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http://dx.doi.org/10.14578/jkfs.2017.106.3.310

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

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)
Publication Information
Journal of Korean Society of Forest Science / v.106, no.3, 2017 , pp. 310-319 More about this Journal
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.
Keywords
oak wilt disease; aerial photographs; geographical features; Moran's I statistical analysis; hotspot analysis;
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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.   DOI
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.   DOI
6 Dubayah, R.C. 1994. Modeling a solar radiation topoclimatology for the Rio Grande River Basin. Journal of Vegetation Science, 5(5): 627-640.   DOI
7 Getis, A. and Ord, J.K. 1992. The analysis of spatial association by use of distance statistics. Geographical analysis, 24(3): 189-206.   DOI
8 Goodchild, M.F. 1986. Spatial autocorrelation. Volume 47. Geo Books.
9 Griffith, D.A. 1987. Spatial Autocorrelation: A Primer. Resource Publications in Geography, Association of American Geographers.
10 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.
11 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.
12 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.
13 Kim, K.H. 2005. Oak wilt disease. Tree Protection, 10: 17-25.
14 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.
15 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.   DOI
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.   DOI
19 Ebdon, D. 1985. Statistics in Geography. Blackwell.