The stem diameter is a fundamental parameter for assessing woody vegetation growth and quantifying ecological and economic benefits such as biomass production, carbon sequestration, and urban ecosystem services. Recent advances in handheld mobile laser scanning (HMLS) enable efficient acquisition of high-density point clouds for deriving tree structural attributes in complex urban environments. This study presents an automated workflow for tree detection and diameter at breast height (DBH) estimation in an urban park using two HMLS systems: the GoSLAM RS100i and the FJD Trion S1. The analysis evaluates the influence of point cloud density and subsampling resolution on detection performance and DBH accuracy. Reference measurements for 69 trees were collected using a forestry caliper and total station, while HMLS datasets were georeferenced with RTK-GNSS observations. The workflow included point cloud filtering, terrain modelling, stem extraction, and DBH estimation through cylindrical fitting. Detection performance differed between systems and was strongly affected by point density. The GoSLAM RS100i showed a decline in correctly detected trees from 97.1% at 2 cm spacing to 53.6% at 4 cm, whereas the FJD Trion S1 maintained stable detection rates of approximately 87% across all spacings, likely due to its higher point density. DBH estimation accuracy was comparable for both systems, with RMSE values of 3.3–3.6 cm for filtered datasets and up to 4.9 cm when all detections were included. A systematic positive bias of 1.7–2.5 cm was observed. Results demonstrate that HMLS systems provide reliable DBH estimates for efficient urban tree inventory applications.