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Forest Structure Estimation from a UAV-Based Photogrammetric Point Cloud in Managed Temperate Coniferous Forests

Authors :
Tetsuji Ota
Miyuki Ogawa
Nobuya Mizoue
Keiko Fukumoto
Shigejiro Yoshida
Source :
Forests, Vol 8, Iss 9, p 343 (2017)
Publication Year :
2017
Publisher :
MDPI AG, 2017.

Abstract

Here, we investigated the capabilities of a lightweight unmanned aerial vehicle (UAV) photogrammetric point cloud for estimating forest biophysical properties in managed temperate coniferous forests in Japan, and the importance of spectral information for the estimation. We estimated four biophysical properties: stand volume (V), Lorey’s mean height (HL), mean height (HA), and max height (HM). We developed three independent variable sets, which included a height variable, a spectral variable, and a combined height and spectral variable. The addition of a dominant tree type to the above data sets was also tested. The model including a height variable and dominant tree type was the best for all biophysical property estimations. The root-mean-square errors (RMSEs) for the best model for V, HL, HA, and HM, were 118.30, 1.13, 1.24, and 1.24, respectively. The model including a height variable alone yielded the second highest accuracy. The respective RMSEs were 131.74, 1.21, 1.31, and 1.32. The model including a spectral variable alone yielded much lower estimation accuracy than that including a height variable. Thus, a lightweight UAV photogrammetric point cloud could accurately estimate forest biophysical properties, and a spectral variable was not necessarily required for the estimation. The dominant tree type improved estimation accuracy.

Details

Language :
English
ISSN :
19994907 and 44695039
Volume :
8
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Forests
Publication Type :
Academic Journal
Accession number :
edsdoj.513c84dc7fe04a4bbaa44695039ed1ff
Document Type :
article
Full Text :
https://doi.org/10.3390/f8090343