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Forest fire spread simulation based on VIIRS active fire data and FARSITE model

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China

  • Email：xvbenben@outlook.com
  • Introduction：徐奔奔，研究方向为大气遥感。E-mail： xvbenben@outlook.com
XU Benben1,  
  • Affiliation：

    Center of Emergency Rescue and Safeguard of Heilongjiang Province, Department of Emergency Management of Heilongjiang Province, Harbin 150001, China

WANG Weiye2,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China

CHEN Liangfu1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China

TAO Jinhua1,  
  • Affiliation：

    Jiangsu Environment Monitoring Center, Nanjing 210019, China

JI Xuanyu3,  
  • Affiliation：

    Forest Fire Corps of Heilongjiang Province, Harbin 150000, China

ZHANG Chengjie4,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China

  • Email：fanmeng@radi.ac.cn
  • Introduction：范萌，研究方向为大气遥感。E-mail： fanmeng@radi.ac.cn
FAN Meng1*

реферат

Forest fires seriously affect the environment and social economy, e.g., damaging infrastructure, causing economic losses, and endangering human health. Effective simulation and prediction of forest fire growth are greatly important. Fire behavior models can provide analytical schemes for characterizing and predicting the speeds and directions of fire spread. However, fire spread models are subject to assumptions and limitations that inherently produce compounding errors during simulations. Satellite remote sensing monitoring of forest fire can be used to analyze the spatial dynamic change process of large-scale fires. It is an economical and effective technology for obtaining fire information in a large range and a short period. It can also provide fire location information for fire spread models.This study proposes a new approach of fire spread simulations based on the assessment of simulated fire growth discrepancies by using satellite active fire data. The FARSITE fire spread simulator was used to simulate the spread of forest fires that occurred on May 17, 2017 in Chenbaerhuqi, Inner Mongolia autonomous region, China, and the S-NPP\VIIRS forest active fire data were applied into the FARSITE simulator for calibration and re-initialization. The Landsat-8 and GF-1 data were used to generate the data required by the FARSITE. The fire field for different time periods was monitored by the multisource satellite data Sentinel-2A, GF-1 and GF-4 data. 375 m VIIRS active fire monitoring data were employed for re-initializing FARSITE fire simulation. We combined the satellite fire data and fire spread model for reducing errors of simulation results caused by the condition limitation of fire model, and the Sørensen’s coefficient (SC) was employed to evaluate the accuracy of fire spread simulation results at FARSITE before and after reinitializing the simulator for VIIRS active fire data.The re-initialization results of the FARISTE simulator by VIIRS active fire data showed that the simulation accuracy in each simulation process gradually decreased along with time. The distribution of simulation results indicated that the simulation findings after re-initialization were consistent with the actual fire perimeter monitored by high or moderate resolution remote sensing data. The highest precision in the process using active fire data increased by 56.89%, and the final accuracy increased by 45.45%. The final SC value increased from 54.14% to 78.76% when the satellite data were used to re-initialize the FARSITE fire simulation system, with increment of 42.76%. The maximum SC value was 87.8% for VIIRS active fire data re-initialization during simulation. The re-initialization approach meaningfully improved the accuracy of fire simulation.The use of satellite remote sensing active fire data and the re-initialization of FARSITE limited the further expansion of the error of fire spread model and improved the reliability and accuracy of forest fire simulation. This innovative approach represents a potential scheme for reducing the error of large-scale fire simulation results that can improve the reliability of fire spread model. This method provides an effective data assimilation method for fire prediction. It also provides a basis for fire management departments to manage forests and develop fire suppressing plans. In this study, the actual ground and air fire-fighting forces change the results of fire spread. They are an important factor for the deviation between the simulation results and the actual results.

ключеви́че слова́

remote sensing;forest fires;forest fires spread model;VIIRS;FARSITE;fire behavior simulations

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