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Geohazard Mapping for Community Resilience: Susceptibility, Impact, and Recovery

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 8056

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Special Issue Editors

Dr. F. Albert Liu

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Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: landslides; change detection; community recovery

Dr. Renato Macciotta

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Department of Civil and Environmental Engineering, University of Alberta, 6-207 Donadeo Innovation Centre for Engineering, 9211-116 St, Edmonton, AB T6G 2H5, Canada
Interests: landslide; rock mechanics; rock mass; monitoring; field survey; remote survey; UAV photogrammetry; rockfall risk assessment
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Dr. Lu Zhuo

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School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK
Interests: infrastructure systems; remote sensing; socioeconcomic
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Special Issue Information

Dear Colleagues,

Society’s resilience is tested by the impacts of natural or human-induced geohazards, such as earthquakes, landslides, hurricanes, and tornadoes, which represent the ultimate test of nature–human systems interactions. While each of these ‘full-scale’ extreme events is routinely analyzed by controlled physical experiments or numerical simulations, their potential, existing, and long-term impacts on communities are not yet fully understood. Remote-sensing techniques, such as multi-spectral and hyperspectral imaging, LiDAR, and proximate and in situ instrumentation, can provide multi-scale data for regional- and/or community-scale use, allowing a) susceptibility and risk mapping for baseline information for rescue, recovery, and reconstruction; (b) calibration and validation of physical and computational simulation for real geohazards–infrastructure interactions; (c) realistic assessment of the complex post-disaster recovery process of a community under major and cascading effects of geohazards; and (d) robust evaluations of large-scale regional variations in extreme events. Considering climate change, and the associated increase in frequency and intensity of extreme weather events and natural hazards, enhancing our capabilities to adequately perform these tasks is of increasing importance.

This Special Issue focuses on remote sensing, geohazards, disaster resilience, change detection, data processing and machine learning, uncertainty characterization of monitoring information, recovery planning and reconstruction, and the future variation of geohazard frequency and intensity due to climate change. We welcome contributions to advanced and novel remote sensing applications on geohazard mapping, especially within the context of potential, immediate, and long-term impacts on urban and/or rural communities of different scales.

Dr. F. Albert Liu
Dr. Renato Macciotta
Dr. Lu Zhuo
Guest Editors

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Keywords

geohazard
susceptibility mapping
change detection
disaster resilience
post-disaster recovery
climate change

Published Papers (3 papers)

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Research

26 pages, 19665 KiB

Open AccessArticle

Evaluation of Susceptibility by Mass Movements through Stochastic and Statistical Methods for a Region of Bucaramanga, Colombia

by Joaquín Andrés Valencia Ortiz, Antonio Miguel Martínez-Graña and Lenny Mejía Méndez

Remote Sens. 2023, 15(18), 4567; https://doi.org/10.3390/rs15184567 - 16 Sep 2023

Cited by 1 | Viewed by 932

Abstract

Mass movements are one of the hydrometeorological phenomena with the most negative impacts on the study area, and their evaluation through the calculation of susceptibility provides a tool of vital importance within territorial planning and disaster risk management on natural and anthropic environments. Their evaluation took algorithms designed within stochastic and statistical methods, such as the artificial neural network, the bivariate statistical method, and the logistic regression method, which integrate inherent variables (geoenvironmental characterization) against events or dependent variables. This correlation simulates regions with a probability of occurrence of mass movement under training or weight assignment. Its construction for this study took, as a basis, 50% of the events (test) and 50% of the events (validation) randomly and with equivalent area distribution against the inherent variables. As a result, it was observed that the bivariate method presented a good performance in spatial prediction. This model presents values of AUC = 82.2% (test) and AUC = 76.9% (validation), grouping a total of 591 events of the 856 events in the high category (69%). In turn, from a second evaluation carried out by this method to each hydrographic basin, a condition was established in the area (50 km²) for coherent results at a level of analysis 1:25,000, based on the idea that the variables do not present changes greater than 20% in their attributes, added to a knowledge of the area evaluated. Full article

