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

Abstract

Forest fires have a huge impact on 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 of great importance. 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, which is an economical and effective technology for obtaining fire information in a large range and a short period, and can 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 using satellite active fire data. The FARSITE fire spread simulator was used to simulate the fire 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 was 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 were monitored by the multi-source satellite data Sentinel-2A, GF-1 and GF-4 data. 375m VIIRS active fire monitoring data was 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 SC (Sørensen's coefficient) 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 show that the simulation accuracy in each simulation process gradually decreases along with time. According to the distribution of simulation results, the simulation results after re-initialization are better 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 56.89%, and the final accuracy increased 45.45%. The final SC value is increased from 54.14% to 78.76% after the satellite data being used to re-initialize the FARSITE fire simulation system, with the increasement 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 limit the further expansion of the error of fire spread model and improve 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, which provides a basis for fire management departments to manage forests and make fire suppressing plans. In this study, the actual ground and air fire-fighting forces change the results of fire spread, which is an important factor for the deviation between the simulation results and the actual results.