Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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NDVI Noise Interpolation Using Harmonic Analysis

조화 분석을 이용한 식생지수 보정 기법에 관한 연구

  • Park, Soo-Jae (Dept. of Geoinformatic Engineering, Pukyong National University) ;
  • Han, Kyung-Soo (Dept. of Geoinformatic Engineering, Pukyong National University) ;
  • Pi, Kyoung-Jin (Dept. of Geoinformatic Engineering, Pukyong National University)
  • 박수재 (부경대학교 위성정보과학과) ;
  • 한경수 (부경대학교 위성정보과학과) ;
  • 피경진 (부경대학교 위성정보과학과)
  • Received : 2010.05.17
  • Accepted : 2010.08.27
  • Published : 2010.08.31
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Abstract

NDVI(Normalized Difference Vegetation Index), which is broadly used as short-term data composite, is an important parameter for climate change and long-term land surface monitoring. Although atmospheric correction is performed, NDVI dramatically appears several low peak noise in the long-term time series. They are related to various contaminated sources, such as cloud masking problem and wet ground condition. This study suggests a simple method through harmonic analysis for reducing NDVI noise using SPOT/VGT NDVI 10-day MVC data. The harmonic analysis method is compared with the polynomial regression method suggested previously. The polynomial regression method overestimates the NDVI values in the time series. The proposed method showed an improvement in NDVI correction of low peak and overestimation.

NDVI(Normalized Difference Vegetation Index)는 기후 변화 모니터링과 식생 변화 탐지 모니터링을 위한 주요한 지표이며 주로 단일 기간 합성 자료 형태로 널리 활용되고 있다. 원격탐사 된 식생지수 자료는 전처리 과정을 거치게 되지만 제거되지 못한 cloud pixel, 대기 효과, 지면의 상태 등으로 인하여 NDVI 값이 저평가(low peak)되는 noise가 발생하게 된다. 이러한 문제점을 해결하기 위해 국내 외 연구가 활발히 진행되고 있으며 최근 높은 값(high peak)을 추적하는 방법인 다중 다항 회귀식을 이용하여 noise를 보정하는 방법이 개발되었으나 부분적으로 참값보다 과대 평가되는 문제점이 있다. 따라서 본 연구에서는 과대 평가되는 문제점을 해결하고자 조화 분석을 이용하여 low peak 탐지 후 보간하는 종합적인 기법을 개발하였다. 이를 검증하기 위해 SPOT/VGT NDVI 10-day MVC 자료를 이용하여 다중 다항 회귀식을 이용한 방법과의 비교 분석을 수행한 결과 전반적인 식생 지수의 시계열 특성이 잘 나타났고 NDVI 실제 값(raw value)을 보다 현실적으로 재생산하여 조화 분석을 이용한 방법이 더 우수한 것으로 판단된다.

Keywords

References

  1. 염종민, 한경수, 김영섭, 2005. Identification of Contaminated pixels in 10-day NDVI image, 2005년도 한국기상학회 가을 학술대회 논문집, 300-301.
  2. Chen, J., Jonsson, P., and Tamura, M., et al, 2004. A simple method for reconstructing a highquality NDVI time-series data set based on the Savitzky-Golay filter, Remote Sensing of Environment, 91: 332-344. https://doi.org/10.1016/j.rse.2004.03.014
  3. Eidenshink, J. C. and J. L. Faundeen, 1994. The 1 km AVHRR global land data set: first stages in implementation, International Journal of Remote Sensing, 15(17): 3443-3462. https://doi.org/10.1080/01431169408954339
  4. Goward, S. N., Markham, B., Dye, D. G., Dulaney, W., and Yang, J, 1991. Normalized difference vegetation index measurement from the advanced very high resolution radiometer, Remote Sensing of Environment, 35: 257-277. https://doi.org/10.1016/0034-4257(91)90017-Z
  5. Gutman, G., 1987. The derivation of vegetation indices from AVHRR data, International Journal of Remote Sensing, 8: 1235-1243. https://doi.org/10.1080/01431168708954768
  6. Holben, B. N., 1986. Characteristics of maximum value compositing imagines for AVHRR data, International Journal of Remote Sensing, 7: 1417- 1437. https://doi.org/10.1080/01431168608948945
  7. Jakubauskas, M. E., Legates, D. R., and Kastens, J. H., 2001. Harmonic analysis of time-series AVHRR NDVI data, Photogrammetric Engineering and Remote Sensing, 67: 461-470.
  8. Jonsson, P. and Eklundh, L., 2002. Seasonality extraction by function fitting to time-series of satellite sensor data, IEEE Transactions on Geoscience and Remote Sensing, 40(8): 1824-1832. https://doi.org/10.1109/TGRS.2002.802519
  9. L.Monika Moska, 2005. Temporal signatures and harmonic analysis of natural and anthropogenic disturbances of forested landscapes: a case study in the Yellowstone region, Proc. OF MultiTemp 2005, 3rd International Workshop on the Analysis of Multi- Temporal Remote Sensing images, Biloxi, Mississippi, USA, May 16-18, 2005, 15-19.
  10. Miura, T., Huete, A. R., van Leeuwen, W. J. D., and Didan, K., 1998. Vegetation detection through smoke-filled AVIRIS images: An assessment using MODIS band passes, Journal of Geophysical Research, 103(24): 32001-32011. https://doi.org/10.1029/98JD00051
  11. Moody, A. and Johnson, D. M., 2001. Land-surface phenologies from AVHRR using the discrete Fourier transform, Remote Sensing of Environment, 75: 305-323. https://doi.org/10.1016/S0034-4257(00)00175-9
  12. Prince, S. D. and Tucker, C. J., 1986, Satellite remote sensing of rangelands in Botswana.II. NOAA AVHRR and herbaceous vegetation, International Journal of Remote Sensing, 7: 1555- 1570. https://doi.org/10.1080/01431168608948953
  13. Roerink, G. J., Menenti, M., and Verhoef, W., 2000. Reconstructing cloudfree NDVI composites using Fourier analysis of time series, International Journal of Remote Sensing, 21(9): 1911-1917. https://doi.org/10.1080/014311600209814
  14. Stowe, L. L., McClain, E. P., and Carey, R., et al., 1991. Global distribution of cloud cover derived from NOAA/AVHRR operational satellite data, Advances in Space Research, 11(3): 51-54. https://doi.org/10.1016/0273-1177(91)90402-6
  15. Swets, D. L., Reed, B. C., Rowland, J. R., and Marko, S. E., 1999. A weighted least-squares approach to temporal smoothing of NDVI smoothing, 1999 American Society for Photogrammetry and Remote Sensing (ASPRS) Annual Conference, 526-536.
  16. Viovy, N., Arino, O., and Belward, A., 1992. The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series, International Journal of Remote Sensing, 13(8), 1585-1590. https://doi.org/10.1080/01431169208904212
  17. White et al., 1997. A continental phenology model for monitoring vegetation responses to interannual climatic variability, Global Biogeochemical Cycles, 11(2): 217-234. https://doi.org/10.1029/97GB00330
  18. Y. Richard and I. Poccard, 1998. A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in Southern Africa, International Journal of Remote Sensing, 19(15): 2907-2920. https://doi.org/10.1080/014311698214343