Special Issue "Mapping and Assessing Natural Disasters Using Geospatial Technologies"

A special issue of Geosciences (ISSN 2076-3263).

Deadline for manuscript submissions: closed (31 May 2016).

Special Issue Editor

Prof. Dr. Ruiliang Pu
E-Mail Website
Guest Editor
School of Geosciences, University of South Florida, 4204 E Fowler Ave., NES 107, Tampa, FL 33620, USA
Tel. 813-974-1508; Fax: 813-974-4808
Interests: Hyperspectral data analysis; remote sensing; invasive species mapping and monitoring; land cover change detection; image processing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The overall goal of this Special Issue of Geosciences is to explore and evaluate the potential of application of geospatial technologies such as remote sensing, GIS, GPS and spatial statistics in mapping, predicting, monitoring and assessing natural disasters. Natural disasters, including floods, wildfires, volcanic eruptions, earthquakes, tsunamis, landslides can cause immense loss of life and/or property. A natural disaster is a major adverse event resulting from natural processes of the Earth. Such processes could be efficiently investigated and well understood with modern geospatial technologies.

Specifically, this Special Issue aims to provide an outlet for rapid, widely accessible publication of peer-reviewed studies utilizing geospatial technologies to map, monitor, predict, and assess natural disasters. This special issue aims to cover, without being limited to, the following areas:

  • Wildfires: Hotspot detection and burn scar mapping using satellite remote sensing data, GIS, GPS, etc.;
  • Landslides: Monitoring, mapping and assessing landslides using RADAR/LiDAR and/or optical remote sensing devices, GIS and GPS;
  • Earthquakes/tsunamis: Mapping condition pre- and post-, and evaluation of loss and damage after earthquakes/tsunamis using multitemporal remote sensing and GIS techniques.
  • Other natural disasters: Volcanic eruptions, flooding and tornado/hurricane damage, etc. using geospatial technologies and modeling tools.

Dr. Ruiliang Pu
Guest Editor

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. Geosciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Geospatial technology
  • Remote sensing
  • GIS
  • GPS
  • Spatial statistics
  • Wildfire
  • Hotspot detection
  • Burn scar mapping
  • Landslide monitoring
  • Assessment after disaster
  • Earthquake

Published Papers (10 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Review

Open AccessEditorial
A Special Issue of Geosciences: Mapping and Assessing Natural Disasters Using Geospatial Technologies
Geosciences 2017, 7(1), 4; https://doi.org/10.3390/geosciences7010004 - 16 Jan 2017
Cited by 3
Abstract
Natural disasters, including floods, wildfires, volcanic eruptions, earthquakes, tsunamis, tropical storms, droughts, and landslides, can cause major losses of human lives and livelihoods, the destruction of economic and social infrastructure, as well as environmental damages.[...] Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)

Research

Jump to: Editorial, Review

Open AccessArticle
Anomaly Detection from Hyperspectral Remote Sensing Imagery
Geosciences 2016, 6(4), 56; https://doi.org/10.3390/geosciences6040056 - 12 Dec 2016
Cited by 4
Abstract
Hyperspectral remote sensing imagery contains much more information in the spectral domain than does multispectral imagery. The consecutive and abundant spectral signals provide a great potential for classification and anomaly detection. In this study, two real hyperspectral data sets were used for anomaly [...] Read more.
Hyperspectral remote sensing imagery contains much more information in the spectral domain than does multispectral imagery. The consecutive and abundant spectral signals provide a great potential for classification and anomaly detection. In this study, two real hyperspectral data sets were used for anomaly detection. One data set was an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data covering the post-attack World Trade Center (WTC) and anomalies are fire spots. The other data set called SpecTIR contained fabric panels as anomalies compared to their background. Existing anomaly detection algorithms including the Reed–Xiaoli detector (RXD), the blocked adaptive computation efficient outlier nominator (BACON), the random selection based anomaly detector (RSAD), the weighted-RXD (W-RXD), and the probabilistic anomaly detector (PAD) are reviewed here. The RXD generally sets strict assumptions to the background, which cannot be met in many scenarios, while BACON, RSAD, and W-RXD employ strategies to optimize the estimation of background information. The PAD firstly estimates both background information and anomaly information and then uses the information to conduct anomaly detection. Here, the BACON, RSAD, W-RXD, and PAD outperformed the RXD in terms of detection accuracy, and W-RXD and PAD required less time than BACON and RSAD. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
Show Figures

