• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.149-152
• /
• 2005

A new methodology to detect forest fire burnt scars at near real time using MODIS (Moderate-resolution Imaging Spectroradiometer) data is presented here with a goal of introducing a new and improved capability to detect forest fire burnt scars in Thailand. This new technology is expected to increase the efficiency and effectiveness of the forest fire tackling resources distribution and management of the country. Using MODIS data in burnt scars detection has two major advantages - high availability of data and high resolution per performance ratio. Results prove the near real time algorithm suitable and working well in order to monitor the forest fire dynamic movement. The algorithm is based on the threshold separated linear equation of burnt and un-burnt. A ground truth experiment confirms the burnt and un-burnt? areas characteristics (temperature and NDVI). A threshold line on a scatter plot of Band I and Band 2 is determined to separate the burnt from un-burnt pixels. The different threshold values of NDVI and temperature use to identify pixels' anomaly, abnormal low NDVI and high temperature. The overlay (superimpose) method is used to verify burnt pixels. Since forest fire is a dynamic phenomenon, MODIS burnt scars information is suiting well to fill in the missing temporal information of LANDSAT for the forest fire control managing strategy in Thailand. This study was conducted in the Huai-Kha-Kaeng (HKK) Wildlife Sanctuary, Thailand

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.747-749
• /
• 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.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.6
• /
• pp.1506-1514
• /
• 2015

In this paper, we proposed a forest fires prediction and detection system. It could provide a situation of fire prediction and detection methods using context awareness sensor. A fire occurs wide range of sensing a fire in a single camera sensor, it is difficult to detect the occurrence of a fire. In this paper, we propose an algorithm for real-time by using a temperature sensor, humidity, Co2, the flame presence information acquired and comparing the data based on multiple conditions, analyze and determine the weighting according to fire in complex situations. In addition, it is possible to differential management of intensive fire detection and prediction for required dividing the state of fire zone. Therefore we propose an algorithm to determine the prediction and detection from the fire parameters as an temperature, humidity, Co2 and the flame in real-time by using a context awareness sensor and also suggest algorithm that provide the path of fire diffusion and service the secure safety zone prediction.

• Journal of Convergence for Information Technology
• /
• v.10 no.10
• /
• pp.123-127
• /
• 2020

In order to obtain the overall fire line information of medium and large forest fires at night, the ground control system was developed to determine whether forest fires occurred through real-time video clips and to calculate the location of the forest fires determined using the location of drones, angle information of video cameras, and altitude information on the map to reduce the time required for regular video matches obtained after the completion of the mission. To verify the reliability of the developed function, the error distance of the aiming position information of the flight altitude star and the image camera was measured, and the location information within the reliable range was displayed on the map. As the function developed in this paper allows real-time identification of multiple locations of forest fires, it is expected that overall fire line information for the establishment of forest fire extinguishing measures will be obtained more quickly.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.3
• /
• pp.159-164
• /
• 2019

Detecting wildfire using only color in image information is a very difficult issue. This paper proposes an algorithm to detect forest fire area by analyzing color and motion of the area in the video including forest fire. The proposed algorithm removes the background region using the Gaussian Mixture based background segmentation algorithm, which does not depend on the lighting conditions. In addition, the RGB channel is changed to an HSV channel to extract flame candidates based on color. The extracted flame candidates judge that it is not a flame if the area moves while labeling and tracking. If the flame candidate areas extracted in this way are in the same position for more than 2 minutes, it is regarded as flame. Experimental results using the implemented algorithm confirmed the validity.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_2
• /
• pp.1083-1093
• /
• 2019

The purpose of this study is to detect the burned area change by direct burning of tree canopies and post-fire mortality of trees via analyzing satellite imageries from the Korea multi-purpose satellite-2 and -3 (KOMPSAT-2 and -3) for two large-fires over the Goseong-Sokcho and Gangneung-Donghae regions in April 2019. For each case, the burned area was compared between two dates: the day when the fire occurred and 15-18 days after it. As the results, within these two dates, there was no substantial difference in burned area of sites whose severities were marked as "Extreme", but sites with "High" and "Low" severities showed significant differences in burned area between the two dates. These differences were resulted from the lagged post-fire browning of canopies which was detected by images from in-situ observation,satellite, and the unmanned aerial vehicle. The post-fire browning started after 3-4 days and became apparent after 10-15 days. This study offers information about the timing to quantify the burned area by large fire and about the mechanism of post-fire mortality. Also, the findings can support policy makers in planning the restoration of the damaged areas.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.10 no.6
• /
• pp.626-632
• /
• 2017

