Natural Spread Pattern of Damaged Area by Pine Wilt Disease Using Geostatistical Analysis

공간통계학적 방법에 의한 소나무 재선충 피해의 자연적 확산유형분석

  • Son, Min-Ho (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Lee, Woo-Kyun (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Lee, Seung-Ho (Korea Forest Research Institute) ;
  • Cho, Hyun-Kook (Korea Forest Research Institute) ;
  • Lee, Jun-Hak (Department of Environmental Science, Policy and Management, University of California)
  • Received : 2006.08.16
  • Accepted : 2006.05.08
  • Published : 2006.09.30

Abstract

Recently, dispersion of damaged forest by pine wilt disease has been regarded as a serious social issue. Damages by pine wilt disease have been spreaded by natural area expansion of the vectors in the damaged area, while the national wide damage spread has induced by human-involved carrying infected trees out of damaged area. In this study, damaged trees were detected and located on the digital map by aerial photograph and terrestrial surveys. The spatial distribution pattern of damaged trees, and the relationship of spatial distribution of damaged trees and some geomorphological factors were geostatistically analysed. Finally, we maked natural spread pattern map of pine wilt disease using geostatistical CART(Classification and Regression Trees) model. This study verified that geostatistical analysis and CART model are useful tools for understanding spatial distribution and natural spread pattern of pine wilt diseases.

최근, 소나무재선충(Bursaphelenchus xylophilus)에 의한 소나무림의 피해에 대한 사회적 심각성이 크게 대두되고 있다. 소나무 재선충에 의한 산림피해는 피해지 내에서는 매개충인 솔수염하늘소의 자연적인 영역확장에 의해 확산되는 반면, 전국적으로는 감염목의 인위적 반출 및 이동에 의해 확산이 진행되고 있다. 본 연구에서는 부산 대변항의 재선충 피해지내에서 항공사진 및 현지조사에 의해 피해목의 공간적인 위치를 파악하였고, 공간통계학적인 방법을 통하여 피해목의 공간분포유형, 피해발생과 지형인자간의 관계를 분석하였다. 또한, 지형공간자료를 통계학적 Tree 모형에 적용한 CART(Classification and Regression Trees)모형을 이용하여 재선충 피해의 자연적인 확산 예측 지도를 작성하였다. 본 연구를 통해 공간통계학적인 분석과 CART모형이 소나무재선충 피해의 공간분포 및 자연적 확산유형을 파악하는데 유용한 도구로 활용될 수 있음을 확인할 수 있었다.

