• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1341-1347
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• 2021

The need for an effective disaster management system has grown these days to protect public safety as the number of disasters causing massive damage increases. Since disaster-induced damage can develop in various ways, rapid and accurate countermeasures must be prepared soon after disasters occur. Numerous studies have continuously developed remote sensing and GIS (Geographic Information System)-based techniques for disaster monitoring and damage analysis. This special issue presents the research results on disaster prediction and monitoring based on various remote sensors on different platforms from ground to space and disaster management using GIS techniques. The developed techniques help manage various disasters such as storms, floods, and forest fires and can be combined to achieve an integrated and effective disaster management system.

• Geomechanics and Engineering
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• v.14 no.5
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• pp.421-428
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• 2018

In deep mining, the lateral deformation of strip coal pillar appears to be a new characteristic. In order to study the lateral deformation of coal-mass, a monitoring method and monitoring instrument were designed to investigate the lateral deformation of strip coal pillar in Tangkou Coalmine with the mining depth of over 1000 m. Because of without influence of repeated mining, the bedding sandstone roof is easy to break and the angle between maximum horizontal stress and the roadway is small, the maximum lateral deformation is only about 287 mm lower than the other pillars in the same coalmine. In deep mining, the energy accumulation and release cause a discontinuous damage in the heterogeneous coal-mass, and the lateral deformation of coal pillar shows discontinuity, step and mutation characters. These coal-masses not only show a higher plasticity but also the high brittleness at the same time, and its burst tendency is more obvious. According to the monitoring results and theoretical calculations, the yield zone of the coal pillar width is determined as 15.6 m. The monitoring results presented through this study are of great significance to the stability analysis and design of coal pillar.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.1
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• pp.98-103
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• 2009

The forecasting, mitigation and preparedness of the natural disaster impacts require relevant information regarding the disaster desirable in real time. In the meantime it is requiring the rapid and continuous data and information generation or gathering for possible prediction and monitoring of the natural disaster. Since disasters that cause huge social and economic disruptions normally affect large areas or territories and are linked to global change. The use of traditional and conventional methods for management of the natural disaster impact can not be effectively implemented for intial data col1ection with the further processing. The space technology or remote sensing tools offer excellent possibilities of collecting vital data. The main reason is capability of this technology of collecting data at global and regional scales rapidly and repetitively. This is unchallenged advantage of the space methods and technology. The satellite or remote sensing techniques can be used to monitor the current situation, the situation before based on the data in sight. as well as after disaster occurred. They can be used to provide baseline data against which future changes can be compared while the GIS techniques provide a suitable framework for integrating and analyzing the many types of data sources required for disaster monitoring. Developed GIS is an excellent instrument for definition of the social impact status of the natural disaster which can be undertaken in the future database developments. This methodology is a good source for analysis and dynamic change studies of the natural disaster impacts.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1231-1244
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• 2020

Landsat satellites are representative optical satellites that have observed the Earth's surface for a long-term, and are suitable for long-term changes such as disaster preparedness/recovery monitoring, land use change, change detection, and time series monitoring. In this paper, clouds and cloud shadows were detected using QA bands to detect and remove clouds simply and efficiently. Then, the missing area of the experimantal image is restorated through the SSG algorithm, which does not directly refer to the pixel value of the reference image, but performs restoration to the pixel value in the Experimental image. Through this study, we presented the possibility of utilizing the modified SSG algorithm by quantitatively and qualitatively evaluating information on variousl and cover conditions in the thermal wavelength band as well as the visible wavelength band observing the surface.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.861-871
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• 2019

Landsat satellite images have been increasingly used for disaster damage analysis and disaster monitoring because they can be used for periodic and broad observation of disaster damage area. However, periodic disaster monitoring has limitation because of areas having missing data due to clouds as a characteristic of optical satellite images. Therefore, a study needs to be conducted for restoration of missing areas. This study detected and removed clouds and cloud shadows by using the quality assessment (QA) band provided when acquiring Landsat-8 images, and performed image restoration of removed areas through a spatial and temporal adaptive reflectance fusion (STARFM) algorithm. The restored image by the proposed method is compared with the restored image by conventional image restoration method throught MLC method. As a results, the restoration method by STARFM showed an overall accuracy of 89.40%, and it is confirmed that the restoration method is more efficient than the conventional image restoration method. Therefore, the results of this study are expected to increase the utilization of disaster analysis using Landsat satellite images.

