Abstract: Objectives： Airborne laser scanning (ALS) data have achieved full provincial coverage in parts of China, providing important support for high-precision forest resource monitoring. However, at the county scale, effective integration of ALS data with dense forest plot inventory data to improve sampling estimation of mean forest stock volume has received limited attention. Methods： We selected Mengyin County in Shandong Province as the study area and Nonlinear logistic regression models were fitted using ALS-derived variables and dense plot data to estimate plot-level forest stock volume. Traditional design-based inference, various stratification methods (including post-stratification based on ALS predictions and dual-factor post-stratification with slope), and model-assisted estimation using ALS variables (with and without slope) were compared. Performance was evaluated through standard error and sampling precision. Results： Model-assisted estimation based on ALS variables yielded the lowest standard errors: 1.35 m³·hm^-2 (without slope) and 1.33 m³·hm^-2 (with slope). The version incorporating slope achieved the highest sampling precision of 93.90%, representing around 3% improvement over traditional design-based inference (90.28%). Post-stratification using ALS predictions and dual-factor post-stratification (ALS + slope) also performed well, with standard errors of 1.39 m³·hm^-2 and 1.37 m³·hm^-2, and sampling precisions of 93.77% and 93.87%, respectively. The results revealed that incorporating slope effectively addressed terrain heterogeneity in complex areas. Conclusions： ALS-assisted sampling methods, particularly model-assisted estimation and targeted post-stratification, can substantially improve the precision of county-level mean forest stock volume estimation. These approaches offer feasible solutions for optimizing forest resource inventory methods and achieving the goal of annual high-frequency forest monitoring and reporting, especially in regions with complex topography.