大兴安岭雷击火时空聚集性及其驱动因素

doi:10.11707/j.1001-7488.LYKX20250583

摘要/Abstract

摘要：

目的: 全球气候变暖、极端天气频发，导致雷击火灾的规模更大、强度更高、破坏性更强。探究大兴安岭雷击火灾的时空分布及聚集性演变规律，并识别其关键驱动因素，为雷击火灾的防控和管理提供科学依据。方法: 基于2013—2024年大兴安岭雷击火灾历史数据，综合运用统计分析、二维高斯核密度分析、地理探测器等方法，分析雷击火灾的动态演化规律、空间聚集特征及关键驱动因素的解释力。结果: 1） 12年间共发生雷击火灾791次，2019年达到峰值。雷击火灾主要集中在4—10月，尤其是春季防火期和夏季生长期，最早记录为4月24日，最晚为9月19日。春季防火期结束后雷击火灾数量回落，但于7月中旬再次达到高峰。每日13:00—17:00（不含 17:00 时刻）为雷击火灾的高发时段，占总发生次数的50%以上。2） 12年间雷击火灾整体在研究区西北部呈明显聚集。逐年分析显示，聚集区呈现年度差异。采用自然断裂法将雷击火灾核密度值划分为5个风险等级，南部地区通常为极低风险区域（仅2022年出现少量雷击火灾）。Getis-Ord Gi^*显著性检验显示，极高风险区具有稳定的空间聚集特征。3）前3日平均气温、月最高气温、海拔、月平均气温、0~7 cm土层土壤湿度和归一化植被指数是各年份中解释力最强的核心驱动因素。相对湿度和月平均气温（较高解释力出现次数9次）、海拔和归一化植被指数（7次）等组合在历年数据中解释力普遍较强，表明其对雷击火灾的发生影响较为显著。此外，月平均气温、月最高气温和海拔与其他驱动因素的组合亦具有较高解释力。结论: 近年来大兴安岭雷击火灾发生整体呈波动趋势，建议在夏季（特别是午后14:00时左右）加强对重点林区的防范。12年间雷击火灾在乌玛、永安山、图强和阿木尔地区持续呈显著聚集特征，部分年份（2015和2018年）雷击火灾事件分布较散，呈随机分布特征，无显著聚集。不同类型因子的交互作用存在差异，其中气象因子之间的交互效应较为明显，在雷击火灾的发生机制中占据主导作用。本研究揭示了雷击火灾的时空演化规律及关键驱动机制，为防火决策的精细化和区域化管理提供了科学依据。

关键词: 雷击火灾, 驱动因素, 二维高斯核密度, 地理探测器, 大兴安岭

Abstract:

