Estimating Forest Stock Volume Based on Airborne Lidar Data

Abstract

Forest stock volume (FSV) stands as an important indicator in evaluating the potential for carbon sequestration. It is crucial for forest resource management at local, regional, and national scales. In order to achieve an accurate estimation of FSV, this article takes Mengyin County, Shandong Province, China as the research area, builds a random forest (RF) model for four tree species based on airborne Lidar data, and forms a monitoring system of "individual tree - grid - county" granularities. The results demonstrated that all four models exhibited excellent generalization capabilities, with no signs of overfitting. In the test phase, the R2 of the poplar and pine models exceeded 0.9, while the R2 of the cypress model was 0.81, and the rRMSE was controlled within 20%, indicating that the fitting effect of the three tree species models was better; the accuracy of the robinia pseudoacacia model was relatively poor, with R2 of 0.60 and rRMSE of 20.60%. This study provides a feasible method for estimating forest stock volume within the county, which provides strong technical support for forest resource management and planning, and helps promote sustainable forestry development.