Special Issue "Natural Hazards and Geospatial Information"

Special Issue Editor

Guest Editor
Prof. Dr. Jason K. Levy

Disaster Preparedness and Emergency Management, University of Hawaii, Kapolei, HI 96707, USA
Website | E-Mail
Interests: fuzzy systems; evolutionary algorithms; neural networks; artificial intelligence; network security

Special Issue Information

Dear Colleagues,

 

The scale, intensity and prevalence of emergencies and disasters are increasing around the world. Advances in geospatial information can help scientists, citizens, government leaders and industry to understand what is needed, and how to find it, in the event of a crisis. Geomatics engineering and geospatial services can help to save lives, protect the environment and protect key resources and critical infrastructure systems. Geospatial information is critical for all phases of the disaster management cycle, from developing preparedness solutions to supporting command and control. After a disaster, GIS, remote sensing and GPS systems can help to prioritize response and recovery efforts.

 

Prof. Jason K.  Levy
Guest Editor

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Keywords

  • disaster management

  • disaster risk

  • geospatial information

  • comprehensive emergency management

Published Papers (9 papers)

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Research

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Open AccessArticle Applicability of Remote Sensing-Based Vegetation Water Content in Modeling Lightning-Caused Forest Fire Occurrences
ISPRS Int. J. Geo-Inf. 2019, 8(3), 143; https://doi.org/10.3390/ijgi8030143
Received: 21 February 2019 / Revised: 11 March 2019 / Accepted: 15 March 2019 / Published: 18 March 2019
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Abstract
In this study, our aim was to model forest fire occurrences caused by lightning using the variable of vegetation water content over six fire-dominant forested natural subregions in Northern Alberta, Canada. We used eight-day composites of surface reflectance data at 500-m spatial resolution, [...] Read more.
In this study, our aim was to model forest fire occurrences caused by lightning using the variable of vegetation water content over six fire-dominant forested natural subregions in Northern Alberta, Canada. We used eight-day composites of surface reflectance data at 500-m spatial resolution, along with historical lightning-caused fire occurrences during the 2005–2016 period, derived from a Moderate Resolution Imaging Spectroradiometer. First, we calculated the normalized difference water index (NDWI) as an indicator of vegetation/fuel water content over the six natural subregions of interest. Then, we generated the subregion-specific annual dynamic median NDWI during the 2005–2012 period, which was assembled into a distinct pattern every year. We plotted the historical lightning-caused fires onto the generated patterns, and used the concept of cumulative frequency to model lightning-caused fire occurrences. Then, we applied this concept to model the cumulative frequencies of lightning-caused fires using the median NDWI values in each natural subregion. By finding the best subregion-specific function (i.e., R2 values over 0.98 for each subregion), we evaluated their performance using an independent subregion-specific lightning-caused fire dataset acquired during the 2013–2016 period. Our analyses revealed strong relationships (i.e., R2 values in the range of 0.92 to 0.98) between the observed and modeled cumulative frequencies of lightning-caused fires at the natural subregion level throughout the validation years. Finally, our results demonstrate the applicability of the proposed method in modeling lightning-caused fire occurrences over forested regions. Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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Open AccessArticle A Comparative Study of Statistics-Based Landslide Susceptibility Models: A Case Study of the Region Affected by the Gorkha Earthquake in Nepal
ISPRS Int. J. Geo-Inf. 2019, 8(2), 94; https://doi.org/10.3390/ijgi8020094
Received: 23 December 2018 / Revised: 2 February 2019 / Accepted: 12 February 2019 / Published: 18 February 2019
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Abstract
As a result of the Gorkha earthquake in 2015, about 9000 people lost their lives and many more were injured. Most of these losses were caused by earthquake-induced landslides. Sustainable planning and decision-making are required to reduce the losses caused by earthquakes and [...] Read more.
As a result of the Gorkha earthquake in 2015, about 9000 people lost their lives and many more were injured. Most of these losses were caused by earthquake-induced landslides. Sustainable planning and decision-making are required to reduce the losses caused by earthquakes and related hazards. The use of remote sensing and geographic information systems (GIS) for landslide susceptibility mapping can help planning authorities to prepare for and mitigate the consequences of future hazards. In this study, we developed landslide susceptibility maps using GIS-based statistical models at the regional level in central Nepal. Our study area included the districts affected by landslides after the Gorkha earthquake and its aftershocks. We used the 23,439 landslide locations obtained from high-resolution satellite imagery to evaluate the differences in landslide susceptibility using analytical hierarchy process (AHP), frequency ratio (FR) and hybrid spatial multi-criteria evaluation (SMCE) models. The nine landslide conditioning factors of lithology, land cover, precipitation, slope, aspect, elevation, distance to roads, distance to drainage and distance to faults were used as the input data for the applied landslide susceptibility mapping (LSM) models. The spatial correlation of landslides and these factors were identified using GIS-based statistical models. We divided the inventory into data used for training the statistical models (70%) and data used for validation (30%). Receiver operating characteristics (ROC) and the relative landslide density index (R-index) were used to validate the results. The area under the curve (AUC) values obtained from the ROC approach for AHP, FR and hybrid SMCE were 0.902, 0.905 and 0.91, respectively. The index of relative landslide density, R-index, values in sample datasets of AHP, FR and hybrid SMCE maps were 53%, 58% and 59% for the very high hazard classes. The final susceptibility results will be beneficial for regional planning and sustainable hazard mitigation. Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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Open AccessArticle Scenario-Based Risk Assessment of Earthquake Disaster Using Slope Displacement, PGA, and Population Density in the Guyuan Region, China
