Forest fire spread simulation based on VIIRS active fire data and FARSITE model
- Vol. 26, Issue 8, Pages: 1575-1588(2022)
DOI： 10.11834/jrs.20219427
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摘要
针对森林火灾模型模拟、预测火灾蔓延结果精度随时间增加而降低的问题，提出了使用卫星遥感火监测数据重初始化火灾模型降低模拟结果误差的方法。使用FARSITE（Fire Area Simulator）火灾模拟系统模拟内蒙古自治区一场森林火灾，并使用375 m的VIIRS（Visible infrared Imaging Radiometer Suite）火监测数据对FARSITE重初始化，探究该方法在中国林火蔓延模拟中的应用。使用SC（Sørensen’s Coefficient）值，辅以多源卫星遥感数据，评估该方法模拟结果精度。结果显示，各模拟过程中，模拟结果精度均随模拟时间的增加而降低，重初始化的模拟结果与实际过火区一致性更高，VIIRS重初始化模拟结果SC值最高提高56.89%，最终SC值提高了45.45%；最终模拟结果的SC值从最初的54.14%经过VIIRS火点修正后提高到78.76%；模拟过程中的VIIRS火点重初始化的模拟结果SC值最高为87.8%。使用VIIRS火监测数据重初始化FARSITE火灾模拟系统，将火灾分为多次进行模拟，缩短模拟时间，有效控制模拟误差的传递，提高了森林火灾蔓延模拟的精度，这种方法提高了火灾模型在大规模、长时间森林火灾应用中的可靠性。
Abstract
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.
关键词
遥感森林火灾林火蔓延VIIRSFARSITE火行为模拟
Keywords
remote sensingforest firesforest fires spread modelVIIRSFARSITEfire behavior simulations
references
Anderson K R, Englefield P, Little J M and Reuter G. 2009. An approach to operational forest fire growth predictions for Canada. International Journal of Wildland Fire, 18(8): 893-905 [DOI: 10.1071/WF08046http://dx.doi.org/10.1071/WF08046]
Benali A, Sá A C L, Ervilha A R, Trigo R M, Fernandes P M and Pereira J M C. 2017. Fire spread predictions: sweeping uncertainty under the rug. Science of the Total Environment, 592: 187-196 [DOI: 10.1016/j.scitotenv.2017.03.106http://dx.doi.org/10.1016/j.scitotenv.2017.03.106]
Cencerrado A, Cortés A and Margalef T. 2014. Response time assessment in forest fire spread simulation: an integrated methodology for efficient exploitation of available prediction time. Environmental Modelling and Software, 54: 153-164 [DOI: 10.1016/j.envsoft.2014.01.008http://dx.doi.org/10.1016/j.envsoft.2014.01.008]
Chen Y Y. 2018. Study on Remote Sensing Monitoring of Forest Fire and Simulation of Fire Spread and Estimation after Forest Fire Disaster. Hohhot: Inner Mongolia University
陈云云. 2018. 森林火灾遥感监测、蔓延模拟及灾后评估研究. 呼和浩特: 内蒙古大学
Coen J L and Schroeder W. 2013. Use of spatially refined satellite remote sensing fire detection data to initialize and evaluate coupled weather-wildfire growth model simulations. Geophysical Research Letters, 40(20): 5536-5541 [DOI: 10.1002/2013gl057868http://dx.doi.org/10.1002/2013gl057868]
Finney M A. 1998. FARSITE: fire Area Simulator-Model Development and Evaluation. USDA Forest Service.
He B H, Chen L F, Tao J H, Su L, Wang Z F, Li S S, Han D, Zhang Y and Yu C. 2011. A contextual fire detection algorithm based on observation geometry for HJ-1B-IRS. Journal of Infrared and Millimeter Waves, 30(2): 104-108, 114
贺宝华, 陈良富, 陶金花, 苏林, 王子峰, 李莘莘, 韩冬, 张莹, 余超. 2011. 基于观测几何的环境卫星红外相机遥感火点监测算法. 红外与毫米波学报, 30(2): 104-108, 114
Hilton J E, Miller C, Sullivan A L and Rucinski C. 2015. Effects of spatial and temporal variation in environmental conditions on simulation of wildfire spread. Environmental Modelling and Software, 67: 118-127 [DOI: 10.1016/j.envsoft.2015.01.015http://dx.doi.org/10.1016/j.envsoft.2015.01.015]
Justice C O, Román M O, Csiszar I, Vermote E F, Wolfe R E, Hook S J, Friedl M, Wang Z S, Schaaf C B, Miura T, Tschudi M, Riggs G, Hall D K, Lyapustin A I, Devadiga S, Davidson C and Masuoka E J. 2013. Land and cryosphere products from Suomi NPP VIIRS: overview and status. Journal of Geophysical Research: Atmospheres, 118(17): 9753-9765 [DOI: 10.1002/jgrd.50771http://dx.doi.org/10.1002/jgrd.50771]
Li X W, Niu Z C, Jiang S and Shi H. 2014. Operational characteristics of environmental monitoring satellite suomi NPP and usage in ecological environment monitoring. Environmental Monitoring and Forewarning, 6(3): 1-6
李旭文, 牛志春, 姜晟, 侍昊. 2014. 环境监测卫星Suomi NPP业务特性及生态环境监测应用. 环境监控与预警, 6(3): 1-6
Li Y, Feng Z K, Wang W and Ge B. 2007. Establishment of forest fire spreading model based on the remote sensing image. Journal of Beijing Forestry Univerisity, 29(S2): 93-97
李勇, 冯仲科, 王维, 葛斌. 2007. 基于遥感图像的林火蔓延模型的研建. 北京林业大学学报, 29(S2): 93-97
Nashun T, Wu G Z, Dai H Y and Su D Y. 2013. Forest fire spread simulation based on FARSITE. Journal of Anhui Agricultural Sciences, 41(19): 8203-8205
那顺陶格陶, 吴国周, 代海燕, 苏东玉. 2013. 基于FARSITE的森林火蔓延模拟研究. 安徽农业科学, 41(19): 8203-8205 [DOI: 10.13989/j.cnki.0517-6611.2013.19.100http://dx.doi.org/10.13989/j.cnki.0517-6611.2013.19.100]
