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Estimations of Forest Growing Stocks in Small-area Level Considering Local Forest Characteristics

산림의 지역적 특성을 고려한 시군구 임목축적량 통계 산출 기법 개발

  • Kim, Eun-Sook (Center for Forest and Climate Change, Korea Forest Research Institute) ;
  • Kim, Cheol-Min (Center for Forest and Climate Change, Korea Forest Research Institute)
  • 김은숙 (국립산림과학원 기후변화연구센터) ;
  • 김철민 (국립산림과학원 기후변화연구센터)
  • Received : 2014.08.18
  • Accepted : 2014.11.02
  • Published : 2015.03.31

Abstract

Forest statistics of local administrative districts have many social needs, nevertheless we have some difficulties for working out an accurate statistics because of insufficient data in small-area level. Thus, new small-area estimation method has to set aside additional data, decrease errors of statistics and consider the local forest characteristics at the same time. In this study, we researched the spatial divisions that can set aside additional data for statistics production and satisfy the major premise, which is "forest characteristics of spatial divisions have to be equal to that of small-area". And we compared synthetic estimation methods based on three different spatial divisions(provinces, neighbor districts and new expanded districts). New expanded districts were divided based on the criteria of climate, soil type and tree species composition that affects local forest characteristics. Small-area statistics were assessed in terms of the ability to estimate local forest characteristics and consistency within large-area statistics. As a result, new expanded districts synthetic estimation was assessed to calculate statistics that reflects local forest characteristics better than other two estimation methods. Moreover, this synthetic estimation method produced the statistics that was included within 95% confidence interval of large-area statistics and was the closer to large-area statistics than the neighbor districts synthetic estimation.

시군구 단위 산림통계의 사회적 필요성에도 불구하고 자료의 부족으로 인하여 현실을 반영한 산림통계 산출이 어려운 상황에 있다. 따라서, 시군구 산림 통계 산출을 위하여 해당 시군구의 국가산림자원조사 자료와 주변 지역의 자료를 함께 활용하여 통계량의 오차 수준을 감소시키고 소면적 통계량이 해당 지역 산림의 지역적 특수성을 반영할 수 있는 새로운 소면적 통계산출 방법의 개발이 필요하다. 본 연구에서는 소면적 지역과 특성 구조가 유사하다는 가정을 만족하면서 통계산출을 위한 최소한의 표본점 개수를 확보하기에 적정한 공간 단위에 대한 연구를 수행하였다. 그리고 산림의 지역적 특성을 결정하는 주요 요인인 기후, 토양, 수종 구성 등의 동질성을 기준으로 구획된 확장시군구 기반의 합성추정법, 시 도 단위 자료를 이용하는 기본계획구 합성추정법, 인접 시군구 자료를 이용하는 이웃시군 합성추정법을 비교하고, 이 방법들을 통해 산출된 임목축적 통계의 지역적 특성 설명력과 상위 통계와의 관계에 대한 평가를 수행했다. 그 결과, 확장시군구 합성추정법이 기본계획구 합성추정과 이웃시군 합성추정보다 지역적 특성을 보다 잘 반영하는 통계를 산출하는 것으로 평가되었다. 또한 확장시군구 합성추정법을 통해 산출된 통계량은 시도 단위로 산출된 통계량의 95% 신뢰구간 내에 포함되었으며, 이웃시군 합성추정법에 의해 산출된 결과보다 시도단위 통계량과의 차이가 적게 발생하였다.

