Bayesian Network and Factor Analysis for Modeling Pine Wilt Disease Prevalence

Mingxiang Huang, Liang Guo, Jianhua Gong, Weijun Yang

GuangZhou Urban Planning & Design Survey Research Institute, GuangZhou ,China.
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing ,China.
T Information Center, Ministry of Environmental Protection of China, Beijing ,China.

DOI: 10.4236/jsea.2013.63B004                

Abstract

A Bayesian network (BN) model was developed to predict susceptibility to PWD(Pine Wilt Disease). The distribution of PWD was identified using QuickBird and unmanned aerial vehicle (UAV) images taken at different times. Seven factors that influence the distribution of PWD were extracted from the QuickBird images and were used as the independent variables. The results showed that the BN model predicted PWD with high accuracy. In a sensitivity analysis, elevation (EL), the normal differential vegetation index (NDVI), the distance to settlements (DS) and the distance to roads (DR) were strongly associated with PWD prevalence, and slope (SL) exhibited the weakest association with PWD prevalence. The study showed that BN is an effective tool for modeling PWD prevalence and quantifying the impact of various factors.

Keywords

Pine Wilt Disease; Bayesian Network; Modeling; Factor Analysis

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Liebhold, A.M., Macdonald, W.L., Bergdahl, D., Maestro, V.C., INVASION BY EXOTIC FOREST PESTS - A THREAT TO FOREST ECOSYSTEMS. Forest Science 41(2) 1-49. (1995)
[2]	Waage, J.K., Reaser, J.K., A global strategy to defeat invasive species. Science 292(5521) 1486-1486. (2001)
[3]	Beckenbach, K., Smith, M.J., Webster, J.M., Taxonomic affinities and intra-and interspecific variation in Bursaphelenchus spp. as determined by polymerase chain reaction. Journal of Nematology 24(1) 140. (1992)
[4]	Iwahori, H., Tsuda, K., Kanzaki, N., Izui, K., Futai, K., PCR-RFLP and se-quencing analysis of ribosomal DNA of Bursaphelenchus nematodes related to pine wilt disease. Fundamental and Applied Nematology 21(6) 655-666. (1998)
[5]	Mamiya, Y., History of pine wilt disease in Japan. Journal of Nematology 20(2) 219. (1988)
[6]	Tares, S., Abad, P., Bruguier, N., de Guiran, G., Identification and evidence for relationships among geographical isolates of Bursaphelenchus spp.(pinewood nematode) using homologous DNA probes. Heredity 68(2) 157-164. (1992)
[7]	Togashi, K., Shigesada, N., Spread of the pinewood nematode vectored by the Japa-nese pine sawyer: modeling and analytical approaches. Population Ecology 48(4) 271-283. (2006)
[8]	Zhao, B.G., 2008. Pine Wilt Disease in China, Pine Wilt Dis-ease. . Springer Japan, pp. Part I,18-25.
[9]	Pearl, J., 1988. Probabilistic reasoning in intelligent systems: net-works of plausible inference. Morgan Kauf-mann.
[10]	McCann, R.K., Marcot, B.G., Ellis, R., Bayesian belief networks: applications in ecology and natural resource management. Canadian Journal of Forest Research 36(12) 3053-3062. (2006)
[11]	Heckerman, D., A tutorial on learning with Bayesian networks. Innovations in Bayesian Networks 33-82. (2008)
[12]	Bromley, J., Guidelines for the use of Bayesian networks as a participatory tool for Water Resource Management. (2005)
[13]	Castelletti, A., Soncini-Sessa, R., Bayesian Networks and participatory modelling in water resource management. Environmental Modelling & Software 22(8) 1075-1088. (2007)
[14]	Marcot, B.G., Holthausen, R.S., Raphael, M.G., Rowland, M.M., Wisdom, M.J., Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. Forest Ecology and Management 153(1-3) 29-42. (2001)
[15]	Nyberg, J.B., Marcot, B.G., Sulyma, R., Using Bayesian belief networks in adaptive management. Canadian Journal of Forest Research 36(12) 3104-3116. (2006)
[16]	Stelzenm, uuml, ller, V., Lee, J., Garnacho, E., Rogers, S.I., 2010. Assessment of a Bayesian Belief Network–GIS framework as a practical tool to support marine planning.
[17]	Dlamini, W.M., A Bayesian belief network analysis of factors influencing wildfire occur-rence in Swaziland. Environmental Modelling & Soft-ware 25(2) 199-208. (2010)
[18]	Friedman, N., Linial, M., Nachman, I., Pe'er, D., Using Bayesian networks to analyze expression data. Journal of Computational Biology 7(3-4) 601-620. (2000).