Figure 1

Open AccessArticle
Identifying Spatio-Temporal Landslide Hotspots on North Island, New Zealand, by Analyzing Historical and Recent Aerial Photography
Geosciences 2016, 6(4), 48; https://doi.org/10.3390/geosciences6040048 - 02 Nov 2016
Cited by 8
Abstract
Accurate mapping of landslides and the reliable identification of areas most affected by landslides are essential for advancing the understanding of landslide erosion processes. Remote sensing data provides a valuable source of information on the spatial distribution and location of landslides. In this [...] Read more.
Accurate mapping of landslides and the reliable identification of areas most affected by landslides are essential for advancing the understanding of landslide erosion processes. Remote sensing data provides a valuable source of information on the spatial distribution and location of landslides. In this paper we present an approach for identifying landslide-prone “hotspots” and their spatio-temporal variability by analyzing historical and recent aerial photography from five different dates, ranging from 1944 to 2011, for a study site near the town of Pahiatua, southeastern North Island, New Zealand. Landslide hotspots are identified from the distribution of semi-automatically detected landslides using object-based image analysis (OBIA), and compared to hotspots derived from manually mapped landslides. When comparing the overlapping areas of the semi-automatically and manually mapped landslides the accuracy values of the OBIA results range between 46% and 61% for the producer’s accuracy and between 44% and 77% for the user’s accuracy. When evaluating whether a manually digitized landslide polygon is only intersected to some extent by any semi-automatically mapped landslide, we observe that for the natural-color images the landslide detection rate is 83% for 2011 and 93% for 2005; for the panchromatic images the values are slightly lower (67% for 1997, 74% for 1979, and 72% for 1944). A comparison of the derived landslide hotspot maps shows that the distribution of the manually identified landslides and those mapped with OBIA is very similar for all periods; though the results also reveal that mapping landslide tails generally requires visual interpretation. Information on the spatio-temporal evolution of landslide hotspots can be useful for the development of location-specific, beneficial intervention measures and for assessing landscape dynamics. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
Show Figures

Graphical abstract

Open AccessArticle
Feasibility Study of Land Cover Classification Based on Normalized Difference Vegetation Index for Landslide Risk Assessment
Geosciences 2016, 6(4), 45; https://doi.org/10.3390/geosciences6040045 - 20 Oct 2016
Cited by 7
Abstract
Unfavorable land cover leads to excessive damage from landslides and other natural hazards, whereas the presence of vegetation is expected to mitigate rainfall-induced landslide potential. Hence, unexpected and rapid changes in land cover due to deforestation would be detrimental in landslide-prone areas. Also, [...] Read more.
Unfavorable land cover leads to excessive damage from landslides and other natural hazards, whereas the presence of vegetation is expected to mitigate rainfall-induced landslide potential. Hence, unexpected and rapid changes in land cover due to deforestation would be detrimental in landslide-prone areas. Also, vegetation cover is subject to phenological variations and therefore, timely classification of land cover is an essential step in effective evaluation of landslide hazard potential. The work presented here investigates methods that can be used for land cover classification based on the Normalized Difference Vegetation Index (NDVI), derived from up-to-date satellite images, and the feasibility of application in landslide risk prediction. A major benefit of this method would be the eventual ability to employ NDVI as a stand-alone parameter for accurate assessment of the impact of land cover in landslide hazard evaluation. An added benefit would be the timely detection of undesirable practices such as deforestation using satellite imagery. A landslide-prone region in Oregon, USA is used as a model for the application of the classification method. Five selected classification techniques—k-nearest neighbor, Gaussian support vector machine (GSVM), artificial neural network, decision tree and quadratic discriminant analysis support the viability of the NDVI-based land cover classification. Finally, its application in landslide risk evaluation is demonstrated. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
Show Figures

Figure 1

Open AccessArticle
Measuring Beach Profiles along a Low-Wave Energy Microtidal Coast, West-Central Florida, USA
Geosciences 2016, 6(4), 44; https://doi.org/10.3390/geosciences6040044 - 19 Oct 2016
Cited by 6
Abstract
Monitoring storm-induced dramatic beach morphology changes and long-term beach evolution provides crucial data for coastal management. Beach-profile measurement using total station has been conducted along the coast of west-central Florida over the last decade. This paper reviews several case studies of beach morphology [...] Read more.
Monitoring storm-induced dramatic beach morphology changes and long-term beach evolution provides crucial data for coastal management. Beach-profile measurement using total station has been conducted along the coast of west-central Florida over the last decade. This paper reviews several case studies of beach morphology changes based on total-station survey along this coast. The advantage of flexible and low-cost total-station surveys is discussed in comparison to LIDAR (light detection and ranging) method. In an attempt to introduce total-station survey from a practical prospective, measurement of cross-shore beach profile in various scenarios are discussed, including: (1) establishing a beach profile line with known instrument and benchmark locations; (2) surveying multiple beach profiles with one instrument setup; (3) implementation of coordinate rotation to convert local system to real-earth system. Total-station survey is a highly effective and accurate method in documenting beach profile changes along low-energy coasts. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
Show Figures

