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Special Issue "Advances in Remote Sensing and GIS for Natural Hazards Assessment"

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 15286

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Special Issue Editor

Dr. Salvador García-Ayllón Veintimilla

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Guest Editor

Department of Civil Engineering, Technical University of Cartagena, Paseo Alfonso XIII, 52, Cartagena, Spain
Interests: territorial anthropization; GIS analysis; landscape planning; land use policy; territorial management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural risk assessment is one of the disciplines that has seen the greatest advances in the field of GIS and remote sensing in recent years. The implementation of more sophisticated analysis methodologies, more accurate remote sensing systems, more innovative damage assessment protocols, etc. are some of the various tools that have improved the management of these phenomena. This Special Issue seeks contributions involving innovative approaches or relevant case studies regarding natural hazards assessment related to GIS and remote sensing in topics such as:

- Earthquakes and landslides;

- Flooding and tsunamis;

- Wildfires;

- Hurricanes and similar meteorological phenomena;

- Extreme drought and other risks associated with climate change.

Innovative methodologies, frameworks, or significant results from relevant case studies related to all these topics are welcome, but similar ones may also be considered for publication if they fit within the scope of this Special Issue. Also, the Issue is open to all interested researchers from the 1st Conference on Future Challenges in Sustainable Urban Planning & Territorial Management SUPTM 2022.

Dr. Salvador García-Ayllón Veintimilla
Guest Editor

Guidance References

Chalkias, C.; Ferentinou, M.; Polykretis, C. GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method. ISPRS Int. J. Geo-Inf. 2014, 3, 523-539.

Fernández-Guisuraga, J. M.; Suárez-Seoane, S.; Calvo, L. Modeling Pinus pinaster forest structure after a large wildfire using remote sensing data at high spatial resolution. For. Ecol. Manage. 2019, 446, 257–271.

Garcia-Ayllon, S. Long-Term GIS Analysis of Seaside Impacts Associated to Infrastructures and Urbanization and Spatial Correlation with Coastal Vulnerability in a Mediterranean Area. Water 2018, 10, 1642.

García-Ayllón, S.; Tomás, A.; Ródenas, J.L. The Spatial Perspective in Post-Earthquake Evaluation to Improve Mitigation Strategies: Geostatistical Analysis of the Seismic Damage Applied to a Real Case Study. Appl. Sci. 2019, 9, 3182.

Li, X.; Yu, L.; Xu, Y.; Yang, J.; Gong, P. Ten years after Hurricane Katrina: monitoring recovery in New Orleans and the surrounding areas using remote sensing. Sci. Bull. 2016, 61, 1460–1470.

Poursanidis, D.; Chrysoulakis, N. Remote Sensing, natural hazards and the contribution of ESA Sentinels missions. Remote Sens. Appl. Soc. Environ. 2017, 6, 25–38

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 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

natural hazards assessment
GIS
remote sensing
earthquake
landslide
flooding
tsunami
climate change
wildfire
hurricane

Published Papers (6 papers)

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Research

Open AccessArticle

Soil Depth Prediction Model Using Terrain Attributes in Gangwon-do, South Korea

Jinwook Kim

and

Hosung Shin

Appl. Sci. 2023, 13(3), 1453; https://doi.org/10.3390/app13031453 - 22 Jan 2023

Viewed by 491

Abstract

Soil depth is a crucial parameter in slope stability analysis in mountainous areas. The drilling survey is the most reliable method for determining soil depth, but it requires a high cost for the vast geographical area. Therefore, this study proposes a soil depth prediction model for mountainous areas that uses Terrain Attributes (TAs) from digital maps. Gangwon-Do, a predominantly mountainous region in South Korea, is selected as the study target area. The study area is classified by parent rock type into igneous rocks, metamorphic rocks, and sedimentary rocks. The correlation with TAs is analyzed through multi-collinearity using drilling data published in the Korea drilling information database. In addition, the most suitable combination of variables is selected through multi-collinearity analysis, and the regression model using STI, TWI, and SLOPE is found to be the most appropriate model (VIF < 10). The proposed model for soil depth shows significance at p < 0.001, and the correlation coefficient (

R^{2}

) is figured out for igneous rock (0.702), metamorphic rock (0.686), and sedimentary rock (0.693). In addition, the reliability of the proposed model was verified by using data from regions not included in the model development, and the correlation coefficients were igneous rock (0.867), metamorphic rock (0.801), and sedimentary rock (0.814). The model proposed is more suitable for Korean topography than the existing statistical models; it can help to increase the accuracy of slope stability analysis. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Assessment)

