Research

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22 pages, 8294 KiB

Open AccessArticle

Geoinformatics and Machine Learning for Comprehensive Fire Risk Assessment and Management in Peri-Urban Environments: A Building-Block-Level Approach

by Anastasia Yfantidou, Melpomeni Zoka, Nikolaos Stathopoulos, Martha Kokkalidou, Stella Girtsou, Michail-Christos Tsoutsos, Diofantos Hadjimitsis and Charalampos Kontoes

Appl. Sci. 2023, 13(18), 10261; https://doi.org/10.3390/app131810261 - 13 Sep 2023

Viewed by 1475

Abstract

Forest fires can result in loss of life, damage to infrastructure, and adverse environmental impacts. This study showcases an integrated approach for conducting high-detail fire risk assessment and supporting strategic planning and management of fire events in peri-urban areas that are susceptible to [...] Read more.

Forest fires can result in loss of life, damage to infrastructure, and adverse environmental impacts. This study showcases an integrated approach for conducting high-detail fire risk assessment and supporting strategic planning and management of fire events in peri-urban areas that are susceptible to forest fires. The presented methodology encompasses fire hazard modeling, vulnerability and exposure assessment, and in situ observations. Numerous fire hazard scenarios were tested, simulating the spatiotemporal spread of fire events under different wind characteristics. The vulnerability of the studied areas was assessed by combining population data (density and age) and building characteristics, while the exposure parameter employed land value (EUR/m²) as an indicator for qualitatively estimating potential economic effects in the study area. Field campaigns facilitated the identification and recording of critical areas and points, including high-risk buildings and population gathering areas, which subsequently informed the mitigation and fire management planning suggestions. Moreover, field recordings acted as an iterative process for validating and updating the fire risk maps. This research work utilizes state-of-the-art techniques to achieve an analysis of fire risk at a building-block level. Overall, the study presents an applied and end-to-end methodology for effectively addressing forest fire risk in peri-urban areas. Full article

(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)

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17 pages, 10329 KiB

Open AccessArticle

Frequency Ratio Model as Tools for Flood Susceptibility Mapping in Urbanized Areas: A Case Study from Egypt

by Hanaa A. Megahed, Amira M. Abdo, Mohamed A. E. AbdelRahman, Antonio Scopa and Mohammed N. Hegazy

Appl. Sci. 2023, 13(16), 9445; https://doi.org/10.3390/app13169445 - 21 Aug 2023

Cited by 1 | Viewed by 1236

Abstract

The occurrence of flash floods is a natural yet unavoidable occurrence over time. In addition to harming people, property, and resources, it also undermines a country’s economy. This paper attempts to identify areas of flood vulnerability using a frequency ratio approach. The frequency [...] Read more.

The occurrence of flash floods is a natural yet unavoidable occurrence over time. In addition to harming people, property, and resources, it also undermines a country’s economy. This paper attempts to identify areas of flood vulnerability using a frequency ratio approach. The frequency ratio (FR) model was used to produce flood prediction maps for New Cairo City, Egypt. Using field data and remote sensing data, 143 spatial flooded point sites were mapped to build a flood inventory map. The primary driving criteria for flash floods were determined to be elevation, slope, aspect, Land Use Land Cover (LULC), lithology, stream distance, stream density, topographic wetness index (TWI), surface runoff, and terrain ruggedness index (TRI), in that order of importance. A flood susceptibility map (FSM) has been created using the FR model, which combines geographical flooded sites and environmental variables. Our findings from FSM, roughly a fifth of the city is very highly susceptible to flooding (19.32%), while the remaining 40.09% and 13.14% of the study area rank very low and low risk, respectively. The receiver operating characteristic curve (ROC) technique was also used to validate the FSM, and the resulting results showed an area under the curve (AUC) of 90.11%. In conclusion, decision makers can employ models to extract and generate flood risk maps in order to better understand the effects of flash floods and to create alternative measures to prevent this hazard in similar regions. The results of this study will aid planners and decision makers in developing some likely actions to reduce floods vulnerability in this area. Full article

