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Chinese Journal of Applied Ecology ›› 2021, Vol. 32 ›› Issue (3): 836-844.doi: 10.13287/j.1001-9332.202103.001

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Estimating standing stocks of the typical conifer stands in Northeast China based on airborne lidar data

YUAN Yu-na1, PENG Dao-li1*, WANG Wei2, ZENG Wei-sheng2   

  1. 1College of Fores-try, Beijing Fores-try University, Beijing 100083, China;
    2Academy of Forest Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China
  • Received:2020-07-09 Accepted:2020-11-28 Online:2021-03-15 Published:2021-09-15
  • Contact: * E-mail: dlpeng@bjfu.edu.cn
  • Supported by:
    National Key R&D Program of China (2016YFD-0600205) and the China National Land Survey and Planning Institute Bidding Project (GXTC-A-19070081)

Abstract: 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.

Key words: airborne lidar, conifer stand, standing stock, partial least-squares regression