ISPRS Int. J. Geo-Inf. 2019, 8(2), 85; https://doi.org/10.3390/ijgi8020085
Received: 17 December 2018 / Revised: 17 January 2019 / Accepted: 11 February 2019 / Published: 14 February 2019
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Abstract
Mega-earthquakes that occur in mountainous areas of densely populated cities are particularly catastrophic, triggering large landslides, destroying more buildings, and usually resulting in significant death tolls. In this paper, earthquake scenarios in the Guyuan Region of China are used as an example to [...] Read more.
Mega-earthquakes that occur in mountainous areas of densely populated cities are particularly catastrophic, triggering large landslides, destroying more buildings, and usually resulting in significant death tolls. In this paper, earthquake scenarios in the Guyuan Region of China are used as an example to study earthquake disaster risk assessment and a method of assessment is proposed that uses the peak ground acceleration (PGA), landslides triggered by the earthquake, and the effects on the population. The method is used to develop scenarios for earthquake disaster risk assessment along the Haiyuan and Liupanshan Faults for earthquake magnitudes of Ms 7.0, 7.5, 8.0, and 8.5 triggered by one of the two faults. The quantitative earthquake disaster risk maps in the study area were developed by integrating the values of the at-risk elements for the earthquake factor, population, and landslide hazard. According to the model results, the high-hazard zone was mainly located in the severely affected areas along the faults and on the western side of the faults. These results can be useful for emergency preparation planning, response plans, and resource assessment. Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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Open AccessArticle Multi-Criteria Decision Making (MCDM) Model for Seismic Vulnerability Assessment (SVA) of Urban Residential Buildings
ISPRS Int. J. Geo-Inf. 2018, 7(11), 444; https://doi.org/10.3390/ijgi7110444
Received: 28 September 2018 / Revised: 4 November 2018 / Accepted: 12 November 2018 / Published: 14 November 2018
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Abstract
Earthquakes are among the most catastrophic natural geo-hazards worldwide and endanger numerous lives annually. Therefore, it is vital to evaluate seismic vulnerability beforehand to decrease future fatalities. The aim of this research is to assess the seismic vulnerability of residential houses in an [...] Read more.
Earthquakes are among the most catastrophic natural geo-hazards worldwide and endanger numerous lives annually. Therefore, it is vital to evaluate seismic vulnerability beforehand to decrease future fatalities. The aim of this research is to assess the seismic vulnerability of residential houses in an urban region on the basis of the Multi-Criteria Decision Making (MCDM) model, including the analytic hierarchy process (AHP) and geographical information system (GIS). Tabriz city located adjacent to the North Tabriz Fault (NTF) in North-West Iran was selected as a case study. The NTF is one of the major seismogenic faults in the north-western part of Iran. First, several parameters such as distance to fault, percent of slope, and geology layers were used to develop a geotechnical map. In addition, the structural construction materials, building materials, size of building blocks, quality of buildings and buildings-floors were used as key factors impacting on the building’s structural vulnerability in residential areas. Subsequently, the AHP technique was adopted to measure the priority ranking, criteria weight (layers), and alternatives (classes) of every criterion through pair-wise comparison at all levels. Lastly, the layers of geotechnical and spatial structures were superimposed to design the seismic vulnerability map of buildings in the residential area of Tabriz city. The results showed that South and Southeast areas of Tabriz city exhibit low to moderate vulnerability, while some regions of the north-eastern area are under severe vulnerability conditions. In conclusion, the suggested approach offers a practical and effective evaluation of Seismic Vulnerability Assessment (SVA) and provides valuable information that could assist urban planners during mitigation and preparatory phases of less examined areas in many other regions around the world. Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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Open AccessArticle Landslide Susceptibility Mapping Using Logistic Regression Analysis along the Jinsha River and Its Tributaries Close to Derong and Deqin County, Southwestern China
ISPRS Int. J. Geo-Inf. 2018, 7(11), 438; https://doi.org/10.3390/ijgi7110438
Received: 5 September 2018 / Revised: 25 October 2018 / Accepted: 4 November 2018 / Published: 8 November 2018
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Abstract
The objective of this study was to identify the areas that are most susceptible to landslide occurrence, and to find the key factors associated with landslides along Jinsha River and its tributaries close to Derong and Deqin County. Thirteen influencing factors, including (a) [...] Read more.
