Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

Retrieval of the Fraction of Photosynthetically Active Radiation (FPAR) using SPOT/VEGETATION over Korea

SPOT/VEGETATION 자료를 이용한 한반도의 광합성유효복사율(FPAR)의 산출

  • Pi, Kyoung-Jin (Dept. of Geoinformatic Engineering, Pukyong National University) ;
  • Han, Kyung-Soo (Dept. of Geoinformatic Engineering, Pukyong National University)
  • 피경진 (부경대학교 위성정보과학과) ;
  • 한경수 (부경대학교 위성정보과학과)
  • Received : 2010.08.21
  • Accepted : 20100931
  • Published : 2010.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

The importance of vegetation in studies of global climate and biogeochemical cycles is well recognized. Especially. the FPAR (fraction of photosynthetically active radiation) is one of the important parameters in ecosystem productivity and carbon budget models. Therefore, accurate estimates of vegetation parameters are increasingly important in environmental impact assessment studies. In this study, optical FPAR using the Terra MODIS (MODerate resolution Imaging Spectroradiometer), SPOT VEGETATION and ECOCLIMAP data reproduced on the Korean peninsula. We applied the empirical method which is usually estimated as a linear or nonlinear function of vegetation indices. As results, we estimated the accurate expression which is 0.9039 of $R^2$ in cropland and 0.7901 of $R^2$ in forest. Finally, this study could be demonstrated to calibrate that produced FPAR while the overall pattern and random noise through the comparative analysis of FPAR on the reference data. Optimal use of input parameter on the Korean peninsula should be helping the accuracy of output as well as the improved quality of research.

FPAR는 다양한 육상 생태계 모텔에서 중요한 입력변수로 사용된다. 이 때문에 다양한 global product의 형태로 제공되고 있다. 하지만 한반도를 영역으로 하는 연구에 이를 바로 적용 시 오차가 발생할 수 있고, 이것은 위성자료를 이용한 지면 정보 산출에 있어서 직접적인 오차요인이 된다. 따라서 본 연구에서는 Terra/MODIS와 SPOT/VEGETATION 그리고 ECOCLIMAP 자료를 이용해 한반도에 최적화된 FPAR를 산출 하였고, 또한 기존에 사용하였던 LAI와의 관계식을 사용하지 않고, SPOT/VGT NDVI 로부터 계산된 FVC (Fraction Vegetation Cover)를 직접 이용하여 FPAR를 산출 하였다. 이를 위해 식생지수의 선형/비선형 관계를 이용하여 구하는 경험적인 방법을 적용하여 회귀분석을 수행한 결과 cropland와 forest에서 각각 결정계수 (Coefficient of Determination, $R^2$)가 0.9039. 0.7901으로 정확도가 높은 관계식을 도출해내었다. 최종적으로 Reference FPAR 자료와의 비교 분석을 통해 본 연구에서 산출된 FPAR가 전반적인 패턴을 잘 표현하면서 불규칙하게 발생하던 노이즈 또한 보정된 것을 확인 할 수 있었다. 이렇게 한반도에 최적화된 입력변수의 사용은 산출물의 정확도뿐만 아니라 연구의 질 향상에도 도움을 줄 것으로 사료된다.