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Open AccessArticle

Ecosystem Services Assessment for Their Integration in the Analysis of Landslide Risk

Patricia Arrogante-Funes

Adrián G. Bruzón

Fátima Arrogante-Funes

Ana María Cantero

Ariadna Álvarez-Ripado

René Vázquez-Jiménez

and

Rocío N. Ramos-Bernal

Appl. Sci. 2022, 12(23), 12173; https://doi.org/10.3390/app122312173 - 28 Nov 2022

Viewed by 983

Abstract

Landslides are disasters that cause damage to anthropic activities, innumerable loss of human life, and affect the natural ecosystem and its services globally. The landslide risk evaluated by integrating susceptibility and vulnerability maps has recently become a manner of studying sites prone to landslide events and managing these regions well. Developing countries, where the impact of landslides is frequent, need risk assessment tools to address these disasters, starting with their prevention, with free spatial data and appropriate models. However, to correctly understand their interrelationships and social affection, studying the different ecosystem services that relate to them is necessary. This study is the first that has been attempted in which an integrated application methodology of ecosystem services is used to know in a systematic way if the information that ecosystem services provide is useful for landslide risk assessment. For the integration of ecosystem services into the landslide risk evaluation, (1) eight ecosystem services were chosen and mapped to improve understanding of the spatial relationships between these services in the Guerrero State (México), and (2) areas of synergies and trade-offs were identified through a principal component analysis, to understand their influence on risk analysis better. These are extracted from the models of the ARIES platform, artificial intelligence, and big data platform. Finally, (3) the similarity between the risk characteristics (susceptibility and vulnerability, already mapped by the authors) and the ecosystem services assessment was analysed. The results showed that the ecosystem services that most affect the synergy are organic carbon mass and the potential value of outdoor recreation; meanwhile, the possible removed soil mass was the most important trade-off. Furthermore, the lowest similarity value was found between landslide vulnerability and ecosystem services synergy, indicating the importance of including these ecosystem services as a source of valuable information in the risk analysis methodologies, especially with respect to risk vulnerability. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Assessment)

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Open AccessArticle

Geostatistical Analysis of the Spatial Correlation between Territorial Anthropization and Flooding Vulnerability: Application to the DANA Phenomenon in a Mediterranean Watershed

Salvador Garcia-Ayllon

and

John Radke

Appl. Sci. 2021, 11(2), 809; https://doi.org/10.3390/app11020809 - 16 Jan 2021

Cited by 15 | Viewed by 2791

Abstract

Climate change is making intense DANA (depresión aislada en niveles altos) type rains a more frequent phenomenon in Mediterranean basins. This trend, combined with the transformation of the territory derived from diffuse anthropization processes, has created an explosive cocktail for many coastal towns due to flooding events. To evaluate this problem and the impact of its main guiding parameters, a geostatistical analysis of the territory based on GIS indicators and an NDVI (Normalized Difference Vegetation Index) analysis is developed. The assessment of the validity of a proposed methodology is applied to the case study of the Campo de Cartagena watershed located around the Mar Menor, a Mediterranean coastal lagoon in Southeastern Spain. This area has suffered three catastrophic floods derived from the DANA phenomenon between 2016 and 2019. The results show that apart from the effects derived from climate change, the real issue that amplifies the damage caused by floods is the diffuse anthropization process in the area, which has caused the loss of the natural hydrographic network that traditionally existed in the basin. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Assessment)

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Open AccessArticle

GIS-Based Optimum Geospatial Characterization for Seismic Site Effect Assessment in an Inland Urban Area, South Korea

and

Appl. Sci. 2020, 10(21), 7443; https://doi.org/10.3390/app10217443 - 23 Oct 2020

Cited by 7 | Viewed by 1763

Abstract

Soil and rock characteristics are primarily affected by geological, geotechnical, and terrain variation with spatial uncertainty. Earthquake-induced hazards are also strongly influenced by site-specific seismic site effects associated with subsurface strata and soil stiffness. For reliable mapping of soil and seismic zonation, qualification and normalization of spatial uncertainties is required; this can be achieved by interactive refinement of a geospatial database with remote sensing-based and geotechnical information. In this study, geotechnical spatial information and zonation were developed while verifying database integrity, spatial clustering, optimization of geospatial interpolation, and mapping site response characteristics. This framework was applied to Daejeon, South Korea, to consider spatially biased terrain, geological, and geotechnical properties in an inland urban area. For developing the spatially best-matched geometry with remote sensing data at high spatial resolution, the hybrid model blended with two outlier detection methods was proposed and applied for geotechnical datasets. A multiscale grid subdivided by hot spot-based clusters was generated using the optimized geospatial interpolation model. A principal component analysis-based unified zonation map identified vulnerable districts in the central old downtown area based on the integration of the optimized geoprocessing framework. Performance of the geospatial mapping and seismic zonation was discussed with digital elevation model, geological map. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Assessment)