Objective: Global warming and the increasing frequency of extreme weather events have resulted in lightning-ignited forest fires with larger scale, higher intensity, and stronger destructiveness. This study investigates the spatiotemporal distribution and clustering evolution of lightning-ignited forest fires in the Daxing’anling Mountains, so as to provide a scientific basis for the prevention and management of lightning-ignited forest fires. Method: Based on historical records of lightning-ignited forest fires in the Daxing’ anling Mountains from 2013 to 2024, this study comprehensively applied statistical analysis, two-dimensional Gaussian kernel density analysis, and the geographical detector method to analyze the dynamic evolution patterns, spatial clustering characteristics, and explanatory power of key driving factors. Result: 1) A total of 791 lightning-ignited forest fires occurred during the past 12-year period, showing an overall fluctuating trend with a peak occurring in 2019. These fires were mainly concentrated between April and October, particularly during the spring fire prevention period and the summer growing season, with the earliest event on April 24 and the latest one on September 19. After the spring fire prevention period, the number of fires declined but rose again to a peak in mid-July. The period from 13:00 to 17:00 (excluding 17:00) was the high-incidence time, accounting for more than 50% of the events. 2) Spatially, lightning-ignited forest fires were significantly clustered in the northwestern of study area. Annual analysis revealed interannual variations in cluster locations. Using the natural breaks method, kernel density values were classified into five risk levels, with the southern region generally identified as very low-risk zones (only a few events occurred in 2022). The Getis-Ord Gi^* significance test confirmed that high-risk areas had stable spatial clustering characteristics. 3) The three-day mean temperature before a lightning-ignited fire, monthly maximum temperature, elevation, monthly mean temperature, 0?7 cm soil layer soil moisture, and normalized difference vegetation index (NDVI) were identified as the core driving factors with the strongest explanatory power across different years. Combinations such as relative air humidity and monthly mean temperature (which showed higher explanatory power nine times), as well as elevation and NDVI (seven times), generally exhibited strong explanatory power across the years, indicating that they have a significant influence on the occurrence of lightning-ignited fires. Moreover, the combinations of monthly mean temperature, monthly maximum temperature, and elevation with other factors also demonstrated high explanatory power. Conclusion: This study summarizes the recent occurrence trends and characteristics of lightning-ignited fires in Daxing’anling Mountains, which show overall fluctuating patterns over time. It is recommended to strengthen fire prevention efforts in summer in key forest areas, particularly around 14:00. Lightning-ignited fires in the Wuma, Yong’anshan, Tuqiang and Amur regions have exhibited persistent and significant clustering during the past 12-year period, although in certain years (e.g., 2015 and 2018) the fire events were more scattered and random, showing no significant clustering. The interactions vary among different factor types, with meteorological interactions being particularly prominent and playing a dominant role in the mechanism of lightning-ignited fire occurrence. This study reveals the spatiotemporal evolution patterns and key driving mechanisms of lightning-ignited forest fires, providing scientific basis for more refined and region-specific forest fire prevention and management strategiess.

Key words: lightning-ignited forest fires, driving factors, two-dimensional Gaussian kernel density, geographical detector, Daxing’ anling Mountains

中图分类号:

S762

高博洋,徐健楠,李伟克,王明玉,宁吉彬,杨光. 大兴安岭雷击火时空聚集性及其驱动因素[J]. 林业科学, 2026, 62(6): 56-70.

Boyang Gao,Jiannan Xu,Weike Li,Mingyu Wang,Jibin Ning,Guang Yang. Spatiotemporal Clustering of Lightning-Ignited Forest Fires in Daxing’ anling Mountains and Itʼs Driving Factors[J]. Scientia Silvae Sinicae, 2026, 62(6): 56-70.

