Comparison in linear and nonlinear estimation models of carbon storage of plantations based on UAV LiDAR

  Objective  Forest carbon storage is an important indicator of the composition and function of ecosystem. It will be benefit to forest resource management by investigating the state of forest carbon storage. The LiDAR can be used to monitor forest resources, however, there are many problems in forest parameter estimation, such as multi variable model, uncertainty and lack of variables with analytical significance of stand three-dimensional structure. Therefore, it is necessary to select appropriate stand analysis variables and models.

  Method  This paper uses UAV LiDAR point cloud and sample plot data to analyze the plantation in Wangyedian Forest Farm of Kalaqin Banner, Chifeng City, Inner Mongolia of northern China. The multiple linear model and the multiple power models were used to estimate the forest carbon storage using different variables, and select the optimal model.

  Result  (1) The nonlinear models (R² ranged in 0.66−0.86, rRMSE ranged in 23.51%−9.91%) were better than linear models (R² ranged in 0.52−0.85, rRMSE ranged in 27.70%−12.38%). (2) The mean height of point cloud and canopy cover were used as basic variables. The combinations of different variables were emulated to select the best model of forest parameters. The nonlinear model based on average height of the laser point cloud, canopy cover, height variation coefficient and leaf area variation coefficient (R²=0.86, rRMSE=9.91%) had the highest estimating accuracy.

  Conclusion  The vertical structure variables could improve the estimating accuracy of carbon storage of plantation using LiDAR. The non-linear model is more suitable for the estimation of carbon storage of plantation.