The objective of this study was to identify the areas that are most susceptible to landslide occurrence, and to find the key factors associated with landslides along Jinsha River and its tributaries close to Derong and Deqin County. Thirteen influencing factors, including (a) lithology, (b) slope angle, (c) slope aspect, (d) TWI, (e) curvature, (f) SPI, (g) STI, (h) topographic relief, (i) rainfall, (j) vegetation, (k) NDVI, (l) distance-to-river, (m) and distance-to-fault, were selected as the landslide conditioning factors in landslide susceptibility mapping. These factors were mainly obtained from the field survey, digital elevation model (DEM), and Landsat 4–5 imagery using ArcGIS software. A total of 40 landslides were identified in the study area from field survey and aerial photos’ interpretation. First, the frequency ratio (FR) method was used to clarify the relationship between the landslide occurrence and the influencing factors. Then, the principal component analysis (PCA) was used to eliminate multiple collinearities between the 13 influencing factors and to reduce the dimension of the influencing factors. Subsequently, the factors that were reselected using the PCA were introduced into the logistic regression analysis to produce the landslide susceptibility map. Finally, the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the logistic regression analysis model. The landslide susceptibility map was divided into the following five classes: very low, low, moderate, high, and very high. The results showed that the ratios of the areas of the five susceptibility classes were 23.14%, 22.49%, 18.00%, 19.08%, and 17.28%, respectively. And the prediction accuracy of the model was 83.4%. The results were also compared with the FR method (79.9%) and the AHP method (76.9%), which meant that the susceptibility model was reasonable. Finally, the key factors of the landslide occurrence were determined based on the above results. Consequently, this study could serve as an effective guide for further land use planning and for the implementation of development. Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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Open AccessArticle Geospatial Assessment of the Post-Earthquake Hazard of the 2017 Pohang Earthquake Considering Seismic Site Effects
ISPRS Int. J. Geo-Inf. 2018, 7(9), 375; https://doi.org/10.3390/ijgi7090375
Received: 15 July 2018 / Revised: 17 August 2018 / Accepted: 5 September 2018 / Published: 10 September 2018
Cited by 2 | PDF Full-text (5851 KB) | HTML Full-text | XML Full-text | Correction
Abstract
The 2017 Pohang earthquake (moment magnitude scale: 5.4) was South Korea’s second strongest earthquake in decades, and caused the maximum amount of damage in terms of infrastructure and human injuries. As the epicenters were located in regions with Quaternary sediments, which involve distributions [...] Read more.
The 2017 Pohang earthquake (moment magnitude scale: 5.4) was South Korea’s second strongest earthquake in decades, and caused the maximum amount of damage in terms of infrastructure and human injuries. As the epicenters were located in regions with Quaternary sediments, which involve distributions of thick fill and alluvial geo-layers, the induced damages were more severe owing to seismic amplification and liquefaction. Thus, to identify the influence of site-specific seismic effects, a post-earthquake survey framework for rapid earthquake damage estimation, correlated with seismic site effects, was proposed and applied in the region of the Pohang earthquake epicenter. Seismic zones were determined on the basis of ground motion by classifying sites using the multivariate site classification system. Low-rise structures with slight and moderate earthquake damage were noted to be concentrated in softer sites owing to the low focal depth of the site, topographical effects, and high frequency range of the mainshocks. Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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Open AccessArticle Shaking Maps Based on Cumulative Absolute Velocity and Arias Intensity: The Cases of the Two Strongest Earthquakes of the 2016–2017 Central Italy Seismic Sequence
ISPRS Int. J. Geo-Inf. 2018, 7(7), 244; https://doi.org/10.3390/ijgi7070244
Received: 29 March 2018 / Revised: 4 June 2018 / Accepted: 13 June 2018 / Published: 22 June 2018
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Abstract
By referring to the two strongest earthquakes of the 2016–2017 Central Italy seismic sequence, this paper presents a procedure to make shaking maps through empirical relationships between macroseismic intensity and ground-motion parameters. Hundreds of waveforms were processed to obtain instrumental ground-motion features which [...] Read more.
By referring to the two strongest earthquakes of the 2016–2017 Central Italy seismic sequence, this paper presents a procedure to make shaking maps through empirical relationships between macroseismic intensity and ground-motion parameters. Hundreds of waveforms were processed to obtain instrumental ground-motion features which could be correlated with the potential damage intensities. To take into account peak value, frequency, duration, and energy content, which all contribute to damage, cumulative absolute velocity and Arias intensity were used to quantify the features of the ground motion. Once these parameters had been calculated at the recording sites, they were interpolated through geostatistical techniques on the whole struck area. Finally, empirical relationships were used for mapping intensities, i.e., potential effects on the built environment. The results referred to both earthquake scenarios that were analyzed and were also used for assessing the influence of the spatial coverage of the instrumental network. In fact, after the first events, the Italian seismic network was subjected to the addition and thickening of sensors in the epicentral area, especially. The results obtained by models only dependent on ground-motion parameters or even on the epicentral distance were compared with the official ShakeMaps and the observed intensities for assessing their reliability. Finally, some suggestions are proposed to improve the procedure that could be used for rapidly assessing ground shaking and mapping damage potential producing useful information for non-expert audience. Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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Review

