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Advances in GIS and Remote Sensing Applications in Natural Hazards
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Advances in GIS and Remote Sensing Applications in Natural Hazards

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (15 May 2025) | Viewed by 10192

Special Issue Editors

Dr. Weibo Liu

E-Mail Website
Guest Editor
Department of Geosciences, Florida Atlantic University, Boca Raton, FL, USA
Interests: GIScience; geocomputational methods in GIS; GIS and RS applications in natural hazards, urbanization, and coastal environment
Dr. Yi Qiang

E-Mail Website
Guest Editor
School of Geosciences, University of South Florida, Tampa, FL 33620, USA
Interests: GIScience; spatial data science; disaster resilience; visual analytics; geocomputation
Special Issues, Collections and Topics in MDPI journals
Dr. Qunying Huang

E-Mail Website
Guest Editor
Department of Geography, University of Wisconsin-Madison, Madison, WI 53706-1491, USA
Interests: spatial big data analytics and mining; cloud computing, distributed computing, and high-performance computing; remote sensing; natural hazards
Special Issues, Collections and Topics in MDPI journals
Dr. Manzhu Yu

E-Mail Website
Guest Editor
Department of Geography, Penn State University, State College, PA 16801, USA
Interests: GIScience; spatiotemporal analysis; natural hazards/extreme weather events; spatial data science and deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing global population and the impact of climate change have led to a rise in natural hazards, such as droughts, heat waves, storm surges, hurricanes, wildfires, and flooding. These events can result in the loss of life, property damage, socio-economic disruption, and environmental damage globally. Natural hazard modeling and analysis is the foundation of natural disaster risk management, assessment, and policymaking. Understanding the impacts of natural disasters often involves a broad and interdisciplinary research approach. The development of recent technologies, such as Geographic Information System (GIS), Remote Sensing (RS), and artificial intelligence (AI) / machine learning (ML) provides the opportunity to better monitor, model, and quantify natural hazards. The use of the varied spatial, temporal, and spectral resolution of data such as satellite images, social media feeds, and real-time sensor measurements also help better model and understand the spatiotemporal patterns and socio-economic impacts of natural hazards.

This Special Issue seeks original contributions on the advanced applications of GIS, RS, and other geospatial tools and technologies in understanding various dimensions of natural hazards through new theory, modeling, data products, and robust methodologies. The topics may include but are not limited to:

  • Natural hazard modeling;
  • Disaster mapping and damage assessment;
  • Hazard and vulnerability assessments;
  • Risk mapping and quantifications;
  • Applications of GIS, RS, AI, and ML;
  • Droughts, heat waves, storm surges, and coastal environments;
  • Multi-scale modeling and real-time data application;
  • Multi-source multimodal data fusion for natural hazard applications.

Dr. Weibo Liu
Dr. Yi Qiang
Dr. Qunying Huang
Dr. Manzhu Yu
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • GIS
  • natural hazards
  • remote sensing
  • vulnerability
  • machine learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

