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Mapping and estimating forest carbon absorption using time-series MODIS imagery in South Korea

시계열 MODIS 영상자료를 이용한 산림의 연간 탄소 흡수량 지도 작성

  • Cha, Su-Young (Asian Institute for Energy, Environment & Sustainability, Seoul National University) ;
  • Pi, Ung-Hwan (High Performance Device Group, Samsung Advanced Institute of Technology) ;
  • Park, Chong-Hwa (Graduate School of Environmental Studies, Seoul National University)
  • 차수영 (서울대학교 아시아에너지지속가능발전연구소) ;
  • 피웅환 (삼성종합기술원) ;
  • 박종화 (서울대학교 환경대학원)
  • Received : 2012.07.18
  • Accepted : 2013.10.18
  • Published : 2013.10.31

Abstract

Time-series data of Normal Difference Vegetation Index (NDVI) obtained by the Moderate-resolution Imaging Spectroradiometer(MODIS) satellite imagery gives a waveform that reveals the characteristics of the phenology. The waveform can be decomposed into harmonics of various periods by the Fourier transformation. The resulting $n^{th}$ harmonics represent the amount of NDVI change in a period of a year divided by n. The values of each harmonics or their relative relation have been used to classify the vegetation species and to build a vegetation map. Here, we propose a method to estimate the annual amount of carbon absorbed on the forest from the $1^{st}$ harmonic NDVI value. The $1^{st}$ harmonic value represents the amount of growth of the leaves. By the allometric equation of trees, the growth of leaves can be considered to be proportional to the total amount of carbon absorption. We compared the $1^{st}$ harmonic NDVI values of the 6220 sample points with the reference data of the carbon absorption obtained by the field survey in the forest of South Korea. The $1^{st}$ harmonic values were roughly proportional to the amount of carbon absorption irrespective of the species and ages of the vegetation. The resulting proportionality constant between the carbon absorption and the $1^{st}$ harmonic value was 236 tCO2/5.29ha/year. The total amount of carbon dioxide absorption in the forest of South Korea over the last ten years has been estimated to be about 56 million ton, and this coincides with the previous reports obtained by other methods. Considering that the amount of the carbon absorption becomes a kind of currency like carbon credit, our method is very useful due to its generality.

매일 단위로 수신되는 MODIS 인공위성자료를 이용하여 계산한 시계열 식생지수 자료는 1년 주기의 생물계절 특성을 나타내는 복잡한 파형으로 표현될 수 있다. 이러한 복잡한 파형도 단순한 파형의 합성으로 이루어지는데 이산 퓨리에 변환 분석 기법은 이들을 각각의 하모닉들로 추출해 내어 다양한 주기별로 생육을 달리하는 식생의 특성을 설명할 수 있다. 특히 이산 퓨리에 분석을 통해 도출된 시계열 식생지수 자료의 1차 하모닉 값은 1년 동안 변화하는 총 잎의 생장량을 나타내는 것으로써 나무의 상대성장회귀식 추정에 의해 식생이 1년 동안 탄소를 흡수한 양을 나타내는 지상부 바이오매스양을 설명한다. 따라서 1차 하모닉 값의 변화량은 1년 동안 식생이 탄소를 흡수하는 양을 나타낸다고 할 수 있는데, 시계열 MODIS 자료에서 추출된 6220여개의 표본들의 1차 하모닉 10년 평균값과 산림청의 입목 축적량 데이터를 통해 추정된 연간 단위면적당 이산화탄소 흡수량을 이용하여 수종별 비례상수를 도출할 수 있었다. 남한 산림지역에 한하여 총 이산화탄소 흡수량은 2000년 이후 10년 평균 약 5천6백만톤으로 계산되었고 이것은 발표된 남한 산림의 연간 이산화탄소 흡수량에 근접하였다. 본 연구에서 제시한 방법은 보편적 비례상수를 이용하여 식생의 연간 탄소 흡수량을 추정함으로써 시계열 위성영상 자료를 이용하여 매년 변화하는 산림의 이산화탄소 흡수량 지도를 반복하여 정량적으로 제작할 수 있는 환경공간정보를 제공한다.

