Journal of the Korean Society of Environmental Restoration Technology (한국환경복원기술학회지)

The Korea Society of Environmental Restoration Technology (한국환경복원기술학회)
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Principal Component Analysis Based Ecosystem Differences between South and North Korea Using Multivariate Spatial Environmental Variables

다변량 환경 공간변수 주성분 분석을 통한 남·북 생태계 차이

  • Yu, Jaeshim (Division of Global Forestry, Korea Forest Research Institute) ;
  • Kim, Kyoungmin (Division of Global Forestry, Korea Forest Research Institute)
  • 유재심 (국립산림과학원 국제산림연구과) ;
  • 김경민 (국립산림과학원 국제산림연구과)
  • Received : 2015.05.28
  • Accepted : 2015.07.17
  • Published : 2015.08.31
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Abstract

The objectives of this study are to analyze the quantitative ecological principal components of Korean Peninsula using the multivariate spatial environmental datasets and to compare the ecological difference between South and North Korea. Ecological maps with GIS(Geographical Information System) are constructed by PCA(Principal Component Analysis) based on seventeen raster(cell based) variables at 1km resolution. Ecological differences between South and North Korea are extracted by Factor Analysis using ecosystem maps masked from Korean ones. Spatial data include SRTM(Shuttle Radar Topography Mission), Temperature, Precipitation, SWC(Soil Water Content), fPAR(Fraction of Photosynthetically Active Radiation) representing for a productivity, and SR(Solar Radiation), which all cover Korean peninsula. When it performed PCA, the first three scores were assigned to red, green, and blue color. This color triplet indicates the relative mixture of the seventeen environmental conditions inside each ecological region. The first red one represents for 'physiographic conditions' worked by high elevation and solar radiation and low temperature. The second green one stands for 'seasonality' caused by seasonal variations of temperature, precipitation, and productivity. The third blue one means 'wetness condition' worked by high value such as precipitation and soil water contents. FA extraction shows that South Korea has relatively warm and humid ecosystem affected by high temperature, precipitation, and soil water contents whereas North Korea has relatively cold and dry ecosystem due to the high elevation, low temperature and precipitation. Results would be useful at environmental planning on inaccessible land of North Korea.

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References

  1. Andrew, M. E..Nelson, T. A..Wulder, M. A.. Hobart, G. W..Coops, N. C. and C. J. Q. Farmer. 2013. Ecosystem classifications based on summer and winter conditions, Environmental Monitoring Assessment, 185: 3057-3079. https://doi.org/10.1007/s10661-012-2773-z
  2. Bailey, R. G. 2005. Identifying Ecoregion Boundaries, Environmental Management 34(1): s14-s26.
  3. Bailey, R. G..Zoltai, S. C. and E. B. Wiken. 1985. Ecological regionalization in Canada and the United States. Geoforum 116(3): 265-275.
  4. Coops, N. C..Wulder, M. A. and D. Iwanicka. 2009. An environmental domain clasification of Canada using earth observation data for biodiversity assessment, Ecological Informatics 4: 8-22. https://doi.org/10.1016/j.ecoinf.2008.09.005
  5. Coops, N. C..Wulder, M. A..Duro, D. C..Han, T. and S. Berry. 2008. The development of a Canadian dynamic habitat index using multi-temporal satellite estimates of canopy light absorbance. Ecological Indicators, 8(5): 754-766. https://doi.org/10.1016/j.ecolind.2008.01.007
  6. Duro, D. C..Coops, N. C..Wulder, M. A. and T. Han. 2007. Development of a large area biodiversity monitoring system driven by remote sensing in Physical Geography, 31: 1-26.
  7. Fitterer, J. L..Nelson, T. A..Coops, N. C. and M. A. Wulder. 2012. Modelling the ecosystem indicators of British Columbia using Earth observation data and terrain indices, Ecological Indicators, 20: 151-162. https://doi.org/10.1016/j.ecolind.2012.02.024
  8. Franklin, J. 1995. Pridictive vegetation mapping: geographic modeling of biospatial patterns in relation to environmental gradients. Progress in Physics Geography 19(4): 474-499. https://doi.org/10.1177/030913339501900403
  9. Hagrove, W. W. and F. M. Hoffman. 2005. Potential of Multivariate Quantitiative Methods for Delineation and Visualization of Ecoregions, Environmental Management 34(5): 1-21.
  10. Hargrove, W. W. and R. J. Luxmoore. 1998. A New High-Resolution National Map of Vegetation Ecoregions Produced Empirically Using Multivariate Spatial Clustering. Conference paper to be published electronically by Environmental Systems Research Institute, Redlands CA.
  11. Hargrove, W. W..Hoffman, F. M. and B. E. Law. 2003. New analysis reveals representativeness of the Ameriflux Network, EOS, 84(48): 529-544.
  12. Hijmans. R. J..Cameron, S. E..Parra, J. L.. Jones, P. G. and Jarvis, A. 2005. Very high resolution interpolated climate surfaces for global land areas, International Journal of Climatology 25: 1965-1978. https://doi.org/10.1002/joc.1276
  13. Hirzel, A. H..Hausser, J..Chessel, D. and N. Perrin. 2002. Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data. Ecology 83(7): 2027-2036. https://doi.org/10.1890/0012-9658(2002)083[2027:ENFAHT]2.0.CO;2
  14. Jensen, J. R. 2004. Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd edition, New York: Pentice Hall.
  15. Kang, S..Kim, Y. and Y. Kim. 2005. Errors of MODIS product of gross primary productivity by using data assimilation office meteorological data. Korean Journal of Agricultural and Forest Meteorology, 7(2): 171-183.
  16. Kumar, L..Skidmore, A. K. and E. Knowles. 1997. Modeling topographic variation in solar radiation in a GIS environment. Int. J. Geogr. Info. Sys. 11(5): 475-497. https://doi.org/10.1080/136588197242266
  17. Mouratidis, A..Briole, P. and K. Katsambalos. 2010. SRTM 3" DEM(version 1,2,3,4) validation by means of extensive kinematic GPS measurements: a case study from North Greece, International Journal of Remote Sensing 31(23): 6205-4222. https://doi.org/10.1080/01431160903401403
  18. Tansley, A. G. 1935. The Use and Abuse of Vegetational Concepts and Terms. Ecology, 16(3): 284-307. https://doi.org/10.2307/1930070
  19. Volkoff, B..Castro Mello, F. F..Ferreira Maia, S. M. and C. E. Pellegrino Cerri. 2012. Landscape and soil regionalization in sourthern Brazilian Amazon and contiguous area: methodology and relevance for ecological studies, Scientin Agricola, 69(3): 217-225. https://doi.org/10.1590/S0103-90162012000300007
  20. Williams, C. L..Hargrove, W. W..Liebman, M. and D. E. James. 2008. Agro-ecoregionalization of Iowa using multivariate geographical clustering, Agriculture, Ecosystems and Environment, 123: 161-174. https://doi.org/10.1016/j.agee.2007.06.006
  21. Wulder, M. A..Ortlepp, S. M..White, J. C.. Nelson, T. and N. C. Coops. 2010. A provincial and regional assessment of the mountain pine beetle epidemic in British Columbia: 1999-2008. J. Environ. Inform. 15: 1-13. https://doi.org/10.3808/jei.201000161
  22. http://www.cgiar-csi.org/
  23. http://www.worldclim.org/
  24. http://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/global-historical-climatology-network-ghcn
  25. http://www.fao.org/home/en/
  26. https://www.wmo.int/pages/index_en.html
  27. http://www.r-hydronet.sr.unh.edu/english/