Study on the Stand Spatial Model and Pattern Based on Voronoi Diagram

Abstract

The secondary forest of Mt. Longhu in the south Dongting Lake is chosen as the research object in this paper which tries to establish a stand model based on Voronoi diagram. This diagarm is applied here to illustrates the stand spatial structure and extract forest spatial quantitative information. Based on this, coefficient of variation (CV) is used to quantify the change rate of Voronoi polygon area, transferring the analytic results of stand spatial pattern to the calculation of the CV based on Voronoi diagram, and comparing the calculation results with uniform angle index. The result shows that the stand model of Voronoi diagram can accurately display some important spatial information, such as the correlation, distance and angle between adjacent trees as well as spatial density, etc. When sampling large scale(above 76m) , CV tends to remain stable, and the overall stand spatial pattern shows mild aggregation which is close to random distribution. The CV of Voronoi diagram and uniform angle index have notable positive correlation, which further proves the feasibility and effectiveness of the analytical methods applied in this paper.The spatial pattern of main tree species of abor layer in different development stages shows a distinct temporal and spatial dynamic characteristics due to many factors. The small鄄diameter wood belongs to aggregated distribution, while the large鄄diameter belongs to mild aggregation or random distribution. The stand spatial model and its pattern analytical method, both based on Voronoi diagram, charaterize the tree growth feature, intraspecific and interspecific relationship, characteristics of resources and enviornment distribution from the perspective of spatial structure, providing a new way of thinking for stand spatial management by applying computational geometry method.

Single-Seed Precise Sowing of Maize Using Computer Simulation

“…Then how is the floor space determined? Seedling missing spot is shared by maize around it and its area is determined by Voronoi chart [8][9][10][11].…”

How does the landscape uniformity in different forest landscapes affect the visual behavior and preference evaluation intention of participants—a case study of forest landscape in northern China (Liaoning)

“…Finally, through Python, the distribution of trees (20 m × 20 m) in various plots is intuitively reflected by the Voronoi diagram of the point set (Qin et al, 2008;Liu et al, 2014), see Figure 2.…”

“…Attributes of scenes (type, shape, density, etc.) affect people's visual behavior patterns Liu et al, 2014;Wu et al, 2019;Gao et al, 2020). The plant structural characteristics, growth status, plant coverage, seasonal changes, hierarchical structure, and even the selection of characteristics of individual plants and special plants affect participants' visual behavior.…”

“…However, in China, most forest parks are developed from stateowned forest farms with rich plantation resources, so early scholars focused on studying in-forest landscapes in terms of tree growth (Deng, 2009;Deng et al, 2013). For example, Liu claimed that the spatial structure of forests determines the competitive advantage and spatial niche among trees, which greatly affects the health and stability of the forest (Liu et al, 2014). The Voronoi diagram is used to study forest spaces and their distribution patterns.…”