• 융합정보논문지
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• 제11권11호
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• pp.143-150
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• 2021

산불감시를 위해 한반도 지역을 24시간 상시 감시하고 감시정보의 전파가 가능한 정지궤도위성 기반의 산불감시 위성체계 구축을 위한 내용을 소개하고, 산불감시 시스템의 구축과 다양한 활용 방안에 대해 기술한다. 위성 활용 산불감시 시스템을 구축하기 위해 문헌연구, 기술적 원리, 산불감시 수단, 위성 산불감시 시스템에 대해 기술하고, 결론을 도출하겠다. 위성 활용 산불감시 시스템은 적외선 탐지 광학센서를 탑재한 정지궤도 위성 1기와 위성에서 수신된 자료를 처리하여 감시정보를 전파하는 지상처리 소로 구성될 수 있다. 산불감시 위성은 우리나라 상공 정지궤도에 위치하며 하루 24시간 365일 상시 운용되어야 한다. 산불감시 기술은 적외선탐지 기술로서 산불 감시 등의 국가 공공 이익 분야와 국가 안보분야에 활용이 가능하다. 하루 24시간 상시 운용되어야 하며 이를 만족시키기 위해서는 정지궤도 위성 기반의 산불감시 위성 시스템의 구축이 효율적이라고 할 수 있겠다.

• 한국환경복원기술학회지
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• 제25권5호
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• pp.43-57
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• 2022

This study aims to classify forest fire-affected areas, identify forest types by the intensity of forest fire damage using multi-time Landsat-satellite images before and after forest fires and to analyze the effects of artificial restoration sites and natural restoration sites. The difference in the values of the Normalized Burned Ratio(NBR) before and after forest fire damage not only maximized the identification of forest fire affected and unaffected areas, but also quantified the intensity of forest fire damage. The index was also used to confirm that the higher the intensity of forest fire damage in all forest fire-affected areas, the higher the proportion of coniferous forests, relatively. Monitoring was conducted after forest fires through Normalized Difference Vegetation Index(NDVI), an index suitable for the analysis of effects by restoration type and the NDVI values for artificial restoration sites were found to no longer be higher after recovering the average NDVI prior to the forest fire. On the other hand, the natural restoration site witnessed that the average NDVI value gradually became higher than before the forest fires. The study result confirms the natural resilience of forests and these results can serve as a basis for decision-making for future restoration plans for the forest fire affected areas. Further analysis with various conditions is required to improve accuracy and utilization for the policies, in particular, spatial analysis through forest maps as well as review through site checks before and immediately after forest fires. More precise analysis on the effects of restoration will be available based on a long term monitoring.

• 대한임베디드공학회논문지
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• 제18권6호
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• pp.327-333
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• 2023

Existing studies on the UAV (Unmanned Aerial Vehicle)-based CPS (Cyber Physical System) environment lack forest fire monitoring and forest fire reconnaissance using real-world UAVs. So, it is necessary to monitor forest fires early through CPS based on real-world UAVs with high reliability and resource management efficiency. In this paper presents an MFG (Misstion File Generater) that automatically generates a flight path of an UAV for forest fire monitoring in a CPS environment. MFG generates flight paths based on a hiking trail with a high fire probability due to a true story of an entrant. We have confirmed that the flight path generated by MFG can be applied to the UAV. Also, we have verified that the UAV flies according to the flight path generated by MFG in simulation, with a negligible error rate.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1295-1297
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• 2003

Owing to the higher temporal resolution, meteorological satellite data is widely used to monitor the disasters happened on the earth's surface. However, the precision of identifying disaster information is limited by the poor spatial resolution. As known, GIS technology is good at processing and analyzing the geographic information. The result shows, integrating with GIS technology, the ability of monitoring forest fire using meteorological satellite data has been greatly improved.

