Real-time extraction of fire line and optimization of spread simulation fire line based on infrared sequence images

ZHANG Xiaodi, LI Mingze, WANG Bin, WU Zechuan, MO Zhukun, FAN Zhongzhou

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (6) : 192-202.

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南京林业大学学报（自然科学版）
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JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (6) : 192-202. DOI: 10.12302/j.issn.1000-2006.202207036

Real-time extraction of fire line and optimization of spread simulation fire line based on infrared sequence images

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Abstract

【Objective】 In recent years, forest fire disturbances have seriously affected forest structure and environment. The accurate simulation of forest fire spread is an effective way to reduce forest fire damage. The Rothermel-Huygens principle was developed and optimized to predict and simulate the spread of forest fires, explore the law of forest fire spread, and provide theoretical support and effective plans for forest fire prevention. 【Method】 An ignition experiment was conducted on fine combustibles in the forests of typical flammable tree species in Heilongjiang Province. The fire line was extracted using the threshold segmentation and Canny edge detection algorithms, and the forest fire spread rate was calculated. Eight factors affecting forest fire spread were collected, and Pearson’s correlation analysis and partial correlation coefficients were used to conduct a correlation analysis on forest fire spread factors. On this basis, the Rothermel-Huygens principal model was constructed to simulate the forest fire spreading process, and a comparison and analysis were performed using the actual real-time fire line contours. 【Result】 Correlation analysis revealed significant positive correlations of wind speed, slope, and fire site temperature with fire spread rate. Moisture content, thickness, and load of the combustibles were significantly negatively correlated with fire spread rate. The overall accuracy (PT) of the indoor ignition experiment of the constructed Rothermel-Huygens model reached 79.25%, sensitivity (S) was 78.42%, F-measure value was 79.11%, and Kappa coefficient was 0.804. The PT of the outdoor ignition experiment of the constructed Rothermel-Huygens model reached 85.15%, S was 82.31%, F-measure value was 82.85%, and the Kappa coefficient was 0.832. The findings indicated that the Rothermel-Huygens principle model could accurately predict the fire spreading process and the performance of the model was stable. 【Conclusion】 Wind speed, moisture content of combustibles, and slope are the dominant factors affecting the forest fire spread rate in Heilongjiang. The constructed Rothermel-Huygens principal model was suitable for simulating the spread of forest fires. With good simulation results for indoor and outdoor ignitions, the model can accurately predict and capture the position of the fire line.

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ignition experiment / Rothermel-Huygens model / image segmentation / forest fire spread speed / Heilongjiang Province

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ZHANG Xiaodi , LI Mingze , WANG Bin , et al . Real-time extraction of fire line and optimization of spread simulation fire line based on infrared sequence images[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2023, 47(6): 192-202 https://doi.org/10.12302/j.issn.1000-2006.202207036