Keywords

References

  1. Bae, S.W., Lim, J.H., Kim, S.J., and Lee, M.B. 2012. Economic tree species(1) Pine Tree. Korea Forest Research Institute, Seoul, Korea. pp. 250.
  2. Bechtold, W.A., Patterson, P.L.(Editors). 2005. The enhanced forest inventory and analysis program - national sampling design and estimation procedures. General Technical Report SRS080. USDA Forest Service. NC. U.S.A. pp. 85.
  3. Cho, H.K., Yoo, B.O., Kim, S.H., Ryu, J.H., Kim, J.C., Seo, S.H., Kim, J.S., Lee. J.B., and Kim, H.H. 2012. Production and update of large scale forest type map using the digital airphoto. Korea Forest Research Institute. Seoul. Korea. pp. 135.
  4. Chung, S.Y., Yim, J.S., Cho, H.K., Jeong, J.H., Kim, S.H., and Shin, M.Y. 2009. Estimation of forest biomass for Muju county using biomass conversion table and remote sensing data. Journal of Korean Forest Society 98(4): 409-416.
  5. Jeong, J.H., Won, H.K., and Kim, I.H.. 2004. Forest Site in Korean -Forest Soil-. Korea Forest Research Institute. Seoul. Korea. pp. 621.
  6. Kang, Y. and Jang, S. 2007. A Study on the Delimitation of Census Output Areas in Korea. Journal of Korean Urban Geographical Society 10(1): 15-36.
  7. Kim, D.H. and Kim, J.K. 2004. Estimating local household income and expenditure survey. Statistics Korea. pp. 123.
  8. Kim, E.S., Kim, K.M., Lee, J.B., Lee. S.H., and Kim, C.C. 2011. Spatial upscaling of aboveground biomass estimation using National Forest Inventory data and forest type map. Journal of Korean Forest Society 100(3): 455-465.
  9. Kim, J.S., Hwang, H.J., and Shin, K.I. 2008. Comparison of spatial small-area estimators based on neighborhood information systems. The Korean Journal of Applied Statistics 21(5): 855-866. https://doi.org/10.5351/KJAS.2008.21.5.855
  10. Kim, S.H., Kim, J.C., Yoo, B.O., Yim, J.S., and Jung, I.B. 2011. The 5th National Forest Inventory Report. Korea Forest Research Institute, Seoul, Korea. pp. 166.
  11. Korean Forestry Promotion Institute. 2013. Assessment for forest resources of Korea : 2006-2012. Korean Forestry Promotion Institute, Seoul, Korea. pp. 267
  12. Kwon, S.P. 2007. Small-area estimation of employment statistics. pp. 54-80. In : Improvement of statistics technique for development of national statistics, Statistical Research Institute, Daejeon, Korea.
  13. Lee, S.W., Won, H.K., Jung, Y.H., Jeong, J.H., Koo, K.S., Kang, Y.H., Son, Y.M., and Shin, M.Y. 2009. History and application of optimum site determination system of the tree species, Korea Forest Research Institute, Seoul, Korea. pp. 128.
  14. Lee, K.S. and Shin, K.I. 2008. Comparison of neighborhood information systems for lattice data analysis. The Korean Journal of Applied Statistics 21(3): 387-397. https://doi.org/10.5351/KJAS.2008.21.3.387
  15. Shin, J.H. and Kim, C.M. 1996. Ecosystem classification in Korea(I): Ecoprovince classification. FRI Journal of Forest Science 54. pp. 188-199.
  16. Shin, J.H., Kwon, J.H., Oh, J.H., Chun, J.H., Lim, J.H., Yang, H.M., and Kim, Y.K. 2009. Korean forest landscape and ecosystem zoning. Korea Forest Research Institute. Seoul. Korea. pp. 52.
  17. Smith, W.B. 2002. Forest Inventory and analysis: a national inventory and monitoring program. Environmental Pollution 116. pp. S233-S242. https://doi.org/10.1016/S0269-7491(01)00255-X
  18. Tomppo, E., Katila, M., Haakana, M., and Persaari, J. 2010. The multi-source national forest inventory of Finland-methods and results 2005. Finnish Forest Research Institute. Vantaa, Finland. pp. 59.
  19. Yim, J.S., Han, W.S., Jung, I.B., Kim, S.H., and Shin, M.Y. 2010. Application of synthetic estimator for estimating forest growing stock volumes at the small-area level. Journal of Korean Forest Society 99(3): 285-291.
  20. Yim, J.S., Yoo, B.O., and Shin, M.Y. 2012. Comparison of forest growing stock estimates by distance-weighting and stratification in k-Nearest Neighbor Technique. Journal of Korean Forest Society 101(3): 374-380.
  21. Yoo, S.H., Heo, J., Jung, J.H., Han, S.H., and Kim, K.M. 2011. Estimation of aboveground biomass carbon stock using Landsat TM and ratio images-kNN algorithm and regression model priority. Journal of Korean Society for Geospatial Information System 19(2): 39-48.

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  2. Estimation of the Forest Stand Volumes from Forest Inventory Data Based on Synthetic Estimation Method: A Case of the Economic Forest in Gangwon-do, Republic of Korea vol.32, pp.2, 2016, https://doi.org/10.7747/JFES.2016.32.2.140