Analysis on Three NDVI Time-series Reconstruction Methods and Their Applications in North Tibet

  • 1. State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Institute of Environment and Sustainable Development in Agriculture|Chinese Academy of Agricultural Sciences, Beijing 100081, China

Received date: 2010-06-29

  Revised date: 2010-10-22

  Online published: 2011-02-25


Remote sensing vegetation index time-series datasets have been applied widely in various fields, such as monitoring and simulation of terrestrial ecological environment system on global and regional scales, analysis of vegetation cover dynamics, recognizing features and extracting information of vegetation phenology and so on. However, due to errors from information collection and transmission process of sensors, different atmospheric conditions and random ground situations etc, various residual noise exists in NDVI time-series data. Therefore, a series of NDVI time-series data reconstruction methods have been developed to solve the problem. In this paper, various newly proposed and modified reconstruction methods are summarized and evaluated firstly. Then the three primary methods, i.e. asymmetric Guassian (AG) function fitting, double logistic (D-L) fitting method and Savitzky-Golay (S-G) filteration, are introduced in terms of basic principles and features. Based on the TIMESAT program, by taking some NDVI time-series of pixel samples of different land cover types as instance, the suitability and defects of the three approaches are analyzed and discussed. The result shows that the former two methods generally present similar performances, but on fittings of peak of these time-series curves there are minor differences. Besides, the three methods have inconsistence effects in different study areas, land cover and research purposes. In detail, the S-G method has a more excellent performance in reconstructing NDVI curves in alpine desert and forest-covered area with much noise, while the D-L and AG function fitting methods could conduct more accurate reconstructed time-series dataset in alpine shrub or marshy grassland regions, especially for fitting the start and end period of vegetation growing season. For crops-planted land cover, the double logistic fitting method can't fit the correct curve of NDVI time-series, and the asymmetric Guassian function fitting has the best performance. Finally, based on AG function fitting method, the NDVI time-series of 2007-2009 in North Tibet are reconstructed, and the processed NDVI values are clearly more homogeneous than those in raw data.

Cite this article

SONG Chunqiao, YOU Songcai, KE Linghong, LIU Gaohuan . Analysis on Three NDVI Time-series Reconstruction Methods and Their Applications in North Tibet[J]. Journal of Geo-information Science, 2011 , 13(1) : 133 -143 . DOI: 10.3724/SP.J.1047.2011.00133


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