Graphical abstract

Open AccessArticle
Assessing Floods and Droughts in Ungauged Small Reservoirs with Long-Term Landsat Imagery
Geosciences 2016, 6(4), 42; https://doi.org/10.3390/geosciences6040042 - 27 Sep 2016
Cited by 8
Abstract
Small reservoirs have developed across semi-arid areas as a low cost solution for millions of rural small holders to harvest scarce water resources. Studies have highlighted limited agricultural water use and low water availability on individual reservoirs, but no information exists on the [...] Read more.
Small reservoirs have developed across semi-arid areas as a low cost solution for millions of rural small holders to harvest scarce water resources. Studies have highlighted limited agricultural water use and low water availability on individual reservoirs, but no information exists on the drought patterns of multiple small reservoirs. Their small size and dispersion prevents individualised hydrological monitoring, while hydrological modelling suffers from rainfall variability and heterogeneity across data sparse catchments and reservoirs. A semi-automated original approach exploiting free, archive Landsat satellite images is developed here for long-term monitoring of multiple ungauged small water bodies. Adapted and tested against significant hydrometric time series on three lakes, the method confirms its potential to monitor water availability on the smallest water bodies (1–10 ha) with a mean RMSE of 20,600 m3 (NRMSE = 26%). Uncertainties from the absence of site-specific and updated surface-volume rating curves were here contained through a power relationship adapted over time for silting based on data from 15 surrounding lakes. Applied to 51 small reservoirs and 546 images over 1999–2014, results highlight the ability of this transposable method to shed light on flood dynamics and allow inter annual and inter lake comparisons of water availability. In the Merguellil upper catchment, in Central Tunisia, results reveal the significant droughts affecting over 80% of reservoirs, confirming the need for small reservoirs to maintain a supplementary irrigation objective only. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
Show Figures

Graphical abstract

Open AccessArticle
A Monitoring Network to Map and Assess Landslide Activity in a Highly Anthropized Area
Geosciences 2016, 6(3), 40; https://doi.org/10.3390/geosciences6030040 - 13 Sep 2016
Cited by 4
Abstract
Mapping landslide activity in a highly anthropized area entails specific problems. The integration of different monitoring techniques in order to measure the displacements rate within the slope is mandatory. We describe our activity for the Mortisa landslide which is located on the western [...] Read more.
Mapping landslide activity in a highly anthropized area entails specific problems. The integration of different monitoring techniques in order to measure the displacements rate within the slope is mandatory. We describe our activity for the Mortisa landslide which is located on the western flank of the Cortina d’Ampezzo valley (northeastern Italy) in a highly anthropized area in the heart of the Dolomites, a UNESCO world heritage site. The mass movement threatens some houses, an important national road, and part of the area that will be the venue for the upcoming 2021 Alpine Skiing World Championship. The hazardous context along with its prestigious location makes the construction of new settlements and infrastructure very challenging. Owing to that, precise mapping and assessment of the activity of the Mortisa landslide is extremely important. To achieve this task, multitemporal aerial photo interpretation, A-DInSAR analysis, Global Navigation Satellite System (GNSS) surveys, and inclinometric measurements were performed. Through the integration of the monitoring data and geomorphological interpretation, a hazard map of the Mortisa area was produced with the intent to assist the local authorities in the definition of the new urban development plan. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
Show Figures