Keywords

References

  1. Blackard, 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 R.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
  2. DeFries, R., F. Achard, S. Brown, M. Herold, D. Murdiyarso, B. Schlamadinger, and C. de Souza, 2007. Earth observations for estimating greenhouse gas emissions from deforestation in developing countries, Environmental Science and Policy, 10(4):385-394. https://doi.org/10.1016/j.envsci.2007.01.010
  3. Fuchs, H., P. Magdon, C. Kleinn, and H. Flessa, 2009. Estimating aboveground carbon in a catchment of the Siberian forest tundra: combining satellite imagery and field inventory, Remote Sensing of Environment, 113(3):518 -531. https://doi.org/10.1016/j.rse.2008.07.017
  4. Gibbs, H.K., S. Brown, J.O. Niles, and J.A. Foley, 2007. Monitoring and estimating tropical forest carbon stocks: making REDD a reality, Environmental Research Letters, 2(4): 045023. https://doi.org/10.1088/1748-9326/2/4/045023
  5. Goetz S., A. Baccini, N. Laporte, T. Johns, W. Walker, J. Kellndorfer, R. A Houghton, and M. Sun, 2009. Mapping and monitoring carbon stocks with satellite observations: a comparison of methods, Carbon Balance and Management, 4(1):2. https://doi.org/10.1186/1750-0680-4-2
  6. Jakubauskas, M.E., D.R. Legates, and J.H. Kastens, 2002. Crop identification using harmonic analysis of time-series AVHRR NDVI data, Computers and Electronics in Agriculture, 37(1):127-139. https://doi.org/10.1016/S0168-1699(02)00116-3
  7. Herold, M. and T. Johns, 2007. Linking requirements with capabilities for deforestation monitoring in the context of the UNFCCC-REDD process, Environmental Research Letters, 2(4): 045025. https://doi.org/10.1088/1748-9326/2/4/045025
  8. 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(I):54-62. https://doi.org/10.1007/s11431-006-8107-z
  9. IPCC, 2003. Good Practice Guidance for Land Use, Land-Use Change and Forestry, Institute for Global Environment Strategies.
  10. Niels, A. and J. Sathaye, 2008. Reducing deforestation and trading emissions: Economic implications for the post-Kyoto carbon market, ZEW-Centre for European Economic Research Discussion Paper, 08-016.
  11. Otsuka T., W. Mo, T. Satomura, M. Inatomi, and H. Koizumi, 2007. Biometric based carbon flux measurements and net ecosystem production (NEP) in a temperate deciduous broad-leaved forest beneath a flux tower, Ecosystems, 10:324-334. https://doi.org/10.1007/s10021-007-9017-z
  12. Patenaude G., R. Milne, and T.P. Dawson, 2005. Synthesis of remote sensing approaches for forest carbon estimation: reporting to the Kyoto Protocol, Environmental Science and Policy, 8(2): 161-178. https://doi.org/10.1016/j.envsci.2004.12.010
  13. 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
  14. Reed, B.C., J.F. Brown, D. VanderZee, T.R. Loveland, J.W. Merchant, and D.O. Ohlen, 1994. Measuring phonological variability from satellite imagery, Journal of Vegetation Science, 5(5): 703-714. https://doi.org/10.2307/3235884
  15. Smith, B., W. Knorr, J.L. Widlowski, B. Pinty, and N. Gobron, 2008. Combining remote sensing data with process modelling to monitor boreal conifer forest carbon balances, Forest Ecology and Management, 255(12): 3985-3994. https://doi.org/10.1016/j.foreco.2008.03.056
  16. Son Y.M., K.H. Lee, and R.H. Kim, 2007. Estimation of Forest Biomass in Korea, Journal of Korean Forestry Society, 96(4): 477-482.
  17. 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
  18. Wylie, B.K., D.J. Meyer, L.L. Tieszen, and S.?Mannel, 2002. Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands - A case study, Remote Sensing of Environment, 79(2-3): 266-278. https://doi.org/10.1016/S0034-4257(01)00278-4
  19. 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. https://doi.org/10.7780/kjrs.2009.25.4.311
  20. Ministry of Environment, 2007, http://egis.me.go.kr/
  21. Korea Forest Service, 2009, http://www.forest.go.kr/
  22. Korea Forest Service, 2010, http://www.klaw.go.kr/

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