Estimating structural parameters of a complex mixed conifer-broadleaf forest using UAV photogrammetry

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.