Sample-Based Vegetation Distribution Information Synthesis.

• Main Authors: Chanchan Xu, Gang Yang, Meng Yang
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2015-01-01
• Series: PLoS ONE
• Online Access: http://europepmc.org/articles/PMC4529179?pdf=render

In constructing and visualizing a virtual three-dimensional forest scene, we must first obtain the vegetation distribution, namely, the location of each plant in the forest. Because the forest contains a large number of plants, the distribution of each plant is difficult to obtain from actual measurement methods. Random approaches are used as common solutions to simulate a forest distribution but fail to reflect the specific biological arrangements among types of plants. Observations show that plants in the forest tend to generate particular distribution patterns due to growth competition and specific habitats. This pattern, which represents a local feature in the distribution and occurs repeatedly in the forest, is in line with the "locality" and "static" characteristics in the "texture data", making it possible to use a sample-based texture synthesis strategy to build the distribution. We propose a vegetation distribution data generation method that uses sample-based vector pattern synthesis. A sample forest stand is obtained first and recorded as a two-dimensional vector-element distribution pattern. Next, the large-scale vegetation distribution pattern is synthesized automatically using the proposed vector pattern synthesis algorithm. The synthesized distribution pattern resembles the sample pattern in the distribution features. The vector pattern synthesis algorithm proposed in this paper adopts a neighborhood comparison technique based on histogram matching, which makes it efficient and easy to implement. Experiments show that the distribution pattern synthesized with this method can sufficiently preserve the features of the sample distribution pattern, making our method meaningful for constructing realistic forest scenes.