Advisor

Evans, L. David

Date of Degree

5-1-2010

Document Type

Dissertation - Open Access

Degree Name

Doctor of Philosophy

College

College of Forest Resources

Abstract

Studies have not been conducted examining the influence of the spatial distribution of LiDAR-derived tree measuresments and their affects the predictive ability of LiDAR-derived forest metrics as input for growth-and-yield analysis on individual trees. This study addresses both of these voids in current knowledge and determines the suitability, concerns and application of LiDAR for time-series analysis, specifically forest growth-and-yield. LiDAR datasets of the same site acquired in 1999, 2000, 2002, and 2006 by different vendors using different specifications were utilized in this study. Directional differences of Lidar-identified tree top locations were examined. Minimal location differences were noted, but no bias occurred. Differences in locations appeared to be from environmental effects such as wind. Improvements on individual-tree identification using a time-series analysis approach were implemented. The treeinding model was improved with a Boolean decision rule yielding significant differences in stand density calculations in 1.4 m spacing plots and for overall calculations of the 2000 and 2002 LiDAR datasets. Individual tree measurements derived from the 1999 LiDAR data were used to estimate growth to the 2006 data. These growth-and-yield values were compared with field-derived and field-measured values. Significant differences were found between the LiDAR- and field-derived measures of growth-and-yield. These increased over time and were believe to be compounded error from the LiDAR-estimated tree diameters. LiDAR datasets can be correlated to previous LiDAR datasets of the same area with very little effort. LiDAR tree identification can be improved using decision criteria based on subsequent LiDAR datasets of the same area. The ability to track individual trees by location over time using LiDAR could yield large datasets to potentially improve growth-and-yield modeling efforts and other stand characterization procedures.

URI

https://hdl.handle.net/11668/15165

Share

COinS