Special Issue "Disaster Management and Geospatial Information"

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

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editor

Dr. Dean Kyne
E-Mail Website
Guest Editor
Department of Sociology, University of Texas Rio Grande Valley1201 W University Dr., Edinburg, TX 78539, USA
Interests: Disaster Studies; Sustainability, Resiliency, and Social Capital; Environmental Justice; Geographic and Information System (GIS); Nuclear Power Induced Emergencies and Disasters Management

Special Issue Information

Dear Colleagues,

I would like to cordially invite you to consider publishing your manuscripts in our Special Issue on the topic of disaster management and geospatial information in the International Journal of Geo-Information Journal (ISPRS).

Disaster management consists of mitigation, preparedness, response, and recovery. The first two are performed before a disaster event takes place, whereas the latter two are executed after the disaster. Due to climate changes, future disasters are anticipated to be more frequent, intense, and costly. At this juncture, building disaster resiliency represents one of the most desired goals commonly shared among key stakeholders to cope with anticipated future disasters. Building disaster resiliency requires effectively managing disaster risk, which is determined by hazard, exposure, and vulnerabilty. Disaster professionals have been utilizing the geographic information system (GIS) as a tool to better understand the natural, man-made, and technological hazards, human exposures to them, and their social, economic, and environmental vulenrabilty in their efforts to manage disaster risks.

The success of managing disaster risk to build disaster resiliency specifically depends on the geospatial information which is the most important component in the GIS system. The widespread use of GIS in disaster managment has led to an increasing need of geospatial information. Governments at the local, county, state, and federal levels currently provide more information, which contains geospatial data. Producing and providing geospatial information presents various challenges and barriers among key stakeholders. The challenges include organizing and managing the geospatial information, sharing among different levels of government, and maintaining confidentiality of geospatial data. Overcoming challenges warrants effective public policies and monitoring by the federal government at the national level. In sum, this Special Issue provides a platform for researchers with an emphasis on disaster management and geospatial information to share their own findings, views, and ideas on overcoming issues and challenges in utilizing GIS in disaster management and disseminating geospatial information to all users.

Dr. Dean Kyne
Guest Editor

Aims

ISPRS International Journal of Geo-Information (ISSN 2220-9964) provides an advanced forum for the science and technology of geographic information. ISPRS International Journal of Geo-Information publishes regular research papers, reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. There are, in addition, three unique features of this journal:

  • manuscripts regarding research proposals and research ideas will be particularly welcomed.
  • electronic files and software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material.
  • we also accept manuscripts communicating to a broader audience with regard to research projects financed with public funds.

Scope

  • understanding risk, exposure, and vulnerability to disasters
  • spatial disaster event databases
  • disaster risk communication
  • disaster preparedness and mitigation
  • visualization of geospatial data for disaster management
  • data sharing initiatives for disaster management
  • promotion of situational awareness of disasters
  • crowdsourcing and volunteered geographic information (vgi) in a disaster and crisis context
  • disaster and crisis related issues in spatial data infrastructures
  • webmapping for disaster and crisis support
  • spatio-temporal modelling for disaster management
  • future challenges for disaster risk related geoinformation management
  • near-real time mapping for response
  • crisis mapping and geovisualization
  • interoperability aspects regarding disaster-related geodata
  • public policies on producing and sharing geospatial information
  • protocols for sharing geospatial information among disaster and emergency management organizations at local, county, state, and federal
  • sharing geospatial information among key stakeholders for managing disasters
  • maintaining confidentiality of geospatial information for disaster management

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. 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 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (9 papers)

