• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1861-1869
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• 2022

Recent climate change can increase the frequency and damage of wildfires worldwide. It can also lead to the deterioration of the forest ecosystem and increase casualties and economic loss. Satellite-based indices for forest damage can facilitate an objective and rapid examination of burned areas and help analyze inaccessible places like North Korea. In this letter, we conducted a detection of burned areas in North Korea using the traditional Normalized Burn Ratio (NBR), the Normalized Difference Vegetation Index (NDVI) to represent vegetation vitality, and the Fire Burn Index (FBI) and Forest Withering Index (FWI) that were recently developed. Also, we suggested a strategy for the satellite-based detection of burned areas in the Korean Peninsula as a result of comparing the four indices. Future work requires the examination of small-size wildfires and the applicability of deep learning technologies.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.545-557
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• 2022

This study used high-resolution satellite images and supervised classification technique based on machine learning method in order to detect the areas affected by wildfires in the demilitarized zone (DMZ) where direct access is difficult. Sentinel-2 A/B was used for high-resolution satellite images. Land cover map was calculated based on the SVM supervised classification technique. In order to find the optimal combination to classify the DMZ wildfire damage area, supervised classification according to various kernel and band combinations in the SVM was performed and the accuracy was evaluated through the error matrix. Verification was performed by comparing the results of the wildfire detection based on satellite image and data by the wildfire statistical annual report in 2020 and 2021. Also, wildfire damage areas was detected for which there is no current data in 2022. This is to quickly determine reliable results.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1489-1503
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• 2022

Drone and sensor technologies are enabling digitalization of agricultural crop's growth information and accelerating the development of the precision agriculture. These technologies could be able to assess damage of crops when natural disaster occurs, and contribute to the scientification of the crop insurance assessment method, which is being conducted through field survey. This study was aimed to calculate lodged damage rate from the vegetation indices extracted by drone based RGB images for soybean. Support Vector Classifier (SVC) models were considered by adding vegetation indices to the Crop Surface Model (CSM) based lodged damage rate. Visible Atmospherically Resistant Index (VARI) and Green Red Vegetation Index (GRVI) based lodged damage rate classification were shown the highest accuracy score as 0.709 and 0.705 each. As a result of this study, it was confirmed that drone based RGB images can be used as a useful tool for estimating the rate of lodged damage. The result acquired from this study can be used to the satellite imagery like Sentinel-2 and RapidEye when the damages from the natural disasters occurred.

• Composites Research
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• v.18 no.6
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• pp.26-33
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• 2005

The mechanical properties of composite materials may severely degrade in the presence of damage. Especially, the high-velocity impact such as bird strike, a hailstorm, and a small piece of tire or stone during high taxing, can cause considerable damage to the structures and sub-system in spite of a very small mass. However, it is not easy to detect the damage in composite plates using a single technique or any conventional methods. In this paper, the PVDF(polyvinylidene fluoride) film sensors were used for monitoring high-velocity impact damage initiation and propagation in composite laminates. The WT(wavelet transform) and STFT(short time Fourier transform) are used to decompose the sensor signals. A ultrasonic C-scan and a digital microscope are also used to examine the extent of the damage in each case. This research shows how various sensing techniques, PVDF sensor in particular, can be used to characterize high-velocity impact damage in advanced composite.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.187-194
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• 2008

LiDAR는 일반 항공사진 및 위성영상과는 달리 사물의 높이를 측정할 수 있어 산림의 3차원 모델링을 수행할 수 있다. 본 연구에서는 이러한 LiDAR의 특성을 이용하여 산불이 발생한 강원도 양양지역 산림의 물리적 피해를 분석하였으며, 디지털 항공사진으로부터 Normalized Difference Vegetation Index (NDVI)를 추출하여 산림의 생물학적 피해를 분석하였다. 산림의 물리적 피해는 임관의 피해정도에 따라 지표면에서 반사되는 Point Data의 개수의 비율로서 추정을 하였다. 피해정도의 고저(高低)를 구분하는 기준은 통계적 방법 (Jenk's Natural Break) 으로부터 추정된 0.3594을 사용하였으며, 지표면 반사비율이 0.3594 이상인 경우 물리적 피해정도를 고(高, Serious Physical Damage; SPD), 지표면 반사비율이 0.3594 이하인 경우 물리적 피해정도를 저(低, Light Physical Damage; LPD)로 나타내었다. 또한 생물학적 피해는 일반적인 NDVI 값의 범위(-1

• Smart Structures and Systems
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• v.5 no.4
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• pp.329-344
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• 2009

