Are bamboo construction materials environmentally friendly? A life cycle environmental impact analysis

The impact of landscape complexity on preference ratings and eye fixation of various urban green space settings

Adaptation of bamboo to heterogeneous habitat: phenotypic plasticity

The use of 3D point cloud-based technology for quantifying standing wood and stand parameters can play a key role in forestry ecological benefit assessment and standing tree cultivation and utilization. With the advance of 3D information acquisition techniques, such as light detection and ranging (LiDAR) scanning, the stand information of trees in large areas and complex terrain can be obtained more efficiently. However, due to the diversity of the forest floor, the morphological diversity of the trees, and the fact that forestry is often planted as large-scale plantations, efficiently segmenting the point cloud of artificially planted forests and extracting standing wood feature parameters remains a considerable challenge. An effective method based on energy segmentation and PointCNN is proposed in this work to address this issue. The network is enhanced for learning point cloud features by geometric feature balance model (GFBM), enabling the efficient segmentation of tree point clouds from forestry point cloud data collected by terrestrial laser scanning (TLS) in outdoor environments. The 3D Forest software is then used to obtain single wood point cloud after semantic segmentation, and the extracted single wood point cloud is finally employed to extract standing wood feature parameters using TreeQSM. The point cloud semantic segmentation method is the most important part of our research. According to our findings, this method can segment datasets of two different artificially planted woodland point clouds with an overall accuracy of 0.95 and a tree segmentation accuracy of 0.93. When compared with the manual measurements, the root-mean-square error (RMSE) for tree height in the two datasets are 0.30272 and 0.21015 m, and the RMSEs for the diameter at breast height are 0.01436 and 0.01222 m, respectively. Our method is a robust framework based on deep learning that is applicable to forestry for extracting the feature parameters of artificially planted trees. It solves the problem of segmenting tree point clouds in artificially planted trees and provides a reliable data processing method for tree information extraction, trunk shape analysis, etc.

/pdf/a-deep-learning-based-method-for-extracting-standing-wood-166pc8nk.pdf

A Deep Learning-Based Method for Extracting Standing Wood Feature Parameters from Terrestrial Laser Scanning Point Clouds of Artificially Planted Forest

The characteristics of plant clusters influence on the cooling effect: A case study in a subtropical Island Park, China

Which ornamental features of bamboo plants will attract the people most

Various distances and orientations influenced the branching traits of Dendrocalamus minor var. amoenus in Dongshan Island, China

Bambusa pervariabilis is mostly produced in south China; usually cultivated on the banks of the rivers and near villages. We determined the complete chloroplast (cp) genome sequence of B. pervariab...

Complete chloroplast genome sequence of Bambusa pervariabilis (Bambusodae).

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Which ornamental features of bamboo plants will attract the people most

Various distances and orientations influenced the branching traits of Dendrocalamus minor var. amoenus in Dongshan Island, China

Complete chloroplast genome sequence of Bambusa pervariabilis (Bambusodae).