地球信息科学学报

地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (8): 1133-1140.doi: 10.3724/SP.J.1047.2016.01133

• 遥感科学与应用技术 • 上一篇    下一篇

随机森林算法在机载LiDAR数据林分平均树高 估算中的应用研究

鲁林1(), 周小成1,*(), 余治忠1, 韩尚2, 汪小钦1   

  1. 1. 福州大学地理空间信息技术国家地方联合工程研究中心、空间数据挖掘与信息共享教育部重点实验室,福州 350002
    2. 福建省测绘院,福州 350002
  • 收稿日期:2015-10-26 修回日期:2016-02-23 出版日期:2016-08-10 发布日期:2016-08-10
  • 通讯作者: 周小成 E-mail:sirc_lulin@163.com;zhouxc@fzu.edu.cn
  • 作者简介:

    作者简介：鲁 林(1990-),男,硕士生,研究方向为林业遥感。E-mail:sirc_lulin@163.com

  • 基金资助:
    国家自然科学基金项目(41201427);福建省科技厅高校产学合作项目(2015H6008)

Plot-level Forest Height Inversion Using Airborne LiDAR Data Based on the Random Forest

LU Lin1(), ZHOU Xiaocheng1,*(), YU Zhizhong1, HAN Shang2, WANG Xiaoqin1   

  1. 1. Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National Engineering Research Center of Geospatial Information Technology, Fuzhou University, Fuzhou 350002, China
    2. Institute of Surveying and Mapping of Fujian Province, Fuzhou 350002, China
  • Received:2015-10-26 Revised:2016-02-23 Online:2016-08-10 Published:2016-08-10
  • Contact: ZHOU Xiaocheng E-mail:sirc_lulin@163.com;zhouxc@fzu.edu.cn

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摘要：

采用机载LiDAR数据估算森林结构参数是当前林业遥感中的研究热点。本文以福建省长汀县朱溪河流域为示范区,探讨了随机森林算法（RF）在机载LiDAR数据林分平均树高估测中的适用性。首先,通过渐进三角网（TIN）算法进行点云滤波并获取相应林分样地的植被点云子集和高程归一化的植被点云;然后,从归一化后的植被点云提取出高度分位数变量以及点云统计特征值等24个变量参数;最后,根据提取的变量参数和野外实测林分均高数据建立研究区林分平均高随机森林回归估测模型。研究结果表明,模型估测的样地平均树高与实测值具有明显线性相关关系,线性回归系数为0.938,相关系数达到0.968。对样地的估测精度都在86%以上,总体平均精度达到了93.17%。研究认为,基于植被点云变量参数的随机森林模型估测林分平均树高具有较高的可靠性。

关键词: 机载LiDAR, 随机森林, 森林结构参数, 朱溪河流域, 林分平均树高

Abstract:

It has been a hot study field to extract forest structure parameter using Airborne LiDAR. This paper evaluated the validity of random forests technique (RF) in the estimation of forest height, based on both of the physical and statistical features of airborne LiDAR data with the utilization of a new detection method to find the crown height. The study area was selected to be the Zhuxi river basin of Changting county in Fujian Province. At first, the ground point dataset, vegetation and elevation normalized vegetation point dataset of stands were generated by using the progressive TIN filter algorithm. Then, 24 independent variables, such as the percentile of heights and the statistical metrics of points, were derived from the normalized vegetation point dataset. Based on the 24 laser-derived features and the field data, the estimation model for the random forest regression of the mean canopy height in the study area was established. 29 of the samples were used to construct the prediction model, and the remaining 11 samples were used to verify the accuracy of the model. Finally, we compared the average value of the estimated tree heights in each plot with the measured values. The result showed that they were highly correlated with each other, the regression coefficient between them was 0.938, and the correlation coefficient was 0.968. The accuracies of all plots were higher than 87% and the total average accuracy was 93.17%. Moreover, the importance of each variable was calculated in this paper to evaluate the accuracy of model estimation closely. And a conclusion was drawn that the importance of the variable sand the model estimation accuracy were positive correlated, which implies that the greater the importance of the variables, the greater their impact on the accuracy of the model estimation. Among all variables, the Mean_P90 and the percentiles between 70%~95% were representatively having a great influence on the accuracy of model estimation. According to the results, it was concluded that the estimation model of forest height based on random forest technique (RF) with multi-factor was proved to be feasible and efficient.

Key words: airborne LiDAR, random forest, forest structural parameters, Zhuxi river basin, average tree height