Keywords

References

  1. 강신규, 김영일, 김영진, 2005. MODIS 총일차생산성산출물의 오차요인 분석: 입력기상자료의 영향, 한국농림기상학회지, 7(2): 171-183.
  2. Gutman, G. and A. Ignatov, 1998. The derivation of the green vegetation fraction from NOAA/ AVHRR data for use in numeric weather prediction models, International Journal of Remote Sensing, 19(8): 1533-1543. https://doi.org/10.1080/014311698215333
  3. Hall, F. G., J. R. Townshend, and E. T. Engman, 1995. Status of remote-sensing algorithms for estimation of land-surface state parameters, Remote Sensing of Environment, 51(1): 135-156.
  4. Jarvis, P. G. and J. W. Leverenz, 1983. Productivity of temperate, deciduous and evergreen forests, Physiological Plant Ecology IV, Ecosystems Processes: Moneral Cycling, Productivity and Man's Influence, Berlin, Springer-Verlag, Vol. 12D, 233-280.
  5. Knyazikhin, Y., J. V. Martonchik, R. B. Myneni, D. J. Diner, and S. W. Running, 1998. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data, Journal of Geophysical Research, 103(D24): 32257-32276. https://doi.org/10.1029/98JD02462
  6. Knyazikhin, Y., W. Yang, H. Dong, T. Bin, N. Shabanov, and R. Myneni, 2003. MODIS LAI and FPAR product on global, regional and local scales, American Geophysical Union Fall Meeting 2003, #B22C-06.
  7. Myneni, R. B., S. Hoffman, Y. Knyazikhin, J. L. Privette, J. Glassy, and Y. Tian, et al., 2002. Global products of vegetation leaf area and fraction absorbed par from year one of MODIS data, Remote Sensing of Environment, 83(1-2): 214-231. https://doi.org/10.1016/S0034-4257(02)00074-3
  8. Myneni, R., Y. Knyazikhin, J. Glassy, P. Votava, and N. Shabanov, 2003. User's guide FPAR, LAI (ESDT: MOD15A2) 8-day composite NASA MODIS land algorithm, http://www.cybele.bu.edu/modismisr/products/modis/userguide.pdf.
  9. Pontailler, J. Y., G. J. Hymus, and B. G. Drake, 2003. Estimation of leaf area index using groundbased remote sensed NDVI measurements: validation and comparison with two indirect techniques, Canadian Journal of Remote Sensing, 29(3): 381-387. https://doi.org/10.5589/m03-009
  10. Prince, S. D. and S. N. Groward, 1995. Global primary production: A remote sensing approach, Journal of Biogeography, 22: 815-835. https://doi.org/10.2307/2845983
  11. Roujean, J. L. and R. Lacaze, 2002. Global mapping of vegetation parameters from POLDER multiangular measurements for studies of surface-atmosphere interactions: A pragmatic method and its validation, Journal of Geophysical Research, 107(D12): 4150-4163. https://doi.org/10.1029/2001JD000751
  12. Ruimy, A., B. Saugier, and G. Dedieu, 1994. Methodology for the estimation of terrestrial net primary production from remotely sensed data, Journal of Geophysical Research, 99(D3): 5263-5283. https://doi.org/10.1029/93JD03221
  13. Ruimy, A., L. Kergoat, and A. Bondeau, 1999. Comparing global models of terrestrial net primary productivity (NPP): Analysis of differences in light absorption and light-use efficiency, Global Change Biology, 5(S1): 56-64. https://doi.org/10.1046/j.1365-2486.1999.00007.x
  14. Running, S. W., P. E. Thornton, R. Nemani, and J. M. Glassy, 2000. Global terrestrial gross and net primary productivity from the Earth Observing System, Methode in ecosystem science, New York, Springer, 44-57.
  15. Sellers, P. J. and D. S. Schmid, 1993. Remote Sensing of the land biosphere and biogeochemistry in the EOS era: Science priorities-EOS land biosphere and biogeochemical cycles panels, Global Planetary Change, 7(4): 279-297. https://doi.org/10.1016/0921-8181(93)90002-6
  16. Tian, Y., Y. Zhang, R. B. Myneni, J. M. Glassy, G. Dedieu, and S. W. Running, 2000. Prototyping of MODIS LAI and FPAR Algorithm with LASUR and LANDSAT data, IEEE Transactions on Geoscience and Remote Sensing, 38(5): 2387-2401. https://doi.org/10.1109/36.868894
  17. Xiao, X., D. Hollinger, J. Aber, M. Goltz, E. A. Davidson, Q. Zhang, and B. Moore, 2004. Satellite-based modeling of gross primary production in an evergreen needleleaf forest, Remote Sensing of Environment, 89(4): 519-534. https://doi.org/10.1016/j.rse.2003.11.008