Quantitative extraction of fine contour parameters for forest fire using satellite remote sensing
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Institute of Forest Resource Information Technique, State Laboratory for Remote Sensing and Information Techniques, Chinese Academy of Forestry,Institute of Forest Resource Information Technique; Communication University of China,Institute of Forest Resource Information Technique, State Laboratory for Remote Sensing and Information Techniques, Chinese Academy of Forestry,Information Center of Forest Fire Prediction and Monitoring, State Forestry Administration

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    Abstract:

    To fulfil quantitative monitoring of fire line contour (location, length and area) for forest fire prevention and suppression, the algorithm for fire identification have been developed using dynamic thresholds of window combining the images obtained in thermal band, short wave infrared band and near infrared band of Landsat TM/ETM+. In order to get the quantitative result of fire line contour, the identified fire image has been processed by a series method such as the connectivity identification, hole filling, little speckle elimination and edge smooth. An automatic fire line contour identification program has also been developed using Landsat TM/ETM images in ENVI 4.8 IDL condition. The method and program have been validated by selected typical forest fires which had taken place in northeast forest region of China and Амрская бласть of far east region of Russian. The validation results showed that the total accurate percent is 86.44%, the total false percent is 13.56% (the total omission is 1.77% and the commission is 11.79%). So, the methodology can be satisfied the need to knowing the fire line contour parameters for forest fire prevention and suppression operation.

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QIN Xian-Lin, YANG Fei, ZU Xiao-Feng, ZHANG Zi-Hui. Quantitative extraction of fine contour parameters for forest fire using satellite remote sensing[J]. Journal of Infrared and Millimeter Waves,2014,33(6):642~648

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History
  • Received:June 17,2013
  • Revised:September 28,2014
  • Adopted:September 06,2013
  • Online: November 27,2014
  • Published: