UBC Theses and Dissertations
The use of airborne LiDAR to assess tree species and forest biomass in subtropical forests Cao, Lin
The subtropical forest biome accounts for approximately a quarter of the area of China and is particularly important for local economies, and for maintaining biodiversity and the carbon balance of forest ecosystems. Despite their importance, there is still considerable uncertainty about the characterization and spatial distribution of tree species, as well as the carbon budgets of these forests, many of which have been altered by anthropogenic activities. Remote sensing has the potential to provide quantitative, spatially explicit information for mapping and monitoring forest ecosystems. It is also a cost-effective tool to provide temporally uniform and “wall-to-wall” observations over time. Light Detection and Ranging (LiDAR) is an active remote sensing laser technology that provides an advantage over most other remote sensing technologies in its ability to provide detailed three-dimensional information of forest canopy structure, which is particularly useful for studying forest biophysical and structural properties. The aim of this dissertation is to investigate novel approaches for using and examining the effectiveness of LiDAR technologies, in order to classify tree species and estimate forest biomass and dynamics, across a study site within the subtropical region of southeast China. Specifically, airborne LiDAR was evaluated for its ability to: (i) discriminate tree species using small-footprint full-waveform LiDAR metrics; (ii) estimate forest biomass components by discrete-return and full-waveform LiDAR metrics; (iii) spatially extrapolate the estimation of forest biomass components, and (iv) predict and map biomass dynamics using multi-temporal LiDAR data. The results of this dissertation confirm that LiDAR-based approaches can make significant contributions to analyze the structure, composition and distribution of tree species across the study site, and provide effective methodologies and techniques for developing high resolution, spatially explicit estimations of forest biomass (and its dynamics). These methods have important applications to sustainable forest management, forest carbon cycling studies and carbon accounting projects.
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