constellation of four satellites, in combination with a Digital Elevation Model for the extraction of flood extent and spatially distributed water depth in a flat area with complex topography during a flood event. These SAR-based hazard maps were then used to perform a bi-dimensional hydraulic model calibration on the November 2010 flood event at the mouth of the Bradano River in Basilicata, Italy. Once the best fit between flood predictions of hydrodynamic models was identified and the efficacy of SAR data in correcting hydrodynamic inconsistencies with regard to reliable assessment of flood extent and water-depth maps was shown by validation with the December 2013 Bradano River event. Based on calibration and validation results, the paper aims to show how the combination of the time series of Synthetic Aperture Radar (SAR) and Digital Elevation Model (DEM) derived water-depth maps with the data from the hydrodynamic model can provide valuable information for flood dynamics monitoring in a flat area with complex topography. Future research should focus on the integration and implementation of the semi-automatic proposed method in an operational system for near real-time flood management. Full article

(This article belongs to the Special Issue Geospatial Methods and Tools for Natural Risk Management and Communications)

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18 pages, 3483 KiB

Open AccessArticle

Assessment of Tangible Direct Flood Damage Using a Spatial Analysis Approach under the Effects of Climate Change: Case Study in an Urban Watershed in Hanoi, Vietnam

by Mohamed Kefi, Binaya Kumar Mishra, Pankaj Kumar, Yoshifumi Masago and Kensuke Fukushi

ISPRS Int. J. Geo-Inf. 2018, 7(1), 29; https://doi.org/10.3390/ijgi7010029 - 16 Jan 2018

Cited by 32 | Viewed by 8069

Abstract

Due to climate change, the frequency and intensity of Hydro-Meteorological disasters, such as floods, are increasing. Therefore, the main purpose of this work is to assess tangible future flood damage in the urban watershed of the To Lich River in Hanoi, Vietnam. An approach based on spatial analysis, which requires the integration of several types of data related to flood characteristics that include depth, in particular, land-use classes, property values, and damage rates, is applied for the analysis. To simulate the future scenarios of flooding, the effects of climate change and land-use changes are estimated for 2030. Additionally, two scenarios based on the implementation of flood control measures are analyzed to demonstrate the effect of adaptation strategies. The findings show that climate change combined with the expansion of built-up areas increases the vulnerability of urban areas to flooding and economic damage. The results also reveal that the impacts of climate change will increase the total damage from floods by 26%. However, appropriate flood mitigation will be helpful in reducing the impacts of losses from floods by approximately 8% with the restoration of lakes and by approximately 29% with the implementation of water-sensitive urban design (WSUD). This study will be useful in helping to identify and map flood-prone areas at local and regional scales, which can lead to the detection and prioritization of exposed areas for appropriate countermeasures in a timely manner. In addition, the quantification of flood damage can be an important indicator to enhance the awareness of local decision-makers on improving the efficiency of regional flood risk reduction strategies. Full article

(This article belongs to the Special Issue Geospatial Methods and Tools for Natural Risk Management and Communications)

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22 pages, 4661 KiB

Open AccessArticle

Developing an Agent-Based Simulation System for Post-Earthquake Operations in Uncertainty Conditions: A Proposed Method for Collaboration among Agents

by Navid Hooshangi and Ali Asghar Alesheikh

ISPRS Int. J. Geo-Inf. 2018, 7(1), 27; https://doi.org/10.3390/ijgi7010027 - 15 Jan 2018

Cited by 29 | Viewed by 5556

Abstract

Agent-based modeling is a promising approach for developing simulation tools for natural hazards in different areas, such as during urban search and rescue (USAR) operations. The present study aimed to develop a dynamic agent-based simulation model in post-earthquake USAR operations using geospatial information system and multi agent systems (GIS and MASs, respectively). We also propose an approach for dynamic task allocation and establishing collaboration among agents based on contract net protocol (CNP) and interval-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, which consider uncertainty in natural hazards information during agents’ decision-making. The decision-making weights were calculated by analytic hierarchy process (AHP). In order to implement the system, earthquake environment was simulated and the damage of the buildings and a number of injuries were calculated in Tehran’s District 3: 23%, 37%, 24% and 16% of buildings were in slight, moderate, extensive and completely vulnerable classes, respectively. The number of injured persons was calculated to be 17,238. Numerical results in 27 scenarios showed that the proposed method is more accurate than the CNP method in the terms of USAR operational time (at least 13% decrease) and the number of human fatalities (at least 9% decrease). In interval uncertainty analysis of our proposed simulated system, the lower and upper bounds of uncertain responses are evaluated. The overall results showed that considering uncertainty in task allocation can be a highly advantageous in the disaster environment. Such systems can be used to manage and prepare for natural hazards. Full article

(This article belongs to the Special Issue Geospatial Methods and Tools for Natural Risk Management and Communications)

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