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We evaluated the performance of unmanned aerial system (UAS) airborne light detection and ranging (lidar) data in the species classification of pine, spruce, and broadleaf trees. Classifications were conducted with three machine learning (ML) approaches (multinomial logistic regression, random forest, and multilayer perceptron) using features computed from automatically segmented point clouds that represent individual trees. Trees were segmented from the point cloud using a marker-controlled watershed algorithm, and two types of features were computed for each segment: intensity and texture. Textural features were computed from gray-level co-occurrence matrices built from horizontal cross sections of the point cloud. Intensity features were computed as the average intensity values within voxels. The classification accuracies were validated on 39 rectangular $30\times30$ m field plots using leave-one-plot out cross-validation. The results showed only very small differences in the classification performance between different ML approaches. Intensity features provided greater classification accuracy (kappa 0.73–0.77) than textural features (kappa 0.60–0.64). However, the best classification results (kappa 0.81) were achieved when both intensity and textural features were used. Feature importance in different ML approaches was also similar. We conclude that the accurate classification of the three tree species considered in this study is possible using single-sensor UAS lidar data.