A brief comparison of seven different sensors in scanning the same tree

Introduction

This post compares the outputs of seven different LiDAR and photogrammetry sensors when scanning the same tree. The goal is to highlight differences in data quality and density depending on the sensor.

Results

Analysis

Data density and detail vary widely**

• Terrestrial laser scanners (TLS) like the Faro Focus 3D X330 and Riegl VZ400i produce the highest point counts and densities, capturin g the most detailed representations of the tree.

• Photogrammetry (GoPro) also achieves high density, but with different noise characteristics.

• File size reflects data richness. Higher point counts and densities generally result in larger file sizes. The Faro Focus 3D X330 produces the largest file, reflecting its very dense data.

• Sensor type impacts output. Mobile laser scanners (MLS) like GeoSlam ZEB Horizon and LiBackpack DGC50 generate less dense data, but are more portable and faster to deploy.

Conclusion

For applications requiring high detail (e.g., scientific analysis, detailed modeling), TLS or high-quality photogrammetry is preferable.

For rapid mapping or where portability is key, MLS or consumer devices like iPad Lidar may suffice.

References

• Murtiyoso, A., Overney, N., & Suwardhi, D. (2024). Systematic synthesis of the notion of scale levels and digital representation in the application of 3D geospatial technology for virtual forests. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 343-348.

• Chen, Y. C., Hollaus, M., Bronne, G., & Pfeifer, N. (2023). Characterization of SilviLaser 2021 Benchmark Data Set.