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Research

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Article
Risk Assessment of Population Loss Posed by Earthquake-Landslide-Debris Flow Disaster Chain: A Case Study in Wenchuan, China
ISPRS Int. J. Geo-Inf. 2021, 10(6), 363; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060363 - 28 May 2021
Viewed by 450
Abstract
Earthquakes often cause secondary disasters in mountainous areas, forming the typical earthquake-landslide-debris flow disaster chain for a long time that results in a series of losses. It is important to improve the risk assessment method from the perspective of cascading effect of such [...] Read more.
Earthquakes often cause secondary disasters in mountainous areas, forming the typical earthquake-landslide-debris flow disaster chain for a long time that results in a series of losses. It is important to improve the risk assessment method from the perspective of cascading effect of such a disaster chain, by strengthening quantitative research on hazards of the debris flows which are affected by landslide volume and rainstorm intensity. Taking Wenchuan County as an example, the risk assessment method for population loss of the disaster chain is established and the risks are evaluated in this paper. The results show that the population loss risk is 2.59–2.71 people/km2 under the scenarios of the Wenchuan Ms8.0 earthquake and four rainstorm intensities. The impacts of landslide and debris flow after the earthquake were long-term and profound. A comparison of risks caused by each element of the chain revealed that the risk associated with the earthquake accounted for the highest proportion, and landslide and debris flow accounted for 38.82–37.18% and 3.42–7.50%, respectively. As the earthquake intensity increases, the total risk posed by the disaster chain increases significantly. The risk caused by the earthquake is the highest in high earthquake intensity zones; while in the lower-intensity zones, landslides and debris flows pose relatively high risks. The risk assessment results were verified through comparison with actual data, indicating that the simulation results are quite consistent with the existing disaster information and that the risk assessment method based on the earthquake-landslide-debris flow cascade process is significant for future risk estimation. Full article
(This article belongs to the Special Issue Disaster Management and Geospatial Information)
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Article
Exploring Spatial Patterns of Virginia Tornadoes Using Kernel Density and Space-Time Cube Analysis (1960–2019)
ISPRS Int. J. Geo-Inf. 2021, 10(5), 310; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050310 - 07 May 2021
Viewed by 403
Abstract
This study evaluates the spatial-temporal patterns in Virginia tornadoes using the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database. In addition to descriptive statistics, the analysis employs Kernel Density Estimation for spatial pattern analysis and space-time cubes to visualize the [...] Read more.
This study evaluates the spatial-temporal patterns in Virginia tornadoes using the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database. In addition to descriptive statistics, the analysis employs Kernel Density Estimation for spatial pattern analysis and space-time cubes to visualize the spatiotemporal frequency of tornadoes and potential trends. Most of the 726 tornadoes between 1960–2019 occurred in Eastern Virginia, along the Piedmont and Coastal Plain. Consistent with other literature, both the number of tornadoes and the tornado days have increased in Virginia. While 80% of the tornadoes occurred during the warm season, tornadoes did occur during each month including two deadly tornadoes in January and February. Over the 60-year period, a total of 28 people were killed in the Commonwealth. Most tornado activity took place in the afternoon and early evening hours drawing attention to the temporal variability of risk and vulnerability. Spatial analysis results identify significant, non-random clusters of tornado activity and increasing temporal frequency. While this study improves weather-related literacy and addresses a need in the Commonwealth, more research is necessary to further evaluate the synoptic and mesoscale mechanisms of Virginia tornadoes. Full article
(This article belongs to the Special Issue Disaster Management and Geospatial Information)
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Article
Assessing Influential Factors on Inland Property Damage from Gulf of Mexico Tropical Cyclones in the United States
ISPRS Int. J. Geo-Inf. 2021, 10(5), 295; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050295 - 04 May 2021
Viewed by 622
Abstract
The Gulf and southeast coastal communities in the United States are particularly vulnerable to tropical cyclones. Coastal areas generally receive the greatest economic losses from tropical cyclones; however, research suggests that losses in the inland zone can occasionally be higher than the coastal [...] Read more.
The Gulf and southeast coastal communities in the United States are particularly vulnerable to tropical cyclones. Coastal areas generally receive the greatest economic losses from tropical cyclones; however, research suggests that losses in the inland zone can occasionally be higher than the coastal zone. Previous research assessing the inland impacts from tropical cyclones was limited to the areas that are adjacent to the coastal zone only, where losses are usually higher. In this study, we assessed the spatial distribution of inland property damage caused by tropical cyclones. We included all the inland counties that fall within the inland zone in the states of Louisiana, Mississippi, and Alabama. Additionally, different factors, including meteorological storm characteristics (tropical cyclone wind and rain), elevation, and county social-economic vulnerability (county social vulnerability index and GDP) were assessed to measure their influence on property damage, using both ordinary least squares (OLS) and geographically weighted regression (GWR) models. GWR performs better than the OLS, signifying the importance of considering spatial variations in the explanation of inland property damage. Results from the tristate region suggest that wind was the strongest predictor of property damage in OLS and one of the major contributing factors of property damage in the GWR model. These results could be beneficial for emergency managers and policymakers when considering the inland impacts of tropical cyclones. Full article