• Journal of Korean Society of Disaster and Security
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• v.5 no.1
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• pp.13-19
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• 2012

This paper introduces platform of platform (POP) for global network on climate adaptation change and disaster risk reduction (CCA/DRR). The POP consists of disaster prevention technology e-market platform, e-learning platform, information sharing platform, and monitoring platform for AMCDRR action plan. The POP is developing based on Korean e-Government standard framework and supports Web and mobile service. Additionally the POP uses special product and technology to search and classify data about CCA/DRR.

• Structural Monitoring and Maintenance
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• v.5 no.2
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• pp.313-323
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• 2018

The presence of water inside concrete structures is an essential condition for the deterioration of the structures. The free water in the concrete pores and micro-cracks is the culprit for the durability related problems, such as alkali-aggregate reaction, carbonation, freeze-thaw damage, and corrosion of steel reinforcement. To ensure the integrity and safe operation of the concrete structures, it is very important to monitor water seepage inside the concrete. This paper presents the experimental investigation of water seepage monitoring in a concrete slab using piezoelectric-based smart aggregates. In the experimental setup, an $800mm{\times}800mm{\times}100mm$ concrete slab was fabricated with 15 SAs distributed inside the slab. The water seepage process was monitored through interrogating the SA pairs. In each SA pair, one SA was used as actuator to emit harmonic sine wave, and the other was used as sensor to receive the transmitted stress wave. The amplitudes of the received signals were able to indicate the water seepage process inside the concrete slab.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.147-155
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• 2024

Purpose: The purpose of this study is to analyze the danger signs of buildings and present a plan to install a building monitoring system to develop measurement technology for small private buildings in the blind spot of disaster safety. Method: The cause of building risk behavior, components of monitoring measuring equipment, location of measuring equipment installation, management plan, etc. are presented. Result: Measuring instruments essentially include acceleration sensors, tilt sensors, gyro sensors, GPS, etc. The measuring instrument should take into account the height and cross-sectional area of the pillar. Conclusion: The results of this study can strengthen disaster safety capabilities in preparation for disasters arising from building collapses that may occur in small private buildings.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.90-95
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• 2012

Remotely sensed data provide valuable information on land monitoring due to multi-temporal observation over large areas. Especially, high resolution imagery with 0.6~1.0 m spatial resolutions contain a wealth of information and therefore are very useful for thematic mapping and monitoring change in urban areas. Recently, remote sensing technology has been successfully utilized for natural disaster monitoring such as forest fire, earthquake, and floods. In this paper, an efficient change detection method based on texture differences observed from high resolution multi-temporal data sets is proposed for mapping disaster damage and extracting damage information. It is composed of two parts: feature extraction and detection process. Timely and accurate information on disaster damage can provide an effective decision making and response related to damage.

• Smart Structures and Systems
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• v.29 no.4
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• pp.589-598
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• 2022

Timber structures are susceptible to structural damages caused by variations in moisture content (MC), inducing severe durability deterioration and safety issues. Therefore, it is of great significance to detect MC levels in timber structures. Compared to current methods for timber MC detection, which are time-consuming and require bulky equipment deployment, Lead Zirconate Titanate (PZT)-enabled stress wave sensing combined with statistic machine learning classification proposed in this paper show the advantage of the portable device and ease of operation. First, stress wave signals from different MC cases are excited and received by PZT sensors through active sensing. Subsequently, two non-baseline features are extracted from these stress wave signals. Finally, these features are fed to a statistic machine learning classifier (i.e., naïve Bayesian classification) to achieve MC detection of timber structures. Numerical simulations validate the feasibility of PZT-enabled sensing to perceive MC variations. Tests referring to five MC cases are conducted to verify the effectiveness of the proposed method. Results present high accuracy for timber MC detection, showing a great potential to conduct rapid and long-term monitoring of the MC level of timber structures in future field applications.