Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

► Journal Browser

Natural Hazard Assessment and Disaster Management Using Remote Sensing

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 17870

Share This Special Issue

Special Issue Editors

Prof. Dr. Issaak Parcharidis

E-Mail Website
Guest Editor

Department of Geography, Harokopio University of Athens, Eleftheriou Venizelou 70, Kallithea-Attiki, PC 176 71 Athens, Greece
Interests: earth observation applications; hazard and risk assessment; SAR interferometry; geospatial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Fulong Chen

E-Mail Website
Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidan District, Beijing 100094, China
Interests: SAR interferometry; remote sensing for cultural applications; space archaeology; MTInSAR for displacement monitoring; remote sensing for geohazards
Special Issues, Collections and Topics in MDPI journals

Dr. Olga Markogiannaki

E-Mail Website
Guest Editor

Dept. of Mechanical Engineering, University of Western Macedonia, Bakola & Sialvera Τ.Κ., 50132 Kozani, Greece
Interests: structural health monitoring; remote sensing for critical infrastructure; bridge engineering; vulnerability and risk assessment; post-disaster impact; structural retrofit
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Critical infrastructure systems provide essential services to modern societies. A wide range of socioeconomic sectors, from public health to energy and transportation, rely on the operation of critical infrastructure (e.g. hospitals, dams, pipelines, transport networks, etc.). To avoid unexpected interruptions in the operation of critical infrastructure and to reduce disaster risk due to extreme events (e.g. earthquakes), it is vital to be aware of the condition and serviceability of these systems. Monitoring infrastructure addresses the urgent need to detect unsafe conditions or issues that require appropriate corrective measures at the early stages (prevention phase). Possible failures can cause great disasters with enormous social, economic, and ecological costs.

This means that the safety and security of these are imperative. Space-based Earth Observation data and methods support Disaster Risk Reduction activities and additionally allows for a competent workforce that will be recruited by local, regional and national authorities. In recent years, significant progress has been made for the innovative exploitation of Earth Observation data and technology.

The Special Issue “Natural Hazard Assessment and Disaster Management Using Remote Sensing” is jointly organized between “Remote Sensing” and “Earth” journals. Contributors are required to check the website below and follow the specific instructions for authors:
https://www.mdpi.com/journal/remotesensing/instructions
https://www.mdpi.com/journal/earth/instructions

The other special issue could be found at:
https://www.mdpi.com/journal/earth/special_issues/Hazard_Assessment

Prof. Dr. Issaak Parcharidis
Prof. Dr. Fulong Chen
Dr. Olga Markogiannaki
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

Linear and non linear infrastructure
Structural health monitoring
Natural hazards
Geospatial intelligence
Pre and post-disaster phase
Optical and SAR images
SAR interferometry (InSAR)
Multitemporal InSAR
Differential SAR tomography (DTomoSAR)

Published Papers (7 papers)

Download All Papers

Research

22 pages, 8743 KiB

Open AccessArticle

Distribution and Characteristics of Damming Landslides Triggered by 1920 M~8 Haiyuan Earthquake, NW China

by Weiheng Zhang, Yueren Xu, Xinyi Guo, Wenqiao Li, Peng Du and Qinjian Tian

Remote Sens. 2022, 14(10), 2427; https://doi.org/10.3390/rs14102427 - 18 May 2022

Cited by 1 | Viewed by 1789

Abstract

Earthquake-triggered damming landslides threaten downstream residents and affect the regional landscape by disrupting water and sediment fluxes. Therefore, it is essential to study the distribution characteristics and distinctive controlling factors of earthquake-triggered damming landslides to provide a reference for treating landslide dams caused by damming landslides. This study uses the 1920 M~8 Haiyuan earthquake-triggered landslides as an example to study the characteristics and topographic effects of damming landslides in the Loess Plateau in Northwestern China. A detailed Haiyuan-earthquake-triggered damming landslide inventory was established. The statistics of terrain, geology, seismic factors, and information gain rankings were used to quantify the significance of the controlling factors. The aspect ratio, equivalent coefficient of friction, area, and slope position was calculated. Damming landslides’ distinctive geomorphic and morphological characteristics were summarized through comparisons with non-damming landslides. The results showed that damming landslides were concentrated in areas with thick loess sediment, low relief, and close proximity to a river. Loess thickness was the most critical control factor among them. Damming landslides have the geomorphological characteristics of a large ratio of length to width (L/W), a low ratio of height to length (H/L), large scales, and entire-slope failure. Moreover, damming landslides can transform the topography of the Loess Plateau through their long-term effects. These findings highlight the characteristics of damming landslides in the Loess Plateau and supplement the global landslide dam inventory. They provide a reference for assisting in earthquake-triggered damming landslides treatments in the Loess Plateau. Full article

