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Geosciences
Special Issues
Mapping and Assessing Natural Disasters Using GIScience Technologies
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Mapping and Assessing Natural Disasters Using GIScience Technologies

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Natural Hazards".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 19687

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
Interests: hyperspectral data analysis; remote sensing; invasive species mapping and monitoring; land cover change detection; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The overall goal of this session is to explore and evaluate the potential of application of advanced GIScience technologies, such as remote sensing (RS), GIS, GPS, and spatial statistics in mapping, modeling, monitoring, and assessing various natural disasters. Natural disasters, such as floods, wildfires, volcanic eruptions, earthquakes, tsunamis, and 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 will provide an outlet for the state of the art of utilizing advanced GIScience technologies to map, model, monitor, predict, and assess natural disasters. This Special Issue invites contributions that cover but are not limited to the following areas:

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

Prof. 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 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. 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 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Geospatial technology
  • Remote sensing
  • GIS
  • GPS
  • Spatial statistics
  • Wildfire
  • Hotspot detection
  • Burn scar mapping
  • Landslide
  • Earthquake/tsunamis
  • Hurricane/tornado
  • Volcanic eruptions

Published Papers (4 papers)

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Research

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17 pages, 4512 KiB  
Article
Comparing the Accuracy of sUAS Navigation, Image Co-Registration and CNN-Based Damage Detection between Traditional and Repeat Station Imaging
by Andrew C. Loerch, Douglas A. Stow, Lloyd L. Coulter, Atsushi Nara and James Frew
Geosciences 2022, 12(11), 401; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences12110401 - 28 Oct 2022
Cited by 2 | Viewed by 1245
Abstract
The application of ultra-high spatial resolution imagery from small unpiloted aerial systems (sUAS) can provide valuable information about the status of built infrastructure following natural disasters. This study employs three methods for improving the value of sUAS imagery: (1) repeating the positioning of [...] Read more.
The application of ultra-high spatial resolution imagery from small unpiloted aerial systems (sUAS) can provide valuable information about the status of built infrastructure following natural disasters. This study employs three methods for improving the value of sUAS imagery: (1) repeating the positioning of image stations over time using a bi-temporal imaging approach called repeat station imaging (RSI) (compared here against traditional (non-RSI) imaging), (2) co-registration of bi-temporal image pairs, and (3) damage detection using Mask R-CNN, a convolutional neural network (CNN) algorithm applied to co-registered image pairs. Infrastructure features included roads, buildings, and bridges, with simulated cracks representing damage. The accuracies of platform navigation and camera station positioning, image co-registration, and resultant Mask R-CNN damage detection were assessed for image pairs, derived with RSI and non-RSI acquisition. In all cases, the RSI approach yielded the highest accuracies, with repeated sUAS navigation accuracy within 0.16 m mean absolute error (MAE) horizontally and vertically, image co-registration accuracy of 2.2 pixels MAE, and damage detection accuracy of 83.7% mean intersection over union. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using GIScience Technologies)
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21 pages, 4638 KiB  
Article
Characterizing Spatial Patterns of Amazon Rainforest Wildfires and Driving Factors by Using Remote Sensing and GIS Geospatial Technologies
by Cong Ma, Ruiliang Pu, Joni Downs and He Jin
Geosciences 2022, 12(6), 237; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences12060237 - 05 Jun 2022
Cited by 7 | Viewed by 3470
Abstract
Known as the “lung of the planet”, the Amazon rainforest produces more than 20% of the Earth’s oxygen. Once a carbon pool for mitigating climate change, the Brazilian Amazônia Biome recently has become a significant carbon emitter due to increasingly frequent wildfires. Therefore, [...] Read more.
Known as the “lung of the planet”, the Amazon rainforest produces more than 20% of the Earth’s oxygen. Once a carbon pool for mitigating climate change, the Brazilian Amazônia Biome recently has become a significant carbon emitter due to increasingly frequent wildfires. Therefore, it is of crucial importance for authorities to understand wildfire dynamics to manage them safely and effectively. This study incorporated remote sensing and spatial statistics to study both the spatial distribution of wildfires during 2019 and their relationships to 15 environmental and anthropogenic factors. First, broad-scale spatial patterns of wildfire occurrence were explored using kernel density estimation, Moran’s I, Getis-Ord Gi*, and optimized hot spot analysis (OHSA). Second, the relationships between wildfire occurrence and the environmental and anthropogenic factors were explored using several regression models, including Ordinary Least Squares (OLS), global (quasi) Poisson, Geographically-weighted Gaussian Regression (GWGR), and Geographically-weighted Poisson Regression (GWPR). The spatial analysis results indicate that wildfires exhibited pronounced regional differences in spatial patterns in the vast and heterogeneous territory of the Amazônia Biome. The GWPR model outperformed the other regression models and explained the distribution and frequency of wildfires in the Amazônia Biome as a function of topographic, meteorologic, and environmental variables. Environmental factors like elevation, slope, relative humidity, and temperature were significant factors in explaining fire frequency in localized hotspots, while factors related to deforestation (forest loss, forest fragmentation measures, agriculture) explained wildfire activity over much of the region. Therefore, this study could improve a comprehensive study on, and understanding of, wildfire patterns and spatial variation in the target areas to support agencies as they prepare and plan for wildfire and land management activities in the Amazônia Biome. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using GIScience Technologies)
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23 pages, 28017 KiB  
Article
Spatial Assessment of Wildfires Susceptibility in Santa Cruz (Bolivia) Using Random Forest
by Marcela Bustillo Sánchez, Marj Tonini, Anna Mapelli and Paolo Fiorucci
Geosciences 2021, 11(5), 224; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences11050224 - 20 May 2021
Cited by 21 | Viewed by 4454
Abstract
Wildfires are expected to increase in the near future, mainly because of climate changes and land use management. One of the most vulnerable areas in the world is the forest in central-South America, including Bolivia. Despite that this country is highly prone to [...] Read more.
Wildfires are expected to increase in the near future, mainly because of climate changes and land use management. One of the most vulnerable areas in the world is the forest in central-South America, including Bolivia. Despite that this country is highly prone to wildfires, literature is rather limited here. To fill this gap, we implemented a dataset including the burned area that occurred in the department of Santa Cruz in the period of 2010–2019, and the digital spatial data describing the predisposing factors (i.e., topography, land cover, ecoregions). The main goal was to develop a model, based on Random Forest, in which probabilistic outputs allowed to elaborate wildfires susceptibility maps. The overall accuracy was finally estimated by using 5-fold cross-validation. In addition, the last three years of observations acted as the testing dataset, allowing to evaluate the predictive performance of the model. The quantitative assessment of the variables revealed that “flooded savanna” and “shrub or herbaceous cover, flooded, fresh/saline/brakish water” are respectively the ecoregions and land cover classes with the highest probability of predicting wildfires. This study contributes to the development and validation of an innovative mapping tool for fire risk assessment, implementable at a regional scale in different areas of the globe. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using GIScience Technologies)
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Review