(This article belongs to the Special Issue Geohazard Mapping for Community Resilience: Susceptibility, Impact, and Recovery)

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20 pages, 4553 KiB

Open AccessArticle

Coexistence of a Marginal Mountain Community with Large-Scale and Low Kinematic Landslide: The Intensive Monitoring Approach

by Danilo Godone, Paolo Allasia, Davide Notti, Marco Baldo, Flavio Poggi and Francesco Faccini

Remote Sens. 2023, 15(13), 3238; https://doi.org/10.3390/rs15133238 - 23 Jun 2023

Cited by 1 | Viewed by 4190

Abstract

Mountain territories affected by natural hazards are vulnerable areas for settlements and inhabitants. Additionally, those areas are characterized by socio-economic marginality, further favoring their abandonment. The study area is located in Liguria (Italy), and a large, slow-moving phenomenon endangers the settlements in the region. Monitoring such phenomena requires the use of instruments capable of detecting yearly, millimetric displacements and, due to their size, the use of remote techniques which can provide deformation measurement of the entire extent of the phenomenon. The methodology proposed here couples long-term interferometric remote sensing data analysis with intensive in situ monitoring (inclinometer, piezometers and global navigation satellite systems). Furthermore, the inclinometric measurements were carried out with an experimental, robotized inclinometer. The aim is to frame the overall context of ground deformation, assure information for inhabitants, stakeholders and land-planners, and secure coexistence with the phenomenon. Remote sensing provided a time series of 28 years of deformation measurements while in situ instrumentations allowed, in the last years, a better understanding of the surficial and deep behavior of the phenomenon, confirming the satellite data. Additionally, the high-frequency monitoring allowed us to record acceleration after precipitation peaks. The proposed approach, including the experimental instruments, proved its viability and can be replicated in similar mountain contexts. Full article

(This article belongs to the Special Issue Geohazard Mapping for Community Resilience: Susceptibility, Impact, and Recovery)

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22 pages, 54888 KiB

Open AccessEditor’s ChoiceArticle

Landslide Risk Assessment in Eastern Kentucky, USA: Developing a Regional Scale, Limited Resource Approach

by Matthew M. Crawford, Jason M. Dortch, Hudson J. Koch, Yichuan Zhu, William C. Haneberg, Zhenming Wang and L. Sebastian Bryson

Remote Sens. 2022, 14(24), 6246; https://doi.org/10.3390/rs14246246 - 09 Dec 2022

Cited by 4 | Viewed by 1956

Abstract

Rapidly changing remote sensing technologies (lidar, aerial photography, satellites) provide opportunities to improve regional-scale landslide risk mapping. However, data limitations regarding landslide hazard and exposure data influence how landslide risk is calculated. To develop risk assessments for a landslide-prone region of eastern Kentucky, USA, we assessed risk modeling and applicability using variable quality data. First, we used a risk equation that incorporated the hazard as a logistic regression landslide susceptibility model using geomorphic variables derived from lidar data. Susceptibility is calculated as a probability of occurrence. The exposure data included population, roads, railroads, and land class. Our vulnerability value was assumed to equal one (worst-case scenario for a degree of loss) and consequence data was economic cost. Results indicate 64.1 percent of the study area is classified as moderate to high socioeconomic risk. To develop a more data-limited approach, we used a 30 m slope-angle map as the hazard input and simplified exposure data. Results for the slope-based approach show the distribution of risk that is less uniform, with large areas of over-and under-prediction. Changes in the hazard and exposure inputs result in significant changes in the quality and applicability of the maps and demonstrate the broad range of risk modelling approaches. Full article

(This article belongs to the Special Issue Geohazard Mapping for Community Resilience: Susceptibility, Impact, and Recovery)

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