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Title: On leaf and wood separation from Terrestrial LiDAR data
Author: Boni Vicari, Matheus
ISNI:       0000 0004 7965 169X
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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TLS can provide high resolution measurements to calibrate/validate remote sensing (RS) efforts to monitor forests and to extend the knowledge underpinning many ecological theories. The application of current TLS methods is hampered by the effects of material mixtures in TLS data. Studies in the literature suggest that mixture of materials in point clouds can lower the accuracy of TLS estimates. Leaf-wood separation methods have previously been proposed in the literature. Given their need for manual input, they are restricted to a small number of trees. Also, approaches used to test these methods are subjective and hard to reproduce. The impact of leaf-wood separation is, so far, poorly understood, as very few quantitative analyses of pre- and post-separation TLS estimates are available in the literature. Reports found in the literature highlight the need for an automated separation method that is able to accurately separate leaf and wood points from TLS data. There is also a gap with regards to reproducing tests that are necessary to validate these separation methods. The final issue identified here is the gap in the knowledge about the impact of leaf-wood separation on TLS estimates. The main objective of this thesis is to propose a method to accurately separate leaf-wood material from TLS data in an automated fashion. Two other methods were proposed: a testing framework to validate the leaf-wood separation method; and a method to estimate leaf angle distribution from leaf-separated point clouds. An initial attempt to quantify the impact of leaf-wood separation on LAD and wood volume estimates from TLS was presented. The method proposed in this thesis is able to automatically separate leaf and wood with accuracy above 80%. The testing framework provided the tools to quantify separation accuracy. LAD estimates from TLS were shown to be accurate. Finally, the leaf-wood separation was found to improve TLS estimates.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available