(This article belongs to the Special Issue Natural Hazard Assessment and Disaster Management Using Remote Sensing)

► Show Figures

Figure 1

19 pages, 10514 KiB

Open AccessArticle

An Improved Method for Automatic Identification and Assessment of Potential Geohazards Based on MT-InSAR Measurements

by Shuran Luo, Guangcai Feng, Zhiqiang Xiong, Haiyan Wang, Yinggang Zhao, Kaifeng Li, Kailiang Deng and Yuexin Wang

Remote Sens. 2021, 13(17), 3490; https://doi.org/10.3390/rs13173490 - 02 Sep 2021

Cited by 15 | Viewed by 3092

Abstract

Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been widely used for ground motion identification and monitoring over large-scale areas, due to its large spatial coverage and high accuracy. However, automatically locating and assessing the state of the ground motion from the massive Interferometric Synthetic Aperture Radar (InSAR) measurements is not easy. Utilizing the spatial-temporal characteristics of surface deformation on the basis of the Small Baseline Subsets InSAR (SBAS-InSAR) measurements, this study develops an improved method to locate potential unstable or dangerous regions, using the spatial velocity gradation and the temporal evolution trend of surface displacements in large-scale areas. This method is applied to identify the potential geohazard areas in a mountainous region in northwest China (Lajia Town in Qinghai province) using 73 and 71 Sentinel-1 images from the ascending and descending orbits, respectively, and an urban area (Dongguan City in Guangdong province) in south China using 32 Sentinel-1 images from the ascending orbit. In the mountainous area, 23 regions with potential landslide hazards have been identified, most of which have high to very high instability levels. In addition, the instability is the highest at the center and decreases gradually outward. In the urban area, 221 potential hazards have been identified. The moderate to high instability level areas account for the largest proportion, and they are concentrated in the farmland irrigation areas, and construction areas. The experiment results show that the improved method can quickly identify and evaluate geohazards on a large scale. It can be used for disaster prevention and mitigation. Full article

(This article belongs to the Special Issue Natural Hazard Assessment and Disaster Management Using Remote Sensing)

► Show Figures

Figure 1

16 pages, 21891 KiB

Open AccessArticle

A Spatio-Temporal Brightness Temperature Prediction Method for Forest Fire Detection with MODIS Data: A Case Study in San Diego

by Adu Gong, Jing Li and Yanling Chen

Remote Sens. 2021, 13(15), 2900; https://doi.org/10.3390/rs13152900 - 23 Jul 2021

Cited by 7 | Viewed by 2446

Abstract

Early detection of forest fire is helpful for monitoring the spread of fire promptly, minimizing the loss of forests, wild animals, human life, and economy. The performance of brightness temperature (BT) prediction determines the accuracy of fire detection. Great efforts have been made on BT prediction model building, but there still remains some uncertainty. Based on the widely used contextual BT prediction model (CM) and temporal-contextual BT prediction model (TCM), we proposed a spatio-temporal contextual BT prediction model (STCM), which involves historical images to contrast the BT correlation matrix between the pixel to be predicted and its background pixels within a dynamic window, and the spatial distance factor was introduced to modify the BT correlation matrix. We applied the STCM to a fire-prone area in San Diego, California, US, and compared it with CM and TCM. We found that the average RMSE of STCM was 12.54% and 9.12% lower than that of CM and TCM, and the standard deviation of RMSE calculated by STCM was reduced by 12.04% and 15.57% compared with CM and TCM, respectively. In addition, the bias of STCM was concentrated around zero and the range of bias of STCM was 88.7% and 15.3% lower than that of CM and TCM, respectively. The results demonstrated that the STCM can be used to obtain the highest BT prediction accuracy and most robust performance, followed by TCM, and CM performed worst. Our research on the BT prediction of potential fire pixels is helpful for improving the fire detection accuracy and is potentially useful for the prediction of other environmental variables with high spatial and temporal autocorrelation. However, the requirement of high-quality continuous data will limit the application of STCM in cloudy and rainy areas. Full article

