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Characterizing the Spatial Distribution of Oak Wilt Disease Using Remote Sensing Data

원격탐사자료를 이용한 참나무시들음병 피해목의 공간분포특성 분석

  • Cha, Sungeun (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Lee, Woo-Kyun (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Kim, Moonil (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Lee, Sle-Gee (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Jo, Hyun-Woo (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Choi, Won-Il (Division of Forest Insect Pest and Disease, National Institute of Forest Science)
  • 차성은 (고려대학교 환경생태공학과) ;
  • 이우균 (고려대학교 환경생태공학과) ;
  • 김문일 (고려대학교 환경생태공학과) ;
  • 이슬기 (고려대학교 환경생태공학과) ;
  • 조현우 (고려대학교 환경생태공학과) ;
  • 최원일 (국립산림과학원 산림병해충연구과)
  • Received : 2017.04.19
  • Accepted : 2017.06.28
  • Published : 2017.09.30

Abstract

This study categorized the damaged trees by Supervised Classification using time-series-aerial photographs of Bukhan, Cheonggae and Suri mountains because oak wilt disease seemed to be concentrated in the metropolitan regions. In order to analyze the spatial characteristics of the damaged areas, the geographical characteristics such as elevation and slope were statistically analyzed to confirm their strong correlation. Based on the results from the statistical analysis of Moran's I, we have retrieved the following: (i) the value of Moran's I in Bukhan mountain is estimated to be 0.25, 0.32, and 0.24 in 2009, 2010 and 2012, respectively. (ii) the value of Moran's I in Cheonggye mountain estimated to be 0.26, 0.32 and 0.22 in 2010, 2012 and 2014, respectively and (iii) the value of Moran's I in Suri mountain estimated to be 0.42 and 0.42 in 2012 and 2014. respectively. These numbers suggest that the damaged trees are distributed in clusters. In addition, we conducted hotspot analysis to identify how the damaged tree clusters shift over time and we were able to verify that hotspots move in time series. According to our research outcome from the analysis of the entire hotspot areas (z-score>1.65), there were 80 percent probability of oak wilt disease occurring in the broadleaf or mixed-stand forests with elevation of 200~400 m and slope of 20~40 degrees. This result indicates that oak wilt disease hotspots can occur or shift into areas with the above geographical features or forest conditions. Therefore, this research outcome can be used as a basic resource when predicting the oak wilt disease spread-patterns, and it can also prevent disease and insect pest related harms to assist the policy makers to better implement the necessary solutions.

본 연구는 참나무시들음병이 수도권에 피해가 집중되어 있는 점을 고려해 북한산, 청계산, 수리산의 시계열 항공사진을 사용하여 감독분류기법(supervised classification)으로 피해목을 분류하였으며, 피해지의 공간적인 특성을 분석하기 위해 피해목 위치의 지형적 특성을 통계처리 하여 고도와 경사와의 밀접한 상관관계를 확인하였다. 또한, Moran's I 통계분석을 이용한 북한산의 Moran's I 값은 2009, 2010, 2012년 각 0.25, 0.32, 0.24, 청계산은 2010, 2012, 2014년 각 0.26, 0.32, 0.22, 수리산은 2012, 2014년 각 0.42, 0.42의 값을 갖으며, 이는 피해목이 군집하여 분포함을 의미한다. 아울러, 피해목 군집의 이동성을 파악하기 위해 hotspot 분석을 실시하여 시계열적으로 hotspot이 이동하는 특성을 확인하였다. 참나무 시들음병의 전체 hotspot 면적(z-score>1.65) 중 고도 200~400 m, 경사 $20{\sim}40^{\circ}$에 분포하는 활엽수 및 혼효림에서의 발생비율은 약 80%로 나타났다. 이는 미래의 피해지역 hotspot은 상기의 지형 및 임상조건에서 발생 또는 이동될 수 있음을 시사한다. 본 연구의 결과는 참나무시들음병의 이동경로 예측의 기초자료로 이용될 수 있으며, 향후 병해충 피해의 사전 방제 및 시스템 구축에 사용될 수 있다.

