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Characterizing Forest Structure Using Terrestrial LiDAR Remote Sensing
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- Author / Creator
- Taheriazad,Leila
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Forests are one of the important natural resources, because of their benefits toward the economy and ecosystem services including goods, climate control, pollution reduction, carbon storage, wildlife habitat protection, nutrient cycling, social and cultural benefits. Hence, sustainable development requires assessment of forest structure with the goal of efficient resource management. The goal of this thesis is to investigate forest structure using Light Detection and Ranging (LiDAR) technology. LiDAR is an active remote sensing method that is suitable for this purpose due to its capability to capture both distribution and three-dimensional structure of canopies into a 3D point cloud with millions of points. This thesis introduces new simple algorithms for automatic processing of the point cloud data collected by ground-based LiDAR and simple assessment of forest structure. Also, the major challenges in handling the huge amount of data generated by LiDAR are discussed and proper solutions are offered and examined. In this light, chapter one reviews the background and capabilities of the LiDAR technology. In chapter two, an algorithm is developed for processing LiDAR data to generate Digital Terrain Model (DTM). Chapter three targets separation of photosynthetic components from non-photosynthetic components using a combination of distance and intensity attribute which are both provided by LiDAR. A comprehensive qualitative/quantitative error analysis is also presented along with the chapters on DTM and separation methods. Chapter four deals with Leaf Area Index (LAI) which makes one of the important assessment parameters of any forest. The drawbacks of current methods for calculation of LAI including the application of uniform voxels of arbitrary size and ignoring the effect of LiDAR scan resolution are discussed. Then, LAI is calculated by non-uniform voxels based on local sampling resolution of LiDAR. This technique avoids using the common radiative transfer model and gap fraction model which involve a high level of approximation. Hence, LAI is calculated directly from the LiDAR data and without intermediate auxiliary models. Finally, chapter five reviews the main contributions and significance of this study. Collectively, ground-based LiDAR is demonstrated as a suitable technology for forest survey. The 3D point cloud data from LiDAR can be processed by proper algorithms to generate DTM, separate leaves from wood, and calculate LAI with high precision.
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- Graduation date
- Fall 2018
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- Type of Item
- Thesis
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- Degree
- Doctor of Philosophy
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- License
- Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.