College of Fores-try, Beijing Fores-try University, Beijing 100083, China.
Academy of Forest Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China.
Ying Yong Sheng Tai Xue Bao. 2021 Mar;32(3):836-844. doi: 10.13287/j.1001-9332.202103.001.
To promote the application of lidar technology in estimating standing stocks of the typical conifer stands in Northeast China, , spruce-fir forest, larch forest, Korean pine forest, var. 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 (=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 (=0.79, RMSE=60.92, RRMSE=22.9%) and larch forest (=0.76, RMSE=28.39, RRMSE=25.8%), followed by spruce-fir forest (=0.81, RMSE=46.96, RRMSE=27.7%) and var. mongolica forest (=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.
为了推动激光雷达技术在估计中国东北地区典型针叶林蓄积量中的应用，我们结合机载激光雷达点云数据和 800 个地面样地数据，采用逐步回归和偏最小二乘法分别建立了 4 种针叶林蓄积量模型。偏最小二乘法优于逐步回归法（=0.05-0.15，RRMSE=2.6%-4.2%）。在建模所涉及的 3 类特征变量中，高度变量（被选择 26 次）比其他变量（分别被选择 12 次和 11 次）更为重要。基于偏最小二乘法平均值建立的模型精度最高，其中对于红松（=0.79，RMSE=60.92，RRMSE=22.9%）和落叶松（=0.76，RMSE=28.39，RRMSE=25.8%）林分效果最好，其次是云冷杉（=0.81，RMSE=46.96，RRMSE=27.7%）和 var. mongolica 林（=0.50，RMSE=55.49，RRMSE=30.4%）。本研究为估计中国东北地区 4 种典型针叶林蓄积量提供了一种有效方法。