Keywords

Acknowledgement

Grant : GIS 및 공간통계학적 기법을 이용한 산림병해충 피해발생 및 확산 모형개발

Supported by : 과학기술부

References

  1. 고제호. 1969. 봄철의 솔잎혹파리 유충밀도의 변동 조사. 한국임학회지 9: 45-48
  2. 국립산림과학원. 2004. 소나무재선충병, 산림과학속보 04-06
  3. 국토연구원. 2004. 공간분석기법. 한울. 서울
  4. 김규헌. 1995. 원격탐사 및 수치지형모델을 이용한 솔잎혹파리 피해지역의 피해동태 및 지형분석. 한국임학회 춘계학술지
  5. 김동수, 이상명, 정영진, 최광식, 문일성, 박정규. 2003. 소나무 재선충의 매개충인 솔수염 하늘소 성충의 우화 상태. Korean J. Applied Entomology. 42(4): 307-313
  6. 김준범, 조명희, 오정수, 선동호. 2003. GIS와 위성영상을 이용한 소나무 재선충 피해지역과 기상인자와의 시공간적인 상관분석. 한국 임학회 92(4): 362-366
  7. 송은태. 2004. CART에서 변수선택 편의에 관한 연구. 서울대학교 석사학위 논문 10-12
  8. 이범영, 우건석. 1987. 솔입혹파리 생장지역에 따른 온도별 생육기간의 차이. The Korean J. of Entomology 17(3): 185-190
  9. 이우균, 이준학, 정기현, 전은진. 2001. IKONOS 영상과 지리형태 인자에 근거한 임상분포의 공간적 특징. 한국산림측정학회 4(1): 74-82
  10. Bennett, R.J. and Haining, R.P. 1985. Spatial structure and spatial interaction : Modelling approaches to the sitatstical analysis of geographical data. Journal of the royal statistical society. Series A(General). 148(1): 1-36 https://doi.org/10.2307/2981508
  11. Bergdahl, D.R. 1988. Impact of pinewood nematode in North America: Presnet and future. J. Nematology. 20: 260-265
  12. Beven, K.H. and Kirkby, M.J. 1979. A physically-based variable contributing area model of basin hydrology. Hydrol. Sci. Bull. 24: 43-69 https://doi.org/10.1080/02626667909491834
  13. Breiman, L., Friedman, J., Olshen, R. and Stone, C. 1984. Classification and regression trees. Chapman and Hall. New York
  14. Dwinell, L.D. 1997. The pinewood nematode: Regulation and mitigation. Annu. Rev. Phytopathol. 35: 153-166 https://doi.org/10.1146/annurev.phyto.35.1.153
  15. Dubayah, R.C. 1994. Modeling a solar radiation topoclimatology for the Rio Grande River Basin. Plant Ecology 142: 71-85 https://doi.org/10.1023/A:1009857824912
  16. Edwards. O.R. and Linit. M.J. 1992. Transmission of Bursaphelenchus xylophilus through oviposition wounds of Monochamus carolinesis. J. Nematology. 24: 133-139
  17. Goreaud, F. and Pelissier, R. 1999. On explicit formulas of edge effect correction for Ripley's K-function. Journal of Vegetaton Science 10: 433-438 https://doi.org/10.2307/3237072
  18. GOettingen Univ. 2002. Department of physical geographic, Germany, DiGeM Program, http://www.geogr.unigoettingen.de/pg/saga/digem/(2005. 04. 20)
  19. Haase, P. 1995. Spatial pattern analysis in ecology based on Ripley's K-function : Introduction and methods of edge correction. Journal of vegetation science 6: 575-582 https://doi.org/10.2307/3236356
  20. Kelly, M. 2002. Landscape dynamics of the spread of sudden oak death, Photogrametric Engineering & remote sensing 68(10): 1001-1009
  21. Kelly, M. 2003. Terrain Modeling and visualization to understand spatial pattern and spread of sudden oak death in california. Terrain data: Application and visualization Making the Connection
  22. Knowles, K., Beaublen, Y. Wingfield, M.J. Baker, F.A. and French, D.W. 1983. The pinewood nematode new in Cannada. Forestry Chronicle 59: 40
  23. Korea Forest Research Institute. 2003. Annual research report of forest pests monitoring in 2002, Korea Forest Research Institute. Seoul, Korea: 259pp
  24. Mamiya, Y. and Enda, N. 1972, Transmission of Bursaphelenchuslignicolus by Monochamus alternatus, Nematologica, 18: 159-162 https://doi.org/10.1163/187529272X00395
  25. Mamiya, Y. and Enda, N. 1972. Transmission of Bursaphelenchus lignicolus (Nematoda: Aphelenchoidae) by Monochamus altematus (Coleoptera:Cerambycidae) Nematologica. 18: 159-162 https://doi.org/10.1163/187529272X00395
  26. Morimoto, K. and Iwasaki, A. 1972. Role of Monochamus alternatus (Coleoptera: Cerambycidae). as a vector of Bursaphelenchus lignicolus (Nematoda: Aphelenchoididae). J. Japan Forest Society. 54: 177-183
  27. Parker, I.M., Simberloff, D., Lonsdale, K., Goodell, M., Wonham, P.M., Kareiva, M.H., Williamson, B.V., Holle, P.B., Moyle, J.E., Byers, and Goldwasser, L. 1999. Impact: toward a framework for understanding the ecological effects of invaders. Biological Invasions 1: 3-19 https://doi.org/10.1023/A:1010034312781
  28. Qinghua G., Kelly, M. and Catherine H. Graham. 2005. Support vector machines for predicting distribution of Sudden Oak Death in California. Ecological Modeling 182: 75-90 https://doi.org/10.1016/j.ecolmodel.2004.07.012
  29. Quinn, P. and Beven, K. 1991. The prediction of hills-lope flow paths for distributed hydrologycal modeling using digital terrain models. Hydrological processes 5: 59-79 https://doi.org/10.1002/hyp.3360050106
  30. Ripley, B.D. 1976. The second order analysis of stationary point processes. J. Applied Probability. 13: 255-266 https://doi.org/10.2307/3212829
  31. Ristaino, J.B. and Gumpertz, M. 2000. New frontiers in the study of dispersal and spatial analysis of epidemics caused by species in the genus phytophthora, Annu. Rev., Phytopathology. 38: 541-576 https://doi.org/10.1146/annurev.phyto.38.1.541
  32. Sanford system, USA, http://salford-systems.com (2005. 06. 10)
  33. Steiner, G. and Buhrer, E.M. 1934. Aphelenchoides xylophilus n.sp., a nematode associated with blue stain and other fungi in timber. J. Agricultural Research 48: 949-951
  34. Thrall, P.H. and Burdon, J.J. 1999. The spatial scale of pathogen dispersal : Consequences for disease dynamics and persistence. Evolutionary Ecolgy Research 1(6): 681-701
  35. Vious, P.K., D'Antonio, C.M., L.L. Loope, and R. Westbrooks. 1996. Biological invasions as global environmental change, American Scientist 84: 468-479
  36. Weihua Z. and David R.M. 1994. Digital elevation model grid size, landscape representation and hydrologic simulations. Water resource research 30(40): 1019-1028 https://doi.org/10.1029/93WR03553
  37. Wingfield, M.J. and Blanchette. R.A. 1983. The pinewood namatode. Bursaphelenchus xylophilus, in Minnesota and Wisconsin: Insect associates and transmission studies. Canada J. Forest Research. 13: 1068-1076 https://doi.org/10.1139/x83-143
  38. Wingfield, M.J., Blanchette, R.A., and Nicholls, T.H. 1984. Is th pine wood nematode an important pathogen in the United States, J. Forest. 82: 232-235