Forest Fire Severity Classification Using Probability Density Function and KOMPSAT-3A

확률밀도함수와 KOMPSAT-3A를 활용한 산불피해강도 분류

  • Lee, Seung-Min (Department of Geoinformatics Engineering, Namseoul University) ;
  • Jeong, Jong-Chul (Department of Geoinformatics Engineering, Namseoul University)
  • 이승민 (남서울대학교 공간정보공학과) ;
  • 정종철 (남서울대학교 공간정보공학과)
  • Received : 2019.10.31
  • Accepted : 2019.11.26
  • Published : 2019.12.31


This research deals with algorithm for forest fire severity classification using multi-temporal KOMPSAT-3A image to mapping forest fire areas. The recent satellite of the KOMPSAT series, KOMPSAT-3A, demonstrates high resolution and multi-spectral imagery with infrared and high resolution electro-optical bands. However, there is a lack of research to classify forest fire severity using KOMPSAT-3A. Therefore, the purpose of this study is to analyze forest fire severity using KOMPSAT-3A images. In addition, this research used pre-fire and post-fire Sentinel-2 with differenced Normalized Burn Ratio (dNBR) to taking for burn severity distribution map. To test the effectiveness of the proposed procedure on April 4, 2019, Gangneung wildfires were considered as a case study. This research used the probability density function for the classification of forest fire damage severity based on R software, a free software environment of statistical computing and graphics. The burn severities were estimated by changing NDVI before and after forest fire. Furthermore, standard deviation of probability density function was used to calculate the size of each class interval. A total of five distribution of forest fire severity were effectively classified.


Grant : 국토위성정보 수집 및 활용기술개발

Supported by : 국토교통부


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