(This article belongs to the Special Issue Disaster Management and Geospatial Information)
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Article
Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source Data
ISPRS Int. J. Geo-Inf. 2021, 10(4), 253; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040253 - 09 Apr 2021
Viewed by 454
Abstract
The early identification of potential landslide hazards is of great practical significance for disaster early warning and prevention. The study used different machine learning methods to identify potential active landslides along a 15 km buffer zone on both sides of Jinsha River (Panzhihua-Huize [...] Read more.
The early identification of potential landslide hazards is of great practical significance for disaster early warning and prevention. The study used different machine learning methods to identify potential active landslides along a 15 km buffer zone on both sides of Jinsha River (Panzhihua-Huize section), China. The morphology and texture features of landslides were characterized with InSAR deformation monitoring data and high-resolution optical remote sensing data, combined with 17 landslide influencing factors. In the study area, 83 deformation accumulation areas of potential landslide hazards and 54 deformation accumulation areas of non-potential landslide hazards were identified through spatial overlay analysis with 64 potential active landslides, which have been confirmed by field verification. The Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were trained and tested through attribute selection and parameter optimization. Among the 17 landslide influencing factors, Drainage Density, NDVI, Slope and Weathering Degree play an indispensable role in the machine learning and recognition of landslide hazards in our study area, while other influencing factors play a certain role in different algorithms. A multi-index (Precision, Recall, F1) comparison shows that the SVM (0.867, 0.829, 0.816) has better recognition precision skill for small-scale unbalanced landslide deformation datasets, followed by RF (0.765, 0.756, 0.741), DT (0.755, 0.756, 0.748) and NB (0.659, 0.659, 0.659). Different from the previous study on landslide susceptibility and hazard mapping based on machine learning, this study focuses on how to find out the potential active landslide points more accurately, rather than evaluating the landslide susceptibility of specific areas to tell us which areas are more sensitive to landslides. This study verified the feasibility of early identification of landslide hazards by using different machine learning methods combined with deformation information and multi-source landslide influencing factors rather than by relying on human–computer interaction. This study shows that the efficiency of potential hazard identification can be increased while reducing the subjective bias caused by relying only on human experts. Full article
(This article belongs to the Special Issue Disaster Management and Geospatial Information)
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Article
Distribution of Lightning Accidents in Sri Lanka from 1974 to 2019 Using the DesInventar Database
ISPRS Int. J. Geo-Inf. 2021, 10(3), 117; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030117 - 27 Feb 2021
Viewed by 516
Abstract
The reported lightning accidents that are available in the DesInventar database—which consist of 549 deaths, 498 injured people, 39 destroyed houses, and 741 damaged houses—were analyzed in terms of their geographical and temporal variation. The average lightning flash densities were calculated using zonal [...] Read more.
The reported lightning accidents that are available in the DesInventar database—which consist of 549 deaths, 498 injured people, 39 destroyed houses, and 741 damaged houses—were analyzed in terms of their geographical and temporal variation. The average lightning flash densities were calculated using zonal statistics using the geographic information system (GIS), referring to the respective raster maps generated based on Lightning Imaging Sensor (LIS) data from the Tropical Rainfall Measurement Mission (TRMM) Satellite. Hence, the variations of the lightning accidents—monthly and climate season-wise—in response to the lightning flash density were also reported. The calculated average lightning flash density in Sri Lanka is 8.26 flashes km−2 year−1, and the maximum average lightning flash density of 31.33 flashes km−2 year−1 is observed in April in a calendar year. April seems to be more vulnerable to lightning accidents, as the maximum number of deaths (150 deaths) and injuries (147 injuries) were recorded in this month. Most of the high-risk lightning accident regions that were identified in Sri Lanka are well known for agricultural activities, and those activities will eventually create the platform for lightning victims. In Sri Lanka, in a year, 12 people were killed and 11 people were injured, based on the reported accidents from 1974 to 2019. Conversely, a substantial increase in the number of deaths, injuries, and incidents of property damage has been observed in the last two decades (2000–2019). On average, for the period from 2000 to 2019, 18 people were killed and 16 people were injured per year. Furthermore, considering the population of the country, 0.56 people per million per year were killed, and 0.51 people per million per year were injured due to lightning accidents based on the reported accidents from 1974 to 2019. Moreover, for the 2000–2019 period, these estimated values are significantly higher; 0.86 people per million per year were killed, and 0.77 people per million per year were injured. Full article
(This article belongs to the Special Issue Disaster Management and Geospatial Information)
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Article
Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions
ISPRS Int. J. Geo-Inf. 2021, 10(3), 114; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030114 - 27 Feb 2021
Viewed by 830
Abstract
The Himalayan region and hilly areas face severe challenges due to landslide occurrences during the rainy seasons in India, and the study area, i.e., the Rudraprayag district, is no exception. However, the landslide related database and research are still inadequate in these landslide-prone [...] Read more.
