Asia-Pacific Journal of Atmospheric Sciences

Korean Meteorological Society (한국기상학회)
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Detection of Contaminated Pixels Based on the Short-term Continuity of NDVI and Correction Using Spatio-Temporal Continuity

  • Cho, A-Ra (Department of Atmospheric Sciences, Kongju National University) ;
  • Suh, Myoung-Seok (Department of Atmospheric Sciences, Kongju National University)
  • Published : 2013.08.31
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Abstract

The present study developed and assessed a correction technique (CSaTC: Correction based on Spatial and Temporal Continuity) for the detection and correction of contaminated Normalized Difference Vegetation Index (NDVI) time series data. Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data from 1982 to 2006 with a 15-day period and an 8-km spatial resolution was used. CSaTC utilizes short-term continuity of vegetation to detect contaminated pixels, and then, corrects the detected pixels using the spatio-temporal continuity of vegetation. CSaTC was applied to the NDVI data over the East Asian region, which exhibits diverse seasonal and interannual variations in vegetation activities. The correction skill of CSaTC was compared to two previously applied methods, IDR (iterative Interpolation for Data Reconstruction) and Park et al. (2011) using GIMMS NDVI data. CSaTC reasonably resolved the overcorrection and spreading phenomenon caused by excessive correction of Park et al. (2011). The validation using the simulated NDVI time series data showed that CSaTC shows a systematically better correction skill in bias and RMSE irrespective of phenology types of vegetation and noise levels. In general, CSaTC showed a good recovery of the contaminated data appearing over the short-term period on a level similar to that obtained using the IDR technique. In addition, it captured the multi-peak of NDVI, and the germination and defoliating patterns more accurately than that by IDR, which overly compensates for seasons with a high temporal variation and where NDVI data exhibit multi-peaks.

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References

  1. Ackerman, S. A., K. I. Strabala, W. P. Menzel, R. A. Frey, C. C. Moeller, and L. E. Gumley, 1998: Discriminating clear-sky from clouds with MODIS. J. Geophys. Res., 103(D24), 32141-32157. https://doi.org/10.1029/1998JD200032
  2. Batjargal, Z., 1998: Desertification in Mongolia. Rala Report 200, 107- 113.
  3. 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
  4. 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
  5. Chen, J., P. Jönsson, M. Tamura, Z. Gu, B. Matsushita, and L. Eklundh, 2004: A simple method for reconstructing a high-quality NDVI timeseries data set based on the Savitzky-Golay filter. Remote Sens. Environ., 91, 332-344. https://doi.org/10.1016/j.rse.2004.03.014
  6. Cihlar, J., and J. Howarth, 1994: Detection and removal of cloud contamination from AVHRR composite images. IEEE Trans. Geosci. Remote Sens., 32, 427-437. https://doi.org/10.1109/36.295057
  7. 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
  8. Frey, R. A., S. A. Ackerman, Y. Liu, K. I. Strabala, H. Zhang, J. R. Key, and X. Wang, 2008: Cloud detection with MODIS. Part I: Improvements in the MODIS cloud mask for collection 5. J. Atmos. Oceanic. Technol., 25, 1057-1072. https://doi.org/10.1175/2008JTECHA1052.1
  9. Hall, F. G., J. R. Townshend, and E. T. Engman, 1995: Status of remote sensing algorithm for estimation of land surface state parameter. Remote Sens. Environ., 51, 138-156. https://doi.org/10.1016/0034-4257(94)00071-T
  10. Jonsson, P., and L. Eklundh, 2002: Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote Sens., 40(8), 1824-1832. https://doi.org/10.1109/TGRS.2002.802519
  11. Julien, Y., and J. A. Sobrino, 2010: Comparison of cloud-reconstruction methods for time series of composite NDVI data. Remote Sens. Environ., 114, 618-625. https://doi.org/10.1016/j.rse.2009.11.001
  12. Kang, J. H., M. S. Suh, and C. H. Kwak, 2010: Land cover classification over East Asian region using recent MODIS NDVI data (2006-2008). J. Korean Meteor. Soc., 20(4), 415-426.
  13. Park, S. J., K. S. Han, and K. J. Pi, 2010: NDVI Noise interpolation using Harmonic analysis. Korean J. of Remote Sens., 26(4), 403-410.
  14. Park, J. H., A. R. Cho, J. H. Kang, and M. S. Suh, 2011: Detection and Correction of Noisy Pixels Embedded in NDVI Time Series Based on the Spatio-temporal Continuity. J. Korean Meteor. Soc., 21(4), 337-347
  15. Pinzon, J., 2002: Using HHT to successfully uncouple seasonal and interannual components in remotely sensed data. Proc. Abstract, SCI 2002 Conference, Orlando, Florida, SCI International.
  16. Pinzon, J., M. E. Brown, and C. J. Tucker, 2004: Satellite time series correction of orbital drift artifacts using empirical mode decomposition. In: N. Huang(Editor). Hilbert-Huang Transform: Introduction and Application, 167-186 pp.
  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. Smith, P. M., S. N. V. Kalluri, S. D. Prince, and R. S. Defries, 1997: The NOAA/NASA Pathfinder AVHRR 8-km land data set. Photogramm. Eng. and Rem. S., 63(1): 12-13, 27-31.
  19. Suh, M. S., and A. S. Suh, 2003: Characteristics of meteorological satellite data (PAL) over East Asia and 3-D method of noise detection and correction. J. Korean Meteor. Soc., 39(1), 125-138.
  20. Suh, M. S., J. R. Lee, J. H. Kang, D. K. Lee, and M. H. Ahn, 2005: On the relationship between seasonal change of vegetation and climate elements in East Asia. J. Korean Meteor. Soc., 41(4), 557-570.
  21. Tateishi, R., and K. Kajiwara, 1994: Consideration on problems of NOAA/ GVI data for global land cover monitoring. Geocarto Int., 9(4), 5-15.
  22. Tucker, C. J., 1979: Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127-150. https://doi.org/10.1016/0034-4257(79)90013-0
  23. Tucker, C. J., J. E. Pinzon, M. E. Brown, D. Slayback, E. W. Pak, R. Mahoney, E. F. Vermote, and N. E. 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
  24. Yang, X., K. Zhang, B. Jia, and L. Ci, 2005: Desertification assessment in China: An overview. J. Arid. Environ., 63(2), 517-531. https://doi.org/10.1016/j.jaridenv.2005.03.032

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