Journal of the Korean Association of Geographic Information Studies (한국지리정보학회지)

The Korean Association of Geographic Information Studies (한국지리정보학회)
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Comparison of Three Kinds of Methods on Estimation of Forest Carbon Stocks Distribution Using National Forest Inventory DB and Forest Type Map

국가산림자원조사 DB와 임상도를 이용한 산림탄소저장량 공간분포 추정방법 비교

  • Kim, Kyoung-Min (Center for Forest & Climate Change, Korea Forest Research Institute) ;
  • Roh, Young-Hee (Korean Institute of Geographical Research, Sungshin Women's University) ;
  • Kim, Eun-Sook (Center for Forest & Climate Change, Korea Forest Research Institute)
  • 김경민 (국립산림과학원 기후변화연구센터) ;
  • 노영희 (성신여자대학교 한국지리연구소) ;
  • 김은숙 (국립산림과학원 기후변화연구센터)
  • Received : 2014.08.24
  • Accepted : 2014.12.09
  • Published : 2014.12.31
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Abstract

Carbon stocks of NFI plots can be accurately estimated using field survey information. However, an accurate estimation of carbon stocks in other unsurveyed sites is very difficult. In order to fill this gap, various spatial information can be used as an ancillary data. In South Korea, there is the 1:5,000 forest type map that was produced by digital air-photo interpretation and field survey. Because this map contains very detailed forest information, it can be used as the high-quality spatial data for estimating carbon stocks. In this study, we compared three upscaling methods based on the 1:5,000 forest type map and 5th national forest inventory data. Map algebra(method 1), RK(Regression Kriging)(method 2), and GWR(Geographically Weighted Regression)(method 3) were applied to estimate forest carbon stock in Chungcheong-nam Do and Daejeon metropolitan city. The range of carbon stocks from method 2(1.39~138.80 tonC/ha) and method 3(1.28~149.98 tonC/ha) were more similar to that of previous method(1.56~156.40 tonC/ha) than that of method 1(0.00~93.37 tonC/ha). This result shows that RK and GWR considering spatial autocorrelation can show spatial heterogeneity of carbon stocks. We carried out paired t-test for carbon stock data using 186 sample points to assess estimation accuracy. As a result, the average carbon stocks of method 2 and field survey method were not significantly different at p=0.05 using paired t-test. And the result of method 2 showed the lowest RMSE. Therefore regression kriging method is useful to consider spatial variations of carbon stocks distribution in rugged terrain and complex forest stand.

기존의 산림탄소저장량 통계는 현지 조사 표본 기반의 통계로 표본점 단위에서는 비교적 정확하지만 미조사 지점에 대해서는 정확도가 떨어질 수 있다. 이를 보완하기 위한 것이 공간 정보를 보조 자료로 함께 활용하는 면적 기반 추정이며 우리나라의 경우 디지털 항공사진 판독과 현지 조사를 통해 상세 수준의 산림정보를 얻을 수 있는 1:5,000 임상도를 보유하고 있으므로 임상도의 활용성에 주목할 필요가 있다. 본 연구에서는 1:5,000 임상도와 제5차 국가산림자원조사 자료에 기반한 세 가지 업스케일링 방법을 비교하였다. 충청남도와 대전시를 대상으로 지도대수(방법 1), 회귀크리깅(방법 2) 및 지리가중회귀(방법 3)를 이용하여 산림탄소저장량을 각각 추정하였다. 탄소저장량 범위의 경우, 방법 2(1.39~138.80 tonC/ha)와 방법 3(1.28~149.98 tonC/ha)이 방법 1(0.00~93.37 tonC/ha)에 비해 기존의 현지 조사 표본 기반 방법의 추정치 범위(1.56~156.40 tonC/ha)와 유사한 범위로 추정하여 공간자기상관성을 고려한 회귀크리깅과 지리가중회귀 방법이 탄소저장량 분포의 공간이질성을 잘 반영하는 것으로 나타났다. 정확도 평가를 위해 독립검증 지점 186개소의 탄소저장량에 대한 대응표본 t-검정을 수행한 결과, 방법 2의 평균 추정치와 NFI 표본 기반 평균 추정치는 통계적으로 유의한 차이가 없으며(p>0.05) 방법 2의 결과가 가장 낮은 RMSE를 보였다. 따라서 지형과 임분 구조가 복잡한 우리나라 산림의 경우, 회귀크리깅이 기존 통계 방법과 가장 유사한 평균 탄소저장량을 산출하면서 탄소저장량의 국지적 변이를 나타내기에 유용할 것으로 판단된다.