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis
[1]
KALANTAR B, UEDA N, IDREES M O, et al. Forest fire susceptibility prediction based on machine learning models with resampling algorithms on remote sensing data[J]. Remote Sens, 2020, 12(22): 3682, DOI: 10.3390/rs12223682.
Cited in this article [1]
[2]
李滨, 刘可宁. 基于无人机光学遥感的森林生物量估算研究[J]. 森林工程, 2022, 38(5): 83-92.
LI B, LIU K N. Forest biomass estimation based on UAV optical remote sensing[J]. Forest Engineering, 2022, 38(5): 83-92.
Cited in this article [1]
[3]
NÖLTE A, MEILBY H, YOUSEFPOUR R. Multi-purpose forest management in the tropics: incorporating values of carbon, biodiversity and timber in managing Tectona grandis (teak) plantations in Costa Rica[J]. For Ecol Manag, 2018, 422: 345-357. DOI: 10.1016/j.foreco.2018.04.036.
Cited in this article [1]
[4]
LI X Y, JIN H J, WANG H W, et al. Influences of forest fires on the permafrost environment: a review[J]. Adv Clim Change Res, 2021, 12(1): 48-65. DOI: 10.1016/j.accre.2021.01.001.
Cited in this article [1]
[5]
WU Z C, LI M Z, WANG B, et al. Using artificial intelligence to estimate the probability of forest fires in Heilongjiang, northeast China[J]. Remote Sens, 2021, 13(9): 1813. DOI: 10.3390/rs13091813.
Cited in this article [1]
[6]
翟春婕, 曹兆楼, 郑怀兵. 基于图像的火焰锋面法向速度场测量研究[J]. 火灾科学, 2016, 25(1):28-33.
ZHAI C J, CAO Z L, ZHENG H B. Measurement of normal velocity field at the fire front based on imaging analysis[J]. Fire Saf Sci, 2016, 25(1):28-33. DOI: 10.3969/j.issn.1004-5309.2016.01.04.
Cited in this article [2]
[7]
MARTÍNEZ-DE DIOS J R, ANDRÉ J C, GONÇALVES J C, et al. Laboratory fire spread analysis using visual and infrared images[J]. Int J Wildland Fire, 2006, 15(2): 179. DOI: 10.1071/wf05004.
Cited in this article [1]
[8]
李丹丹, 金森. 用可见光图像法测定林火蔓延速率的误差[J]. 中南林业科技大学学报, 2014, 34(3): 61-67.
LI D D, JIN S. Study on errors of forest fire spread rate measured by visible light image method[J]. J Central South Univ For Technol, 2014, 34(3): 61-67. DOI: 10.3969/j.issn.1673-923X.2014.03.012.
Cited in this article [2]
[9]
金森. 林火图像测量中的火场边缘提取技术[J]. 林业科学, 2007, 43(9):44-47, 157.
JIN S. Fire boundary extracting technique in automatic measurement of rate of spread of forest fire by analyzing series of images[J]. Sci Silvae Sin, 2007, 43(9): 44-47, 157. DOI: 10.3321/j.issn:1001-7488.2007.09.009.
Cited in this article [2]
[10]
苏漳文, 曾爱聪, 蔡奇均, 等. 基于Gompit回归模型的大兴安岭林火预测模型及驱动因子研究[J]. 林业工程学报, 2019, 4(4):135-142.
SU Z W, ZENG A C, CAI Q J, et al. Study on prediction model and driving factors of forest fire in Da Hinggan Mountains using Gompit regression method[J]. J For Eng, 2019, 4(4): 135-142. DOI: 10.13360/j.issn.2096-1359.2019.04.020.
Cited in this article [1]
[11]
冯俊伟. 华南地区森林地表可燃物火蔓延速度特性的实验研究[J]. 消防科学与技术, 2022, 41(6): 832-835.
FENG J W. Experimental study on fire spread velocity characteristics of forest surface combustible materials in south China[J]. Fire Sci Technol, 2022, 41(6):832-835. DOI: 10.3969/j.issn.1009-0029.2022.06.023.
Cited in this article [2]
[12]
张运林, 宋红, 胡海清. 云南3种乔木防火期内含水率变化及预测模型[J]. 西北林学院学报, 2019, 34(1):157-162.
ZHANG Y L, SONG H, HU H Q. Prediction model of moisture content of 3 tree species in Yunnan Province[J]. J Northwest For Univ, 2019, 34(1):157-162. DOI: 10.3969/j.issn.1001-7461.2019.01.23.
