In order to meet the demand for accurate monitoring of forest resources， this paper explores the potential of backpack Light Detection and Ranging （LiDAR） on extracting forest structure parameters for the practical applications. Taking Jiande Forest Farm in Zhejiang Province as the study area， based on backpack LiDAR data collected from eight sample plots， an improved K-means hierarchical clustering algorithm is proposed for individual tree segmentation. Then， six individual tree structural parameters， including diameter at breast height， tree height， crown diameter， crown area， crown volume and gap fraction， as well as 56 cloud point layer height variables were calculated based on the segmented individual tree point cloud data. After that， the random forest method is applied to estimate the volume of individual trees and sample plots. The results showed that， the average comprehensive segmentation accuracy F of the improved K-means hierarchical clustering algorithm was 0.87， and the extraction accuracy of single tree diameter at breast height was 91.26%， and the tree height accuracy was 85.77%. The individual tree volume estimation model using only six tree structural parameters obtained an accuracy of the coefficient of determination（R2） of 0.89， and the Root-Mean-Square Error （RMSE） was 0.053 m³. After using the Pearson correlation coefficient and the importance of random forest features to select the optimal features from the individual tree structure parameters and layer height parameters， the outperformed model was obtained with an estimation accuracy of R2 was 0.93， RMSE was 0.041 m³， and the overall plots’ accuracy reached 94.20%. This study indicated that the proposed K-means hierarchical clustering algorithm can effectively segment individual tree point clouds， and the random forest method can estimate individual tree volume and sample plots volume well， which can provide an important reference for backpack LiDAR in extracting forest resource parameters.