(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)

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23 pages, 31350 KiB

Open AccessArticle

Evaluating the Impact of Seismic Activity on the Slope Stability of the Western Coast of Lefkada Island Using Remote Sensing Techniques, Geographical Information Systems, and Field Data

by Konstantinos G. Nikolakopoulos, Ioannis K. Koukouvelas, Aggeliki Kyriou, Dionysios Apostolopoulos and George Pappas

Appl. Sci. 2023, 13(16), 9434; https://doi.org/10.3390/app13169434 - 20 Aug 2023

Viewed by 1222

Abstract

The current research aims to examine the long-term evolution of the western cliffs of Lefkada Island following the occurrence of the last two strong earthquakes, on 14 August 2003 and 17 November 2015, respectively. Medium resolution satellite data (Landsat) and very high-resolution data [...] Read more.

The current research aims to examine the long-term evolution of the western cliffs of Lefkada Island following the occurrence of the last two strong earthquakes, on 14 August 2003 and 17 November 2015, respectively. Medium resolution satellite data (Landsat) and very high-resolution data (Ikonos, Pleiades, and airphotos) were processed in Google Earth Engine and Erdas imagine software, respectively. The study area covers a 20 km-long region of the western cliffs of Lefkada Island, extending from Egremni beach to the South to Komilio beach to the North. Relief, vegetation, and inclination changes were detected in the ArcGis environment. The results were associated with in situ data provided through the installation of a sediment trap. The analysis of the results proved that seismicity is the main factor that formed the western coastline of Lefkada Island, affecting the integrity of the cliffs. Specifically, large earthquakes cause immediate vegetation and topographic (inclination changes, mass movements) modifications in the western cliffs of the island. Meanwhile, small earthquakes (magnitudes < 4.1) contribute to the cliff’s evolution during the inter-seismic era. The intensity of these aforementioned changes was closely related to the seismic activity that occurred in the vicinity of the study area. In addition, it was found that precipitation and wind do not exert a similar influence on the cliff’s evolution. Full article

(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)

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18 pages, 7026 KiB

Open AccessArticle

Research on Fine Estimation of People Trapped after Earthquake on Single Building Level Based on Multi-Source Data

by Shizhe Xie, Dongping Ming, Jin Yan, Huaining Yang, Ran Liu and Zhi Zhao

Appl. Sci. 2023, 13(9), 5430; https://doi.org/10.3390/app13095430 - 27 Apr 2023

Cited by 1 | Viewed by 1395

Abstract

Risk assessments of people who are trapped are an important basis for scientific and effective emergency rescue after an earthquake. Currently, most models are based on the kilometer grid scale or community scale that gauge the population and extent of the earthquake burial [...] Read more.

Risk assessments of people who are trapped are an important basis for scientific and effective emergency rescue after an earthquake. Currently, most models are based on the kilometer grid scale or community scale that gauge the population and extent of the earthquake burial under distinct intensities. The estimation results of the methods are on coarse scales; therefore, the methods cannot meet the requirements of rapid rescue after an earthquake. In response to the above statements, this study uses multi-source data to propose a way to estimate the number and distribution of people trapped under the scale of single buildings. Firstly, we use pre-earthquake optical high spatial resolution remote sensing images for building detection, and then we combine them with multi-source data for population distribution simulation. Secondly, indoor ratio assessment models are constructed by analyzing human behavior. Then, aerial remote sensing images are used for building seismic damage level detection. Finally, based on these three factors, a single building crush burial estimation model is constructed to obtain the number and distribution of personnel trapped. In this paper, the reliability of the proposed workflow is demonstrated by the casualty results in experiments conducted in the nearby Moxi town after the Luding 6.8 magnitude earthquake on 5 September 2022. For future natural disaster events, this method can provide reliable information support and decision references for earthquake emergency rescue. Full article