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参考文献 0

	白　夜, 李　晖, 王　博, 等. 森林雷击火成因与防控对策. 林业资源管理, 2019 (6): 7- 11, 37.
	Bai Y, Li H, Wang B, et al. Review on causes to forests struck by lightning and countermeasures. Forest Resources Management, 2019 (6): 7- 11, 37.
	陈乐奇, 徐　鑫, 孔祥宁, 等. 雷击森林火险区划模型及其在泰山林区的应用. 林业调查规划, 2024, 49 (5): 214- 220.
	Chen L Q, Xu X, Kong X N, et al. Forest fire risk zoning model of lightning strike and its application in Mount Taishan forest area. Forest Inventory and Planning, 2024, 49 (5): 214- 220.
	冯雨林, 刘爱柱, 郭常来, 等. 大兴安岭林区雷击火成因机理及影响因素研究进展. 地质与资源, 2024, 33 (5): 701- 706.
	Feng Y L, Liu A Z, Guo C L, et al. Research progress on the genetic mechanism and influencing factors of lightning fire in Daxing’anling forest area. Geology and Resources, 2024, 33 (5): 701- 706.
	焦强英, 韩宗甫, 王炜烨, 等. 基于多源数据和机器学习方法的大兴安岭地区雷击火驱动因子及火险预测模型. 林业科学, 2023, 59 (6): 74- 87.
	Jiao Q Y, Han Z F, Wang W Y, et al. Driving factors and forecasting model of lightning-caused forest fires in the Daxing’anling Mountains based on multi-sources data and machine learning method. Scientia Silvae Sinicae, 2023, 59 (6): 74- 87.
	李　华. 2005. 黑龙江大兴安岭森林雷击火环境及预测预报. 北京: 中国林业科学研究院.
	Li H. 2005. The forecast and environment of lightning fire in daxingan mountain in Heilongjiang Province. Beijing: Chinese Academy of Forestry. ［in Chinese］
	李　伟, 邓凤东, 孟　珍, 等. 基于GIS加权核密度场模型的闪电热点探测与影响因素分析. 气象科技, 2025, 53 (3): 438- 447.
	Li W, Deng F D, Meng Z, et al. Detection and analysis of lightning hotspots and influencing factors based on GIS weighted kernel density field model. Meteorological Science and Technology, 2025, 53 (3): 438- 447.
	李伟克, 舒立福, 王明玉, 等. 黑龙江大兴安岭林区雷击火与人为火发生规律及变化趋势. 林业科学, 2024, 60 (4): 136- 146.
	Li W K, Shu L F, Wang M Y, et al. Occurrence pattern and changing trend of lightning induced fires and human-caused fires in Daxing’anling forest region of Heilongjiang Province. Scientia Silvae Sinicae, 2024, 60 (4): 136- 146.
	李　威, 舒立福, 王明玉, 等. 大兴安岭1980—2021年雷击火时空分布特征. 林业科学, 2023, 59 (10): 22- 31.
	Li W, Shu L F, Wang M Y, et al. Temporal and spatial distribution and dynamic characteristics of lightning fires in the Daxing’anling Mountains from 1980 to 2021. Scientia Silvae Sinicae, 2023, 59 (10): 22- 31.
	李　威, 王明玉, 舒立福, 等. 2000—2022年阿尔泰山林区雷击火时空分布特征. 林业科学, 2025, 61 (4): 169- 179.
	Li W, Wang M Y, Shu L F, et al. Spatiotemporal distribution characteristics of lightning-caused fires in the Altai Mountains forest region from 2000 to 2022. Scientia Silvae Sinicae, 2025, 61 (4): 169- 179.
	钱　勇, 邱贵强, 张华明, 等. 多源气象资料在森林雷击火辨识中的应用. 干旱气象, 2022 (3): 40.
	Qian Y, Qiu G Q, Zhang H M, et al. Application of multi-source meteorological data in lightning-attributed forest fire identification. Journal of Arid Meteorology, 2022 (3): 40.
	孙少辉, 南海涛. 林区干雷暴及雷电监测的研究. 森林工程, 2008 (1): 16- 17.
	Sun S H, Nan H T. Research on dry thunderstorm and thunder monitoring in a forest area. Forest Engineering, 2008 (1): 16- 17.
	舒　洋, 孙子瑜, 张　恒, 2022. 世界森林雷击火研究现状和展望. 世界林业研究, 35(2): 34−40.
	Shu Y, Sun Z Y, Zhang H. 2022. Research on lightning fire in forest: current status and outlook. World Forestry Research, 35(2): 34−40. [ in Chinese]
	苏漳文. 2020. 基于地理信息系统的大兴安岭林火发生驱动因子及预测模型的研究. 哈尔滨: 东北林业大学.
	Su Z W. 2020. Study on driving factors and forecasting models of forest fire in the Great Xing’an Mountains based on geographic information System. Harbin: Northeast Forestry University. ［in Chinese］
	田晓瑞, 舒立福, 赵凤君, 等. 大兴安岭雷击火发生条件分析. 林业科学, 2012, 48 (7): 98- 103.
	Tian X R, Shu L F, Zhao F J, et al. Analysis of the conditions for lightning fire occurrence in Daxing’anling region. Scientia Silvae Sinicae, 2012, 48 (7): 98- 103.
	王明玉, 苑尚博, 李　威, 等. 密集雷电引发大兴安岭群发雷击火过程及其影响因素. 林业科学, 2022, 58 (11): 10- 20.
	Wang M Y, Yuan S B, Li W, et al. Process and influencing factors of mass lightning fires caused by dense lightning in the Daxing’anling Mountains. Scientia Silvae Sinicae, 2022, 58 (11): 10- 20.
	辛　悦, 毕力格, 包山虎, 等. 基于CloudSat-CALIPSO数据的大兴安岭地区云宏微观物理量的垂直结构特征分析. 气象, 2023, 49 (4): 427- 438.