Ultrasonic chaotic excitations combined with sensor prediction algorithms have shown the ability to identify incipient damage (loss of preload) in a bolted joint. In this study we examine a physical experiment on a single-bolt aluminum lap joint as well as a three-dimensional physics-based simulation designed to model the behavior of guided ultrasonic waves through a similarly configured joint. A multiple bolt frame structure is also experimentally examined. In the physical experiment each signal is imparted to the structure through a macro-fiber composite (MFC) patch on one side of the lap joint and sensed using an equivalent MFC patch on the opposite side of the joint. The model applies the waveform via direct nodal displacement and 'senses' the resulting displacement using an average of the nodal strain over an area equivalent to the MFC patch. A novel statistical classification feature is developed from information theory concepts of cross-prediction and interdependence. This damage detection algorithm is used to evaluate multiple damage levels and locations.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.450-453
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• 2005

A forest fire occurs mainly as natural factor such as wind, temperature or human factor such as light. Recently, the most of forest fire prevention is prediction or prevision against forest fire by using remote sensing technology. However in order to forest fire prevention, the remote sensing has many limitations such as high cost and advanced technologies and so on. Therefore, we need to multisensor integration system that utilize not only remote sensing but also in-situ sensing in order to reduce large damage of forest fire though analysis of happen cause and prediction routing of occurred forest fire. In this paper we propose a multisensor integration system that offers prediction information of factors and route of forest fire by integrates collected data from remote sensor and in-situ sensor for forest fire prevention. The proposed system is based on wireless sensor network for collect observed data from various sensors. The proposed system not only offers great quality information because firstly, raw data level fuse different format of collected data from remote and in-situ sensor but also accomplish information level fusion based on result of first stage. Offered information from our system can help early prevention of factor and early prevision against occurred forest fire which transfer to SMS service or alert service into monitoring interface of administrator.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1785-1791
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• 2021

Natural and environmental disasters are recently increasing in frequency and complexity worldwide due to the rapid expansion of overpopulation, industrialization, and urbanization. Thus, analyzing past critical events/disasters in deep and preparing for future disasters in terms of risk identification, assessment and management are imperative requirements. In this special issue, we introduce several interesting studies covering disaster risk management and observation technologies for the heat waves, particulate matters, floods, drought, and earthquake using remote sensing and GIS performed by i-SEED (School of Integrated Science for Sustainable Earth & Environmental Disaster at Pukyong National University). We expect that the results of this special issue provide comprehensive information on the risk management and damage prevention of natural and environmental disasters and offer guidance on the application to future disasters to reduce their risks and impacts.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.191-195
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• 2007

Recently, landslides frequently happen at a natural slope during period of intensive rainfall. With rapidly increasing population of steep terrain in Korea, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide prediction methods have been developed in the world. In this study, a simple landslide prediction system that enables people to escape the endangered area is developed. The system is focused to debris flows which happen frequently during periods of intensive rainfall at steep slopes in Kangwondo. This system is based on the wireless sensor network that is composed of sensor nodes, gateway, and server system. Sensor nodes that are composed of sensing part and communication part are newly developed to detect sensitive ground movement. Sensing part is designed to measure tilt angle and acceleration accurately, and communication part is deployed with Bluetooth (IEEE 802.15. I) module to transmit the data to the gateway. To verify the feasibility of this landslide prediction system, a series of laboratory tests is performed at a small-scale earth slope supplying rainfall by artificial rainfall dropping device. It is found that sensing nodes installed at slope can detect the ground motion when the slope failure starts. It is expected that the landslide prediction system by wireless senor network can provide early warnings when landslides such as debris flow occurs, and can be applied to ubiquitous computing city (U-City) that is characterized by disaster free.

• Structural Monitoring and Maintenance
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• v.3 no.1
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• pp.91-114
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• 2016

In this paper, recent research trends and activities on structural health monitoring (SHM) of civil infrastructure in Korea are reviewed. Recently, there has been increasing need for adopting smart sensing technologies to SHM, so this review focuses on smart sensing, monitoring, and assessment for civil infrastructure. Firstly, the research activities on smart sensor technology is reviewed including optical fiber sensors, piezoelectric sensors, wireless smart sensors, and vision-based sensing system. Then, a brief overview is given to the recent advances in smart monitoring and assessment techniques such as vibration-based global monitoring techniques, local monitoring with piezoelectric materials, decentralized monitoring techniques for wireless sensors, wireless power supply and energy harvest. Finally, recent joint SHM activities on several test beds in Korea are discussed to share the up-to-date information and to promote the smart sensors and monitoring technologies for applications to civil infrastructure. It includes a Korea-US joint research on test bridges of the Korea Expressway Corporation (KEC), a Korea-US-Japan joint research on Jindo cable-stayed bridge, and a comparative study for cable tension measurement techniques on Hwamyung cable-stayed bridge, and a campaign test for displacement measurement techniques on Sorok suspension bridge.