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Open AccessReview Shoreline Detection using Optical Remote Sensing: A Review
ISPRS Int. J. Geo-Inf. 2019, 8(2), 75; https://doi.org/10.3390/ijgi8020075
Received: 30 October 2018 / Revised: 20 December 2018 / Accepted: 20 January 2019 / Published: 5 February 2019
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Abstract
With coastal erosion and the increased interest in beach monitoring, there is a greater need for evaluation of the shoreline detection methods. Some studies have been conducted to produce state of the art reviews on shoreline definition and detection. It should be noted [...] Read more.
With coastal erosion and the increased interest in beach monitoring, there is a greater need for evaluation of the shoreline detection methods. Some studies have been conducted to produce state of the art reviews on shoreline definition and detection. It should be noted that with the development of remote sensing, shoreline detection is mainly achieved by image processing. Thus, it is important to evaluate the different image processing approaches used for shoreline detection. This paper presents a state of the art review on image processing methods used for shoreline detection in remote sensing. It starts with a review of different key concepts that can be used for shoreline detection. Then, the applied fundamental image processing methods are shown before a comparative analysis of these methods. A significant outcome of this study will provide practical insights into shoreline detection. Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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Other

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Open AccessCorrection Correction: Kim, H.; et al. Geospatial Assessment of the Post-Earthquake Hazard of the 2017 Pohang Earthquake Considering Seismic Site Effects. ISPRS Int. J. Geo-Inf. 2018, 7, 375
ISPRS Int. J. Geo-Inf. 2019, 8(2), 62; https://doi.org/10.3390/ijgi8020062
Received: 21 January 2019 / Accepted: 24 January 2019 / Published: 29 January 2019
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Abstract
We have recently been made aware of errata and omissions in the introduction section for describing the seismological characteristics of the 2017 Pohang earthquake as stated in the title of this article, which was recently published in ISPRS [...] Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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