	Xin Y, Bilig, Bao S H, et al. Vertical structure characteristics of cloud macro and micro physical quantities in the Greater Khingan Mountains based on CloudSat-CALIPSO satellite data. Meteorological Monthly, 2023, 49 (4): 427- 438.
	杨淑香, 吴宏伟, 董　越, 等. 内蒙古大兴安岭森林火险等级预报模型研究. 林业调查规划, 2024, 49 (2): 19- 24.
	Yang S X, Wu H W, Dong Y, et al. Prediction model of forest fire risk level in Greater Khingan Mountains of Inner Mongolia. Forest Inventory and Planning, 2024, 49 (2): 19- 24.
	臧桐汝, 舒立福, 王明玉, 等. 黑龙江大兴安岭林区雷击火时空分布及驱动因素分析. 西北农林科技大学学报(自然科学版), 2022, 50 (12): 64- 76.
	Zang T R, Shu L F, Wang M Y, et al. Analysis of spatial and temporal distribution and driving factors of lightning-caused fires in Daxing’anling forest region of Heilongjiang. Journal of Northwest A & F University (Natural Science Edition), 2022, 50 (12): 64- 76.
	张　浩, 陈　倩, 刘群山, 等. 木里县境内雅砻江流域林区雷电活动的时空特征分析. 农业灾害研究, 2025, 15 (5): 257- 259.
	Zhang H, Chen Q, Liu Q S, et al. Spatiotemporal characteristics of lightning activities in the Yalong River Basin of Muli County. Journal of Agricultural Catastrophology, 2025, 15 (5): 257- 259.
	张念慈, 刘　迪, 刘广菊. 大兴安岭地区森林雷击火灾爆发机理与风险评估研究. 森林防火, 2023, 41 (4): 16- 20.
	Zhang N C, Liu D, Liu G J. Study on the mechanism and risk assessment of forest lightning fire outbreak in the Greater Khingan Mountains region. Journal of Wildland Fire Science, 2023, 41 (4): 16- 20.
	张岐岳. 2023. 内蒙古大兴安岭北部林火干扰对冻土和植被的影响. 呼和浩特: 内蒙古农业大学.
	Zhang Q Y. 2023. The impact of forest fire disturbance on permafrost and vegetation in the Northern Great Xing’an Mountains of Inner Mongolia. Hohhot: Inner Mongolia Agricultural University ［in Chinese］
	张雨桐, 凌成星, 刘　华, 等. 基于RSEI模型的大兴安岭区生态质量动态监测与评价. 遥感技术与应用, 2025, 40 (2): 359- 367.
	Zhang Y T, Ling C X, Liu H, et al. Dynamic monitoring and evaluation of ecological quality in Daxing’anling district based on RSEI model. Remote Sensing Technology and Application, 2025, 40 (2): 359- 367.
	周长明, 陈　亮, 陆明明, 等. 高纬度林区干雷暴与雷击火时空分布特征及其相关分析. 广西林业科学, 2022, 51 (4): 520- 526.
	Zhou C M, Chen L, Lu M M, et al. Temporal and spatial distribution characteristics of dry thunderstorm and lightning fire and their correlation in high latitude forest area. Guangxi Forestry Science, 2022, 51 (4): 520- 526.
	周　庆. 2023. 内蒙古大兴安岭森林火灾发生空间格局与驱动因素研究. 呼和浩特: 内蒙古农业大学.
	Zhou Q. 2023. Spatial patterns and drivers of forest fire occurrence in the Daxing’an mountains of Inner Mongolia. Hohhot: Inner Mongolia Agricultural University. ［in Chinese］
	周　庆, 张　恒, 赵鹏武, 等. 内蒙古大兴安岭林火发生概率及驱动因素在1987年森林大火重大历史事件前后的差异. 林业科学, 2024, 60 (7): 81- 94.
	Zhou Q, Zhang H, Zhao P W, et al. Differences in the probability and drivers of forest fires in the Daxing’an Mountains of Inner Mongolia before and after the major historical event of the forest fire in 1987. Scientia Silvae Sinicae, 2024, 60 (7): 81- 94.
	Abatzoglou J T, Kolden C A, Cullen A C, et al. Climate change has increased the odds of extreme regional forest fire years globally. Nature Communications, 2025, 16, 6390. doi: 10.1038/s41467-025-61608-1
	Abatzoglou J T, Williams A P. Impact of anthropogenic climate change on wildfire across western US forests. PNAS, 2016, 113 (42): 11770- 11775. doi: 10.1073/pnas.1607171113
	Clarke H, Gibson R, Cirulis B, et al. Developing and testing models of the drivers of anthropogenic and lightning-caused wildfire ignitions in south-eastern Australia. Journal of Environmental Management, 2019, 235, 34- 41. doi: 10.1016/j.jenvman.2019.01.055
	Coogan S C P, Aftergood O, Flannigan M D. Human and lightning-caused wildland fire ignition clusters in British Columbia, Canada. International Journal of Wildland Fire, 2022, 31 (11): 1043- 1055. doi: 10.1071/WF21177
	Ellis T M, Bowman D M J S, Williamson G J. Human activity augments lightning ignitions to reshape fire seasonality across all biomes on Earth. Nature Ecology & Evolution, 2025, 9 (11): 2069- 2079. doi: 10.1038/s41559-025-02862-w
	Gallo C, Dieppois B, Quilcaille Y, et al. Future impacts of climate change on global fire weather: insight from weighted CMIP6 multimodel ensembles. Journal of Climate, 2025, 38 (22): 6445- 6462. doi: 10.1175/JCLI-D-24-0540.1