• 대한원격탐사학회지
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• 제20권1호
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• pp.13-21
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• 2004

The usefulness of the multi-temporal satellite image to monitoring the vegetation recovery process after forest fire was tested. Using multi-temporal Landsat TM and ETM＋data, NDVI and NBR changes over times were analyzed. Both NDVI and NBR values were rapidly decreased after the fire and gradually increased for all forest type and damage class. However, NBR curve showed much clearer tendency of vegetation recovery than NDVI. Both indices yielded the lowest values in severely damaged red pine forest. The results show the vegetation recovery process after forest fire can detect and monitor using multi-temporal Landsat image. NBR was proved to be useful to examine the recovering and development process of the vegetation after fire. In the not damaged forest, however the NDVI shows more potential capability to discriminate the forest types than NBR..

• 한국정보기술응용학회:학술대회논문집
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• 한국정보기술응용학회 2005년도 6th 2005 International Conference on Computers, Communications and System
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• pp.123-126
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• 2005

Wireless Sensor Networks will revolutionize applications such as environmental monitoring, home automation, and logistics. We developed forest fire surveillance system. In this paper, Considering the fact that in Korea, during November to May, forest fires occur very frequently causing catastrophic damages on the valuable environment, Although exists other forest fire surveillance system such as surveillance camera tower, infrared ray sensor system and satellite system. Preexistence surveillance system can't real-time surveillance, monitoring, database and automatic alarm. But, forest fire surveillance system(FFSS) support above. In this paper, we describes a system development approach for a wireless sensor network based FFSS that is to be used to measure temperature and humidity as well as being fitted with a smoke detector. Such a device can be used as an early warning fire detection system and real-time surveillance in the area of a bush fire or endangered public infrastructure. Once the system has being development, a mesh network topology will be implemented with the chosen sensor node with the aim of developing a sophisticated mesh network.

• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.536-546
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• 2022

A society will lose a lot of something in this field when the forest fire broke out. If a forest fire can be detected in advance, damage caused by the spread of forest fires can be prevented early. So, we studied how to detect forest fires using CCTV currently installed. In this paper, we present a deep learning-based model through efficient image data construction for monitoring forest fire smoke, which is unstructured data, based on the deep learning model YOLOv5. Through this study, we conducted a study to accurately detect forest fire smoke, one of the amorphous objects of various forms, in YOLOv5. In this paper, we introduce a method of self-learning by producing insufficient data on its own to increase accuracy for unstructured object recognition. The method presented in this paper constructs a dataset with a fixed labelling position for images containing objects that can be extracted from the original image, through the original image and a model that learned from it. In addition, by training the deep learning model, the performance(mAP) was improved, and the errors occurred by detecting objects other than the learning object were reduced, compared to the model in which only the original image was learned.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.747-749
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• 2003

For forest fire monitoring in relatively cool area like Siberia, design of Decision Support System (DSS) is proposed. The DSS is consisted of three different algorithms to detect potential fires from NOAA AVHRR image. The algorithm developed by CCRS (Canada Center for Remote Sensing) uses fixed thresholds for multi-channel information like one by ESA (European Space Agency). The algorithm of IGBP (International Geosphere Biosphere Program) involves contextual information in deriving fire pixels. CCRS and IGBP algorithms are rather liberal compared to more conservative ESA algorithm. Fire pixel information from the three algorithms is presented to the user. The user considers all these information in making decision about the location fire takes place.

• 대한원격탐사학회지
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• 제12권1호
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• pp.60-80
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• 1996

AVHRR(Advanced Very High Resolution Radiometer) on NOAA satellite provides data in five spectral, one in visible range, one in near infrared and three in thermal range. In this paper, application of NOAA/AVHRR data is studied for environment monitoring such as cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index, forest fire, flood, snow cover and so on. The analyses for cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index and forest fire showed reasonable agreement. But monitoring for flood and snow cover was uneasy due to the limitations such as cloud contamination, low spatial resolution. So this research had only simple purpose to identify well-defined waterbody for dynamic monitoring of flood. Based on development of these basic algorithms, we have a plan to further reseach for environment monitoring using AVHRR data.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1473-1473
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• 2003