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Multi-Hazard Spatial Modelling and Mapping

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

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 28638

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Dr. Hamid Reza Pourghasemi

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Guest Editor

Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran
Interests: landslide modeling; natural and man-made hazards; spatial modeling in GIS and R; machine learning techniques; multi-hazard modeling; remote sensing applications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. John P. Tiefenbacher

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Guest Editor

Department of Geography, Texas State University-San Marcos, 601 University Dr. San Marcos, TX 78666, USA
Interests: environmental geography; hazards and disasters; air quality; environmental problems of the US–Mexico borderlands; human dimensions of wildlife; states of the former Soviet Union; geography of viniculture; historical environmental geography; genealogy and GIS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural hazards are extreme processes or conditions in nature that yield negative impacts on human lives and properties. Reports by CRED have indicated that nearly 22.3 million people were killed by extreme events between 1900 and 2006—an average of 208,000 people annually. The evaluation of the susceptibility to hazards, their probabilities of occurrence, and likelihood of exposures of people and property is vital. The analysis of individual or multiple hazards may guide integrated management. Multi-hazard management focuses on reducing risk and mitigating exposure to events like landslides, floods, forest fires, land subsidence, avalanches, drought, earthquakes, cyclonic storms, and severe erosion. This term stresses the analysis of a combination of hazards, but in reality, all decisions are made in such a context in areas that are susceptible to these events. There are many tools (GIS, remote sensing, machine learning, meta-heuristics, multi-criteria decision-making, etc.) that can be used to support hazard management. Combinations of these can increase understanding of complex problems and may also help spawn new theories to study, explain, and manage hazards and reduce disasters. This Issue focuses on multi-hazard assessments using new methods and technologies that employ GIS, remote sensing, modeling, and artificial intelligence tools and techniques.

Dr. Hamid Reza Pourghasemi
Prof. Dr. John P. Tiefenbacher
Guest Editors

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Keywords

multi-hazard
natural disaster
hazard and risk mapping
climate change
drought
machine learning
meta-heuristic
decision support systems
geospatial modeling
spatial analysis
UAV
MCDM

Published Papers (9 papers)

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Research

19 pages, 1564 KiB

Open AccessArticle

Climate Change and Vulnerability: The Case of MENA Countries

by Razieh Namdar, Ezatollah Karami and Marzieh Keshavarz

ISPRS Int. J. Geo-Inf. 2021, 10(11), 794; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10110794 - 22 Nov 2021

Cited by 23 | Viewed by 5248

Abstract

Climate is changing and mitigation of the corresponding impacts requires assessment of vulnerability and adaptation building. This issue is particularly important in Middle East and North Africa (MENA), which is recognized as one of the most water scarce regions of the world and vulnerable to climate change. Therefore, the objective of this study was an assessment of the different sectors’ vulnerability as well as the overall vulnerability of the MENA countries to climate change. The Notre Dame-Global Adaptation Index (ND-GAIN) was used to investigate climate change vulnerability. Cluster analysis revealed the very high, high, medium and low levels of vulnerability for the MENA countries by distinguishing their extent of exposure, sensitivity and adaptive capacity. Further results indicated that the MENA countries have an acceptable status of infrastructure and habitat, tolerable health and ecosystem statuses, and inappropriate water and food conditions. Water shortage is also a serious problem in this region, to the extent that it is often assumed that water shortage is the root cause of all other types of vulnerability in MENA. However, the obtained results do not support this assumption. These findings provide insight about the adaptation challenges that should be faced and the choices that should be made in response to climate change, in MENA. Full article

(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)

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21 pages, 3232 KiB

Open AccessArticle

Relationships between Peri-Urbanization Processes and Multi-Hazard Increases: Compared Diachronic Analysis in Basins of the Mediterranean Coast

by Antonio Gallegos Reina and María Jesús Perles Roselló

ISPRS Int. J. Geo-Inf. 2021, 10(11), 759; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10110759 - 10 Nov 2021

Cited by 4 | Viewed by 1524

Abstract

This paper analyzes the relationships between the peri-urbanization process in the surroundings of cities and the increase in the synergistic dangers of flooding and water erosion. An analysis and an evaluation of the conditions causing the flooding in peri-urban basins are carried out, comparing the conditions before and after the peri-urbanization process. For this purpose, a diachronic analysis of the morphological and functional conditions of the territory that conditions flooding and associated dangers is provided. The conditions for the generation of runoff, the incorporation of solids into the flood flow, and the characteristics of urban planning are evaluated in 1956 (date before the peri-urbanization process) and 2010 (the peak of the urbanization process in the area) in order to analyze the changes in the land use model and their consequences on the increase in risk. The study is applied to four river basins (44 km² in total) with varied land use models, in order to collect representative scenarios of the peri-urban coastal basins of the Spanish Mediterranean region. The results show that the risk factors that undergo the most significant changes are the runoff threshold, the vegetation cover, and the soil structure. It is concluded that peri-urbanization constitutes a territorial risk-causing process, and attention is drawn to the convenience of going beyond the sectoral approach in the study of hazards, coming to understand them as a multi-hazard process in which causes have a direct relationship with the underlying territorial model. Full article

