Abstract:
Estimating forest structural parameters in structurally complex forests remains as challengi
ng. Use of highresolution imagery, suitable remote sensing variables and models will highly
contribute to improve estimation accuracy. We used high-
resolution UAV RGB imagery to estimate forest structural parameters in a mixed conifer
broadleaf forest at the University of Tokyo Hokkaido Forest. In addition to DBH, spatial posit
ion and height of dominant trees were measured in the inventory plots. Pix4D software was
used to derive dense point clouds, digital surface model, canopy height model (CHM) and o
rthomosaics. Mean, maximum, percentile and standard deviation of CHM were validated w
ith the height and DBH, basal area (BA), stem volume (V) and tree carbon stock (CST). 75-
99 % percentile heights of CHM were highly correlated with dominant tree height, while CH
M mean was highly correlated with BA, V, and CST. Conifer dominated plots had a higher es
timation accuracy with dominant tree height.
