Journal of Visualization

, Volume 22, Issue 1, pp 177–195 | Cite as

Visual analysis of occurrence and control of forest pests with multi-view collaboration

  • Bo Yang
  • Weiqun CaoEmail author
  • Chengming Tian
Regular Paper


Forest pests are an important aspect of forest pest prevention and control work. However, it is difficult for forest pest researchers to gain a comprehensive understanding of the occurrence and control of pests using traditional statistical methods. It is a considerable challenge to help researchers to find useful information from pest occurrence and control data. Combining features of forest pest occurrence, such as timing, geography, hierarchy, disaster grade and pest species, we propose a multi-view collaborative hybrid visual analysis method to analyze the occurrence and control of forest pests from multiple angles. On this basis, we design and realize a multi-view collaborative hybrid visual analysis system for the occurrence and control of forest pests. Via case studies on the test dataset using the developed system, we complete an omni-directional analysis of the overall situation of forest pests, the overall situation of a certain pest species, the overall situation of pests in a certain region, and the occurrence of a certain pest in a certain region. The experimental results show that the visualization technologies and interactive technologies used in the paper can effectively assist researchers in the analysis of related data, and it is also demonstrated that the system is user-friendly and that the applied visualization methods are effective.

Graphical abstract


Forest pests Visual analysis Visualization Multi-view collaboration 



The authors would like to thank the forest protection experts from Shanxi Provincial Bureau of Forestry Pest Control and Quarantine for providing valuable feedback and suggestions for this project. This work is supported by the Fundamental Research Funds for the Central Universities (No. 2015ZCQ-XX) and the National Key Research and Development Program (2017YFD0600105).


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Copyright information

© The Visualization Society of Japan 2018

Authors and Affiliations

  1. 1.School of Information Science and TechnologyBeijing Forestry UniversityBeijingChina
  2. 2.College of ForestryBeijing Forestry UniversityBeijingChina

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