대기 (Atmosphere)

  • 제21권4호
  • /
  • Pages.337-347
  • /
  • 2011
  • /
  • 1598-3560(pISSN)
  • /
  • 2288-3266(eISSN)
한국기상학회 (Korean Meteorological Society)
권호 표지
DOI QR코드

DOI QR Code

시공간적 연속성을 이용한 오염된 식생지수(GIMMS NDVI) 화소의 탐지 및 보정 기법 개발

Detection and Correction of Noisy Pixels Embedded in NDVI Time Series Based on the Spatio-temporal Continuity

  • Park, Ju-Hee (Department of Atmospheric Science, Kongju National University) ;
  • Cho, A-Ra (Department of Atmospheric Science, Kongju National University) ;
  • Kang, Jeon-Ho (Department of Atmospheric Science, Kongju National University) ;
  • Suh, Myoung-Seok (Department of Atmospheric Science, Kongju National University)
  • 투고 : 2011.08.04
  • 심사 : 2011.09.22
  • 발행 : 2011.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, we developed a detection and correction method of noisy pixels embedded in the time series of normalized difference vegetation index (NDVI) data based on the spatio-temporal continuity of vegetation conditions. For the application of the method, 25-year (1982-2006) GIMMS (Global Inventory Modeling and Mapping Study) NDVI dataset over the Korean peninsula were used. The spatial resolution and temporal frequency of this dataset are $8{\times}8km^2$ and 15-day, respectively. Also the land cover map over East Asia is used. The noisy pixels are detected by the temporal continuity check with the reference values and dynamic threshold values according to season and location. In general, the number of noisy pixels are especially larger during summer than other seasons. And the detected noisy pixels are corrected by the iterative method until the noisy pixels are completely corrected. At first, the noisy pixels are replaced by the arithmetic weighted mean of two adjacent NDVIs when the two NDVI are normal. After that the remnant noisy pixels are corrected by the weighted average of NDVI of the same land cover according to the distance. After correction, the NDVI values and their variances are increased and decreased by 5% and 50%, respectively. Comparing to the other correction method, this correction method shows a better result especially when the noisy pixels are occurred more than 2 times consistently and the temporal change rates of NDVI are very high. It means that the correction method developed in this study is superior in the reconstruction of maximum NDVI and NDVI at the starting and falling season.

