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.
Highlights
Up-to-date and spatially detailed information on forest biophysical properties is fundamental to allowing managers to ensure sustainable forest management [1]
We investigated the capabilities of the lightweight unmanned aerial vehicle (UAV) photogrammetric point cloud using the SfM approach to estimate forest biophysical properties in managed temperate coniferous forests
When the biophysical properties were regressed with the addition of the dominant tree type information, in terms of R2, adjR2, root-mean-square errors (RMSEs), and relative RMSE, the accuracies of the estimation were comparable to those obtained using the respective independent variable sets without the dominant tree type information
Summary
Up-to-date and spatially detailed information on forest biophysical properties is fundamental to allowing managers to ensure sustainable forest management [1]. A methodology that captures the spatial and periodic information of forest biophysical properties is required. Remote sensing is an important option for capturing the spatio-temporal information of forest biophysical properties. Measuring three-dimensional (3D) forest structure as a point cloud is an established way to capture the information. Airborne light detection and ranging (Lidar) is an active remote sensing system that directly measures 3D structures by emitting laser pulses from an aircraft-borne sensor. Because the emitted laser pulses can reach the ground by penetrating a dense forest canopy, airborne Lidar can provide terrain height, as well as a point cloud. The relative height between the point cloud and the local terrain height is well suited for measuring stand-level forest biophysical properties, including stand volume [2,3], and tree height [2,4].
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