Keywords

References

  1. Anselin, L. and A.K. Bera. 1998. Spatial dependence in linear regression models with an introduction to spatial econometrics. In: A. Ullah and D. Giles (ed). Handbook of Applied Economic Statistics. CRC Press, New York, pp.237-289.
  2. Blackyard, J.A., M.V. Finco, E.H. Helmer, G.R. Holden, M.L. Hoppus, D.M. Jacobs, A.J. Lister, G.G. Moisen, M.D. Nelson, R. Riemann, B. Ruefenacht, D. Salajanu, D.L. Weyermann, K.C. Winterberger, T.J. Brandeis, R.L. Czaplewski, R.E. McRoberts, P.L. Patterson and P.P. Tymcio. 2008. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sensing of Environment 112:1658-1677. https://doi.org/10.1016/j.rse.2007.08.021
  3. Brown, S. and G. Gaston. 1995. Use of forest inventories and geographic information systems to estimate biomass density of tropical forests: application to tropical africa. Environmental Monitoring and Assessment 38:157-168.
  4. Fournier, R.A., J.E. Luther, L. Guindon, M.C. Lambert, D. Piercey, R.J. Hall and M.A. Wulder. 2003. Mapping aboveground tree biomass at the stand level from inventory information: test cases in Newfoundland and Quebec. Canadian Journal of Forest Research 33(10):1846-1863. https://doi.org/10.1139/x03-099
  5. Fotheringham, A.S., C. Brunsdon and M. Charlton. 2002. Geographically Weighted Regression. Wiley, 284pp.
  6. Freeman, E. and G. Moisen. 2006. Evaluating kriging as a tool to improve moderate resolution maps of forest biomass. Environmental Monitoring and Assessment 128(1-3):395-410.
  7. Gjertsen, A.K. 2007. Accuracy of forest mapping based on Landsat TM data and a k-NN based method. Remote Sensing of Environment 110:420-430. https://doi.org/10.1016/j.rse.2006.08.018
  8. Hengl, T., G. Heuvelink and A. Stein. 2004. A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 122(1-2): 75-93.
  9. Huiyan, G., D. Limin, W. Gang, X. Dong, W. Shunzhong and W. Hui. 2006. Estimation of forest volumes by integrating landsat TM imagery and forest inventory data. Science in China: Series E Technological Sciences 49:54-62. https://doi.org/10.1007/s11431-006-8107-z
  10. IPCC. 2003. Good practice guidance for land use, land-use change and forestry, Kanagawa, Institute for Global Environment Strategies(IGES). 576pp.
  11. Jung, J.H., J. Heo, S.H. Yoo, K.M. Kim and J.B. Lee. 2010. Estimation of aboveground biomass carbon stock in Danyang area using kNN algorithm and landsat TM seasonal satellite images. Korean Society for Geospatial Information System 18(4):119-129 (정재훈, 허준, 유수홍, 김경민, 이정빈. 2010. kNN 알고리즘과 계절별 Landsat TM 위성영상을 이용한 단양군 지역의 지상부 바이오매스 탄소저장량 추정. 한국지형공간정보학회지 18(4): 119-129).
  12. Kim, E.S., K.M. Kim, J.B. Lee, S.H. Lee and J.C. Kim. 2011. Spatial upscaling of aboveground biomass estimation using national forest inventory data and forest type map. Journal of Korean Forest Society 100(3):505-515 (김은숙, 김경민, 이정빈, 이승호, 김종찬. 2011. 국가산림 자원조사 자료와 임상도를 이용한 지상부 바이오매스의 공간규모 확장. 한국임학회지 100(3):505-515).
  13. Kim, Y.J. and H.K. Kim. 2007. Environmental Statistics. Dong Hwa, 256pp (김영주, 김희갑. 2007. 환경통계학. 동화기술, 256쪽)
  14. Korea Forest Research Institute. 2009. 5th National Forest Inventory Manual Ver. 1.3. 54pp (국립산림과학원. 2009. 제 5차 국가산림자원조사 요령 ver 1.3. 54쪽).
  15. Korea Forest Research Institute. 2010. Study on the basis of forest carbon accounting in Korea. Korea Forest service. 436pp (국립산림과학원. 2010. 교토의정서 대응 산림탄소계정 기반 구축 연구. 산림청. 436쪽).
  16. Korea Forest Service. 2001. Forestry Dictionary. 1341pp (산림청. 2001. 임업 및 임학 사전. 1341쪽).
  17. Korea Forest Service. 2009. Climate Change and Forest. 244pp (산림청. 2009. 기후변화와 산림. 244쪽).
  18. Korea Forest Service. 2011. Statistical yearbook of forestry. 484pp (산림청. 2011. 임업통계연보. 484쪽).
  19. Kohl, M., S.S. Magnussen and M. Marchetti. 2006. Sampling Methods, Remote Sensing and GIS Multiresource Forest Inventory. Springer. 373pp.
  20. Labrecque, S., R.A. Fournier, J.E. Luther and D. Piercey. 2006. A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland. Forest Ecology and Management 226:129-144. https://doi.org/10.1016/j.foreco.2006.01.030
