Browsing by Subject "Point clouds"

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  • Pyorala, Jiri; Liang, Xinlian; Saarinen, Ninni; Kankare, Ville; Wang, Yunsheng; Holopainen, Markus; Hyyppa, Juha; Vastaranta, Mikko (2018)
    Terrestrial laser scanning (TLS) accompanied by quantitative tree-modeling algorithms can potentially acquire branching data non-destructively from a forest environment and aid the development and calibration of allometric crown biomass and wood quality equations for species and geographical regions with inadequate models. However, TLS's coverage in capturing individual branches still lacks evaluation. We acquired TLS data from 158 Scots pine (Pinus sylvestris L.) trees and investigated the performance of a quantitative branch detection and modeling approach for extracting key branching parameters, namely the number of branches, branch diameter (b(d)) and branch insertion angle (b) in various crown sections. We used manual point cloud measurements as references. The accuracy of quantitative branch detections decreased significantly above the live crown base height, principally due to the increasing scanner distance as opposed to occlusion effects caused by the foliage. b(d) was generally underestimated, when comparing to the manual reference, while b was estimated accurately: tree-specific biases were 0.89cm and 1.98 degrees, respectively. Our results indicate that full branching structure remains challenging to capture by TLS alone. Nevertheless, the retrievable branching parameters are potential inputs into allometric biomass and wood quality equations.
  • Xu, Shan; Zaidan, Martha A.; Honkavaara, Eija; Hakala, Teemu; Viljanen, Niko; Porcar-Castell, Albert; Liu, Zhigang; Atherton, Jon (IEEE, 2020)
    IEEE International Symposium on Geoscience and Remote Sensing IGARSS
    Leaf angle distribution (LAD) is a key canopy structural parameter, playing an important role in light transfer. LAD can be estimated from fixed point of view photography, however this is time consuming and spatially limited. Recently, Terrestrial LiDAR Scanning (TLS) has been used to estimate LAD through 3D canopy space. The downside of TLS it is more costly than the cameras used in the photographic method. We propose a cost effective method to estimate LAD from drone based photogrammetry. We compare LAD estimates in different water treatment plots. Results show that LAD can be obtained from photogrammetric point clouds. Leaf angles were enhanced in stressed plots, presumably due to wilting. Further, the leaf azimuth distribution was not random but concentrated around 0 and 180 degrees. In summary, drone based photogrammetry can be used to estimate remote sensing parameters such as LAD paving the way for cost effective trait estimation.