The Himalayan region and hilly areas face severe challenges due to landslide occurrences during the rainy seasons in India, and the study area, i.e., the Rudraprayag district, is no exception. However, the landslide related database and research are still inadequate in these landslide-prone areas. The main purpose of this study is: (1) to prepare the multi-temporal landslide inventory map using geospatial platforms in the data-scarce environment; (2) to evaluate the landslide susceptibility map using weights of evidence (WoE) method in the Geographical Information System (GIS) environment at the district level; and (3) to provide a comprehensive understanding of recent developments, gaps, and future directions related to landslide inventory, susceptibility mapping, and risk assessment in the Indian context. Firstly, 293 landslides polygon were manually digitized using the BHUVAN (Indian earth observation visualization) and Google Earth® from 2011 to 2013. Secondly, a total of 14 landslide causative factors viz. geology, geomorphology, soil type, soil depth, slope angle, slope aspect, relative relief, distance to faults, distance to thrusts, distance to lineaments, distance to streams, distance to roads, land use/cover, and altitude zones were selected based on the previous study. Then, the WoE method was applied to assign the weights for each class of causative factors to obtain a landslide susceptibility map. Afterward, the final landslide susceptibility map was divided into five susceptibility classes (very high, high, medium, low, and very low classes). Later, the validation of the landslide susceptibility map was checked against randomly selected landslides using IDRISI SELVA 17.0 software. Our study results show that medium to very high landslide susceptibilities had occurred in the non-forest areas, mainly scrubland, pastureland, and barren land. The results show that medium to very high landslide susceptibilities areas are in the upper catchment areas of the Mandakini river and adjacent to the National Highways (107 and 07). The results also show that landslide susceptibility is high in high relative relief areas and shallow soil, near thrusts and faults, and on southeast, south, and west-facing steep slopes. The WoE method achieved a prediction accuracy of 85.7%, indicating good accuracy of the model. Thus, this landslide susceptibility map could help the local governments in landslide hazard mitigation, land use planning, and landscape protection. Full article
(This article belongs to the Special Issue Disaster Management and Geospatial Information)
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Article
A GIS-Based System for Spatial-Temporal Availability Evaluation of the Open Spaces Used as Emergency Shelters: The Case of Victoria, British Columbia, Canada
ISPRS Int. J. Geo-Inf. 2021, 10(2), 63; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020063 - 02 Feb 2021
Viewed by 495
Abstract
Canadian emergency management planners have historically ignored the self-motivated evacuation procedures of people who cannot initially choose the safest evacuation areas. In densely developed urban areas, open spaces can be seen as ideal evacuation areas and should thus be included in shelter planning. [...] Read more.
Canadian emergency management planners have historically ignored the self-motivated evacuation procedures of people who cannot initially choose the safest evacuation areas. In densely developed urban areas, open spaces can be seen as ideal evacuation areas and should thus be included in shelter planning. In this study, the public open spaces in Great Victoria were selected as the study area and evaluated using GIS technologies. A multi-criteria TOPSIS evaluation model was used to conduct comprehensive quantitative evaluations of the open spaces’ safety, accessibility, and availability. Through hybrid process, service area, and POI aggregation coupling analyses, a model is created that provides an overall evaluation at the district level. In addition to providing a model for evaluating open spaces as emergency shelters, applicable to most Canadian cities, this study emphasizes the importance and disadvantages of open space emergency shelters in Canada, which have heretofore been ignored by decision makers. In Great Victoria, we found that the distribution of open spaces does not match the dynamics of the population distribution, meaning that through inadequate preparation some districts lack a safe evacuation place—this in an area where people are at high risk of earthquake disasters and their subsequent effects. Full article
(This article belongs to the Special Issue Disaster Management and Geospatial Information)
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Article
Flood Susceptibility Mapping through the GIS-AHP Technique Using the Cloud
ISPRS Int. J. Geo-Inf. 2020, 9(12), 720; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120720 - 02 Dec 2020
Cited by 4 | Viewed by 785
Abstract
Flood susceptibility mapping is essential for characterizing flood risk zones and for planning mitigation approaches. Using a multi-criteria decision support system, this study investigated a flood susceptible region in Bihar, India. It used a combination of the analytical hierarchy process (AHP) and geographic [...] Read more.
Flood susceptibility mapping is essential for characterizing flood risk zones and for planning mitigation approaches. Using a multi-criteria decision support system, this study investigated a flood susceptible region in Bihar, India. It used a combination of the analytical hierarchy process (AHP) and geographic information system (GIS)/remote sensing (RS) with a cloud computing API on the Google Earth Engine (GEE) platform. Five main flood-causing criteria were broadly selected, namely hydrologic, morphometric, permeability, land cover dynamics, and anthropogenic interference, which further had 21 sub-criteria. The relative importance of each criterion prioritized as per their contribution toward flood susceptibility and weightage was given by an AHP pair-wise comparison matrix (PCM). The most and least prominent flood-causing criteria were hydrologic (0.497) and anthropogenic interference (0.037), respectively. An area of ~3000 sq km (40.36%) was concentrated in high to very high flood susceptibility zones that were in the vicinity of rivers, whereas an area of ~1000 sq km (12%) had very low flood susceptibility. The GIS-AHP technique provided useful insights for flood zone mapping when a higher number of parameters were used in GEE. The majorities of detected flood susceptible areas were flooded during the 2019 floods and were mostly located within 500 m of the rivers’ paths. Full article
(This article belongs to the Special Issue Disaster Management and Geospatial Information)
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Review

