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Understanding the forest structure: development of tools for identification and delineation of individual trees using LiDAR.

LiDAR (Light Detection and Ranging) is currently being used to extract the
biophysical characteristics of forests. LiDAR can provide extensive information about
tree canopies; pulses reflected back to the sensor can represent understory vegetation as
well as partial tree canopies below the dominant trees. Canopy structure can yield
valuable clues about the biodiversity, and processes affecting the ecology of forest stands.
In addition, structural information can provide insight into other processes such as fire behaviour and the distribution of fuels. This thesis focuses in developing tools to better understand the forest structure. The tools are computer-based algorithms that use LiDAR data as input and provide output with detailed information about the different layers of vegetation in a forested area. Three main modules are used in this study: (1) treetop identification, (2) delineation of canopies for the dominant layer of vegetation, and (3) delineation of partial canopies underneath this dominant layer. The study area was located in the Greater Victoria Water District, west of Victoria, British Columbia, Canada. Nine plots were chosen to represent the study area. A complete census was conducted in the summer of 2005 to provide information about tree, diameter at breast height (DBH), and tree dominance (based on the criteria: suppressed, intermediate, codominant and dominant). Results show that the algorithm is able to properly identify and delineate the majority of trees in the study areas. The third module, partial canopy
delineation, also presents promising results with the dataset used. Newer LiDAR systems,
with higher number of returns, will definitely provide better datasets with more
information of the different layers within the forest, increasing the identification and
delineation of these partial trees. Need of new field data is a must for future work and for further tests with the algorithm.

Identiferoai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/3110
Date16 November 2010
CreatorsLoos, Rafael
ContributorsNiemann, K. O.
Source SetsUniversity of Victoria
LanguageEnglish, English
Detected LanguageEnglish
TypeThesis
RightsAvailable to the World Wide Web

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