Parameter estimation and effective tree trunk model establishment are significant to tree biomass measurements and three-dimensional (3D) reconstruction. In this study, we proposed a trunk model establishment and parameter estimation method for a single tree through multistation terrestrial laser scanning (TLS). This approach obtains a more accurate and dense point cloud and yields several improvements. First, to correct the tree position, the angle between the trunk centreline and the vertical line was extracted to normalize the tree point clouds. Subsequently, some tree model parameters were accurately calculated. The tree height was extracted based on individual differences rather than directly adopting the Digital Elevation Model (DEM) model; The B-Spline curve fitting was added to the diameters at breast height (DBH) measure, which better determined the shape of the DBH compared with circle fitting; The CBH was measured by multi-view voxel projection instead of Single-view fixed threshold segmentation, which confirmed the position of the CBH more accurately. To highlight the effectiveness and pertinence of the proposed method, we analysed three plots that the proposed method can achieve more accurate results, where the mean relative error (MRE) of the tree height, DBH and CBH were 1.89%, 1.74% and 2.91% satisfying the accuracy standard specified by the technical indicators (± 5%). The experimental results validate the trunk model accuracy and the effectiveness of the parameter measurements (close to the field measurements). The proposed method is significantly different from the accuracy of the previous method. The proposed method has the best accuracy in Multistation Terrestrial Laser Scanning. Hence the proposed method has outstanding performance in the high-precision extraction of trunk parameters and the construction of trunk models which will perform forest inventory and construction of virtual forest more accurately.
The pandemic of COVID-19 has caused economic and social crisis across the world. Existing studies have shown that the uncertain social context has profoundly affected people’s life, triggering a variety of social psychological phenomena including the deterioration of mental health and the change of political behavioral patterns. However, little has been known about the differences in people’s pre-pandemic political ideology and their influences on people’s mental health and political behaviors after the pandemic. Using the secondary data from the 2018 and 2020 China Family Panel Studies, we measured nationalism tendencies, state performance ratings, social justice evaluation and life satisfaction of 29,629 adults before the pandemic. Using latent profile analysis (LPA), we examined the typologies of respondents’ political ideology and values. Five types emerged to identify respondents with different political ideology and values: (Class-1) High nationalism tendency, country evaluation, social justice perception, and life satisfaction; (Class-2) Low life satisfaction; (Class-3) Moderate ratings; (Class-4) Low nationalism tendency; and (Class-5) Low country evaluation, low social justice perception. We further explored the predicting roles of those typologies on people’s depressive symptoms and political engagement behaviors after the pandemic. We found that, after the pandemic, although the depressive symptoms of people with low life satisfaction (Class-2) and low country and society ratings (Class-5) eased, they still tended to have more severe depressive symptoms than the Moderate ratings group (Class-3). People with low life satisfaction (Class-2) were also less likely to follow political information than the moderate group (Class-3). Our research revealed how the psychology and behaviors of Chinese people with different political views changed when faced with uncertainty in social context. Further research needs to be carried out to depict how these changes occur.
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