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Review
Implementation of FAIR Principles for Ontologies in the Disaster Domain: A Systematic Literature Review
ISPRS Int. J. Geo-Inf. 2021, 10(5), 324; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050324 - 11 May 2021
Viewed by 486
Abstract
The success of disaster management efforts demands meaningful integration of data that is geographically dispersed and owned by stakeholders in various sectors. However, the difficulty in finding, accessing and reusing interoperable vocabularies to organise disaster management data creates a challenge for collaboration among [...] Read more.
The success of disaster management efforts demands meaningful integration of data that is geographically dispersed and owned by stakeholders in various sectors. However, the difficulty in finding, accessing and reusing interoperable vocabularies to organise disaster management data creates a challenge for collaboration among stakeholders in the disaster management cycle on data integration tasks. Thus the need to implement FAIR principles that describe the desired features ontologies should possess to maximize sharing and reuse by humans and machines. In this review, we explore the extent to which sharing and reuse of disaster management knowledge in the domain is inline with FAIR recommendations. We achieve this through a systematic search and review of publications in the disaster management domain based on a predefined inclusion and exclusion criteria. We then extract social-technical features in selected studies and evaluate retrieved ontologies against the FAIR maturity model for semantic artefacts. Results reveal that low numbers of ontologies representing disaster management knowledge are resolvable via URIs. Moreover, 90.9% of URIs to the downloadable disaster management ontology artefacts do not conform to the principle of uniqueness and persistence. Also, only 1.4% of all retrieved ontologies are published in semantic repositories and 84.1% are not published at all because there are no repositories dedicated to archiving disaster domain knowledge. Therefore, there exists a very low level of Findability (1.8%) or Accessibility (5.8%), while Interoperability and Reusability are moderate (49.1% and 30.2 % respectively). The low adherence of disaster vocabularies to FAIR Principles poses a challenge to disaster data integration tasks because of the limited ability to reuse previous knowledge during disaster management phases. By using FAIR indicators to evaluate the maturity in sharing, discovery and integration of disaster management ontologies, we reveal potential research opportunities for managing reusable and evolving knowledge in the disaster community. Full article
(This article belongs to the Special Issue Disaster Management and Geospatial Information)
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