Handheld Terrestrial LiDAR Scanning of sub-canopy woodland habitats: Quantifying understorey canopy vegetation density to measure the impacts of occlusion

Abstract

This study aimed to validate a low-cost self-built handheld terrestrial lidar scanning (HTLS) system, examine its tree detection capabilities, and investigate the impacts of understory occlusion on Light Detection and Ranging (LiDAR) survey data in forested areas. Two sites, Hen Faes and Treborth, were surveyed using LiDAR technology, and the detection rates of trees using the HTLS system were compared to manual field survey. The results indicated a significant difference in tree detection rates between the two sites, with Hen Faes exhibiting a higher mean detection rate (99.3%, SD = 0.203) compared to Treborth (94.9%, SD = 3.83) when utilising the LiDAR system. Furthermore, the study demonstrated that the HTLS system provides accurate measurements of tree diameter at breast height (DBH) and can serve as a cost-effective alternative to commercial systems for forest inventory and validation studies.
Furthermore, this study highlighted the significant impact (Pearsons coefficient (r) 0.785 (p < 0.001)) of understory occlusion on LiDAR survey data, with missing tree data resulting from increased understory density. The novel approach used in the study was to quantify understory vegetation using volumetric and area values obtained from CloudCompare, which enabled the measurement of occlusion impacts and standardised the comparisons of stand characteristics between sites and plots. The importance of accounting for understory occlusion is underscored by the significant positive relationship (Pearson coefficient (r) 0.785 (p < 0.001)) found between missing tree percentage and undergrowth vegetation density.
Overall, this study emphasizes the need to improve the accuracy and completeness of forest remote sensing data for effective forest management and conservation. To achieve this, a more comprehensive understanding of the impacts of understory vegetation on LiDAR survey data is necessary. Further research is required to explore the potential benefits and limitations of LiDAR technology in different forest types and to optimize its use for different applications.
With such knowledge, HTLS could become a valuable asset in research, conservation, and sustainable forestry practices. Therefore, this study provides valuable insights into the potential of HTLS systems and the impacts of understory occlusion on LiDAR survey data in forested areas.

Date of Award	23 Jul 2024
Original language	English
Awarding Institution	Bangor University
Supervisor	Sopan Patil (Supervisor)