The plantation area of China is the largest in the world. It is very important to precisely monitor plantation structure. The study area is located at Wangyedian forest farm, Chifeng, Inner Mongolia. The dominated tree species include Larix principis-rupprechtii and Pinus tabuliformis. The stand height models of plantation were established using the UAV (Unmanned Aerial Vehicle) LiDAR (Light Detection and Ranging) data and in situ sample plots measurements. The significant independent variables were selected based on the Pearson’s correlations between the six stand heights (Arithmetic mean height, Lorey’s height, Dominated height, Maximum height, Median height and Crown area weighted height) and the statistical metrics of discrete point cloud. The branch-and-bound search of best subset was conducted to fit the estimation models of stand height. The model accuracy was assessed by the cross validation. The results showed that the correlations between the height metrics of LiDAR point cloud and the different stand heights were high. The linear regression obtained the best result for different stand heights. The independent variables of the estimation model were all height metrics. For the six stand heights, the Lorey’s height (R² = 0.91—0.97, rRMSE = 2.75%—3.96%), dominated height (R² = 0.86—0.97, rRMSE = 3.72%—3.83%) and Crown area weighted height (R² =0.86—0.96, rRMSE = 3.81%—4.73%) had the highest accuracy, while arithmetic mean height (R² =0.85—0.94, rRMSE = 4.52%—6.07%) and median height (R² =0.80—0.95, rRMSE =5.37%—7.34%) had a lower accuracy, maximum height (R² = 0.69—0.87, RMSE = 1.30—1.40 m) was the lowest. Considering the forest types, the estimation accuracies of larch plantation stands were better the estimation accuracies of all forest types (ΔR² = 0—0.05, ΔrRMSE = ‒0.69%—1.97%), which were better than the estimation accuracy of the stand height models of pine stands (ΔR² = 0.06—0.18, ΔrRMSE = ‒1.90%—1.13%). The UAV LIDAR can be used to estimate the stand height of the northern temperate coniferous forest, and applied for the rapid and accurate investigation of plantation resources.

remote sensing;UAV;lidar;point clouds;plantation;stand height