(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)

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27 pages, 6192 KiB

Open AccessArticle

A Fully Automatic, Interpretable and Adaptive Machine Learning Approach to Map Burned Area from Remote Sensing

by Daniela Stroppiana, Gloria Bordogna, Matteo Sali, Mirco Boschetti, Giovanna Sona and Pietro Alessandro Brivio

ISPRS Int. J. Geo-Inf. 2021, 10(8), 546; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10080546 - 13 Aug 2021

Cited by 8 | Viewed by 2566

Abstract

The paper proposes a fully automatic algorithm approach to map burned areas from remote sensing characterized by human interpretable mapping criteria and explainable results. This approach is partially knowledge-driven and partially data-driven. It exploits active fire points to train the fusion function of factors deemed influential in determining the evidence of burned conditions from reflectance values of multispectral Sentinel-2 (S2) data. The fusion function is used to compute a map of seeds (burned pixels) that are adaptively expanded by applying a Region Growing (RG) algorithm to generate the final burned area map. The fusion function is an Ordered Weighted Averaging (OWA) operator, learnt through the application of a machine learning (ML) algorithm from a set of highly reliable fire points. Its semantics are characterized by two measures, the degrees of pessimism/optimism and democracy/monarchy. The former allows the prediction of the results of the fusion as affected by more false positives (commission errors) than false negatives (omission errors) in the case of pessimism, or vice versa; the latter foresees if there are only a few highly influential factors or many low influential ones that determine the result. The prediction on the degree of pessimism/optimism allows the expansion of the seeds to be appropriately tuned by selecting the most suited growing layer for the RG algorithm thus adapting the algorithm to the context. The paper illustrates the application of the automatic method in four study areas in southern Europe to map burned areas for the 2017 fire season. Thematic accuracy at each site was assessed by comparison to reference perimeters to prove the adaptability of the approach to the context; estimated average accuracy metrics are omission error = 0.057, commission error = 0.068, Dice coefficient = 0.94 and relative bias = 0.0046. Full article

(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)

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28 pages, 13905 KiB

Open AccessArticle

Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing

by A’kif Al-Fugara, Ali Nouh Mabdeh, Mohammad Ahmadlou, Hamid Reza Pourghasemi, Rida Al-Adamat, Biswajeet Pradhan and Abdel Rahman Al-Shabeeb

ISPRS Int. J. Geo-Inf. 2021, 10(6), 382; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060382 - 04 Jun 2021

Cited by 23 | Viewed by 2824

Abstract

Fires are one of the most destructive forces in natural ecosystems. This study aims to develop and compare four hybrid models using two well-known machine learning models, support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS), as well as two meta-heuristic models, the whale optimization algorithm (WOA) and simulated annealing (SA) to map wildland fires in Jerash Province, Jordan. For modeling, 109 fire locations were used along with 14 relevant factors, including elevation, slope, aspect, land use, normalized difference vegetation index (NDVI), rainfall, temperature, wind speed, solar radiation, soil texture, topographic wetness index (TWI), distance to drainage, and population density, as the variables affecting the fire occurrence. The area under the receiver operating characteristic (AUROC) was used to evaluate the accuracy of the models. The findings indicated that SVR-based hybrid models yielded a higher AUROC value (0.965 and 0.949) than the ANFIS-based hybrid models (0.904 and 0.894, respectively). Wildland fire susceptibility maps can play a major role in shaping firefighting tactics. Full article

(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)

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26 pages, 36286 KiB

Open AccessArticle

Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models

by Hilal Ahmad, Chen Ningsheng, Mahfuzur Rahman, Md Monirul Islam, Hamid Reza Pourghasemi, Syed Fahad Hussain, Jules Maurice Habumugisha, Enlong Liu, Han Zheng, Huayong Ni and Ashraf Dewan

ISPRS Int. J. Geo-Inf. 2021, 10(5), 315; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050315 - 07 May 2021

Cited by 22 | Viewed by 3866

Abstract

The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale. Full article

(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)

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19 pages, 3769 KiB

Open AccessArticle

The Road Map to Classify the Potential Risk of Wind Erosion

by Hana Středová, Jana Podhrázská, Filip Chuchma, Tomáš Středa, Josef Kučera, Petra Fukalová and Martin Blecha

ISPRS Int. J. Geo-Inf. 2021, 10(4), 269; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040269 - 20 Apr 2021

Cited by 5 | Viewed by 2510

Abstract

Environmental degradation, for example, by wind erosion, is a serious global problem. Despite the enormous research on this topic, complex methods considering all relevant factors remain unpublished. The main intent of our paper is to develop a methodological road map to identify key soil–climatic conditions that make soil vulnerable to wind and demonstrate the road map in a case study using a relevant data source. Potential wind erosion (PWE) results from soil erosivity and climate erosivity. Soil erosivity directly reflects the wind-erodible fraction and indirectly reflects the soil-crust factor, vegetation-cover factor and surface-roughness factor. The climatic erosivity directly reflects the drought in the surface layer, erosive wind occurrence and clay soil-specific winter regime, making these soils vulnerable to wind erosion. The novelty of our method lies in the following: (1) all relevant soil–climatic data of wind erosion are combined; (2) different soil types “sand” and “clay” are evaluated simultaneously with respect to the different mechanisms of wind erosion; and (3) a methodological road map enables its application for various conditions. Based on our method, it is possible to set threshold values that, when exceeded, trigger landscape adjustments, more detailed in situ measurements or indicate the need for specific management. Full article

