Forest topsoil supports vegetation growth and contains the majority of soil nutrients that are important indices of soil fertility and quality. Therefore, estimating forest topsoil properties, such as soil organic matter (SOM), total nitrogen (Total N), pH, litter-organic (O-A) horizon depth (Depth) and available phosphorous (AvaP), is of particular importance for forest development and management. As an emerging technology, light detection and ranging (LiDAR) can capture the three-dimensional structure and intensity information of scanned objects, and can generate high resolution digital elevation models (DEM) using ground echoes. Moreover, great power for estimating forest topsoil properties is enclosed in the intensity information of ground echoes. However, the intensity has not been well explored for this purpose. In this study, we collected soil samples from 62 plots and the coincident airborne LiDAR data in a Korean pine forest in Northeast China, and assessed the effectiveness of both multi-scale intensity data and LiDAR-derived topographic factors for estimating forest topsoil properties. The results showed that LiDAR-derived variables could be robust predictors of four topsoil properties (SOM, Total N, pH, and Depth), with coefficients of determination (R2) ranging from 0.46 to 0.66. Ground-returned intensity was identified as the most effective predictor for three topsoil properties (SOM, Total N, and Depth) with R2 values of 0.17–0.64. Meanwhile, LiDAR-derived topographic factors, except elevation and sediment transport index, had weak explanatory power, with R2 no more than 0.10. These findings suggest that the LiDAR intensity of ground echoes is effective for estimating several topsoil properties in forests with complicated topography and dense canopy cover. Furthermore, combining intensity and multi-scale LiDAR-derived topographic factors, the prediction accuracies (R2) were enhanced by negligible amounts up to 0.40, relative to using intensity only for topsoil properties. Moreover, the prediction accuracy for Depth increased by 0.20, while for other topsoil properties, the prediction accuracies increased negligibly, when the scale dependency of soil–topography relationship was taken into consideration.