A comparative analysis of grid-based and object-based modeling approaches for poplar forest growing stock volume estimation in plain regions using airborne LiDAR data

Poplar (PopulusL.) is one of the most widely distributed tree species planted in the plains of China and plays an important role in wood products and ecological services. Accurate estimation of poplar Growing Stock Volume (GSV) is crucial for better understanding the ecological functions and economic values in plain regions. However, the striped distribution feature of poplar forests in plain regions makes traditional grid-based GSV modeling methods highly uncertain. This research took Lixin County and Yongqiao District as case studies to examine the advantages of using object-based GSV modeling approach over the traditional grid-based approaches for poplar GSV estimation. The canopy height variables and density variables were extracted from airborne LIDAR-derived Canopy Height Model (CHM) data through different grid sizes and segmentation unit for constructing the poplar GSV estimation models using the linear regression. The results indicate that (1) Significantly linear relationships exist between GSV and height percentile variables; (2) The estimation accuracy in Lixin can be effectively improved by incorporating the CHM density variables into height variables, with the coefficient of determination (R2) increasing from 0.46 to 0.71 and Root Mean Square Error (RMSE) decreasing from 20.23 to 14.94 m3/ha when a grid-based approach was implemented at grid size of 26 m by 26 m (plot size). However, CHM density variables have no effect on estimation modeling in Yongqiao district. The patch sizes and shapes considerably affect the selection of modeling variables and accuracy of modeling prediction; (3) The object-based mapping approach outperforms the grid-based approach in solving the mixed plot problem. This is especially valuable in the study areas with striped forest distribution. This study shows that differences in poplar stand structure affect the selection of modeling variables and GSV modeling performance, and an object-based modeling approach is recommended for GSV estimation in the plain areas.