Assessing the performance of a handheld laser scanning system for individual tree mapping. A mixed forest show case in Spain
Creators: name: Tupinamba-Simoes, Frederico
name: Pascual, Adrián
name: Guerra-Hernández, Juan
name: Ordóñez, Cristóbal
name: Bravo, Felipe
Publisher: name: Zenodo
Year: 2022
Subjects: LiDAR
precision forestry
terrestrial laser scanning
forest monitoring
mobile laser scanning
forest inventory

description: Handheld laser scanning (HLS)


The HLS data was collected in early February 2022 using a handheld laser scanner model ZEB-Horizon (GeoSLAM Ltd., Nottingham, UK). This HLS system is equipped with a LiDAR Velodyne VLP-16 sensor, mounted in a spinning head, with 100-meter range and accuracy ranging from 1 to 3 cm, vertical angular resolution of 2.0° and horizontal resolution of 0.1° - 0.4°. The HLS system can capture 300,000 points per second in single return, can scan in double-return mode, and uses SLAM technology in data collection. The toolkit for the scanning operations includes a data-logger connected to the handheld scanner where the optical RGB camera and the LiDAR sensor are set. Activating the HLS system for scanning is straightforward and takes approximately 3 minutes. After departing from the initial control point, successive ground control points (GCP) must be recorded along the track to support the optimal co-registration of the meshed point cloud in post-processing. The ZEB HLS system recognizes a GCP in post-processing when the scanner stops in the same position for several seconds. We surveyed a grid of 25 by 25-meter that define the sub-plots of a marteloscopio, with 30 possible candidates to be used as GCP. For this we used the total station GTS 220, This grid were supported on two external vertices to the perimeter of the marteloscopio, collected with Global Navigation Satellite System (GNSS) receiver (Topcon Hiper SR), in fixed mode through Networked Transport of RTCM via Internet Protocol (NTRIP) to be able to project point clouds based on HLS to eventually integrate ALS data for validation of tree height.


Two scanning routes were designed to evaluate whether a short, simplified path would render point clouds of similar quality to a more thorough path design to potentially render better results in terms of tree mapping accuracy (Figure 3). Both alternatives started and finished in the same GCP as recommended by the manufacturer. The first scan (Scan 01, 632.4 m) started in the center of the marteloscope from where HLS data acquisition started towards the NW, then SW and finally back to the origin GCP before replicating the same loop to the eastern sections. Scan 01 transited along 11 CGPs. The second scanning path (Scan 02, 916.8 m) increased its length 50% compared to Scan 01 to have a better coverage of the 16 plot sections. Scan 02 started and ended in the SW corner, the first of the 17 GCPs required along the track. Four GCPs were coincident as forward and backward trajectories matched from the SW reference point. After scanning, the GeoSLAM HUB 6.0 software was used to build the 3D point cloud using coordinates from GCPs surveyed with the total station and transformed to the UTM system. The ZEB HLS system provides two transformation modes for generating the 3D point clouds: the Rigid and the None-Rigid modes. The characteristics and outcomes of the rigid and none-rigid transformations are further explored in this study. 


Under the Rigid option, the point cloud is rotated and adjusted, and GCP locations are matched without changing the scale factor – i.e., all laser echoes are transformed in one step. and the relative position of points remains invariant. The None-Rigid mode does not preserve the relative positions of points, by moving, rotating or changing the scale between points.. It is expected to perform better for conditions where SLAM technology better accommodates GCP locations to known GCP coordinates (GeoSLAM Ltd., Nottingham, UK). Hereby, laser points that are closer to the reference points are shifted more than the points that are farther away. Sinuosity in the scanning paths and overlapping along the tracks increase the areas close to GCP for which the switch can affect coordinates values. We explore the effect of transformation mode in this study by processing both Scan 01 and Scan 02 under both options to confirm the expected better performance of the None-Rigid mode in terms of consistency in point cloud density, processing time and effects towards the estimation of tree locations and tree attributes.