Cited in this article [1]
[13]
于宏洲, 柳慧, 张运林, 等. 地表细小可燃物含水率野外自动记录监测系统设计与试验[J]. 森林工程, 2022, 38(5): 38-47.
YU H Z, LIU H, ZHANG Y L, et al. Design and experiment of monitoring system for surface fine fuel moisture[J]. Forest Engineering, 2022, 38(5): 38-47.
Cited in this article [1]
[14]
ROSSA C, FERNANDES P. On the fire-spread rate influence of some fuel bed parameters derived from Rothermel’s model thermal energy balance[J]. Sumarski List, 2018, 142(1/2): 77-80. DOI: https://doi.org/10.31298/sl.142.1-2.7.
Cited in this article [2]
[15]
DASDEMIR I, AYDIN F, ERTUGRUL M. Factors affecting the behavior of large forest fires in Turkey[J]. Environ Manag, 2021, 67(1): 162-175. DOI: 10.1007/s00267-020-01389-z.
Cited in this article [2]
[16]
ABDI O, KAMKAR B, SHIRVANI Z, et al. Spatial-statistical analysis of factors determining forest fires: a case study from Golestan, northeast Iran[J]. Geomat Nat Hazards Risk, 2018, 9(1): 267-280. DOI: 10.1080/19475705.2016.1206629.
Cited in this article [1]
[17]
耿道通, 宁吉彬, 李兆国, 等. 基于Rothermel模型的红松人工林地表可燃物蔓延速率及参数修正[J]. 北京林业大学学报, 2021, 43(11): 79-88.
GENG D T, NING J B, LI Z G, et al. Spread rate and parameter correction of surface combustible materials in Korean pine plantation based on Rothermel model[J]. J Beijing For Univ, 2021, 43(11): 79-88. DOI: 10.12171/j.1000-1522.20200247.
Cited in this article [2]
[18]
何诚, 舒立福, 张思玉, 等. 大兴安岭森林草原地下火阴燃特征研究[J]. 西南林业大学学报, 2020, 40(2):103-110.
HE C, SHU L F, ZHANG S Y, et al. Research on underground fire smouldering characteristics of forest steppe in Daxing’an mountains[J]. J Southwest For Univ, 2020, 40(2):103-110. DOI: 10.11929/j.swfu.201907002.
Cited in this article [1]
[19]
陶长森, 牛树奎, 陈锋, 等. 北京山区主要针叶林潜在火行为及冠层危险指数研究[J]. 北京林业大学学报, 2018, 40(9):55-62.
TAO C S, NIU S K, CHEN F, et al. Potential fire behavior and canopy hazard index of main coniferous forests in Beijing Mountain area[J]. J Beijing For Univ, 2018, 40(9):55-62. DOI: 10.13332/j.1000-1522.20180109.
Cited in this article [1]
[20]
尹赛男, 王东昶, 单延龙, 等. 黑龙江省3种主要火源引发森林火灾的次数和面积时空分布特征[J]. 林业科学, 2021, 57(6):115-124.
YIN S N, WANG D C, SHAN Y L, et al. Spatial and temporal distribution of forest fires (frequency and area) caused by three main fire sources in Heilongjiang Province[J]. Sci Silvae Sin, 2021, 57(6):115-124. DOI: 10.11707/j.1001-7488.20210613.
Cited in this article [1]
[21]
滕跃. 樟子松松瘤锈病对其燃烧性的影响[D]. 哈尔滨: 东北林业大学, 2019.
TENG Y. Effect of Pinus sylvestris var.mongolica on its combustibility[D]. Harbin: Northeast Forestry University, 2019.
Cited in this article [2]
[22]
RIM C B, OM K C, REN G Y, et al. Establishment of a wildfire forecasting system based on coupled weather-wildfire modeling[J]. Appl Geogr, 2018, 90: 224-228. DOI: 10.1016/j.apgeog.2017.12.011.
Cited in this article [2]
[23]
WU Z C, WANG B, LI M Z, et al. Simulation of forest fire spread based on artificial intelligence[J]. Ecol Indic, 2022, 136: 108653. DOI: 10.1016/j.ecolind.2022.108653.
Cited in this article [2]
[24]
FONS W L. Analysis of fire spread in light forest fuels[J]. Journal of Agricultural Research, 1946, 72(3): 92-121.
Cited in this article [1]
[25]
ROCHOUX M C, EMERY C, RICCI S, et al. Towards predictive data-driven simulations of wildfire spread-Part II: ensemble Kalman filter for the state estimation of a front-tracking simulator of wildfire spread[J]. Nat Hazards Earth Syst Sci, 2015, 15(8):1721-1739. DOI: 10.5194/nhess-15-1721-2015.
