To promote the application of lidar technology in estimating standing stocks of the typical conifer stands in Northeast China, i.e., spruce-fir forest, larch forest, Korean pine forest, Pinus sylvestris var. mongolica forest, we combined the point cloud data obtained by airborne lidar with the data of 800 ground plots and established models of standing stocks for the four conifer stands by stepwise regression and partial least square. Partial least squares method was better than stepwise regression method (R2=0.05-0.15, RRMSE=2.6%-4.2%). Among the three types of feature variables involved in modeling, height variable (selected for 26 times) is more important than others (selected for 12 times and 11 times, respectively). With respect to the accuracy of models established based on the means of the partial least square, they worked best for Korean pine forest (R2=0.79, RMSE=60.92, RRMSE=22.9%) and larch forest (R2=0.76, RMSE=28.39, RRMSE=25.8%), followed by spruce-fir forest (R2=0.81, RMSE=46.96, RRMSE=27.7%) and P. sylvestris var. mongolica forest (R2=0.50, RMSE=55.49, RRMSE=30.4%). This study provi-ded an effective way to estimate standing stocks of four typical conifer stands in Northeast China.
为了推广激光雷达技术在森林蓄积量估测计量方面的应用,本研究以东北林区云冷杉林、落叶松林、红松林和樟子松林4种典型针叶林为对象,基于机载激光雷达获取的点云数据提取特征变量,结合800块地面样地数据,采用逐步回归方法和偏最小二乘方法,建立4种针叶林的蓄积量模型。结果表明: 偏最小二乘法建立的模型精度优于逐步回归方法(ΔR2=0.05～0.15,ΔRRMSE=2.6%～4.2%);在参与建模的3类点云特征变量中,贡献最大的是点云高度变量(被选择26次),其他变量有一定的辅助作用(分别被选择12次和11次);使用偏最小二乘方法建立的林分蓄积量模型中,红松林(R2=0.79,RMSE=60.92,RRMSE=22.9%)和落叶松林(R2=0.76,RMSE=28.39,RRMSE=25.8%)的精度最高,云冷杉林(R2=0.81,RMSE=46.96,RRMSE=27.7%)次之,樟子松林(R2=0.50,RMSE=55.49,RRMSE=30.4%)的精度稍低。研究结果为东北林区4种典型针叶林蓄积量估测提供了一种有效的方法。.
Keywords: airborne lidar; conifer stand; partial least-squares regression; standing stock.