(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)

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25 pages, 8765 KiB

Open AccessArticle

Assessment of Rainfall-Induced Landslide Distribution Based on Land Disturbance in Southern Taiwan

by Chih-Ming Tseng, Yie-Ruey Chen, Chwen-Ming Chang, Yung-Sheng Chue and Shun-Chieh Hsieh

ISPRS Int. J. Geo-Inf. 2021, 10(4), 209; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040209 - 01 Apr 2021

Cited by 1 | Viewed by 1799

Abstract

This study explores the impact of rainfall on the followed-up landslides after a severe typhoon and the relationship between various rainfall events and the occurrence, scale, and regional characteristics of the landslides, including second landslides. Moreover, the influence of land disturbance was evaluated. The genetic adaptive neural network was used in combination with the texture analysis of the geographic information system for satellite image classification and interpretation to analyze land-use change and retrieve disaster records and surface information after five rainfall events from Typhoon Morakot (2009) to Typhoon Nanmadol (2011). The results revealed that except for extreme Morakot rains, the greater the degree of slope disturbance after rain, the larger the exposed slope. Extreme rainfall similar to Morakot strikes may have a greater impact on the bare land area than on slope disturbance. Moreover, the relationship between the bare land area and the index of land disturbance condition (I_LDC) is positive, and the ratio of the bare land area to the quantity of bare land after each rainfall increases with the I_LDC. With higher effective accumulative rainfall on the slope in the study area or greater slope disturbance, the landslide area at the second landslide point tended to increase. Full article

(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)

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18 pages, 8777 KiB

Open AccessArticle

Landslide Susceptibility Assessment Using an Optimized Group Method of Data Handling Model

by Azam Kadirhodjaev, Fatemeh Rezaie, Moung-Jin Lee and Saro Lee

ISPRS Int. J. Geo-Inf. 2020, 9(10), 566; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100566 - 29 Sep 2020

Cited by 20 | Viewed by 2300

Abstract

Landslides can cause considerable loss of life and damage to property, and are among the most frequent natural hazards worldwide. One of the most fundamental and simple approaches to reduce damage is to prepare a landslide hazard map. Accurate prediction of areas highly prone to future landslides is important for decision-making. In the present study, for the first time, the group method of data handling (GMDH) was used to generate landslide susceptibility map for a specific region in Uzbekistan. First, 210 landslide locations were identified by field survey and then divided randomly into model training and model validation datasets (70% and 30%, respectively). Data on nine conditioning factors, i.e., altitude, slope, aspect, topographic wetness index (TWI), length of slope (LS), valley depth, distance from roads, distance from rivers, and geology, were collected. Finally, the maps were validated using the testing dataset and receiver operating characteristic (ROC) curve analysis. The findings showed that the “optimized” GMDH model (i.e., using the gray wolf optimizer [GWO]) performed better than the standalone GMDH model, during both the training and testing phase. The accuracy of the GMDH–GWO model in the training and testing phases was 94% and 90%, compared to 85% and 82%, respectively, for the standard GMDH model. According to the GMDH–GWO model, the study area included very low, low, moderate, high, and very high landslide susceptibility areas, with proportions of 14.89%, 10.57%, 15.00%, 35.12%, and 24.43%, respectively. Full article

(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)

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31 pages, 18687 KiB

Open AccessEditor’s ChoiceArticle

An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping

by Omid Ghorbanzadeh, Khalil Didehban, Hamid Rasouli, Khalil Valizadeh Kamran, Bakhtiar Feizizadeh and Thomas Blaschke

ISPRS Int. J. Geo-Inf. 2020, 9(10), 561; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100561 - 27 Sep 2020

Cited by 13 | Viewed by 3753

Abstract

In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering eleven conditioning factors of soil type, slope angle, distance to roads, distance to rivers, rainfall, normalised difference vegetation index (NDVI), aspect, altitude, distance to faults, land cover, and lithology. A fuzzy analytic hierarchy process (FAHP) also was used for the susceptibility mapping using expert knowledge. Then, we integrated the data-driven model of the FR with the knowledge-based model of the FAHP to reduce the associated uncertainty in each approach. We validated our resulting landslide inventory map based on 30% of the global positioning system (GPS) points of an extensive field survey in the study area. The remaining 70% of the GPS points were used to validate the performance of the applied models and the resulting landslide susceptibility maps using the receiver operating characteristic (ROC) curves. Our resulting landslide inventory map got a precision of 94% and the AUCs (area under the curve) of the susceptibility maps showed 83%, 89%, and 96% for the F-AHP, FR, and the integrated model, respectively. The introduced methodology in this study can be used in the application of remote sensing data for landslide inventory and susceptibility mapping in other areas where earthquakes are considered as the main landslide-triggered factor. Full article

(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)

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