Cited in this article [2]
[26]
MCARTHUR A G. Weather and grass land fire behaviour[R]. Forest Research Institute, Forestry and Timber Bureau, 1966.
Cited in this article [1]
[27]
STOCKS B J, ALEXANDER M E, LANOVILLE R A. Overview of the international crown fire modelling experiment (ICFME)[J]. Can J For Res, 2004, 34(8): 1543-1547. DOI: 10.1139/x04-905.
Cited in this article [2]
[28]
DARWISH A A, ABUBAKER A M, SALAIMEH A, et al. Ignition and burning mechanisms of live spruce needles[J]. Fuel, 2021, 304: 121371. DOI: 10.1016/j.fuel.2021.121371.
Cited in this article [1]
[29]
ROTHERMEL R C. A mathematical model for predicting fire spread in wildland fuels[M]. Ogden, Utah: Intermountain Forest & Range Experiment Station, Forest Service, U. S. Dept. of Agriculture, 1972.
Cited in this article [2]
[30]
MASSMAN W J, FORTHOFER J M, FINNEY M A. An improved canopy wind model for predicting wind adjustment factors and wildland fire behavior[J]. Can J For Res, 2017, 47(5): 594-603. DOI: 10.1139/cjfr-2016-0354.
Cited in this article [1]
[31]
CRUZ M G, ALEXANDER M E. Uncertainty associated with model predictions of surface and crown fire rates of spread[J]. Environ Model Softw, 2013, 47: 16-28. DOI: 10.1016/j.envsoft.2013.04.004.
Cited in this article [1]
[32]
GHISU T, ARCA B, PELLIZZARO G, et al. An optimal cellular automata algorithm for simulating wildfire spread[J]. Environ Model Softw, 2015, 71: 1-14. DOI: 10.1016/j.envsoft.2015.05.001.
Cited in this article [1]
[33]
BALL G L, GUERTIN D P. Improved fire growth modeling[J]. Int J Wildland Fire, 1992, 2(2): 47. DOI: 10.1071/wf9920047.
Cited in this article [1]
[34]
ZHOU T J, DING L, JI J, et al. Ensemble transform Kalman filter (ETKF) for large-scale wildland fire spread simulation using FARSITE tool and state estimation method[J]. Fire Saf J, 2019, 105: 95-106. DOI: 10.1016/j.firesaf.2019.02.009.
Cited in this article [2]
[35]
KNIGHT I, COLEMAN J. A fire perimeter expansion algorithm-based on Huygens wavelet propagation[J]. Int J Wildland Fire, 1993, 3(2): 73. DOI: 10.1071/wf9930073.
Cited in this article [1]
[36]
祁曼. 林火蔓延仿真及扑救决策平台设计与软件实现[D]. 北京: 北京林业大学, 2017.
QI M. Design and software implementation of forest fire spread simulation and fire fighting decision platform[D]. J Beijing: Beijing Forestry University, 2017.
Cited in this article [2]
[37]
李炳怡, 刘冠宏, 舒立福. 北京门头沟区主要林分类型地表火行为模拟研究[J]. 北京林业大学学报, 2022, 44(6):96-105.
LI B Y, LIU G H, SHU L F. Simulation study on surface fire behavior of main forest types in Mentougou District, Beijing[J]. J Beijing For Univ, 2022, 44(6):96-105. DOI: 10.12171/j.1000-1522.20210204.
Cited in this article [1]
[38]
LI X D, GAO H W, ZHANG M X, et al. Prediction of forest fire spread rate using UAV images and an LSTM model considering the interaction between fire and wind[J]. Remote Sens, 2021, 13(21): 4325. DOI: 10.3390/rs13214325.
Cited in this article [2]
[39]
DE VIVO F, BATTIPEDE M, JOHNSON E. Infra-red line camera data-driven edge detector in UAV forest fire monitoring[J]. Aerosp Sci Technol, 2021, 111: 106574. DOI: 10.1016/j.ast.2021.106574.
Cited in this article [1]
[40]
何晓军, 徐爱功, 李玉. 利用HSI空间相似性的彩色形态学图像处理方法[J]. 计算机科学, 2019, 46(4):285-292.
