Characterization of mobile LiDAR data collected with multiple echoes per pulse from crowns during foliation

Abstract

As a state-of-the-art technology for fine-scale forest investigation, mobile terrestrial light detection and ranging (LiDAR), also referred to as mobile laser scanning (MLS), recently has been increasingly exploited to improve its performance on this task. One potential solution is to apply special MLS systems with the capability of collecting multiple echoes per pulse (multi-echoes, for short) from canopies. The methodologies of this rationale turned out to perform better but still insufficiently for canopy properties retrieval, owing to their common defective premise. That is, the knowledge of the characteristics of MLS scan data comprising multi-echoes, so far, is in shortage, especially when regarding the dynamic process of tree foliation. As a pioneering work for this challenge, this study attempted to comprehensively analyze the characteristics of MLS multi-echoes collected from tree crowns during foliation. Specifically, new stable multi-echoes-related features were deduced under the schematic frame of relative quantification, in both spatial and temporal sense. “Relative” here briefly means the division operation deployed on the attributes of multi-echoes, individually in terms of the number of echoes, echo width and crown volume integrity, between their different return orders. Then, the “relative” schematic was primarily validated for more stably representing crown properties during foliation, based on the real data that was collected by the Sensei MLS system with a maximum of three echoes per pulse. Further, a case of tree species classification was examined using a linear discriminant classifier, and it was testified that the resultant temporal statistical rules of multi-echoes as the reverse clues can enhance the performance of MLS in applications.

Keywords:

• Light detection and ranging (LiDAR)
• mobile laser scanning (MLS)
• multi-echoes
• feature
• relative quantification
• foliation

Acknowledgements

Thanks to the Academy of Finland for financial support (project “Improving forest supply chain by means of advanced laser measurements” and “Science and technology toward precision forestry”) and also to the Finnish Funding Agency for Technology and Innovation for financial support (project “Development of automatic, detailed 3D model algorithms for forests and built environment”).