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倾斜摄影测量技术提取油松单木信息

杜意鸿 尹田 周雪梅 张晓丽

杜意鸿, 尹田, 周雪梅, 张晓丽. 倾斜摄影测量技术提取油松单木信息[J]. 北京林业大学学报, 2021, 43(4): 77-86. doi: 10.12171/j.1000-1522.20200198
引用本文: 杜意鸿, 尹田, 周雪梅, 张晓丽. 倾斜摄影测量技术提取油松单木信息[J]. 北京林业大学学报, 2021, 43(4): 77-86. doi: 10.12171/j.1000-1522.20200198
Du Yihong, Yin Tian, Zhou Xuemei, Zhang Xiaoli. Extraction of individual tree parameters of Chinese pine by oblique photogrammetry[J]. Journal of Beijing Forestry University, 2021, 43(4): 77-86. doi: 10.12171/j.1000-1522.20200198
Citation: Du Yihong, Yin Tian, Zhou Xuemei, Zhang Xiaoli. Extraction of individual tree parameters of Chinese pine by oblique photogrammetry[J]. Journal of Beijing Forestry University, 2021, 43(4): 77-86. doi: 10.12171/j.1000-1522.20200198

倾斜摄影测量技术提取油松单木信息

doi: 10.12171/j.1000-1522.20200198
基金项目: 国家重点研发计划(2017YFD0600900),国家级大学生创新创业训练计划(G201910022004)
详细信息
    作者简介:

    杜意鸿。主要研究方向:地理信息科学。Email:tyduyh@163.com 地址:100083 北京市海淀区清华东路35号北京林业大学林学院

    责任作者:

    张晓丽,教授,博士生导师。主要研究方向:定量遥感。Email:zhang-xl@263.net 地址:同上

Extraction of individual tree parameters of Chinese pine by oblique photogrammetry

  • 摘要:   目的  获取森林单木参数的信息是经营、管理森林过程中的一项重要内容。倾斜摄影测量技术以其多角度拍摄方法,成为目前高效获得单木信息的研究方法之一。  方法  本研究以内蒙古旺业甸油松林为研究对象,利用无人机倾斜摄影测量技术获取油松单木的树高、冠幅和材积,探究了4种不同的相片分辨率(1、0.5、0.25、0.1 m)对单木信息提取能力的影响。采用基于点云数据的均值漂移算法和基于冠层高度模型(CHM)的分水岭算法分割单木树冠,以样地实测单木参数和激光雷达提取的单木数据作为验证数据,探索了相片分辨率与单木提取能力的关系,比较了两种分割方法的准确度及最优分辨率。建立了基于CHM提取树高与单木材积的异速生长模型(y = 0.000 1x2.717R2 = 0.571 7), 并绘制了测区油松单木材积分布图。  结果  (1)摄影测量提取单木油松冠幅,分水岭算法和均值漂移分割算法均在0.5 m相片分辨率的分割准确度最好,且分水岭算法提取的冠幅较均值漂移算法有较少的漏分、过度分割,其F得分分别为0.87和0.82;而在0.5 m分辨率下均值漂移算法提取的冠幅数值准确度较好,分水岭算法和均值漂移算法得到的参考树冠与分割树冠的相关系数分别为0.850和0.892,且在P < 0.01水平上极显著。(2)分水岭分割算法和均值漂移算法提取油松树高的能力相近,0.5 m相片分辨率得到的单木树高平均误差最小且相差不大,分别为0.42和0.66 m。  结论  研究明确了倾斜摄影测量技术提取油松单木的关键方法和最佳相片分辨率参数,提高了调查效率,为设置合理无人机数据获取的参数提供了科学依据。

     

  • 图  1  试验区及样地概览分布图

    Figure  1.  Overview distribution of sample area and plots

    图  2  试验区点云图

    Figure  2.  Point cloud of test area

    图  3  试验区冠层高度模型(CHM)图

    Figure  3.  Canopy height model (CHM) of test area

    图  4  分水岭算法单木分割效果

    Figure  4.  Effects of individual tree segmentation under watershed algorithm

    图  5  均值漂移算法单木分割效果

    a. 0.1 m分割点云;b. 0.1 m分割效果;c. 0.25 m分割点云;d. 0.25 m分割效果;e. 0.5 m分割点云;f. 0.5 m分割效果;g. 1 m分割点云;h. 1 m分割效果。a, point cloud image of 0.1 m; b, segmentation effect of 0.1 m; c, point cloud image of 0.25 m; d, segmentation effect of 0.25 m; e, point cloud image of 0.5 m; f, segmentation effect of 0.5 m; g, point cloud image of 1 m; h, segmentation effect of 1 m.