	Gao C, Shi C M, Li J B, et al. Igniting lightning, wildfire occurrence, and precipitation in the boreal forest of Northeast China. Agricultural and Forest Meteorology, 2024, 354, 110081. doi: 10.1016/j.agrformet.2024.110081
	Hu T Y, Zhou G S. Drivers of lightning- and human-caused fire regimes in the Great Xing’an Mountains. Forest Ecology and Management, 2014, 329, 49- 58. doi: 10.1016/j.foreco.2014.05.047
	Johnson S D, Balice R G. Seasons within the wildfire season: marking weather-related fire occurrence regimes. Fire Ecology, 2006, 2 (2): 60- 78. doi: 10.4996/fireecology.0202060
	Kelley D, Burton C, Giuseppe F, et al. State of wildfires 2024—2025. Earth System Science Data, 2025, 17 (10): 5377- 5488. doi: 10.5194/essd-17-5377-2025
	Larjavaara M, Kuuluvainen T, Rita H. Spatial distribution of lightning-ignited forest fires in Finland. Forest Ecology and Management, 2005, 208 (1): 177- 188. doi: 10.1016/j.foreco.2004.12.005
	Li G L, Hai J Y, Qiu J Z, et al. Revealing future changes in China’s forest fire under climate change. Agricultural and Forest Meteorology, 2025, 371, 110609. doi: 10.1016/j.agrformet.2025.110609
	Li M L, Wu Y D, Liu Y L, et al. Study on the driving factors of the spatiotemporal pattern in forest lightning fires and 3D fire simulation based on cellular automata. Forests, 2024, 15 (11): 1857. doi: 10.3390/f15111857
	MacNamara B R, Schultz C J, Fuelberg H E. Flash characteristics and precipitation metrics of western U. S. lightning-initiated wildfires from 2017. Fire, 2020, 3 (1): 5. doi: 10.3390/fire3010005
	Mandal J, Chatterjee C, Das S. An explainable machine learning technique to forecast lightning density over north-eastern India. Journal of Atmospheric and Solar-Terrestrial Physics, 2024, 259, 106255. doi: 10.1016/j.jastp.2024.106255
	Mu M, Randerson J T, van der Werf G R, et al. 2011,. Daily and 3-hourly variability in global fire emissions and consequences for atmospheric model predictions of carbon monoxide: daily and hourly global fire emissions. Journal of Geophysical Research: Atmospheres, 116(D24): D24303:1−D24303:19.
	Panagos P, Ballabio C, Meusburger K, et al. Towards estimates of future rainfall erosivity in Europe based on REDES and WorldClim datasets. Journal of Hydrology, 2017, 548, 251- 262. doi: 10.1016/j.jhydrol.2017.03.006
	Podur J, Martell D L, Csillag F. Spatial patterns of lightning-caused forest fires in Ontario, 1976—1998. Ecological Modelling, 2003, 164 (1): 1- 20. doi: 10.1016/s0304-3800(02)00386-1
	Song S D, Zhou X, Yuan S B, et al. Interpretable artificial intelligence models for predicting lightning prone to inducing forest fires. Journal of Atmospheric and Solar-Terrestrial Physics, 2025, 267, 106408. doi: 10.1016/j.jastp.2024.106408
	Wang Z, Huang J G, Ryzhkova N, et al. 352 years long fire history of a Siberian boreal forest and its primary driving factor. Global and Planetary Change, 2021, 207, 103653. doi: 10.1016/j.gloplacha.2021.103653
	Wang Z L, He B B, Chen R, et al. Improving wildfire danger assessment using time series features of weather and fuel in the Great Xing’an Mountain Region, China. Forests, 2023, 14 (5): 986. doi: 10.3390/f14050986
	Wendel J. 2014. Lightning strikes predicted to increase as climate warms. Eos, Transactions American Geophysical Union, 95(47): 431.
	Westermayer A T, Groenemeijer P, Pistotnik G, et al. Identification of favorable environments for thunderstorms in reanalysis data. Meteorologische Zeitschrift, 2017, 26 (1): 59- 70. doi: 10.1127/metz/2016/0754
	Zhang Q Y, Homayouni S, Yao H X, et al. Joint analysis of lightning-induced forest fire and surface influence factors in the Great Xing’an range. Forests, 2022, 13 (11): 1867. doi: 10.3390/f13111867
	Zhang Z J, Tian Y, Wang G Y, et al. A forest fire prediction method for lightning stroke based on remote sensing data. Forests, 2024, 15 (4): 647. doi: 10.3390/f15040647
	Zhen S, Zhang J Y, Zhang Z X, et al. Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics. Open Geosciences, 2021, 13 (1): 796- 806. doi: 10.1515/geo-2020-0265

