Using low-resolution LIDAR on a ground-based agile robot to estimate height, DBH and crown volume of trees

Measurements of tree structural properties, such as trunk diameter at breast height (DBH), tree height, tree diameter, and volume, are crucial for estimating aboveground biomass, carbon stocks, or for managing forestry and silviculture applications. Traditional manual surveys are time-consuming, inaccurate, inconsistent, and subject to observer bias. In this study, we explore the capacity of a ground-based robotic quadruped (“Spot”, from Boston Dynamics), equipped with an Enhanced Autonomy Payload (EAP) module and a Velodyne VLP-16 LIDAR sensor, to measure tree height, DBH, and crown volume. Here, we leverage the Spot EAP’s low-beam LIDAR for efficient data processing and maximizing payload capacity without compromising the EAP’s primary navigation function, leading to lower energy consumption. We developed a scanning method and pre-processing pipeline to generate high-quality point clouds for tree structural analysis. Focusing the study in an urban park with 58 trees (22 Erythrina variegata and 36 Ficus altissima ), we collected tree height using a metric staff for reference data and also measurements for DBH. Crown volume reference data were calculated by combining height measurements obtained from the metric staff with crown extent measurements captured by a LiDAR system mounted on an unmanned aerial vehicle (UAV). Implementing a multi-pose scanning strategy improved the vertical field of view from ±15 to ±60 degrees and increased the point cloud density by more than 800%, achieving a point cloud registration root mean square error (RMSE) of 2.09 cm. Efficient 2D-assisted segmentation, which combines a simplified delineation step with automated refinement, along with leveling, produced individual tree point clouds suitable for structural estimations. Height estimation based on the ground-based robot achieved an RMSE of 29.27 cm and a relative RMSE (rRMSE) of 5.23%. The algorithm for identifying bifurcated trees at breast height showed 100% accuracy. DBH estimates had an RMSE of 4.5 cm and a rRMSE of 18.9%. Crown volume estimation achieved a coefficient of determination (R 2 ) of 0.895, outperforming several existing methods. Overall, the study underscores the potential of agile ground-based robots for efficient and accurate tree structural analysis, with likely future improvements possible through automatic segmentation and parameter tuning.