Crown volumes serve as a crucial factor for surface ecological monitoring. Laser point clouds can characterize the fine-scale spatial morphologies of individual trees, providing a data basis for crown volume extraction. However, existing laser point cloud-based methods for extracting crown volumes of individual trees are sensitive to parameters and exhibit low degrees of automation. Based on the analysis of the three-dimensional morphological structures of individual trees, this study proposed a spherical coordinate integration method for extracting crown volumes of individual trees based on the terrestrial laser scanning (TLS) point clouds. First, the crown points were obtained through visual elevation threshold-based segmentation according to the elevation distributions of TLS point clouds. Then, the TLS point clouds were projected onto the spherical coordinate space for infinitesimal segmentation into triangular pyramids. Finally, the crown volumes were determined through the three-dimensional spherical coordinate integration. Six types of TLS point cloud data for individual trees were selected for tests. As indicated by the test results, the proposed method effectively considers factors like crown morphology and point cloud density, achieving a maximum absolute error of 2.33 m³ and a maximum relative error of 3.40% in the crown volume extraction of individual trees. It manifests higher extraction accuracy and stability compared to the existing methods. Therefore, this study holds significant reference value for extracting tree parameters based on TLS point clouds.