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Adaptive threshold method for active fire identification based on GF-4 PMI data

  • role： First author第一作者
  • Affiliation：

    Institute of Forest Reseources Information Technique, Chinese Academy of Forestry, Forestry Remote Sensing, Beijing 100091, China

  • Email：LiuShuchao1992@163.com
  • Introduction：刘树超，1992年生，男，硕士研究生，研究方向为光学遥感影像处理和森林火灾监测。E-mail:LiuShuchao1992@163.com
LIU Shuchao1,  
  • Affiliation：

    Institute of Forest Reseources Information Technique, Chinese Academy of Forestry, Forestry Remote Sensing, Beijing 100091, China

LI Xiaotong1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Forest Reseources Information Technique, Chinese Academy of Forestry, Forestry Remote Sensing, Beijing 100091, China

  • Email：noaags@ifrit.ac.cn
  • Introduction：覃先林，1969年生，男，研究员，研究方向为森林覆盖变化及林火预警监测技术。E-mail:noaags@ifrit.ac.cn
QIN Xianlin1*,  
  • Affiliation：

    Institute of Forest Reseources Information Technique, Chinese Academy of Forestry, Forestry Remote Sensing, Beijing 100091, China

SUN Guifen1,  
  • Affiliation：

    Institute of Forest Reseources Information Technique, Chinese Academy of Forestry, Forestry Remote Sensing, Beijing 100091, China

LIU Qian1

Resümee

GF-4 is China's first geostationary orbit optical remote sensing satellite with high-resolution ground observation. Compared with other meteorological satellites, it has a single medium-wave infrared channel with the characteristics of high spatial and temporal resolution. To explore the application of GF-4 panchromatic multispectral and medium wave infrared (PMI) in forest fire detection and provide a new method for forest fire monitoring in China.Yulong autonomous county of Yunnan province, the Amur region and the outer Baikal frontier of Russia were selected as experimental areas. During the forest fires in the experimental areas in 2017 and 2018, 12 scene images of GF-4 PMI were obtained, of which 8 scene images were used as the experimental group, and 4 scene images were used as the verification group. Statistical analysis was performed on the typical characteristics of the experimental group images, and the ‘split window method’ was used to construct an adaptive threshold detection algorithm, then the detection algorithm was used to detect the images of the verification group and compared with the results by visually interpreted.The results showed that the detection of forest fire points in Yulong autonomous county of Yunnan province, the accuracy of the algorithm in this paper was 80.0%, the omission detection rate was 20.0%, and the comprehensive evaluation index, which was based on the verification of fire detection, was 0.781. The detection of forest fire points in the outer Baikal frontier of Russia, the accuracy of the algorithm detection was 99.1%, the omission detection rate was 24.3%, and the comprehensive evaluation index was 0.858. The forest fire of 2017 in the Amur region, the accuracy of the algorithm detection was 97.7%, the omission detection rate was 22.2%, and the comprehensive evaluation index was 0.866. The forest fire of 2018 in the Amur region, the accuracy of the algorithm detection was 92.4%, the omission detection rate was 14.5%, and the comprehensive evaluation index was 0.889.The accuracy of the fire points detection in these three experimental areas was higher than 80.0%, the comprehensive evaluation index set based on the accuracy verification of the fire point detection was higher than 0.780. This algorithm could realize the fire point detection of GF-4 PMI images, and the algorithm had a higher accuracy rate of fire point detection in a large range of forest fire, but the omission detection rate of the algorithm was high and needs to be further optimized. The experimental results showed that the proposed algorithm was reliable, which could provide a method reference for forest fire monitoring in China.

Schlüsselwort

GF-4;split-window method;adaptive method;fire point;threshold detection

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