(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)

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24 pages, 15393 KiB

Open AccessArticle

Improving Landslide Susceptibility Assessment through Frequency Ratio and Classification Methods—Case Study of Valencia Region (Spain)

by Isidro Cantarino, Miguel Angel Carrion, Víctor Martínez-Ibáñez and Eric Gielen

Appl. Sci. 2023, 13(8), 5146; https://doi.org/10.3390/app13085146 - 20 Apr 2023

Cited by 2 | Viewed by 1674

Abstract

Landslide susceptibility maps are widely used in land management and urban planning to delimit potentially problematic areas. In this article we improve their reliability by acting on the frequency ratio method and map classification systems. For the frequency ratio method, we have worked [...] Read more.

Landslide susceptibility maps are widely used in land management and urban planning to delimit potentially problematic areas. In this article we improve their reliability by acting on the frequency ratio method and map classification systems. For the frequency ratio method, we have worked with continuous variables and established intervals grouped by probability according to the landslide inventory and based on the characteristics of the data rather than on standard divisions. For map classification systems, we have compared the efficacy of conventional classifications and those based on the concepts of sensitivity and specificity, with the specificity classifications being supported by the information offered by available comparative data. Both strategies make it possible to avoid subjective and repetitive procedures that are alien to the nature of the data being assessed. We present a case study in the 23,000 km² Region of Valencia where a total of 48 different susceptibility maps were generated. We demonstrate that the methods applied in this study to calculate the frequency ratio provide an improvement in specificity in areas of high susceptibility while maintaining good sensitivity. In particular, the Area Under Curve (AUC) values increase from 0.67 for the conventional methods to 0.76 with the methods proposed in this work. This improvement is transferred to susceptibility mapping much more clearly when classifications that incorporate sensitivity, and especially specificity parameters, are used. Full article

(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)

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17 pages, 3460 KiB

Open AccessArticle

Forest Fragmentation and Landscape Connectivity Changes in Ecuadorian Mangroves: Some Hope for the Future?

by Julio J. Jaramillo, Carlos A. Rivas, José Oteros and Rafael M. Navarro-Cerrillo

Appl. Sci. 2023, 13(8), 5001; https://doi.org/10.3390/app13085001 - 16 Apr 2023

Cited by 5 | Viewed by 2515

Abstract

This study investigates the impact of fragmentation on Ecuador’s coastal mangrove forests. Fragmentation is identified as a primary cause of aquatic ecosystem degradation. We analyzed the relationship between habitat loss, fragmentation, and mangrove connectivity through a multitemporal approach using Global Mangrove Watch and [...] Read more.

This study investigates the impact of fragmentation on Ecuador’s coastal mangrove forests. Fragmentation is identified as a primary cause of aquatic ecosystem degradation. We analyzed the relationship between habitat loss, fragmentation, and mangrove connectivity through a multitemporal approach using Global Mangrove Watch and fragmentation and connectivity metrics. The terrain was divided into 10 km² hexagons, and six fragmentation metrics were calculated. A Getis–Ord Gi* statistical analysis was used to identified areas with the best and worst conservation status, while connectivity analyses were performed for a generic species with a 5 km dispersion. Findings revealed widespread mangrove fragmentation in Ecuador, with geographical differences between the insular region (Galapagos) and the mainland coast. Minimal loss or even expansion of mangrove forests in areas like the Galapagos Islands contrasted with severe fragmentation along the mainland coast. Transformation of forests into fisheries, mainly prawn factories, was the primary driver of change, while only a weak correlation was observed between mangrove fragmentation and conversion to agriculture, which accounts for less than 15% of all deforestation in Ecuador. Fragmentation may increase or decrease depending on the management of different deforestation drivers and should be considered in large-scale mangrove monitoring. Focusing only on mangrove deforestation rates in defining regional conservation priorities may overlook the loss of ecosystem functions and fragmentation. Full article

