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Toward an Application of Remote Sensing Technology for Decision Making during Natural Disasters

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 17392

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Special Issue Editors

Dr. Hideomi Gokon

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Guest Editor

School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi City 923-1292, Japan
Interests: knowledge science; decision making; disaster prevention; data analytics; remote sensing; tsunami numerical modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Yudai Honma

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Assistant Guest Editor

Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
Interests: spatial analysis; architecture and urban planning; optimization; operations research; social system; mathematical modeling

Prof. Dr. Shunichi Koshimura

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Guest Editor

Disaster Geo-Informatics Laboratory, International Research Institute of Disaster Science, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-8572, Japan
Interests: earth observation; numerical modeling; disaster management; early warning; tsunami; flood; earthquake
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Through the efforts of many researchers around the world, a number of sophisticated technologies for evaluating comprehensive damages caused by natural disasters such as earthquake, tsunami, flood, volcanic eruption, and landslide have been developed. With the improvement of observation technology and the development and spread of machine learning techniques, the accuracies of evaluating the damages caused by these natural disasters have improved rapidly in recent years. In the next stage, it is important to consider how to apply these advanced technologies to decision making during natural disasters to reduce the burden of relief, recovery, and reconstruction activities and to minimize the impact of natural disasters on human societies.

The objective of this Special Issue is to discuss how to utilize recent advanced remote sensing technologies for decision making during natural disasters and find solutions to reduce the impact of natural disasters on human societies. The following are examples of disaster prevention activities to which remote sensing technologies may contribute.

Early warning
Explorations related to isolated people
Search and rescue
Securing emergent road networks
Identification of isolated areas
Deciding on supply quantities
Disaster waste treatment plan
First aid of infrastructures
Issuing damage certificates to individual houses

One of the important social contributions that researchers can make is not only improving remote sensing technologies, but also showing how to implement technologies to societies and its effects. I would welcome submissions from researchers who have the same awareness of this problem.

Dr. Hideomi Gokon
Dr. Yudai Honma
Prof. Dr. Shunichi Koshimura
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

Decision making
Disaster relief, recovery, and reconstruction
Synthetic aperture radar
Optical sensor
Geographic information system
Machine learning
Network analytics

Published Papers (5 papers)

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Research

22 pages, 17907 KiB

Open AccessArticle

Detecting Urban Floods with Small and Large Scale Analysis of ALOS-2/PALSAR-2 Data

by Hideomi Gokon, Fuyuki Endo and Shunichi Koshimura

Remote Sens. 2023, 15(2), 532; https://doi.org/10.3390/rs15020532 - 16 Jan 2023

Cited by 4 | Viewed by 2036

Abstract

When a large-scale flood disaster occurs, it is important to identify the flood areas in a short time in order to effectively support the affected areas afterwards. Synthetic Aperture Radar (SAR) is promising for flood detection. A number of change detection methods have been proposed to detect flooded areas with pre- and post-event SAR data. However, it remains difficult to detect flooded areas in built-up areas due to the complicated scattering of microwaves. To solve this issue, in this paper we propose the idea of analyzing the local changes in pre- and post-event SAR data as well as the larger-scale changes, which may improve accuracy for detecting floods in built-up areas. Therefore, we aimed at evaluating the effectiveness of multi-scale SAR analysis for flood detection in built-up areas using ALOS-2/PALSAR-2 data. First, several features were determined by calculating standard deviation images, difference images, and correlation coefficient images with several sizes of kernels. Then, segmentation on both small and large scales was applied to the correlation coefficient image and calculated explanatory variables with the features at each segment. Finally, machine learning models were tested for their flood detection performance in built-up areas by comparing a small-scale approach and multi-scale approach. Ten-fold cross-validation was used to validate the model, showing that highest accuracy was offered by the AdaBoost model, which improved the F1 Score from 0.89 in the small-scale analysis to 0.98 in the multi-scale analysis. The main contribution of this manuscript is that, from our results, it can be inferred that multi-scale analysis shows better performance in the quantitative detection of floods in built-up areas. Full article

(This article belongs to the Special Issue Toward an Application of Remote Sensing Technology for Decision Making during Natural Disasters)

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28 pages, 8374 KiB

Open AccessArticle

Evaluation and Prediction of Landslide Susceptibility in Yichang Section of Yangtze River Basin Based on Integrated Deep Learning Algorithm

by Lili Chang, Rui Zhang and Chunsheng Wang

Remote Sens. 2022, 14(11), 2717; https://doi.org/10.3390/rs14112717 - 06 Jun 2022

