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基于"星-机-地"技术的缙云山林火分析

Forest fire analysis in Jinyun Mountain based on "star-machine-ground" technology

  • 摘要: 森林林火调查分析是现代林火管理的重要环节。以2022年8月的重庆缙云山大火地点为试验区, 实地选取轻度、中度、重度火烧样地, 进行每木检尺处理和制图。基于调查区域的卫星遥感和无人机遥感影像, 分别计算NDVI, 并与实测样地的树高、胸径、熏黑高度、冠幅进行空间匹配, 并进行灰色关联分析, 分析不同火烧程度样地各项指标与NDVI值的相关性, 进而通过NDVI值延伸到卫星影像。结果表明: 1)根据地面调查数据, 各火烧样地树木的熏黑高度随火烧程度的增加呈明显增加趋势, 树高、胸径平均值的变化具有一致性, 表现为中度火烧最大, 轻度火烧其次, 重度火烧最小。冠幅的均值变化趋势不同, 表现为中度火烧最大, 重度火烧次之, 轻度火烧最小, 大火主要发生在森林的下层区域。2)3块样地的4项指标与其NDVI的关系均超过0.55, 中度火烧样地的熏黑高度、冠幅和重度火烧样地的冠幅指标与其NDVI为中等关联; 轻度火烧样地的树高、胸径、熏黑高度与其NDVI的关联度均超过0.8, 为极强关联; 其余指标关联度在0.6~0.8之间, 为较强关联。说明要素间相关性较强, NDVI能够作为林火火烧等级划分的依据。3)以无人机多光谱影像NDVI阈值为标准, 确定重度、中度、轻度火烧的NDVI范围依次为0~0.245 6、>0.245 6~0.347 1、>0.347 1~0.690 0, 对卫星遥感影像的NDVI进行重分类, 实现该区域的火烧程度等级划分。为较大范围的林火调查提供了新的思路。

     

    Abstract:
    Background The investigation of burned areas after forest fires is a key and difficult point in the field of forest fire research. Traditional manual on-site investigation methods, satellite remote sensing images, and drone technology, as common investigation methods, have their own drawbacks when used alone, which cannot solve the problem of quickly and accurately dividing the severity of large-scale forest fires. Therefore, taking the location of the Chongqing Jinyun Mountain fire in September 2022 as the experimental area, and combining the advantages of the above three methods, a "star machine ground" forest fire investigation system is constructed.
    Methods Selecting mildly, moderatly, and severely fire plots, and ruler processing and mapping for each wood was conducted. Based on satellite remote sensing and unmanned aerial vehicle remote sensing images of the survey area, NDVI was calculated and spatially matched with the measured tree height, chest diameter, scorch height, and crown width of the plots. Grey correlation analysis was conducted to analyze the correlation between various indicators of the sample plots with different degrees of fire and NDVI values, and then NDVI values were extended to satellite images.
    Results 1) According to ground survey data, the scorch height of trees in each fire plot increased with the increase of fire severity. The changes in the average values of tree height and diameter at breast height were consistent, with moderate fire being the largest, mild fire being the second, and severe fire being the smallest. The average change trend of crown diameter was different from these two items, with moderate fire being the largest, followed by severe fire, and mild fire being the smallest. Large fires mainly occur in the lower layers of the forest. 2) The relationship between the four indicators of the three plots and their NDVI was above 0.55. The scorch height, crown diameter, and crown diameter indicators of the moderately burned plots were moderately correlated with their NDVI. The correlation between the tree height, DBH, and the height of the lightly burned plot and their NDVI was above 0.8, indicating a strong correlation. The correlation degree of other indicators was between 0.6 and 0.8, indicating a strong correlation. 3) Using the NDVI threshold of drone multispectral images as the standard, the NDVI ranges for severe, moderate, and mild fires were determined to be 0-0.245 6, >0.245 6-0.347 1, and >0.347 1-0.690 0, respectively.
    Conclusions Through the method of grey correlation analysis, the correlation between UAV NDVI and four ground survey data is strong, that is, the accuracy of UAV image NDVI is verified with ground survey data validation, indicating that NDVI can be used as the basis for forest fire grading. And using the NDVI threshold of each fire plot in the drone multispectral image as the standard to reclassify satellite remote sensing images of forest fire areas, it can achieve rapid and accurate classification of large-scale fire degree levels. On this basis, the soil type, dominant tree species and elevation data of different fire grades are analyzed, which is of great significance to the cause of fire and the restoration of ecological environment after forest fire.

     

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