基于树木定量结构模型的单木参数提取及生物量估测

    Individual tree parameter extraction and biomass estimation based on the quantitative structure model

    • 摘要:
      目的 以安徽省天马国家级自然保护区内落叶阔叶林为研究对象,探究在复杂林境下树木定量结构模型利用无人机激光雷达数据进行单木生物量估测的应用潜力。
      方法 通过地面调查和无人机获取样地与激光雷达点云数据,其中以地面数据为实测参照数据。采用相对最短路径算法对雷达数据进行分割,利用树木定量结构模型提取分割后单木点云的树木参数(胸径、主干体积、枝干体积、分支数、冠层基部高度、冠层面积、冠层体积以及冠幅),并使用Pearson相关系数以及方差膨胀因子对参数进行变量筛选,最后构建基于3种机器学习算法的单木生物量估测模型。
      结果 基于随机森林(RF)构建的生物量模型训练效果最佳(R2=0.880 0,RMSE=192.81 kg,rRMSE=29.88%),多层感知机(MLP)训练效果(R2=0.820 0,RMSE=236.48 kg,rRMSE=36.65%)与支持向量机(SVM)较为相近(R2=0.810 0,RMSE=243.67 kg,rRMSE=37.77%)。
      结论 本文证实了从树木定量结构模型中所提取的树木参数能在落叶期阔叶树种中构建出精度较高的生物量估测模型,可以为复杂森林环境下的资源调查提供新方法。

       

      Abstract:
      Objective This article takes the deciduous broad-leaved forest in the Tianma National Nature Reserve in Anhui Province as the research subject, exploring the application potential of the quantitative structure model in biomass estimation of individual tree in complex environment using unmanned aerial vehicle laser scanning data.
      Method Through ground surveys and the use of unmanned aerial vehicles, plot data and LiDAR point cloud data were collected, with the ground data serving as the reference measurements. The comparative shortest-path algorithm was used for point cloud segmentation. Subsequently, tree parameters (such as diameter at breast height, trunk volume, branch volume, number of branches, canopy base height, canopy area, canopy volume, and crown width) were extracted from the segmented individual tree point clouds using the quantitative structure model. Pearson correlation coefficient and variance inflation factor were then employed for variable selection of the parameters. Finally, an individual tree biomass estimation model was constructed based on the three machine learning algorithms.
      Result Among the biomass models, the one based on random forest (RF) achieved the best training performance (R2 = 0.880 0, RMSE = 192.81 kg, rRMSE = 29.88%). The performance of the multilayer perceptron (MLP) model (R2 = 0.820 0, RMSE = 233.62 kg, rRMSE = 36.65%) was quite similar to that of the support vector machine (SVM) model (R2 = 0.810 0, RMSE = 243.67 kg, rRMSE = 37.77%).
      Conclusion This article confirms that the tree parameters extracted from the quantitative structure model can be used to construct a high-precision biomass estimation model for deciduous broadleaf species, providing a new method for resource surveys in complex forest environment.

       

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