    Figure  5.  Effects of individual tree segmentation under mean-shift algorithm

    图  6  单木冠幅结果精度散点图

    Figure  6.  Precision scatter diagram from individual tree crown results

    图  7  单木材积与提取树高异速生长模型

    Figure  7.  Allometric analysis of single tree volume and extracted tree height

    图  8  试验区油松单木材积分布图

    Figure  8.  Distribution map of individual tree volume of Pinus tabuliformis in test area

    表  1  两种分割方法在不同分辨率下的精度评价指标

    Table  1.   Precision evaluation indexes under different resolution ratios by two segmentation methods

    分割方法
    Segmentation
    method
    评价指标
    Evaluation
    index
    分辨率 Resolution ratio
    0.1 m0.25 m0.5 m1 m
    分水岭分割算法
    Watershed segmentation
    algorithm
    TP1212115580
    FN1766733108
    FP1980137
    r0.060.640.820.43
    p0.390.600.920.92
    F0.110.620.870.58
    均值漂移算法
    Mean-shift
    algorithm
    TP92110142103
    FN96784685
    FP192961731
    r0.490.590.760.55
    p0.320.530.890.77
    F0.390.560.820.64
    注:TP. 正确分割数;FN. 漏分数;FP. 过度分割数;r. 冠部探测率;p. 冠部准确率;F. F得分。Notes: TP, true positive; FN, false negative; FP, false positive; r, recall; p, precision; F, F-score. The same below.
    下载: 导出CSV

    表  2  各样地的两种分割方法在不同分辨率下的精度评价指标

    Table  2.   Precision evaluation indexes of each sample plot under different resolution ratios by two segmentation methods

    分割方法
    Segmentation
    method
    分辨率
    Resolution
    ratio/m
    样地号
    Sample
    plot No.
    实际单木数
    Actual single
    tree number
    评价指标 Evaluation index
    TPFNFPrpF
    分水岭分割算法
    Watershed segmentation
    algorithm
    0.1 1 27 2 25 2 0.07 0.50 0.13
    2 29 3 26 1 0.10 0.75 0.18
    3 38 1 37 5 0.03 0.17 0.05
    4 41 1 40 4 0.02 0.20 0.04
    5 35 3 33 4 0.08 0.43 0.14
    6 18 1 17 3 0.06 0.25 0.09
    0.25 1 27 18 9 10 0.67 0.64 0.65
    2 29 22 7 14 0.76 0.61 0.68
    3 38 22 16 18 0.58 0.55 0.56
    4 41 23 18 19 0.56 0.55 0.55
    5 35 27 8 15 0.77 0.64 0.70
    6 18 9 9 4 0.50 0.69 0.58
    0.5 1 27 23 4 1 0.85 0.96 0.90
    2 29 25 4 1 0.86 0.96 0.91
    3 38 30 8 3 0.79 0.91 0.85
    4 41 33 8 5 0.80 0.87 0.84
    5 35 29 6 1 0.83 0.97 0.89
    6 18 15 3 2 0.83 0.88 0.86
    1 1 27 12 15 1 0.44 0.92 0.60
    2 29 13 16 1 0.45 0.93 0.60
    3 38 16 22 1 0.42 0.94 0.58
    4 41 17 24 2 0.41 0.89 0.57
    5 35 15 20 1 0.43 0.94 0.59
    6 18 7 11 1 0.39 0.88 0.54
    均值漂移算法
    Mean-shift
    algorithm
    0.1 1 27 14 13 29 0.52 0.33 0.40
    2 29 14 15 28 0.48 0.33 0.39
    3 38 18 20 36 0.47 0.33 0.39
    4 41 19 22 45 0.46 0.30 0.36
    5 35 18 17 39 0.51 0.32 0.39
    6 18 9 9 15 0.50 0.38 0.43
    0.25 1 27 16 11 11 0.59 0.59 0.59
    2 29 17 12 12 0.59 0.59 0.59
    3 38 23 15 20 0.61 0.53 0.57
    4 41 21 20 21 0.51 0.50 0.51
    5 35 21 14 18 0.60 0.54 0.57
    6 18 12 6 14 0.67 0.46 0.55
    0.5 1 27 21 6 2 0.78 0.91 0.84
    2 29 23 6 3 0.79 0.88 0.84
    3 38 27 11 3 0.71 0.90 0.79
    4 41 30 11 5 0.73 0.86 0.79
    5 35 27 8 2 0.77 0.93 0.84
    6 18 14 4 2 0.78 0.88 0.82
    1 1 27 16 11 4 0.59 0.80 0.68
    2 29 17 12 3 0.59 0.85 0.69
    3 38 19 19 5 0.50 0.79 0.61
    4 41 21 20 7 0.51 0.75 0.61
    5 35 19 16 7 0.54 0.73 0.62
    6 18 11 7 5 0.61 0.69 0.65
    下载: 导出CSV

    表  3  树高平均提取误差

    Table  3.   Average extraction error of tree height

    分辨率
    Resolution ratio/m
    单木树高平均误差 Average height error of single tree/m
    分水岭分割算法
    Watershed segmentation algorithm
    均值漂移算法
    Mean-shift algorithm
    0.10.98
    0.250.931.06
    0.50.720.66
    11.210.82
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-06-30
  • 修回日期:  2020-08-03
  • 网络出版日期:  2021-04-17
  • 刊出日期:  2021-04-30

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