HE X J, XU A G, LI Y. Color morphology image processing method using similarity in HSI space[J]. Comput Sci, 2019, 46(4):285-292. DOI: 10.11896/j.issn.1002-137X.2019.04.045.
Cited in this article [2]
[41]
刘若男, 辛义忠, 李岩. 基于皮尔逊相关系数的动态签名验证方法[J]. 仪器仪表学报, 2022, 43(7): 279-287.
LIU R N, XIN Y Z, LI Y. Dynamic signature verification method based on Pearson correlation coefficient[J]. Chin J Sci Instrum, 2022, 43(7): 279-287. DOI: 10.19650/j.cnki.cjsi.J2209320.
Cited in this article [1]
[42]
RICHARDS G D. An elliptical growth model of forest fire fronts and its numerical solution[J]. Int J Numer Methods Eng, 1990, 30(6): 1163-1179. DOI: 10.1002/nme.1620300606.
Cited in this article [2]
[43]
TAN L, DE CALLAFON R A, BLOCK J, et al. Improving wildfire simulations by estimation of wildfire wind conditions from fire perimeter measurements[C]// International Conference on Computational Science. Cham: Springer, 2021: 231-244.
Cited in this article [2]
[44]
YARIYAN P, JANIZADEH S, PHONG T, et al. Improvement of best first decision trees using bagging and dagging ensembles for flood probability mapping[J]. Water Resour Manag, 2020, 34(9): 3037-3053. DOI: 10.1007/s11269-020-02603-7.
Cited in this article [1]
[45]
WANG Y, WANG X J, YIN L. Estimation of extrinsic parameters for dynamic binocular stereo vision using unknown-sized rectangle images[J]. Rev Sci Instrum, 2019, 90(6): 065108. DOI: 10.1063/1.5086352.
Cited in this article [2]
[46]
ZHAO Y, MA J L, LI X H, et al. Saliency detection and deep learning-based wildfire identification in UAV imagery[J]. Sensors, 2018, 18(3): 712. DOI: 10.3390/s18030712.
Cited in this article [1]
[47]
JIAO Z T, ZHANG Y M, XIN J, et al. A deep learning based forest fire detection approach using UAV and YOLOv3[C]// 2019 1st International Conference on Industrial Artificial Intelligence (IAI). July 23-27, 2019, Shenyang, China. IEEE, 2019: 1-5. DOI: 10.1109/ICIAI.2019.8850815.
Cited in this article [1]
[48]
张笑一, 胡同欣, 刘方策, 等. 兴安落叶松针叶床层火蔓延的模拟[J]. 东北林业大学学报, 2021, 49(1):117-124.
ZHANG X Y, HU T X, LIU F C, et al. Simulation of fire spread in coniferous bed of Larix gmelinii[J]. J Northeast For Univ, 2021, 49(1):117-124. DOI: 10.3969/j.issn.1000-5382.2021.01.022.
Cited in this article [1]
[49]
HONG H Y, TSANGARATOS P, ILIA I, et al. Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models, the case of Dayu County, China[J]. Sci Total Environ, 2018, 630: 1044-1056.DOI: 10.1016/j.scitotenv.2018.02.278.
Cited in this article [1]
[50]
GUO F T, SU Z W, WANG G Y, et al. Understanding fire drivers and relative impacts in different Chinese forest ecosystems[J]. Sci Total Environ, 2017, 605/606: 411-425. DOI: 10.1016/j.scitotenv.2017.06.219.
Cited in this article [1]
[51]
高仲亮, 舒立福, 王明玉, 等. 云南松烧除火蔓延速率预测模型研究[J]. 安全与环境学报, 2016, 16(4):82-86.
GAO Z L, SHU L F, WANG M Y, et al. Model for predicting the forest fire spreading speed based on the prescriptive combustion of Pinus yunnanensis[J]. J Saf Environ, 2016, 16(4):82-86. DOI: 10.13637/j.issn.1009-6094.2016.04.016.
Cited in this article [1]
[52]
王正非. 山火初始蔓延速度测算法[J]. 山地研究, 1983, 1(2):42-51.
WANG Z F. The measurement method of the wildfire initial spread rate[J]. J Mt Res, 1983, 1(2):42-51.
Cited in this article [1]

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