Journal of Korean Society of Forest Science (한국산림과학회지)

Korean Society of Forest Science (한국산림과학회)
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Spatial Estimation of the Site Index for Pinus densiplora using Kriging

크리깅을 이용한 소나무림 지위지수 공간분포 추정

  • Kim, Kyoung-Min (Division of Forest Economics and Management, Korea Forest Research Institute) ;
  • Park, Key-Ho (Institute for Korea Regional Studies, Department of Geography, Seoul National University)
  • 김경민 (국립산림과학원 산림경제경영과) ;
  • 박기호 (서울대학교 지리학과 국토문제연구소)
  • Received : 2012.09.21
  • Accepted : 2013.11.25
  • Published : 2013.12.31
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Abstract

Site index information given from forest site map only exist in the sampled locations. In this study, site index for unsampled locations were estimated using kriging interpolation method which can interpolate values between point samples to generate a continuous surface. Site index of Pinus densiplora in Danyang area were calculated using Chapman-Richards model by plot unit. Then site index for unsampled locations were interpolated by theoretical variogram models and ordinary kriging. Also in order to assess parameter selection, cross-validation was performed by calculating mean error (ME), average standard error (ASE) and root mean square error (RMSE). In result, gaussian model was excluded because of the biggest relative nugget (37.40%). Then spherical model (16.80%) and exponential model (8.77%) were selected. Site index estimates of Pinus densiplora throughout the entire area in Danyang showed 4.39~19.53 based on exponential model, and 4.54~19.23 based on spherical model. By cross-validation, RMSE had almost no difference. But ME and ASE from spherical model were slightly lower than exponential model. Therefore site index prediction map from spherical model were finally selected. Average site index from site prediction map was 10.78. It can be expected that regional variance can be considered by site index prediction map in order to estimate forest biomass which has big spatial variance and eventually it is helpful to improve an accuracy of forest carbon estimation.

산림입지도의 지위지수 정보는 조사지점에만 존재하므로 미조사 지역에 대한 지위지수는 별도의 추정이 필요하다. 미조사 지역의 지위지수 추정을 위해 본 연구에서는 점자료로부터 연속표면을 생성하는 공간 내삽법인 크리깅 기법을 적용하였다. Chapman-Richards 생장모델을 이용하여 표준지별 지위지수 추정치를 구한 뒤 가우시안, 구형 및 지수형 베리오그램 모델별로 정규크리깅을 이용하여 단양 전역의 소나무림 지위지수를 격자단위($30m{\times}30m$)로 추정하였다. 교차검증을 위해 평균오차(ME), 평균표준오차(ASE) 및 평균제곱근오차(RMSE)를 계산하였다. 베리오그램 모델 적합 결과, 상대 너깃이 가장 큰 가우시안 모델(37.40%)이 제외되었으며 구형 모델(16.80%)과 지수형 베리오그램 모델(8.77%)이 선택되었다. 크리깅에 의한 지위지수 추정치는 지수형 모델을 적용한 경우 4.39~19.53, 구형모델을 적용한 경우 4.54~19.23의 분포를 보였다. 교차 검증 결과, RMSE는 두 모델에서 큰 차이가 없는 것으로 나타났으나 구형모델의 ME와 ASE가 지수형 모델보다 작기 때문에 구형 베리오그램 모델 기반 지위지수 지도를 최종적으로 선정하였다. 지위지수 지도로부터 산출된 단양지역 소나무림 평균 지위지수는 10.78로 추정되었다. 공간이질성이 큰 우리나라 산림의 바이오매스 추정 시 지위지수 지도를 통해 지역적 변이를 고려할 수 있으며 궁극적으로는 탄소저장량 분포 추정의 정확도 제고에 기여할 수 있을 것으로 기대된다.