Graphical abstract

Open AccessArticle
Using SPOT and Aerial False-Color Infrared (fCIR) Imagery to Verify Floodplain Model Results in West Central Florida
Geosciences 2016, 6(2), 24; https://doi.org/10.3390/geosciences6020024 - 27 Apr 2016
Cited by 2
Abstract
Tropical Storm Debby brought severe flooding to portions of southwestern Florida during the summer of 2012. Remotely-sensed images were collected to document the flooding and test the results of Hydrologic and Hydraulic (H & H) storm water models constructed by the Southwest Florida [...] Read more.
Tropical Storm Debby brought severe flooding to portions of southwestern Florida during the summer of 2012. Remotely-sensed images were collected to document the flooding and test the results of Hydrologic and Hydraulic (H & H) storm water models constructed by the Southwest Florida Water Management District (SWFWMD). One image, a satellite, multi-band SPOT image was provided to the SWFWMD by the Federal Emergency Management Agency (FEMA). This image was collected within 48 h of the storm event. The SWFWMD also contracted for a very high resolution (60 cm Ground Sample Distance (GSD)) fCIR image to be captured for selected watersheds in Citrus, Hernando and Pasco counties, the areas most impacted by the flooding. Modeled floodplain results were compared to remotely-sensed images that were georeferenced and analyzed using remote sensing techniques. The higher resolution fCIR images more clearly identified flooding for better comparison with modeled results. Although the fCIR images, which were collected three to four days after the storm event, under predicted the overall extent of the modeled floodplain, as the images could not confirm the presence of flooding in areas obscured by dense vegetation, they did consistently confirm both the location and shape of flooding simulated by the model. By using image analysis methods on the Near-Infrared (NIR) band of the fCIR image in conjunction with the Digital Elevation Model (DEM), however, it was possible to identify the extent of flooding in those obscured areas. Field surveys of high water elevations indicated that many locations had receded within hours of the storm event, limiting the ability of the fCIR image from capturing peak flood level in all areas. Overall, these remotely-sensed images provided a good validation of predicted flood levels for a design storm of the magnitude of Tropical Storm Debby. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
Show Figures

Figure 1

Open AccessArticle
GIS and Optimisation: Potential Benefits for Emergency Facility Location in Humanitarian Logistics
Geosciences 2016, 6(2), 18; https://doi.org/10.3390/geosciences6020018 - 29 Mar 2016
Cited by 8
Abstract
Floods are one of the most dangerous and common disasters worldwide, and these disasters are closely linked to the geography of the affected area. As a result, several papers in the academic field of humanitarian logistics have incorporated the use of Geographical Information [...] Read more.
Floods are one of the most dangerous and common disasters worldwide, and these disasters are closely linked to the geography of the affected area. As a result, several papers in the academic field of humanitarian logistics have incorporated the use of Geographical Information Systems (GIS) for disaster management. However, most of the contributions in the literature are using these systems for network analysis and display, with just a few papers exploiting the capabilities of GIS to improve planning and preparedness. To show the capabilities of GIS for disaster management, this paper uses raster GIS to analyse potential flooding scenarios and provide input to an optimisation model. The combination is applied to two real-world floods in Mexico to evaluate the value of incorporating GIS for disaster planning. The results provide evidence that including GIS analysis for a decision-making tool in disaster management can improve the outcome of disaster operations by reducing the number of facilities used at risk of flooding. Empirical results imply the importance of the integration of advanced remote sensing images and GIS for future systems in humanitarian logistics. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
Show Figures

Figure 1

Review

Jump to: Editorial, Research

Open AccessReview
Getting Ahead of the Wildfire Problem: Quantifying and Mapping Management Challenges and Opportunities
Geosciences 2016, 6(3), 35; https://doi.org/10.3390/geosciences6030035 - 29 Jul 2016
Cited by 17
Abstract
Wildfire is a global phenomenon that plays a vital role in regulating and maintaining many natural and human-influenced ecosystems but that also poses considerable risks to human populations and infrastructure. Fire managers are charged with balancing the short-term protection of human assets sensitive [...] Read more.
Wildfire is a global phenomenon that plays a vital role in regulating and maintaining many natural and human-influenced ecosystems but that also poses considerable risks to human populations and infrastructure. Fire managers are charged with balancing the short-term protection of human assets sensitive to fire exposure against the potential long-term benefits that wildfires can provide to natural systems and wildlife populations. The compressed decision timeframes imposed on fire managers during an incident are often insufficient to fully assess a range of fire management options and their respective implications for public and fire responder safety, attainment of land and resource objectives, and future trajectories of hazard and risk. This paper reviews the role of GIS-based assessment and planning to support operational wildfire management decisions, with a focus on recent and emerging research that pre-identifies anthropogenic and biophysical landscape features that can be leveraged to increase the safety and effectiveness of wildfire management operations. We use a case study from the United States to illustrate the development and application of tools that draw from research generated by the global fire management community. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
Show Figures

Figure 1

Back to TopTop