Abstract:
Recently concerns over anthropogenic carbon pollution have received increased
global attention and research in forest biomass and carbon sequestration has gained
momentum. Light Detection and Ranging (LiDAR) remote sensing has in the last
decade demonstrated forest measurement and biomass estimation potential. The
project objective was to compare LiDAR forest biomass estimates to traditional field
biomass estimates in a conifer predominant forest located in the Pacific Northwest
region of the United States.
Chapter 2 of this dissertation investigated mapping-grade GPS accuracy in
determining tree locations. Results indicated that post processing of coded
pseudorange satellite signals is the most accurate of those we tested for GPS surveying
under a conifer dominant forest canopy. Chapter 3 compared LiDAR, total station, and
GPS receiver discrete point elevations and DEMs across a range of forest settings.
Average total station plot elevation differences ranged from -0.06 m (SD 0.40) to -0.59 m (SD
0.23) indicating that LiDAR elevations are higher than actual elevations. Average plot GPS
elevation differences ranged from 0.24 (SD 1.55) to 2.82 m (SD 4.58), and from 0.27 (SD
2.33) to 2.69 m (SD 5.06) for LiDAR DEMs.
Chapter 4 assessed LiDAR’s ability to measure three-dimensional forest structure
and estimate biomass using single stem (trees and shrubs) remote sensing. The LiDAR
data tree extraction computer software programs FUSION, TreeVaW, and watershed
segmentation were compared. LiDAR spatial accuracy assessment resulted in overall
average error and standard deviation (SD) for FUSION, TreeVaW, and watershed
segmentation of 2.05 m (SD 1.67 m), 2.19 m (SD 1.83 m), and 2.31 m (SD 1.94 m)
respectively. Overall average LiDAR tree height error and standard deviations (SD)
respectively for FUSION, TreeVaW and watershed segmentation were -0.09 m (SD
2.43 m), 0.28 m (SD 1.86 m), and 0.22 m (2.45 m) in even-age, uneven-age, and old
growth plots combined; and for one clearcut plot 0.56 m (SD 1.07 m), 0.28 m (SD
1.69 m), and 1.17 m (SD 0.68 m), respectively. Biomass comparisons included feature
totals per plot, mean biomass per feature by plot, and total biomass by plot for each
extraction method. Overall LiDAR biomass estimations resulted in FUSION and
TreeVaW underestimating by 25 and 31% respectively, and watershed segmentation
overestimating by approximately 10%. LiDAR biomass underestimation occurred in
66% and overestimation occurred in 34% of the plot comparisons.