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31 pages, 6469 KiB  
Review
Fault-Based Geological Lineaments Extraction Using Remote Sensing and GIS—A Review
by Hemayatullah Ahmadi and Emrah Pekkan
Geosciences 2021, 11(5), 183; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences11050183 - 24 Apr 2021
Cited by 43 | Viewed by 9343
Abstract
Geological lineaments are the earth’s linear features indicating significant tectonic units in the crust associated with the formation of minerals, active faults, groundwater controls, earthquakes, and geomorphology. This study aims to provide a systematic review of the state-of-the-art remote sensing techniques and data [...] Read more.
Geological lineaments are the earth’s linear features indicating significant tectonic units in the crust associated with the formation of minerals, active faults, groundwater controls, earthquakes, and geomorphology. This study aims to provide a systematic review of the state-of-the-art remote sensing techniques and data sets employed for geological lineament analysis. The critical challenges of this approach and the diverse data verification and validation techniques will be presented. Thus, this review spanned academic articles published since 1975, including expert reports and theses. Landsat series, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Sentinel 2 are the prevalent optical remote sensing data widely used for lineament detection. Moreover, Shuttle Radar Topography Mission (SRTM) derived Digital Elevation Model (DEM), Synthetic-aperture radar (SAR), Interferometric synthetic aperture radar (InSAR), and Sentinel 1 are the typical radar remotely sensed data which are widely used for the detection of geological lineaments. The geological lineaments acquired via GIS techniques are not consistent even though a variety of manual, semi-automated, and automated techniques are applied. Therefore, a single method may not provide an accurate lineament distribution and may include artifacts requiring integration of multiple algorithms, e.g., manual and automated algorithms. Full article
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using GIScience Technologies)
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