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基于无人机激光雷达的人工林碳储量线性与非线性估测模型比较

陈中超 刘清旺 李春干 李梅 周相贝 余铸

陈中超, 刘清旺, 李春干, 李梅, 周相贝, 余铸. 基于无人机激光雷达的人工林碳储量线性与非线性估测模型比较[J]. 北京林业大学学报. doi: 10.12171/j.1000-1522.20200417
引用本文: 陈中超, 刘清旺, 李春干, 李梅, 周相贝, 余铸. 基于无人机激光雷达的人工林碳储量线性与非线性估测模型比较[J]. 北京林业大学学报. doi: 10.12171/j.1000-1522.20200417
Chen Zhongchao, Liu Qingwang, Li Chungan, Li Mei, Zhou Xiangbei, Xu Zhu. Comparison of linear and nonlinear estimation models of the carbon storage of plantations based on UAV LiDAR[J]. Journal of Beijing Forestry University. doi: 10.12171/j.1000-1522.20200417
Citation: Chen Zhongchao, Liu Qingwang, Li Chungan, Li Mei, Zhou Xiangbei, Xu Zhu. Comparison of linear and nonlinear estimation models of the carbon storage of plantations based on UAV LiDAR[J]. Journal of Beijing Forestry University. doi: 10.12171/j.1000-1522.20200417

基于无人机激光雷达的人工林碳储量线性与非线性估测模型比较

doi: 10.12171/j.1000-1522.20200417
基金项目: 国家重点研发计划(2017YFD0600904)
详细信息
    作者简介:

    陈中超。主要研究方向:激光雷达林业应用。Email:1392885359@qq.com 地址:530004广西壮族自治区南宁市广西大学林学院

    责任作者:

    李春干,教授。主要研究方向:林业遥感与空间信息。Email:gxali@126.com 地址:530004广西壮族自治区南宁市大学东路100号

Comparison of linear and nonlinear estimation models of the carbon storage of plantations based on UAV LiDAR

  • 摘要:   目的  森林碳储量是生态系统结构与功能的重要指标,掌握森林碳储量现状有利于森林资源管理。激光雷达能够用于监测森林资源,但是存在森林参数估测的模型多、变量不确定和缺乏林分三维结构解析意义的变量等问题,因此,需要选择合适的林分解析变量和模型。  方法  借助无人机激光雷达点云数据与样地调查数据,以内蒙古自治区赤峰市喀喇沁旗旺业甸人工林为研究对象,分别使用多元线性模型与多元乘幂模型以不同变量对林分碳储量进行估测,选出最优模型并进行精度评价。  结果  研究表明:(1)模型方法而言,非线性模型的检验效果优于线性模型的检验效果:非线性模型(R2为0.66 ~ 0.86,rRMSE为23.51% ~ 9.91%),线性模型(R2为0.52 ~ 0.85,rRMSE为27.70% ~ 12.38%)。(2)模型使用平均高、郁闭度为基础变量,以穷举法筛选出来的变量组合,估算森林参数得出最佳模型,其中非线性模型以激光点云平均高、郁闭度、高度变动系数和叶面积变动系数的估算精度最高(R2 = 0.86,rRMSE = 9.91%)。  结论  通过激光雷达估测人工林碳储量时,加入垂直结构变量可以提高模型拟合效果,非线性模型比线性模型更适合人工林碳储量的估测。

     

  • 图  1  研究区地理位置

    Figure  1.  Geographic location of the study area

    图  2  碳储量参考值与估算值的散点关系

    实线为1∶1验证线。the solid line is the 1∶1 verification line.

    Figure  2.  The scatter plot of Carbon storage field- measured and estimated value

    表  1  样地林分特征

    Table  1.   Forest stand characteristics of sample plots

    林分特征
    Forest stand
    characteristics
    变化范围
    Range
    均值
    Mean
    标准差
    Standard
    deviation
    地上生物量/(t·hm−2)
    Above-ground biomass/(t·ha−1)
    60.41 ~ 293.85 156.65 61.73
    碳储量/(t·hm−2)
    Carbon storage/(t·ha−1)
    31.47 ~ 153.10 81.48 32.18
    下载: 导出CSV

    表  2  激光雷达点云特征变量

    Table  2.   The metrics of LiDAR point cloud

    点云特征变量变量
    The metrics of point cloud
    特征变量描述
    The description of the metrics
    平均高
    Mean height (Hm)
    归一化高度的平均值
    The average of normalized heights
    高度标准差
    Height standard deviation (Hs)
    归一化高度的标准差
    The standard deviation of the normalized height
    高度变动系数
    Height variation (Hv)
    归一化高度的变异系数(标准差与平均数的比值)
    The coefficient of variation of the normalized height (The ratio of the standard deviation to the mean)
    高度百分位数(hp10, hp15, …, hp95)
    Height percentiles (hp10, hp15, …, hp95)
    激光返回点的高度分布百分位数
    The height percentiles of the LiDAR returns
    郁闭度
    Canopy closure (cc)
    高于2米的激光返回点所占的百分比
    The percentage of LiDAR returns above 2 m divided by the total returns
    密度百分位数(dp10,dp15, …, dp95)
    Density percentiles (dp10, dp15, …, dp95)
    在各百分位高度等级以上的激光返回点在所有返回点中所占的百分比
    The percentage of LiDAR returns above each percentile levels divided by the total returns
    叶面积密度均值
    Mean leaf area density (Lm)
    根据Beer-Lamber[20]法则计算其叶面积,计算平均值
    The leaf area was calculated according to the Beer-Lamber[20] rule and the average value was calculated
    叶面积密度标准差
    Standard deviation of leaf area density (Ld)
    叶面积密度的标准差
    The standard deviation of leaf area density
    叶面积密度变动系数
    Coefficient of leaf area density variation (Lv)
    叶面积密度的变异系数(标准差与平均值的比值)
    Coefficient of variation of leaf area density (The ratio of the standard deviation to the mean)
    下载: 导出CSV

    表  3  多元回归预测模型

    Table  3.   The multi-regression predictive models

    模型 Model碳储量估测模型 Carbon storage predictive models${R_{{\rm{adj}}}^2} $rRMSE/%
    (4_1) $y=-40.16+9.79H_{\rm{m}}+2.89{\rm{cc}}$ 0.69 21.39
    (5_1) $y=-66.53+10.03H_{\rm{m}}+9.95{\rm{cc}}+11.84L_{\rm{v}}$ 0.67 21.31
    (6_1) $y=-135.98+9.78H_{\rm{m}}+4.84{\rm{cc}}+7.77H_{\rm{s}}+72.09L_{\rm{m}}$ 0.88 12.90
    (7_1) $y=0.72{H_{\rm{m}}}^{1.88}{{\rm{cc}}}^{0.15}$ 0.74 19.39
    (8_1) $y=0.67{H_{\rm{m}}}^{1.89}{{\rm{cc}}}^{0.14}{L_{\rm{m}}}^{0.86}$ 0.82 16.18
    (9_1) $y=0.76{H_{\rm{m}}}^{1.98}{{\rm{cc}}}^{0.23}{H_{\rm{v}}}^{0.18}{L_{\rm{v}}}^{0.05}$ 0.90 11.23
    下载: 导出CSV
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  • 收稿日期:  2020-12-28
  • 修回日期:  2021-05-01
  • 网络出版日期:  2021-07-06

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