(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)

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16 pages, 5008 KiB

Open AccessArticle

Spatial Assessment of Soil Erosion Risk Using RUSLE Embedded in GIS Environment: A Case Study of Jhelum River Watershed

by Muhammad Waseem, Fahad Iqbal, Muhammad Humayun, Muhammad Umais Latif, Tayyaba Javed and Megersa Kebede Leta

Appl. Sci. 2023, 13(6), 3775; https://doi.org/10.3390/app13063775 - 15 Mar 2023

Cited by 7 | Viewed by 2278

Abstract

The watershed area of the Mangla Reservoir spans across the Himalayan region of India and Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly terrain and relatively thin vegetation cover, which significantly increases the river’s sediment [...] Read more.

The watershed area of the Mangla Reservoir spans across the Himalayan region of India and Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly terrain and relatively thin vegetation cover, which significantly increases the river’s sediment output, especially during the monsoon season, leading to a decline in the reservoir’s storage capacity. This work assesses the soil erosion risk in the Jhelum River watershed (Azad Jammu and Kashmir (AJ&K), Pakistan) using the Revised Universal Soil Loss Equation of (RUSLE). The RUSLE components, including the conservation support or erosion control practice factor (P), soil erodibility factor (K), slope length and slope steepness factor (LS), rainfall erosivity factor (R), and crop cover factor (C), were integrated to compute soil erosion. Soil erosion risk and intensity maps were generated by computing the RUSLE parameters, which were then integrated with physical factors such as terrain units, elevation, slope, and land uses/cover to examine how these factors affect the spatial patterns of soil erosion loss. The 2021 rainfall data were utilized to compute the rainfall erosivity factor (R), and the soil erodibility (K) map was created using the world surface soil map prepared by the Food and Agriculture Organization (FAO). The slope length and slope steepness factor (LS) were generated in the highly rough terrain using Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). The analysis revealed that the primary land use in the watershed was cultivated land, accounting for 27% of the area, and slopes of 30% or higher were present across two-thirds of the watershed. By multiplying the five variables, the study determined that the annual average soil loss was 23.47 t ha⁻¹ yr⁻¹. In areas with dense mixed forest cover, soil erosion rates ranged from 0.23 t ha⁻¹ yr⁻¹ to 25 t ha⁻¹ yr⁻¹. The findings indicated that 55.18% of the research area has a low erosion risk, 18.62% has a medium erosion risk, 13.66% has a high risk, and 11.6% has a very high erosion risk. The study’s findings will provide guidelines to policy/decision makers for better management of the Mangla watershed. Full article

(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)

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Review

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20 pages, 1537 KiB

Open AccessEditor’s ChoiceReview

Use of Machine Learning and Remote Sensing Techniques for Shoreline Monitoring: A Review of Recent Literature

by Chrysovalantis-Antonios D. Tsiakos and Christos Chalkias

Appl. Sci. 2023, 13(5), 3268; https://doi.org/10.3390/app13053268 - 03 Mar 2023

Cited by 15 | Viewed by 3720

Abstract

Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and [...] Read more.

Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and different geomorphological features, while exhibiting different scales and spectral responses. Thus, the monitoring of changes in the coastal land classes and the extraction of coastlines/shorelines can be a challenging task. Earth Observation data and the application of spatiotemporal analysis methods can facilitate shoreline change analysis and detection. Apart from remote sensing methods, the advent of machine learning-based techniques presents an emerging trend, being capable of supporting the monitoring and modeling of coastal ecosystems at large scales. In this context, this study aims to provide a review of the relevant literature falling within the period of 2015–2022, where different machine learning approaches were applied for cases of coast-line/shoreline extraction and change analysis, and/or coastal dynamic monitoring. Particular emphasis is given on the analysis of the selected studies, including details about their performances, as well as their advantages and weaknesses, and information about the different environmental data employed. Full article

(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)

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