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

Abstract

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 (H L ), mean height (H A ), and max height (H M ). 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, H L , H A , and H M , 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.

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

“…However, it is worth to notice that results are dependent on UAV sensor used (e.g., RGB, LiDAR, CIR), as well as the forest characteristics and species. Taking into account the sensors used in the aforementioned studies, Puliti et al [37] used a UAV-based CIR imagery; Cao et al [84] and Ota et al [85] used a UAV-based RGB imagery; and Cao et al [84] and Guo et al [86] used a UAV LiDAR system. Despite the fact the results obtained are similar, Cao et al [84] refer that the accuracies of models obtained with UAV-LiDAR were higher than those obtained by UAV-RGB.…”

Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements

“…The remote sensing (RS) discipline mainly describes forest structure by summarizing variables that can 2 of 20 be determined from sensor data. These include maximum height, quantiles of height from surface models or point clouds, point densities, or structural complexity indices, tree counts, biomass estimates and many more [3,[13][14][15][16][17][18][19][20][21][22][23][24][25][26]. If the aim is to provide a broader perspective of the forest structure, metrics such as the Stand Structural Complexity Index (SSCI) [4] are for instance applicable.…”

Robinia pseudoacacia L. in Short Rotation Coppice: Seed and Stump Shoot Reproduction as well as UAS-based Spreading Analysis

“…The 3D surface models in this study were important for the classification of the vegetation height of R. pseudoacacia, grass, and forest pine trees. The height of the black locust trees in the analyzed plantation ranged between 0.2 and 3.0 m. This height classification approach is common for tree analysis [53][54][55]57,58,60]. However, a limitation of current UAS images is that depending on stand density only the top of the tree or stump shoot is detectable [138].…”

“…Today, black locust appears in many European countries, especially in Hungary, Northern Germany, Western Poland, Czech Republic, Southern Slovakia, and Eastern Austria [6]. In Germany, a change in analysis [18] and object-based image analysis (OBIA) [70], primarily including structure from motion (SfM) point clouds [53][54][55]57,58,60]. Nevertheless, OBIA has not yet been used to analyze the spreading of black locust in short rotation coppices.…”