Date of Award
1-1-2010
Document Type
Campus Access Thesis
Department
Geography
First Advisor
John R Jensen
Abstract
The dominant height of 73 georeferenced field sample plots were modeled from various canopy height metrics derived by means of a small-footprint laser scanning technology, known as light detection and ranging (or just LiDAR), over young and mature forest stands using regression analysis. LiDAR plot metrics were regressed against field measured dominant height using Best Subsets Regression to reduce the number of models. From those models, regression assumptions were evaluated to determine which model was actually the best. The best model included the 1st and 90th height percentiles as predictors and explained 95% of the variance in average dominant height.
Rights
© 2010, Andrew Maceyka
Recommended Citation
Maceyka, A.(2010). Modeling Loblolly Pine Dominant Height Using Airborne LiDAR. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/1293