  21. Lufafa, A., I. Diedhiou, S. Samba, M. Sene, M. Khouma, F. Kizito, R. Dick, E. Dossa and J. Noller. 2008. Carbon stocks and patterns in native shrub communities of Senegal's Peanut basin. Geoderma 146 (1-2):75-82. https://doi.org/10.1016/j.geoderma.2008.05.024
  22. Magnussen, S., R.E. McRoberts and E.O. Tomppo. 2009. Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories. Remote Sensing of Environment 113:476-488. https://doi.org/10.1016/j.rse.2008.04.018
  23. McRoberts, R.E. and E.O. Tomppo, E. 2007. Remote sensing support for national forest inventories. Remote Sensing of Environment 110:412-419. https://doi.org/10.1016/j.rse.2006.09.034
  24. Meng, Q., C. Cieszewski and M. Madden. 2009. Large area forest inventory using Landsat ETM+: a geostatistical approach. ISPRS Journal of Photogrammetry and Remote Sensing 64:27-36. https://doi.org/10.1016/j.isprsjprs.2008.06.006
  25. Moran, P. 1948. The interpretation of statistical maps. Journal of Royal Statistical Society 10:243-251.
  26. Nijland, W., E.A. Addink, S.M. De Jong and F.D. Van der Meer. 2009. Optimizing spatial image support for quantitative mapping of natural vegetation. Remote Sensing of Envrionment 113: 771-780. https://doi.org/10.1016/j.rse.2008.12.002
  27. Park, H.J., H.S. Shin, Y.H. Roh, K.M. Kim and K.H. Park. 2012. Estimating forest carbon stocks in Danyang using kriging methods for aboveground biomass. Journal of the Korean Association of Geographic Information Studies 15(1): 16-33 (박현주, 신휴석, 노영희, 김경민, 박기호. 2012. 크리깅 기법을 이용한 단양군의 산림탄소저장량 추정 -지상부 바이오매스를 대상으로-. 한국지리정보학회지 15(1):16-33). https://doi.org/10.11108/kagis.2012.15.1.016
  28. Rahman, M.M., E. Csaplovics and B. Koch. 2008. Satellite estimation of forest carbon using regression models. International Journal of Remote Sensing 29(23):6917-6936. https://doi.org/10.1080/01431160802144187
  29. Reese, H., M. Nilsson, P. Sandstro and H. Olson. 2002. Application using estimates of forest parameters derived from satellite and forest inventory data. Computers and Electronics in Agriculture 37:37-55. https://doi.org/10.1016/S0168-1699(02)00118-7
  30. Sales, M., C. Souza Jr, P. Kyriakidis, D. Roberts and E. Vidal. 2007. Improving spatial distribution estimation of forest biomass with geostatistics: a case study for Rondonia, Brazil. Ecological Modelling 205(1-2):221-230. https://doi.org/10.1016/j.ecolmodel.2007.02.033
  31. Tomppo, E., H. Olsson, G. Stahl, M. Nilsson, O. Hagner and M. Katila. 2008. Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sensing of Environment 112:1982-1999. https://doi.org/10.1016/j.rse.2007.03.032
  32. Tomppo, E., M. Nilsson, M. Rosengren, P. Aalto and P. Kennedy. 2002. Simultaneous use of Landsat-TM and IRS-1C wifs data in estimating large area tree stem volume and aboveground biomass. Remote Sensing of Environment 82:156-171. https://doi.org/10.1016/S0034-4257(02)00031-7
  33. Tomppo, E. and M. Siitonen. 1991. The national forest inventory of finland. Paper and Timber 73(2):90−97.
  34. Wulder, M.A., J.C. White, R.A. Fournier, J.E. Luther and S. Magnussen. 2008. Spatially explicit large area biomass estimation: three approaches using forest inventory and remotely sensed imagery in a GIS. Sensors 8:529-560. https://doi.org/10.3390/s8010529
  35. Yim, J.S., W.S. Han, J.H. Hwang, S.Y. Chung, H.K. Cho and M.Y. Shin. 2009. Estimation of forest biomass based upon satellite data and national forest inventory data. Korean Journal of Remote Sensing 25(4):311-320 (임종수, 한원성, 황주호, 정상영, 조현국, 신만용. 2009. 위성영상자료 및 국가 산림자원조사 자료를 이용한 산림 바이오매스 추정. 대한원격탐사학회지 25(4):311-320). https://doi.org/10.7780/kjrs.2009.25.4.311
  36. Zhuang, Q., T. Zhang and J. Xiao. 2009. Quantification of net primary production of Chinese forest ecosystems with spatial statistical approaches. Mitig Adapt Strateg Glob Change 14:85-99. https://doi.org/10.1007/s11027-008-9152-7

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