Keywords

References

  1. Brown, S., Gillespie, A.J.R., and Lugo, A.E. 1989. Biomass estimation methods for tropical forests with applications to forest inventory data. Forest Science 35: 881-902.
  2. Choi, J.K. 2007. Geostatistcs. Sigma press. pp. 386.
  3. EPA. 2004. Developing spatially interpolated surfaces and estimating uncertainty. pp. 160.
  4. Fournier, R.A., Guindon, L., Bernier, P.Y., Ung, C.-H., and Raulier, F. 2000. Spatial implementation of models in forestry. The Forestry Chronicle 76(6): 929-940. https://doi.org/10.5558/tfc76929-6
  5. Hall, R.J., Price, D.T., Siltanem, R.M., Wang, Y., Fournier, R., Fraser, R.H., and Luther, J.E. 2001. Assessing the role of environmental and remote sensing variables in modeling Canada's forest biomass. 23rd Canadian Remote Sensing Symposium. Ste Foy, Quebec, pp. 465-471.
  6. IPCC. 2003. Good practice guidance for land Use, land-use change and forestry. Institute for Global Environment Strategies(IGES), Kanagawa, Japan. pp. 576.
  7. Iverson, L.R., Brown, S., Prasad, A., Mitasova, H., Gillespie, A.J.R., and Lugo, A.E. 1994. Use of GIS for estimating potential and actual forest biomass for continental South and Southeast Asia. In: V.H. Dale(ed.). Effects of land-Use change on atmospheric $CO_2$ concentrations: south and southeast Asia as a case study. Springer, New York, pp. 67-116.
  8. Kim, D.H., Ryu, D.W., Choi, Y.M., and Lee, W.J. 2010. Application of Kriging and Inverse Distance Weighting Method for the Estimation of Geo-Layer of Songdo Area in Incheon. Journal of the Korean Geotechnical Society 26(1): 5-19.
  9. Kimmins, J.P. 1997. Forest ecology 2nd ed. Prentice-Hall. pp. 596.
  10. Kitanidis, P.K. 1997. Introduction to geostatistics: applications in hydrogeology. Cambridge Press. Cambridge, United Kingdom. pp. 251.
  11. Korea Forest Research Institute. 1999. Pine, Pine Forest. pp. 205.
  12. Korea Forest Research Institute. 2004. Forest sites of Korea. pp. 620.
  13. Korea Forest Research Institute. 2009. The 5th National Forest Inventory Guideline. Ver. 1.3. pp. 54.
  14. Korea Forest Research Institute. 2012. Economic Tree Species 1 Pine Trees. Research Book 59. pp. 250.
  15. Korea Forest Service. 2001. Dictionary of Forestry. Korea Forest Policy Research Association. pp. 1341.
  16. Korea Forest Service. 2009. Climate change & forest. pp. 244.
  17. Korea Forest Service. 2011. Result of calculation of Forest Basic Statistics(2010). pp. 48.
  18. Lee, S.H. 2000. Prediction of height growth and derivation site index equation. Korean Journal of Forest Measurements 3(1): 29-34.
  19. Lee, S.W., Won, H.K., Jeon, Y.H., Jeon, J.H., Ku, K.S., Kang, Y.H., Sohn, Y.M., and Shin, M.H. 2009. A study on history and utilization of suitable tree on a site. Research report 09(01). Korea Forest Research Institute. pp. 128.
  20. Lee, H.Y. 2010, GIS: Geographic Information Systems, Bobmunsa. pp. 688.
  21. Meng, Q., Cieszewski, C., and Madden, M. 2009. Large area forest inventory using Landsat ETM+: a geostatistical approach. ISPRS Journal of Photogrammetry and Remote Sensing 64(1): 27-36. https://doi.org/10.1016/j.isprsjprs.2008.06.006
  22. Palmer, D.J., Hock, B.K., Kimberley, M.O., Watt, M.S., Lowe, D.J., and Payn, T.W. 2009. Comparison of spatial prediction techniques for developing Pinus radiata productivity surfaces across New Zealand. Forest Ecology and Management 258: 2046-2055. https://doi.org/10.1016/j.foreco.2009.07.057
  23. Park, N.W. and Jang, D.H. 2008. Mapping of Temperature and Rainfall Using DEM and Multivariate Kriging. The Korean Geographic Society Journal 43(6): 1002-1015.
  24. Robinson, A.P. and Hamann, J.D. 2011. Forest Analytics with R. Springer. pp.339.
  25. Tang, D. and Bian, F. 2009. Forest site evaluation based on GIS and kriging. The 1st International Conference on Information Science and Engineering(ICISE2009). pp. 2063-2067.
  26. SAS Institute. 2000. SAS/STAT user's guide. Version 8.01. SAS Institute, Cary, NC. pp.3809.
  27. Shin, M.Y. and Han, W.S. 2006. Development of a Forest Inventory System for the Sustainable Forest Management. Journal of Korean Forestry 95(3): 370-377.
  28. Skovsgaard, J.P. and Vanclay, J.P. 2008. Forest site productivity: a review of the evolution of dendrometric concepts for even-aged stands. Forestry 81: 13-31. https://doi.org/10.1093/forestry/cpm041
  29. Sousa, V. and Pereira, L.P. 1999. Regional analysis of irrigation water requirements using kriging:Application to potato crop. Agricultural Water Management 40: 221-233. https://doi.org/10.1016/S0378-3774(98)00123-1
  30. Vega, C and St-Onge, B. 2009. Mapping site index and age by linking a time series of canopy height models with growth curves. Forest Ecology and Management 257: 951-959. https://doi.org/10.1016/j.foreco.2008.10.029
  31. Webster, R. and Oliver, M.A. 2007. Geostatistics for Environmental Scientists. Statistics in Practice(2nd). John Wiley & Sons. Chichester, England. pp.330.
  32. Yoon, J.H. 2003. Characteristics and change prediction of spatial distribution of Pinus densiflora stands in Korea. Doctoral Dissertation at Graduate School of Korea University. pp. 160.
  33. Danygang County Office. 2012. The Characteristic of Dangyang-Gun.
  34. http://www.danyang.chungbuk.kr/dy21/happydanyang/?menu= 010104 (2012.09.10.)

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