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首页> 外文期刊>Journal of Forest Planning >Estimation of Biophysical Parameters of Individual Tree Stands derived from LiDAR and Digital Matrix Camera Image
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Estimation of Biophysical Parameters of Individual Tree Stands derived from LiDAR and Digital Matrix Camera Image

机译：激光雷达和数字矩阵相机图像推导的单个林木生物物理参数的估算

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The aim of this study is to examine the performance of a numerical ellipsoid modeling methodology to estimate tree structural characteristics in mixed forest using airborne Light Detection And Ranging (LiDAR) data along with airborne Digital Matrix Camera (DMC) image. In three-dimensional numerical analysis using points cloud of LiDAR data, ellipsoid model has the potential to simultaneously estimate tree top position, diameter and shape of individual tree crown. A Japanese cedar plantation with randomly mixed pine trees was chosen in this study as this type of forest, which is typical of Japanese cedar plantation in Japan. We developed a methodology consisting of both tree species classification and estimation of characteristics of tree structure with the followings steps: (1) classification of area of cedar and pine trees in the mixed plantation by using ortho-DMC image, (2) estimation of number of trees and estimation of tree top location in horizontal plane by standard ellipsoid model for eachspecies, derived from Crown Height Model (CHM) and based on random selections of points clouds on each of the classified areas, (3) estimation of tree top height and realistic shape of individual tree by using a truncated cone shape model and LiDAR points cloud in respective classified areas. The study area is a cedar plantation forest in Northern Japan. LiDAR measurements with a density of 14.65 pulses/m2 and DMC imagery with a spatial resolution of 10cm are used in this study For validation, ground truth data of tree species, geographic tree position and tree height were measured at the study site. The developed methodology could correctly identify a total of 73 out of 89 cedar trees in the areas classified as cedar, and 12 out of 29 pine trees in areas classified as pine. Validation of estimated tree height resulted in coefficient of determination (R2) of 0.72 and 0.78 for pine and cedar respectively. This study indicates that fitting the ellipsoid model and the truncated cone shape model to LiDARpoints cloud is able to simultaneously estimate tree top position, crown shape and diameter of individual tree crown.
机译：这项研究的目的是使用机载光检测和测距（LiDAR）数据以及机载数字矩阵相机（DMC）图像，研究数值椭球建模方法的性能，以估计混交林中的树木结构特征。在使用LiDAR数据的点云进行三维数值分析中，椭球模型有可能同时估算单个树冠的树顶位置，直径和形状。在本研究中，日本杉杉林中随机混有松树被选为这种类型的森林，这是日本杉杉林的典型特征。我们开发了一种包括树种分类和​​树木结构特征估算的方法，该方法包括以下步骤：（1）通过使用正交DMC图像对混合种植园中的雪松和松树面积进行分类，（2）数量估算树冠的估计和每种树的标准椭球模型在水平面中树顶位置的估计，这些模型是根据树冠高度模型（CHM）得出的，并基于每个分类区域上点云的随机选择，（3）估计树顶高度和通过在相应的分类区域中使用截锥形状模型和LiDAR点云来获得单个树的真实形状。研究区域是日本北部的雪松人工林。在这项研究中，使用了密度为14.65脉冲/ m2的LiDAR测量和空间分辨率为10cm的DMC图像进行验证。在研究现场测量了树种的地面真实数据，树的地理位置和树高。所开发的方法可以正确地识别出被分类为雪松的地区的89棵雪松中的73棵，以及被识别为松木的区域的29棵松树中的12棵。估计的树高的验证导致松树和雪松的确定系数（R2）分别为0.72和0.78。这项研究表明，将椭圆形模型和截锥形状模型拟合到LiDARpoints云可以同时估计单个树冠的树顶位置，树冠形状和直径。

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