(This article belongs to the Special Issue Natural Hazard Assessment and Disaster Management Using Remote Sensing)

► Show Figures

Graphical abstract

16 pages, 5520 KiB

Open AccessArticle

Fire Diurnal Cycle Derived from a Combination of the Himawari-8 and VIIRS Satellites to Improve Fire Emission Assessments in Southeast Australia

by Yueming Zheng, Jian Liu, Hongdeng Jian, Xiangtao Fan and Fuli Yan

Remote Sens. 2021, 13(15), 2852; https://doi.org/10.3390/rs13152852 - 21 Jul 2021

Cited by 13 | Viewed by 2370

Abstract

The violent and persistent wildfires that broke out along the southeast coast of Australia in 2019 caused a large number of pollutant emissions, which seriously affected air quality and the global climate. The existing two methods for estimating combustion emissions based on burned area and fire radiative power mainly use a medium resolution imaging spectrometer (MODIS) on the Aqua and Terra satellites. However, the low temporal resolution of MODIS and insensitivity to small fires lead to deviation in the estimation of fire emissions. In order to solve this problem, the Visible Infrared Imaging Radiometer Suite (VIIRS) with better performance is adopted in this paper, combined with the fire diurnal cycle information obtained by geostationary satellite Himawari-8, to explore the spatio-temporal model of biomass combustion emissions. Using this, a high-spatial- and -temporal-resolution fire emission inventory was generated for southeastern Australia from November 2019 to January 2020, which aims to fully consider the highly dynamic nature of fires and small fires (low FRP) that are much lower than the MODIS burned area or active fire detection limit, with emphasis on dry matter burned (DMB). We found that during the study period, the fire gradually moved from north to south, and the diurnal cycle of the fire in the study area changed greatly. The peak time of the fire gradually delayed as the fire moved south. Our inventory shows that the DMB in southeast Australia during the study period was about 146 Tg, with major burned regions distributed along the Great Dividing Range, with December 2019 being the main burning period. The total DMB we calculated is 0.5–3.1 times that reported by the GFAS (Global Fire Assimilation System) and 1.5 to 4 times lower than that obtained using the traditional “Burned Area Based Method (FINN)”. We believe that the GFAS may underestimate the results by ignoring a large number of small fires, and that the excessive combustion rate used in the FINN may be a source of overestimation. Therefore, we conclude that the combination of high-temporal-resolution and high-spatial-resolution satellites can improve FRE estimation and may also allow further verification of biomass combustion estimates from different inventories, which are far better approaches for fire emission estimation. Full article

(This article belongs to the Special Issue Natural Hazard Assessment and Disaster Management Using Remote Sensing)

► Show Figures

Figure 1

19 pages, 22814 KiB

Open AccessArticle

Applicability of Susceptibility Model for Rock and Loess Earthquake Landslides in the Eastern Tibetan Plateau

by Xinyi Guo, Bihong Fu, Jie Du, Pilong Shi, Qingyu Chen and Wenyuan Zhang

Remote Sens. 2021, 13(13), 2546; https://doi.org/10.3390/rs13132546 - 29 Jun 2021

Cited by 11 | Viewed by 1724

Abstract

It is crucial to explore a suitable landslide susceptibility model with an excellent prediction capability for rapid evaluation and disaster relief in seismic regions with different lithological features. In this study, we selected two typical seismic events, the Jiuzhaigou and Minxian earthquakes, which occurred in the Alpine karst and loess regions, respectively. Eight influencing factors and five models were chosen to calculate the susceptibility of landslide, including the information (I) model, certainty factor (CF) model, logistic regression (LR) model, I + LR coupling model, and CF + LR coupling model. Then, the accuracy and the landslide susceptibility distribution of these models were assessed by the area under curve (AUC) and distribution criteria. Finally, the model with high accuracy and good applicability for the rock landslide or loess landslide regions was optimized. Our results showed that the accuracy of the coupling model is higher than that of the single models. Except for the LR model, the landslide susceptibility distribution for the above-mentioned models is consistent with universal cognition. The coupling models are generally better than their single models. Among them, the I + LR model can obtain the best comprehensive results for assessing the distribution and accuracy of both rock and loess landslide susceptibility, which is helpful for disaster relief and policy-making, and it can also provide useful scientific data for post-seismic reconstruction and restoration. Full article

(This article belongs to the Special Issue Natural Hazard Assessment and Disaster Management Using Remote Sensing)