Noble I R, Gill A M and Bary G A V. 1980. McArthur’s fire-danger meters expressed as equations. Australian Journal of Ecology, 5(2): 201-203 [DOI: 10.1111/j.1442-9993.1980.tb01243.xhttp://dx.doi.org/10.1111/j.1442-9993.1980.tb01243.x]
Papadopoulos G D and Pavlidou F N. 2011. A comparative review on wildfire simulators. IEEE Systems Journal, 5(2): 233-243 [DOI: 10.1109/JSYST.2011.2125230http://dx.doi.org/10.1109/JSYST.2011.2125230]
Parks S A. 2014. Mapping day-of-burning with coarse-resolution satellite fire-detection data. International Journal of Wildland Fire, 23(2): 215-223 [DOI: 10.1071/wf13138http://dx.doi.org/10.1071/wf13138]
Qin X L. 2016. Forest Fire Satellite Remote Sensing Monitoring. Beijing: China Forestry Publishing House
覃先林. 2016. 林火卫星遥感监测. 北京: 中国林业出版社
Sá A C L, Benali A, Fernandes P M, Pinto R M S, Trigo R M, Salis M, Russo A, Jerez S, Soares P M M, Schroeder W and Pereira J M C. 2017. Evaluating fire growth simulations using satellite active fire data. Remote Sensing of Environment, 190: 302-317 [DOI: 10.1016/j.rse.2016.12.023http://dx.doi.org/10.1016/j.rse.2016.12.023]
Sá A C L, Benali A, Pinto R, Fernandes P, Russo A, Santos F, Trigo R M, Pereira J M C, Jerez S and Da Camara C. 2014. Improving wildfire spread simulations using MODIS active fires: the FIRE-MODSAT project. VII International Conference on Forest Fire Research [DOI: 10.14195/978-989-26-0884-6_90http://dx.doi.org/10.14195/978-989-26-0884-6_90]
Salis M, Arca B, Alcasena F, Arianoutsou M, Bacciu V, Duce P, Duguy B, Koutsias N, Mallinis G, Mitsopoulos I, Moreno J M, Pérez J R, Urbieta I R, Xystrakis F, Zavala G and Spano D. 2016. Predicting wildfire spread and behaviour in Mediterranean landscapes. International Journal of Wildland Fire, 25(10): 1015-1032 [DOI: 10.1071/WF15081http://dx.doi.org/10.1071/WF15081]
Schroeder W, Oliva P, Giglio L and Csiszar I A. 2014. The New VIIRS 375 m active fire detection data product: algorithm description and initial assessment. Remote Sensing of Environment, 143: 85-96 [DOI: 10.1016/j.rse.2013.12.008http://dx.doi.org/10.1016/j.rse.2013.12.008]
Scott J H and Burgan R E. 2005. Standard Fire Behavior Fuel Models: a Comprehensive Set for Use with Rothermel’s Surface Fire Spread Model. Department of Agriculture [DOI: 10.2737/RMRS-GTR-153http://dx.doi.org/10.2737/RMRS-GTR-153]
Sørensen T. 1948. A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Kongelige Danske Videnskabernes Selskab, 5: 1-34
Taylor S W, Pike R G and Alexander M E. 1996. Field guide to the Canadian Forest Fire Behaviour Prediction (FBP) System. Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre
Taylor S W, Woolford D G, Dean C B and Martell D L. 2013. Wildfire prediction to inform fire management: statistical science challenges. Statistical Science, 28(4): 586-615 [DOI: 10.1214/13-STS451http://dx.doi.org/10.1214/13-STS451]
Veraverbeke S, Sedano F, Hook S J, Randerson J T, Jin Y F and Rogers B M. 2014. Mapping the daily progression of large wildland fires using MODIS active fire data. International Journal of Wildland Fire, 23(5): 655-667 [DOI: 10.1071/wf13015http://dx.doi.org/10.1071/wf13015]
Wang Z F. 1983. The mesurement method of the wildfire initial spread rate. Mountain Research, 1(2): 42-51
王正非. 1983. 山火初始蔓延速度测算法. 山地研究, 1(2): 42-51
Wu Z W, He H S, Chang Y, Liu Z H and Chen H W. 2011. Development of customized fire behavior fuel models for boreal forests of northeastern China. Environmental Management, 48(6): 1148-1157 [DOI: 10.1007/s00267-011-9707-3http://dx.doi.org/10.1007/s00267-011-9707-3]
Wu Z W, He H S, Liang Y, Luo X and Cai L Y. 2012. Spatial distribution characteristics of potential fire behavior in Fenglin Nature Reserve based on FARSITE Model. Acta Ecologica Sinica, 32(19): 6176-6186
吴志伟, 贺红士, 梁宇, 罗旭, 蔡龙炎. 2012. 基于FARSITE模型的丰林自然保护区潜在林火行为空间分布特征. 生态学报, 32(19): 6176-6186 [DOI: 10.5846/stxb201109111333http://dx.doi.org/10.5846/stxb201109111333]
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Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration
Innovation Center for FengYun Meteorological Satellite (FYSIC)
International Institute for Earth System Science, Nanjing University
School of Surveying and Land Information Engineering, Henan Polytechnic University
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