Since natural disaster occurs increasingly and becomes complicated, it causes deaths, disappearances, and damage to property. As a result, there is a growing interest in the development of ICT-based natural disaster response technology which can minimize economic and social losses. In this letter, we introduce the main functions of the forest fire management platform by using images from an UAV. In addition, we propose a disaster image analysis technology based on the deep learning which is a key element technology for disaster detection. The proposed deep learning based disaster image analysis learns repeatedly generated images from the past, then it is possible to detect the disaster situation of forest-fire similar to a person. The validity of the proposed method is verified through the experimental performance of the proposed disaster image analysis technique.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.698-701
• /
• 2005

This paper examines that is extracted certain information in forest areas within high resolution imagery based on wavelet transformation. First of all, study areas are selected one more species distributed spots refer to forest type map. Next, study area is cut 256 x 256 pixels size because of image processing problem in large volume data. Prior to wavelet transformation, five texture parameters (contrast, dissimilarity, entropy, homogeneity, Angular Second Moment (ASM≫ calculated by using Gray Level Co-occurrence Matrix (GLCM). Five texture images are set that shifting window size is 3x3, distance .is 1 pixel, and angle is 45 degrees used. Wavelet function is selected Daubechies 4 wavelet basis functions. Result is summarized 3 points; First, Wavelet transformation images derived from contrast, dissimilarity (texture parameters) have on effect on edge elements detection and will have probability used forest road detection. Second, Wavelet fusion images derived from texture parameters and original image can apply to forest area classification because of clustering in Homogeneous forest type structure. Third, for grading evaluation in forest fire damaged area, if data fusion of established classification method, GLCM texture extraction concept and wavelet transformation technique effectively applied forest areas (also other areas), will obtain high accuracy result.

• Korean Journal of Remote Sensing
• /
• v.35 no.1
• /
• pp.39-55
• /
• 2019

In this study, we have proposed an improved method to detect forest fires by correcting the reflected signals of day images using the middle-wavelength infrared (MWIR) channel. The proposed method is allowed to remove the reflected signals only using the image itself without an existing data source such as a land-cover map or atmospheric data. It includes the processing steps for calculating a solar-reflected signal such as 1) a simple correction model of the atmospheric transmittance for the MWIR channel and 2) calculating the image-based reflectance. We tested the performance of the method using the MODIS product. When compared to the conventional MODIS fire detection algorithm (MOD14 collection 6), the total number of detected fires was improved by approximately 17%. Most of all, the detection of fires improved by approximately 30% in the high reflection areas of the images. Moreover, the false alarm caused by artificial objects was clearly reduced and a confidence level analysis of the undetected fires showed that the proposed method had much better performance. The proposed method would be applicable to most satellite sensors with MWIR and thermal infrared channels. Especially for geostationary satellites such as GOES-R, HIMAWARI-8/9 and GeoKompsat-2A, the short acquisition time would greatly improve the performance of the proposed fire detection algorithm because reflected signals in the geostationary satellite images frequently vary according to solar zenith angle.

• Korean Journal of Remote Sensing
• /
• v.36 no.1
• /
• pp.29-39
• /
• 2020

Forest fire is a critical disaster that causes massive destruction of forest ecosystem and economic loss. Hence, accurate estimation of the burned area is important for evaluation of the degree of damage and for preparing baseline data for recovery. Since most of the area size damaged by wildfires in Korea is less than 1 ha, it is necessary to use satellite or drone images with a resolution of less than 10m for detecting the damage area. This paper aims to detect wildfire-damaged area from a Kompsat-3 image using the indices such as NDVI (normalized difference vegetation index) and FBI (fire burn index) and to examine the classification characteristics according to the methods such as Otsu thresholding and ISODATA(iterative self-organizing data analysis technique). To mitigate the salt-and-pepper phenomenon of the pixel-based classification, a gaussian filter was applied to the images of NDVI and FBI. Otsu thresholding and ISODATA could distinguish the burned forest from normal forest appropriately, and the salt-and-pepper phenomenon at the boundaries of burned forest was reduced by the gaussian filter. The result from ISODATA with gaussian filter using NDVI was closest to the official record of damage area (56.9 ha) published by the Korea Forest Service. Unlike Otsu thresholding for binary classification,since the ISODATA categorizes the images into multiple classes such as(1)severely burned area, (2) moderately burned area, (3) mixture of burned and unburned areas, and (4) unburned area, the characteristics of the boundaries consisting of burned and normal forests can be better expressed. It is expected that our approach can be utilized for the high-resolution images obtained from other satellites and drones.