Accurate single-tree segmentation plays a crucial role in obtaining detailed individual tree information, which underpins the precise estimation and sustainable management of forest resources. In forests with gradient canopy closure and non-uniform tree distribution, clustering methods that rely solely on empirically set parameters often fail to achieve accurate single-tree segmentation, which significantly constrains refined forest resource management. To address this challenge, an adaptive-radius density-based spatial clustering of applications with noise (DBSCAN) algorithm was proposed for single-tree point cloud segmentation in forests with gradient canopy closure.First, the preprocessed point cloud data were sliced along the z-axis and initially clustered using DBSCAN. The horizontal scale of each sub-cluster was analyzed to guide the adaptive adjustment of clustering in the subsequent steps. Second, removing extreme scale values, the mean of the remaining scales was used to determine an adaptive clustering radius for each slice, accommodating structural variations across different canopy heights. Finally, by integrating the adaptive clustering radius, core point identification, and a k-dimensional tree (KD-Tree)-based neighborhood search strategy, the accurate segmentation of individual tree point clouds was achieved. Experimental results show that the proposed method achieves an average recall of 0.94 and precision of 0.99 across five forest plots, outperforming existing segmentation techniques. The proposed method exhibits strong generalization capability and robustness in complex gradient canopy environments, providing a valuable tool for improving the accuracy and efficiency of forest resource surveys.