LiDAR technology has been widely used in forest survey and research, but the high-resolution point cloud data generated by LiDAR equipment also pose challenges in storage and computing. To address this problem, we propose a point cloud simplification method for trees, which considers both higher similarity to the original point cloud and the area of the tree point cloud. The method first determines the optimal search neighborhood using the standard deviation of FPFH information entropy. Based on FPFH information entropy and Poisson disc sampling theory, the point cloud is partitioned and sampled. By optimizing the separation thresholds of significant feature points and less significant feature points using a genetic algorithm with the Hausdorff distance and point cloud area as the objective function, the final simplified point cloud is obtained. Validation with two point cloud data sets shows that the proposed method achieves good retention of the area information of the original point cloud while ensuring point cloud quality. The research provides new approaches and techniques for processing large-scale forest LiDAR scan point clouds, reducing storage and computing requirements. This can improve the efficiency of forest surveys and monitoring.

To reduce the cutting force and cutting energy consumption during the operation of tree-climbing pruning machines for poplar trees, a linear cutting test bench device for branches was designed according to the growth characteristics of poplar branches and pruning forestry requirements in this study. Firstly, the cutting mechanical analysis of poplar branches was carried out to explore the significance parameters affecting the cutting force, and then the cutting performance test and parameter optimization of poplar branches was carried out through the response surface method (RSM). The test results indicated that cutting speed, tool edge angle and tool back angle had significant effects on the ultimate shear stress, cutting power consumption per unit area, and the branch damage rate of poplar branches, and the established regression equation demonstrated high goodness of fit. Meanwhile, a second-order regression mathematical model was developed between ultimate shear stress, cutting the power consumption per unit area of the cut and the branch damage rate, and the significance parameter. The non-dominated Sorting Genetic Algorithm II (NSGA-II) was used for multi-objective optimization computation to obtain the optimal combination of cutting parameters as 3.02 m/s for cutting speed, 15° for tool edge angle, and 3° for tool back angle. In this case, the ultimate shear stress, cutting power consumption per unit area, and branch damage rate of poplar branches were small, which were 346.63 kPa, 9.35 mJ/mm², and 12.36%, respectively. Through the test verification, it can be seen that the relative error between the verification test and the predicted value of model was less than 7%. Moreover, under a cutting tool edge angle of 15°, the ultimate shear stress, cutting power consumption per unit area, and branch damage rate were, respectively, reduced by 17.29%, 14.98%, and 34.21% compared with those under a cutting tool edge angle of 20°, which verifies the validity and reliability of the test results and the research method. This study can provide data support and reference for the research and development of energy-efficient poplar tree-climbing pruning equipment and related branch-cutting performance tests.