基于长时序影像的大兴安岭山脉林火扰动识别与时空分布格局OA
Identification of forest fire disturbances and spatiotemporal distribution patterns in the Greater Khingan Mountains based on long-term remote sensing imagery
[目的]研究林火扰动的时空分布规律,掌握森林火灾变化趋势,为预防高火险区域火灾发生和优化防火资源配置提供科学依据.[方法]采用 LandTrendr时序分割算法提取森林变化特征,结合随机森林模型进行林火扰动区域识别.在此基础上,运用空间统计方法分析林火扰动的年际变化趋势与空间分布特征,并结合地形因子探讨不同林火烈度及二次火灾的分布规律.[结果](1)1987-2022年,大兴安岭山脉的林火扰动面积整体呈显著下降趋势,且无明显阶段性特征.(2)大兴安岭山脉林火在 0~180 km尺度上呈聚集模式,高密度核心区集中在中东部鄂伦春自治旗、根河市与呼玛县交界处,低密度核心区分布于西部与南部.林火扰动范围先向外蔓延,随后逐步回缩,扰动中心南移后部分北返.(3)林火扰动集中分布于缓坡与斜坡,阳坡更易发生高烈度林火扰动;海拔 400~800 m 区域为火灾高发带,低海拔二次火灾复发率显著高于其他区域.陡坡虽面积较小,但重度火灾占比高.[结论]大兴安岭林火整体呈现出"东聚西散中部集中,缓坡阳坡主导,中海拔风险集中"的分布特征.研究建议优先在内蒙古自治区和黑龙江省交界处的鄂伦春自治旗、根河市和呼玛县等高风险林火扰动区域布设防火设施,同时优化阳坡可燃物管理策略,以提升森林火灾防控的精准性.
[Objective]This study aims to investigate the spatiotemporal distribution patterns of wildfire disturbances and identify trends in forest fire dynamics,providing a scientific basis for preventing fires in high-risk areas and optimizing the allocation of fire prevention resources.[Method]We applied the LandTrendr temporal segmentation algorithm to extract forest change characteristics,combined with a random forest model for identifying forest fire disturbance areas.On this basis,spatial statistical methods are utilized to analyze the interannual variation trends and spatial distribution patterns of forest fire disturbances.Furthermore,topographic factors are integrated to investigate the distribution patterns of different fire severities and secondary fires.[Result](1)From 1987 to 2022,the total wildfire disturbance area in the Greater Khingan Mountains exhibited a significant overall decline,without distinct stage characteristics.(2)Wildfire disturbances displayed a clustered pattern at spatial scales of 0-180 km.The high-density core region was located at the intersection of Oroqen Autonomous Banner,Genhe City,and Huma County in the central-eastern area,while low-density zones were found in the western and southern regions.The disturbance extent initially expanded outward,then gradually contracted,with the fire center shifting southward before partially returning northward.(3)Wildfire disturbances were concentrated on gentle and sloping terrains,with high-severity fires more likely to occur on sunlit(south-facing)slopes.Elevations between 400 and 800 m represented a fire-prone belt,and low-elevation areas showed significantly higher recurrence rates of secondary fires.Although steep slopes accounted for a small area,they exhibited a high proportion of severe fires.[Conclusion]Wildfire patterns in the Greater Khingan Mountains are characterized by eastern clustering,western dispersion,and central concentration,and fire occurrence is dominated by gentle and south-facing slopes,with risk concentration at mid-elevations.We recommend prioritizing the deployment of fire prevention infrastructure in high-risk areas,particularly in Oroqen Autonomous Banner,Genhe City,and Huma County at the border of Inner Mongolia Autonomous Region and Heilongjiang Province,and enhancing fuel management strategies on sunlit slopes to improve the precision of wildfire prevention and control.
贾紫晗;王嘉豪;姚宗琦;张晓丽
北京林业大学林木资源高效生产全国重点实验室,精准林业北京市重点实验室,森林培育与保护教育部重点实验室,北京 100083北京林业大学林木资源高效生产全国重点实验室,精准林业北京市重点实验室,森林培育与保护教育部重点实验室,北京 100083北京林业大学林木资源高效生产全国重点实验室,精准林业北京市重点实验室,森林培育与保护教育部重点实验室,北京 100083北京林业大学林木资源高效生产全国重点实验室,精准林业北京市重点实验室,森林培育与保护教育部重点实验室,北京 100083
农业科技
林火干扰时空分布变化趋势时间序列分析大兴安岭
forest fire disturbancespatiotemporal distributionchange trendtime series analysisGreater Khingan Mountains
《北京林业大学学报》 2026 (4)
80-91,12
中欧对地观测合作森林监测技术与示范应用(2021YFE0117700-3),天空地一体化森林资源监测技术示范(2023YFD2201700).
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