The existing methods for single tree detection based on LiDAR point clouds have achieved extraordinary performance. However, the presence of occluded low-layer trees poses a challenge to existing methods for accurately detecting single trees. To improve the detection performance for low-layer trees, we present a method based on the dynamic graph convolutional neural network (DGCNN). At first, this method utilizes a graph-based algorithm to roughly determine the contour of the top crown on the canopy height model and expands it into circular regions. Each circular region is used as the detection sample. Then, the classifier based on DGCNN is used to classify the detection samples. The samples with low-layer crowns will be retained. Finally, to determine the position of the low-layer tree crowns, the statistical analysis method of point clouds is used to remove the point information from the top-layer tree crown. To verify the effectiveness of our method, the experiments are conducted based on two different experimental areas. The experimental results indicate that our method achieves the highest matching score compared with the other four methods. Specifically, our method can achieve an overall matching rate of 88.32% on five experimental plots containing the low-layer trees, with an overall commission rate of only 42.30%. The numbers and rates of omission generated by our method during detection are significantly lower than the compared methods. All the results have demonstrated that our method can effectively detect low-layer trees and exhibit universality across various scenarios.