Dissertation

The following is the abstract from my dissertation. Below you can link to the full document

 

Aerial laser scanning:

Applications for forest biomass management

Forests are complex systems. They convert sunlight, water, and CO2 into carbon rich biomass, provide habitat for many rare and endangered species, and provide critical environmental services to human communities. Globally, forests serve as an important buffer to atmospheric CO2. Economic and social forces impacting forest health, extent, and growth rate influence the flux dynamics of the global carbon cycle. Policy frameworks to reduce anthropogenic greenhouse gas (GHG) emissions such as the Kyoto Protocols, the European Union’s Clean Development Mechanism, and California’s Climate Change Bill aim to align markets with GHG emissions reduction goals. Evidence suggests that such policy frameworks have created new challenges as well as opportunities for forest managers and posed new and unexpected questions. Forest management decisions are influenced by an increasingly broad array of energy, climate, and natural resource policies. The introductory chapter of this dissertation outlines a framework for forest GHG emission analysis that improves upon existing approaches by addressing carbon flux and stability as well as the industrial ecology of energy, fuels, and wood products derived from forest biomass.

The central aspect of this dissertation is the application of aerial laser scanning technology to tree detection and delineation. Rapid and efficient forest resource assessment will enable forest managers to meet challenges presented by evolving climate policies and market forces. This research presents a novel method for quantifying forest biomass volume using high resolution point clouds derived from aerial Light Detection and Ranging technology. The research is distinct from alternate approaches in employing a efficient search algorithm for matching a parametrized tree crown surface model to the point cloud data. The results are tested against field data collected using a variable plot inventory. The method proposed in this study results in strong correlation (P = 0.20) between plot-level mean basal area as measured by field and LiDAR methods.

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