Cited by 5 | Viewed by 2346

Abstract

Landslide susceptibility evaluation (LSE) refers to the probability of landslide occurrence in a region under a specific geological environment and trigger conditions, which is crucial to preventing and controlling landslide risk. The mainstream of the Yangtze River in Yichang City belongs to the largest basin in the Three Gorges Reservoir area and is prone to landslides. Affected by global climate change, seismic activity, and accelerated urbanization, geological disasters such as landslide collapses and debris flows in the study area have increased significantly. Therefore, it is urgent to carry out the LSE in the Yichang section of the Yangtze River Basin. The main results are as follows: (1) Based on historical landslide catalog, geological data, geographic data, hydrological data, remote sensing data, and other multi-source spatial-temporal big data, we construct the LSE index system; (2) In this paper, unsupervised Deep Embedding Clustering (DEC) algorithm and deep integration network (Capsule Neural Network based on SENet: SE-CapNet) are used for the first time to participate in non-landslide sample selection, and LSE in the study area and the accuracy of the algorithm is 96.29; (3) Based on the constructed sensitivity model and rainfall forecast data, the main driving mechanisms of landslides in the Yangtze River Basin were revealed. In this paper, the study area’s mid-long term LSE prediction and trend analysis are carried out. (4) The complete results show that the method has good performance and high precision, providing a reference for subsequent LSE, landslide susceptibility prediction (LSP), and change rule research, and providing a scientific basis for landslide disaster prevention. Full article

(This article belongs to the Special Issue Toward an Application of Remote Sensing Technology for Decision Making during Natural Disasters)

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29 pages, 2481 KiB

Open AccessArticle

On Transfer Learning for Building Damage Assessment from Satellite Imagery in Emergency Contexts

by Isabelle Bouchard, Marie-Ève Rancourt, Daniel Aloise and Freddie Kalaitzis

Remote Sens. 2022, 14(11), 2532; https://doi.org/10.3390/rs14112532 - 25 May 2022

Cited by 10 | Viewed by 3330

Abstract

When a natural disaster occurs, humanitarian organizations need to be prompt, effective, and efficient to support people whose security is threatened. Satellite imagery offers rich and reliable information to support expert decision-making, yet its annotation remains labour-intensive and tedious. In this work, we evaluate the applicability of convolutional neural networks (CNN) in supporting building damage assessment in an emergency context. Despite data scarcity, we develop a deep learning workflow to support humanitarians in time-constrained emergency situations. To expedite decision-making and take advantage of the inevitable delay to receive post-disaster satellite images, we decouple building localization and damage classification tasks into two isolated models. Our contribution is to show the complexity of the damage classification task and use established transfer learning techniques to fine-tune the model learning and estimate the minimal number of annotated samples required for the model to be functional in operational situations. Full article

(This article belongs to the Special Issue Toward an Application of Remote Sensing Technology for Decision Making during Natural Disasters)

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Graphical abstract

21 pages, 7193 KiB

Open AccessCommunication

Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information

by Xingtong Ge, Yi Yang, Jiahui Chen, Weichao Li, Zhisheng Huang, Wenyue Zhang and Ling Peng

Remote Sens. 2022, 14(5), 1214; https://doi.org/10.3390/rs14051214 - 01 Mar 2022

Cited by 23 | Viewed by 5071

Abstract

Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is hard to build an analysis model that can integrate the remote sensing and the large-scale relevant information, particularly at the sematic level. This paper proposes a disaster prediction knowledge graph for disaster prediction by integrating remote sensing information, relevant geographic information, with the expert knowledge in the field of disaster analysis. This paper constructs the conceptual layer and instance layer of the knowledge graph by building a common semantic ontology of disasters and a unified spatio-temporal framework benchmark. Moreover, this paper represents the disaster prediction model in the forms of knowledge of disaster prediction. This paper demonstrates experiments and cases studies regarding the forest fire and geological landslide risk. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster prediction. Full article

(This article belongs to the Special Issue Toward an Application of Remote Sensing Technology for Decision Making during Natural Disasters)

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24 pages, 7048 KiB

Open AccessArticle

Application of Complex Geophysical Methods for the Detection of Unconsolidated Zones in Flood Dikes

by Tomisław Gołębiowski, Bogdan Piwakowski and Michał Ćwiklik

Remote Sens. 2022, 14(3), 538; https://doi.org/10.3390/rs14030538 - 23 Jan 2022

Cited by 1 | Viewed by 2825

Abstract

The flood levees in the vicinity of Krakow city (Poland) are, in some places, over 100 years old. Thereupon, in the flood dike, and its subsoil, can appear unconsolidated zones, which, during the flood stage, can be a simple way of water flow and/or even can be the place where the levee body will be destroyed. This phenomenon took place in Wawrzeńczyce village, near Krakow city, during the flood in 2010. The geophysical research was carried out, in order to develop a methodology of recognition of potential areas where the levee body can be damaged. The geophysical surveys were conducted with the use of electrical and electromagnetic methods, as well as utilizing the seismic method. The general identification of examined media was realized by the electrical resistivity tomography (ERT) method. The ERT surveys were supplemented by capacitively-coupled resistivity (CCR) measurements, in order to analyze the usefulness of the CCR method for the examination of river dikes and reduction of interpretation ambiguity. The ground penetrating radar (GPR) method detected small anomalies in the body dike, due to the very high resolution of this method, which were not detected by the ERT and CCR techniques. During GPR surveys, non-standard measurement techniques were applied. Finally, the high-resolution seismic reflection (HRSR) method provided a clear and high-resolution image of the dike structure up to the water table and assisted with the identification of the hazard non-consolidated zones. Full article

(This article belongs to the Special Issue Toward an Application of Remote Sensing Technology for Decision Making during Natural Disasters)

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