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Forest fire monitoring based on multisensor remote sensing techniques in Muli County, Sichuan Province

  • role： First author第一作者
  • Affiliation：

    The Key Laboratory for Silviculture and Conservation of Ministry of Education,Beijing Forestry University, Beijing100083, China

  • Email：KevinShadow@163.com
  • Introduction：饶月明，1994年生，男，硕士研究生，研究方向为植被定量遥感。E-mail：KevinShadow@163.com
RAO Yueming1,  
  • Affiliation：

    The Key Laboratory for Silviculture and Conservation of Ministry of Education,Beijing Forestry University, Beijing100083, China

WANG Chuan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    The Key Laboratory for Silviculture and Conservation of Ministry of Education,Beijing Forestry University, Beijing100083, China

  • Email：huaguo_huang@bjfu.edu.cn
  • Introduction：黄华国，1978年生，男，教授，研究方向为植被定量遥感。E-mail : huaguo_huang@bjfu.edu.cn
HUANG Huaguo1*

résumé

Forest fires seriously damage the stability of forest ecology and threaten the safety of human lives and properties. Traditional methods cannot detect fire disasters in time because of their limited observation region and time window. Remote sensing can accurately and continuously detect fire disasters in a large area with improved detection efficiency. Meteorological and polar satellites are mainly used to detect forest fires. However, these satellites cannot detect small fire scars and deal with the influence of cloud and rain. In this study, multisensor remote sensing data were combined to monitor the forest fire spots and progress in Muli Tibetan Autonomous County of Sichuan Province since March 30, 2019.First, we located the fire spots and their time of occurrence using the Chinese Gaofen-4 (GF-4) satellite. This geostationary satellite has a PMS (Panchromatic Multispectral ) camera with a high spatial (50 m) resolution and a mid-infrared sensor (IRS, 400 m) that is sensitive to abnormally high temperatures. Second, we computed the spectral difference between burning and unaffected forest stands using Sentinel-2. Third we classified the fire scars on the basis of Sentinel-2 using the OTSU algorithm to set the threshold of differenced Normalized Burn Ratio (dNBR). Finally, we used the synthetic aperture radar data from Sentinel-1A to relate Normalized Difference Vegetation Index (NDVI) to Polarization Ratio (PR).The results show that: (1) The location of fire spots can be accurately determined using the IRS and PMS data from GF-4. (2) The burning time is March 30 using the PMS data from GF-4 by combining the fire spot location and the sharp reduction in NDVI (from 0.7 to 0.25). (3) A difference is observed in the spectral curves of among the burning forest stands, unaffected areas, and different types of burned areas of Sentinel-2 data at 490 and 2200 nm. (4) The total area of damaged fire scars is 41.56 hm2, with 94.67% accuracy using the dNBR of 0.35 as the threshold from Sentinel-2 derived dNBR map. Lightly damaged fire scars are also classified (66.56 hm2, 90.94%). (5) The PR from Sentinel-1A data increases from 6.6 dB to 10.8 dB after burning. NDVI is linearly related to PR (R2=0.58 for fitting and 0.50 for verification).The above results agree well with the fire spot location and area from local field reports, and the burning time error is less than 12 h. This study provides an efficient method that can ignore the influence of cloud and rain and other complex environments to monitor forest fires. Many details can be obtained to capture the fire progress when GF-4 is used in monitoring. This study provides a reference for the identification of small fire disasters after their occurrences. The research methods and results can provide technical support for forest fire emergency.

mots-clés

forest fires monitoring;GF-4;SAR;multi-sensor remote sensing data;fire spreading

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