15 pages, 14788 KiB  
Article
The DEM Registration Method Without Ground Control Points for Landslide Deformation Monitoring
by Yunchuan Wang, Jia Li, Ping Duan, Rui Wang and Xinrui Yu
Remote Sens. 2024, 16(22), 4236; https://doi.org/10.3390/rs16224236 - 14 Nov 2024
Viewed by 982
Abstract
Landslides are geological disasters that are harmful to both humans and society. Digital elevation model (DEM) time series data are usually used to monitor dynamic changes or surface damage. To solve the problem of landslide deformation monitoring without ground control points (GCPs), a [...] Read more.
Landslides are geological disasters that are harmful to both humans and society. Digital elevation model (DEM) time series data are usually used to monitor dynamic changes or surface damage. To solve the problem of landslide deformation monitoring without ground control points (GCPs), a multidimensional feature-based coregistration method (MFBR) was studied to achieve accurate registration of multitemporal DEMs without GCPs and obtain landslide deformation information. The method first derives the elevation information of the DEM into image pixel information, and the feature points are extracted on the basis of the image. The initial plane position registration of the DEM is implemented. Therefore, the expected maximum algorithm is applied to calculate the stable regions that have not changed between multitemporal DEMs and to perform accurate registrations. Finally, the shape variables are calculated by constructing a DEM differential model. The method was evaluated using simulated data and data from two real landslide cases, and the experimental results revealed that the registration accuracies of the three datasets were 0.963 m, 0.368 m, and 2.459 m, which are 92%, 50%, and 24% better than the 12.189 m, 0.745 m, and 3.258 m accuracies of the iterative closest-point algorithm, respectively. Compared with the GCP-based method, the MFBR method can achieve 70% deformation acquisition capability, which indicates that the MFBR method has better applicability in the field of landslide monitoring. This study provides an idea for landslide deformation monitoring without GCPs and is helpful for further understanding the state and behavior of landslides. Full article
(This article belongs to the Special Issue Advances in GIS and Remote Sensing Applications in Natural Hazards)
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25 pages, 27207 KiB  
Article
From Data to Decision: Interpretable Machine Learning for Predicting Flood Susceptibility in Gdańsk, Poland
by Khansa Gulshad, Andaleeb Yaseen and Michał Szydłowski
Remote Sens. 2024, 16(20), 3902; https://doi.org/10.3390/rs16203902 - 20 Oct 2024
Cited by 1 | Viewed by 2118
Abstract
Flood susceptibility prediction is complex due to the multifaceted interactions among hydrological, meteorological, and urbanisation factors, further exacerbated by climate change. This study addresses these complexities by investigating flood susceptibility in rapidly urbanising regions prone to extreme weather events, focusing on Gdańsk, Poland. [...] Read more.
Flood susceptibility prediction is complex due to the multifaceted interactions among hydrological, meteorological, and urbanisation factors, further exacerbated by climate change. This study addresses these complexities by investigating flood susceptibility in rapidly urbanising regions prone to extreme weather events, focusing on Gdańsk, Poland. Three popular ML techniques, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN), were evaluated for handling complex, nonlinear data using a dataset of 265 urban flood episodes. An ensemble filter feature selection (EFFS) approach was introduced to overcome the single-method feature selection limitations, optimising the selection of factors contributing to flood susceptibility. Additionally, the study incorporates explainable artificial intelligence (XAI), namely, the Shapley Additive exPlanations (SHAP) model, to enhance the transparency and interpretability of the modelling results. The models’ performance was evaluated using various statistical measures on a testing dataset. The ANN model demonstrated a superior performance, outperforming the RF and the SVM. SHAP analysis identified rainwater collectors, land surface temperature (LST), digital elevation model (DEM), soil, river buffers, and normalized difference vegetation index (NDVI) as contributors to flood susceptibility, making them more understandable and actionable for stakeholders. The findings highlight the need for tailored flood management strategies, offering a novel approach to urban flood forecasting that emphasises predictive power and model explainability. Full article
(This article belongs to the Special Issue Advances in GIS and Remote Sensing Applications in Natural Hazards)
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18 pages, 10210 KiB  
Article
A Disparate Disaster: Spatial Patterns of Building Damage Caused by Hurricane Ian and Associated Socio-Economic Factors
by Md Zakaria Salim, Yi Qiang, Barnali Dixon and Jennifer Collins
Remote Sens. 2024, 16(20), 3792; https://doi.org/10.3390/rs16203792 - 12 Oct 2024
Cited by 2 | Viewed by 1855
Abstract
The literature shows that communities under different socio-economic conditions suffer different levels of damage in disasters. In addition to the physical intensity of hazards, such differences are also related to the varying abilities of communities to prepare for and respond to disasters. This [...] Read more.
The literature shows that communities under different socio-economic conditions suffer different levels of damage in disasters. In addition to the physical intensity of hazards, such differences are also related to the varying abilities of communities to prepare for and respond to disasters. This study analyzes the spatial patterns of building damage in Hurricane Ian in 2022 and investigates the socio-economic disparities related to the damage. Specifically, this study employs NASA’s Damage Proxy Map (DPM2) to analyze spatial patterns of building damage caused by the hurricane. Then, it uses statistical analysis to assess the relationships between building damage and hurricane intensity, building conditions, and socio-economic variables at the building and census tract levels. Furthermore, the study applies geographically weighted regression (GWR) to examine the spatial variation of the damage factors. The results provide valuable insights into the potential factors related to building damage and the spatial variation in the factors. The results also reveal the uneven distribution of building damage among different population groups, implying socio-economic inequalities in disaster adaptation and resilience. Moreover, the study provides actionable information for policymakers, emergency responders, and community leaders in formulating strategies to mitigate the impact of future hurricanes by identifying vulnerable communities and population groups. Full article
(This article belongs to the Special Issue Advances in GIS and Remote Sensing Applications in Natural Hazards)
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19 pages, 6164 KiB  
Article
Constructing Rainfall Threshold for Debris Flows of a Defined Hazardous Magnitude
by Yajun Li, Mengyu Wang, Fukang Ma, Jun Zhang, Guowei Li, Xingmin Meng, Guan Chen, Dongxia Yue, Fuyun Guo and Yan Zhao
Remote Sens. 2024, 16(7), 1265; https://doi.org/10.3390/rs16071265 - 3 Apr 2024
Cited by 2 | Viewed by 2035
Abstract
Debris flow can cause damage only when its discharge exceeds the drainage capacity of the prevention engineering. At present, most rainfall thresholds for debris flows mainly focus on the initiation of debris flow and do not adequately consider the magnitude and drainage measures [...] Read more.
Debris flow can cause damage only when its discharge exceeds the drainage capacity of the prevention engineering. At present, most rainfall thresholds for debris flows mainly focus on the initiation of debris flow and do not adequately consider the magnitude and drainage measures of debris flows. These thresholds are likely to initiate numerous warnings that may not be related to hazardous processes. This study proposes a method for calculating the rainfall threshold that is related to a defined level of debris flow magnitude, over which certain damage may be caused. This method is constructed by using the transient rainfall infiltration analysis slope stability model (TRIGRS) and the fluid dynamics process simulation model (MassFlow). We first use the TRIGRS model to analyze slope stability in the study area and obtain the distribution of unstable slopes under different rainfall conditions. Afterward, the MassFlow model is employed to simulate the movement process of unstable slope units and to predict the depositional processes at the mouth of the catchment. Lastly a rainfall threshold is constructed by statistically analyzing the rainfall conditions that cause debris flows flushing out of the given drainage ditch. This method is useful to predict debris flow events of a hazardous magnitude, especially for areas with limited historical observational data. Full article
(This article belongs to the Special Issue Advances in GIS and Remote Sensing Applications in Natural Hazards)
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18 pages, 4164 KiB  
Article
Remote Sensing and GIS in Landslide Management: An Example from the Kravarsko Area, Croatia
by Laszlo Podolszki and Igor Karlović
Remote Sens. 2023, 15(23), 5519; https://doi.org/10.3390/rs15235519 - 27 Nov 2023
Cited by 4 | Viewed by 1899
Abstract
The Kravarsko area is located in a hilly region of northern Croatia, where numerous landslides endanger and damage houses, roads, water systems, and power lines. Nevertheless, natural hazard management plans are practically non-existent. Therefore, during the initial research, a landslide inventory was developed [...] Read more.
The Kravarsko area is located in a hilly region of northern Croatia, where numerous landslides endanger and damage houses, roads, water systems, and power lines. Nevertheless, natural hazard management plans are practically non-existent. Therefore, during the initial research, a landslide inventory was developed for the Kravarsko pilot area based on remote sensing data (high-resolution digital elevation models), and some of the landslides were investigated in detail. However, due to the complexity and vulnerability of the area, additional zoning of landslide-susceptible areas was needed. As a result, a slope gradient map, a map of engineering geological units, and a land-cover map were developed as inputs for the landslide susceptibility map. Additionally, based on the available data and a landslide inventory, a terrain stability map was developed for landslide management. Analysis and map development were performed within a geographical information system environment, and the terrain stability map with key infrastructure data was determined to be the “most user-friendly and practically usable” resource for non-expert users in natural hazard management, for example, the local administration. At the same time, the terrain stability map can easily provide practical information for the local community and population about the expected landslide “risk” depending on the location of infrastructure, estates, or objects of interest or for the purposes of future planning. Full article
(This article belongs to the Special Issue Advances in GIS and Remote Sensing Applications in Natural Hazards)
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