类型Category	驱动因素Driving factor
地形Topography	海拔Elevation
	坡度Slope^*
	坡向Aspect^*

气象Meteorological	累计降水量Cumulative precipitation 前3日累计降水量Three-day cumulative precipitation before a lightning-ignited fire^*
	前3日最高气温Three-day maximum temperature before a lightning-ignited fire^* 前3日平均气温Three-day mean temperature before a lightning-ignited fire^* 露点温度（地表以上2 m处空气达到饱和时所需冷却到的温度）Dewpoint temperature （temperature to which the air, at two meters above the surface of the earth, would have to be cooled to reach saturation）气温（陆地、海洋或内陆水体表面以上2 m高度的气温）Temperature （temperature of air at two meters above the surface of land, sea or in-land waters.）月平均气温Monthly mean temperature^* 月最高气温Monthly maximum temperature^*
	0~7 cm土层土壤湿度0–7 cm soil layer soil moisture 空气相对湿度Relative air humidity^*（详见公式1~3 See formula 1–3）
	10 m东向风分量Eastward component of the 10 m wind 10 m北向风分量Northward component of the 10 m wind 风向Wind direction^（详见公式4 See formula 4）风速Wind speed^（详见公式5 See formula 5）

可燃物Fuel	归一化植被指数Normalized difference vegetation index

[1]	郑群,李亦璞,苏中原,郑永林,王云琦. 酸沉降加剧土地利用变化对三峡库区重庆段土壤保持时空异质性的影响[J]. 林业科学, 2026, 62(5): 40-53.
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