Keywords

References

  1. Brown, K.A., Parks, K.E., Bethell, C.A., Johnson, S.E. and Mulligan, M. 2015. Predicting plant diversity patterns in Madagascar: understanding the effects of climate and land cover change in a biodiversity hotspot. PloS one, 10(4): e0122721. https://doi.org/10.1371/journal.pone.0122721
  2. Burrough, P.A. and McDonell, R.A. 1998. Principles of Geographical Information Systems. Oxford University Press, New York, pp. 190.
  3. Caceres, C.F. 2011. Using GIS in Hotspots Analysis and for Forest Fire Risk Zones Mapping in the Yeguare Region, Southeastern Honduras. Volume 13, Papers in Resource Analysis. pp. 14. Saint Mary's University of Minnesota University Central Services Press. Winona.
  4. Choi, E.H. 2010. Comparison in characteristics of Raffaelea species causing oak wilt disease, and analysis efficacy of fungicides injection. Kangwon University master's thesis.
  5. Dietterich, T.G. 1998. Approximate statistical tests for comparing supervised classification learning algorithms. Neural computation, 10(7): 1895-1923. https://doi.org/10.1162/089976698300017197
  6. Dubayah, R.C. 1994. Modeling a solar radiation topoclimatology for the Rio Grande River Basin. Journal of Vegetation Science, 5(5): 627-640. https://doi.org/10.2307/3235879
  7. Ebdon, D. 1985. Statistics in Geography. Blackwell.
  8. Getis, A. and Ord, J.K. 1992. The analysis of spatial association by use of distance statistics. Geographical analysis, 24(3): 189-206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
  9. Goodchild, M.F. 1986. Spatial autocorrelation. Volume 47. Geo Books.
  10. Griffith, D.A. 1987. Spatial Autocorrelation: A Primer. Resource Publications in Geography, Association of American Geographers.
  11. Kelly, M. and Meentemeyer, R.K. 2002. Landscape dynamics of the spread of sudden oak death. Photogrammetric Engineering and Remote Sensing, 68(10): 1001-1010.
  12. 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.
  13. Kim, G.H., Lee, S.H., Jung, Y.J. and Jung, S.H. 1995. Change Detection and Terrain Analysis for the Pine Forests Damaged by Pine-Gall Midge Using Remote Sensing and Digital Terrain Model. Forest Science and Technology Symposium, pp. 9-11.
  14. Kim, K.H. 2005. Oak wilt disease. Tree Protection, 10: 17-25.
  15. Kim, S.R., Lee, J.B., Kim, J., Kim, E.S. and Lee, W.K. 2014. Spatial distribution analysis for damaged trees by Oak wilt disease. Korean Society for GeoSpatial Information Science Symposium, pp. 209-210.
  16. Lee, S.H.. 2010. 2010 Oak Wilt Disease Control Plan. Korea Forest Service. http://www.forest.go.kr/newkfsweb/cop/bbs/selectBoardArticle.do?bbsId=BBSMSTR_1130&orgId=kfri&mn=KFS_14_04_02_03&nttId=2779809 (2017. 04. 10.)
  17. Park, D.H. 2014. Forest disease and insectpest prediction. prevention plan. Tree Protection, 19: 1-25.
  18. Park, I.K., Nam, Y., Seo, S.T., Kim, S.W., Jung, C.S. and Han, H. R. 2015. Development of a mass treapping device for the ambrosia beetle, Platypus koryoensis, an insect vector of oak wilt disease in Korea. Journal of Asia-Pacific Entomology, 19(1): 39-43. https://doi.org/10.1016/j.aspen.2015.11.002
  19. Yeum, J.H., Han, B.H., Choi, J.W. and Jeong. H.U. 2013. Mapping of the Damaged Forest by Oak Wilt Disease in Bukhansan National Park. Korean Journal of Environment and Ecology, 27(6): 704-717. https://doi.org/10.13047/KJEE.2013.27.6.704