키워드

참고문헌

  1. 강전호, 서명석, 곽종흠, 2010: 최근 MODIS 식생지수 자료 (2006-2008)를 이용한 동아시아 지역 지면피복 분류. 한국기상학회지, 20(4), 415-426.
  2. 박수재, 한경수, 피경진, 2010: 조화 분석을 이용한 식생지수 보정 기법에 관한 연구, 대한원격탐사학회지, 26(4), 403-410. https://doi.org/10.7780/kjrs.2010.26.4.403
  3. 서명석, 서애숙, 2002: 동아시아 지역 지면탐사 기상위성 자료(PAL)의 특성과 3차원 잡음 검색 및 보정 기법. 한국기상학회지, 39(1), 125-138.
  4. 서명석, 남재철, 2003: 기상위성 탐사 자료(PAL: 1982-2000)에 나타난 동아시아 지역 식생의 시간 변동. 한국기상학회지, 39(1), 139-150.
  5. 서명석, 이정림, 강전호, 이동규, 안명환, 2005: 동아시아지역에서 식생의 계절 변화와 기후요소와의 관계. 한국기상학회지, 41(4), 557-570.
  6. Bounoua. L., G. J. Collatz, S. O. Los, P. J. Sellers, D. A. Dazlich, C. J. Tucker, and D. A. Randall, 2000: Sensitivity of Climate to Changes in NDVI. J. Climate, 13(13), 2277-2292. https://doi.org/10.1175/1520-0442(2000)013<2277:SOCTCI>2.0.CO;2
  7. Carlson, T. N., and D. A. Riply, 1997: On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ., 62, 241-252. https://doi.org/10.1016/S0034-4257(97)00104-1
  8. Cihlar, J., and J. Howarth, 1994: Detection and removal of cloud contamination from AVHRR composite images. IEEE Transactions on Geoscience and Remote Sens., 32, 427-437. https://doi.org/10.1109/36.295057
  9. Cihlar, J., 1996: Identification of contaminated pixels in AVHRR composite images for studies of land biosphere. Remote Sens. Environ., 56, 149-153. https://doi.org/10.1016/0034-4257(95)00190-5
  10. Hall, F. G., J. R. Townshend, and E. T. Engman, 1995: Status of remote sensing algorithm for estimation of land surface state parameters. Remote Sens. Environ., 51, 138-156. https://doi.org/10.1016/0034-4257(94)00071-T
  11. Jonsson, P., and L. Eklundh, 2002: Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sens., 40(8), 1824-1832. https://doi.org/10.1109/TGRS.2002.802519
  12. Julian, Y., and Sobrino, J. A., 2010: Comparison of cloudreconstruction methods for time series of composite NDVI data. Remote Sens. Environ., 114, 618-625. https://doi.org/10.1016/j.rse.2009.11.001
  13. Los, S. O., G. J. Collatz, P. J. Sellers, C. M. Malmstrom, N. H. Pollack, R. S. Defries, L. Bounoua, M. T. Parris, C. J. Tucker, and D. A. Dazlich, 2000: A Global 9-yr Biophysical Land Surface Dataset from NOAA AVHRR Data. J. Hydrometeorology, 1(2), 183-199. https://doi.org/10.1175/1525-7541(2000)001<0183:AGYBLS>2.0.CO;2
  14. Lu, S., K. Oki, and K. Omasa, 2005: Mapping Snow Cover Using AVHRR/NDVI 10-day Composite Data. J. Agric. Meteorol., 60(6), 1215-1218. https://doi.org/10.2480/agrmet.1215
  15. Pinzon, J., 2002: Using HHT to successfully uncouple seasonal and interannual components in remotely sensed data. SCI 2002 Conference Proceedings, 14-18 Jult, Orlando, Florida.
  16. Pinzon, J., M. E. Brown, and C. J. Tucker, 2004: Satellite time series correction of orbital drift artifacts using empirical mode decomposition. EMD and its Applications, N. E. Huang, and S. S. P. Shen (Eds), 10, 285-295(singapore: World Scientific).
  17. Roerink, G. J., M. Menenti, and W. Verhoef., 2000: Reconstructing cloudfree NDVI composites using Fourier analysis of time series. Int. J. Remote Sens., 21(9), 1911-1917. https://doi.org/10.1080/014311600209814
  18. Sellers, P. J., C. J. Tucker, G. J. Collatz, S. O. Los, C. O. Justice, D. A. Dazlich, and D. A. Randall, 1994: A global 1_1 NDVI data set for climate studies: Part II. The generation of global fields of terrestrial biophysical parameters from the NDVI. Int. J. Remote Sens., 15(17), 3519-3545. https://doi.org/10.1080/01431169408954343
  19. Sellers, P. J., B. W. Meeson, F. G. Hall, G. Asrar, R. E. Murphy, R. A. Schiffer, F. P. Bretherton, R. E. Dichinson, R. G. Ellingson, and C. B. Fieldand, 1995: Remote sensing of the land surface for studies of global change: Modelsalgorithms-experiments. Remote Sens. Environ., 51, 3-26. https://doi.org/10.1016/0034-4257(94)00061-Q
  20. Smith, P. M.m S. N. V. Kalluri, S. D. Prince, and R. Defries, 1997: The NOAA/NASA Pathfinder 8-km land data set. Photogrammetric Engineering and Remote Sens., 63, 12-31.
  21. Tateishi, R., and K. Kajiwara, 1994: Consideration on problems of NOAA/GVI data for global land cover monitoring. Geocarto International, 4, 5-15.
  22. Tucker, C. J., J. E. Pinzon, M. E. Brown, D. Slayback, E. W. Pak, R. Mahoney, E. Vermote, and N. El Saleous, 2005: An extended AVHRR 8-km NDVI data set compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens., 26, 4485-4498. https://doi.org/10.1080/01431160500168686
  23. Viovy, N., O. Arino, and A. Belward, 1992: The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. Int. J. Remote Sens., 13(8), 1585-1590. https://doi.org/10.1080/01431169208904212