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The European Physical Journal Special Topics

, Volume 164, Issue 1, pp 127–139 | Cite as

Identifying spatial pattern of NDVI series dynamics using recurrence quantification analysis

A case study in the region around Beijing, China
  • S.C. LiEmail author
  • Z.Q. Zhao
  • F.Y. Liu
Article

Abstract

Ecosystem is a prototypical complex system, exhibiting a non-stationary temporal dynamics and complicated spatial patterns, the characterization and description of which is riddled with challenges. In this paper recurrence quantification analysis (RQA), an extended branch of recurrence plots (RPs), was used to measure the determinism and predictability of Normalized Difference Vegetation Index (NDVI)series and its spatial patterns. After introducing the theoretical background of RPs and RQA indices, the implementation of this methodology was demonstrated using NDVI data of region around Beijing, China. The results show that the RQA indices can efficiently capture the nonlinear features of NDVI series and explicitly identify the spatial patterns. The temporal variation and dynamics of NDVI series shows significant spatial differences with the change of landuse and landcover types, characterizing by higher determinism and predictability in natural ecosystem and lower determinism and predictability in agricultural ecosystem. The research work indicates that combination of recurrence quantification analysis and geographical information system can offer an alternative approach to identifying the spatial pattern of temporal NDVI series dynamics.

Keywords

European Physical Journal Special Topic Advanced Very High Resolution Radiometer Advanced Very High Resolution Radiometer Recurrence Plot Physical Journal Special Topic Table 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© EDP Sciences and Springer 2008

Authors and Affiliations

  1. 1.College of Urban and Environmental Sciences, Peking UniversityBeijingChina
  2. 2.The Key Laboratory for Environmental and Urban Sciences, Shenzhen Graduate School, Peking UniversityShenzhenChina

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