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Open AccessArticle

ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru

Nancy Alvan Romero

Francesca Cigna

and

Deodato Tapete

Appl. Sci. 2020, 10(18), 6598; https://doi.org/10.3390/app10186598 - 21 Sep 2020

Cited by 9 | Viewed by 3995

Abstract

The coastline environment and urban areas of Peru overlooking the Pacific Ocean are among the most affected by El Niño-Southern Oscillation (ENSO) events, and its cascading hazards such as floods, landslides and avalanches. In this work, the complete archives of the European Space Agency (ESA)’s European Remote-Sensing (ERS-1/2) missions and European Commission’s Copernicus Sentinel-1 constellation were screened to select synthetic aperture radar (SAR) images covering the most severe and recent ENSO-related flooding events that affected Lima, the capital and largest city of Peru, in 1997–1998 and 2017–2018. Based on SAR backscatter color composites and ratio maps retrieved from a series of pre-, cross- and post-event SAR pairs, flooded areas were delineated within the Rímac River watershed. These are mostly concentrated along the riverbanks and plain, where low-lying topography and gentle slopes (≤5°), together with the presence of alluvial deposits, also indicate greater susceptibility to flooding. A total of 409 areas (58.50 km²) revealing change were mapped, including 197 changes (32.10 km²) due to flooding-related backscatter variations (flooded areas, increased water flow in the riverbed, and riverbank collapses and damage), and 212 (26.40 km²) due to other processes (e.g., new urban developments, construction of river embankments, other engineering works, vegetation changes). Urban and landscape changes potentially contributing, either detrimentally or beneficially, to flooding susceptibility were identified and considered in the overall assessment of risk. The extent of built-up areas within the basin was mapped by combining information from the 2011 Global Urban Footprint (GUF) produced by the German Aerospace Center (DLR), the Open Street Map (OSM) accessed from the Quantum GIS (QGIS) service, and 2011–2019 very high-resolution optical imagery from Google Earth. The resulting flooding risk map highlights the sectors of potential concern along the Rímac River, should flooding events of equal severity as those captured by SAR images occur in the future. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Assessment)

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Open AccessArticle

Post-Disaster Recovery Monitoring with Google Earth Engine

Saman Ghaffarian

Ali Rezaie Farhadabad

and

Norman Kerle

Appl. Sci. 2020, 10(13), 4574; https://doi.org/10.3390/app10134574 - 01 Jul 2020

Cited by 18 | Viewed by 4279

Abstract

Post-disaster recovery is a complex process in terms of measuring its progress after a disaster and understanding its components and influencing factors. During this process, disaster planners and governments need reliable information to make decisions towards building the affected region back to normal (pre-disaster), or even improved, conditions. Hence, it is essential to use methods to understand the dynamics/variables of the post-disaster recovery process, and rapid and cost-effective data and tools to monitor the process. Google Earth Engine (GEE) provides free access to vast amounts of remote sensing (RS) data and a powerful computing environment in a cloud platform, making it an attractive tool to analyze earth surface data. In this study we assessed the suitability of GEE to analyze and track recovery. To do so, we employed GEE to assess the recovery process over a three-year period after Typhoon Haiyan, which struck Leyte island, in the Philippines, in 2013. We developed an approach to (i) generate cloud and shadow-free image composites from Landsat 7 and 8 satellite imagery and produce land cover classification data using the Random Forest method, and (ii) generate damage and recovery maps based on post-classification change analysis. The method produced land cover maps with accuracies >88%. We used the model to produce damage and three time-step recovery maps for 62 municipalities on Leyte island. The results showed that most of the municipalities had recovered after three years in terms of returning to the pre-disaster situation based on the selected land cover change analysis. However, more analysis (e.g., functional assessment) based on detailed data (e.g., land use maps) is needed to evaluate the more complex and subtle socio-economic aspects of the recovery. The study showed that GEE has good potential for monitoring the recovery process for extensive regions. However, the most important limitation is the lack of very-high-resolution RS data that are critical to assess the process in detail, in particular in complex urban environments. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Assessment)

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