► Show Figures

Graphical abstract

26 pages, 7303 KiB

Open AccessArticle

Shipborne Mobile Photogrammetry for 3D Mapping and Landslide Detection of the Water-Level Fluctuation Zone in the Three Gorges Reservoir Area, China

by Dingjian Jin, Jing Li, Jianhua Gong, Yi Li, Zheng Zhao, Yongzhi Li, Dan Li, Kun Yu and Shanshan Wang

Remote Sens. 2021, 13(5), 1007; https://doi.org/10.3390/rs13051007 - 07 Mar 2021

Cited by 11 | Viewed by 2748

Abstract

The water-level fluctuation zone (WLFZ) of the Three Gorges Reservoir is a serious landslide-prone area. However, current remote sensing methods for landslide mapping and detection in the WLFZ are insufficient because of difficulties in data acquisition and lack of facade information. We proposed a novel shipborne mobile photogrammetry approach for 3D mapping and landslide detection in the WLFZ for the first time, containing a self-designed shipborne hardware platform and a data acquisition and processing workflow. To evaluate the accuracy and usability of the resultant 3D models in the WLFZ, four bundle block adjustment (BBA) control configurations were developed and adopted. In the four configurations, the raw Global Navigation Satellite System (GNSS) data, the raw GNSS data and fixed camera height, the GCPs extracted from aerial photogrammetric products, and the mobile Light Detection and Ranging (LiDAR) point cloud were used. A comprehensive accuracy assessment of the 3D models was conducted, and the comparative results indicated the BBA with GCPs extracted from the aerial photogrammetric products was the most practical configuration (RMSE 2.00 m in plane, RMSE 0.46 m in height), while the BBA with the mobile LiDAR point cloud as a control provided the highest georeferencing accuracy (RMSE 0.59 m in plane, RMSE 0.40 m in height). Subsequently, the landslide detection ability of the proposed approach was compared with multisource remote sensing images through visual interpretation, which showed that the proposed approach provided the highest landslide detection rate and unique advantages in small landslide detection as well as in steep terrains due to the more detailed features of landslides provided by the shipborne 3D models. The approach is an effective and flexible supplement to traditional remote sensing methods. Full article

(This article belongs to the Special Issue Natural Hazard Assessment and Disaster Management Using Remote Sensing)

► Show Figures

Figure 1

17 pages, 65836 KiB

Open AccessArticle

Temperature Variations in Multiple Air Layers before the Mw 6.2 2014 Ludian Earthquake, Yunnan, China

by Ying Zhang, Qingyan Meng, Zian Wang, Xian Lu and Die Hu

Remote Sens. 2021, 13(5), 884; https://doi.org/10.3390/rs13050884 - 26 Feb 2021

Cited by 15 | Viewed by 2072

Abstract

On 3 August 2014, an Mw 6.2 earthquake occurred in Ludian, Yunnan Province, China (27.245° N 103.427° E). This damaging earthquake caused approximately 400 fatalities, 1800 injuries, and the destruction of at least 12,000 houses. Using air temperature data of the National Center for Environmental Prediction (NCEP) and the tidal force fluctuant analysis (TFFA) method, we derive the temperature variations in multiple air layers between before and after the Ludian earthquake. In the spatial range of 30° × 30° (12°–42° N, 88°–118° E) of China, a thermal anomaly appeared only on or near the epicenter before earthquake, and air was heated from the land, then uplifted by a heat flux, and then cooled and dissipated upon rising. With the approaching earthquake, the duration and range of the thermal anomaly during each tidal cycle was found to increase, and the amplitude of the thermal anomaly varied with the tidal force potential: air temperature was found to rise during the negative phase of the tidal force potential, to reach peak at its trough, and to attenuate when the tidal force potential was rising again. A significance test supports the hypothesis that the thermal anomalies are physically related to Ludian earthquakes rather than being coincidences. Based on these results, we argue that the change of air temperature could reflect the stress changes modulated under the tidal force. Moreover, unlike the thermal infrared remote sensing data, the air temperature data provided by NCEP are not affected by clouds, so it has a clear advantage for monitoring the pre-earthquake temperature variation in cloudy areas. Full article

(This article belongs to the Special Issue Natural Hazard Assessment and Disaster Management Using Remote Sensing)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Natural Hazard Assessment and Disaster Management Using Remote Sensing

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI