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Remote Sens. 2017, 9(1), 76;

Stochastic Spatio-Temporal Models for Analysing NDVI Distribution of GIMMS NDVI3g Images

Department of Statistics and Operations Research, Public University of Navarre, 31006 Pamplona, Spain
Institute for Advanced Materials (InaMat), Public University of Navarre, 31006 Pamplona, Spain
Department of Mathematics, Spanish Open University (UNED), 31006 Pamplona, Spain
Author to whom correspondence should be addressed.
Academic Editors: Lenio Soares Galvao, Clement Atzberger and Prasad S. Thenkabail
Received: 29 June 2016 / Revised: 23 December 2016 / Accepted: 8 January 2017 / Published: 15 January 2017
PDF [8384 KB, uploaded 17 January 2017]


The normalized difference vegetation index (NDVI) is an important indicator for evaluating vegetation change, monitoring land surface fluxes or predicting crop models. Due to the great availability of images provided by different satellites in recent years, much attention has been devoted to testing trend changes with a time series of NDVI individual pixels. However, the spatial dependence inherent in these data is usually lost unless global scales are analyzed. In this paper, we propose incorporating both the spatial and the temporal dependence among pixels using a stochastic spatio-temporal model for estimating the NDVI distribution thoroughly. The stochastic model is a state-space model that uses meteorological data of the Climatic Research Unit (CRU TS3.10) as auxiliary information. The model will be estimated with the Expectation-Maximization (EM) algorithm. The result is a set of smoothed images providing an overall analysis of the NDVI distribution across space and time, where fluctuations generated by atmospheric disturbances, fire events, land-use/cover changes or engineering problems from image capture are treated as random fluctuations. The illustration is carried out with the third generation of NDVI images, termed NDVI3g, of the Global Inventory Modeling and Mapping Studies (GIMMS) in continental Spain. This data are taken in bymonthly periods from January 2011 to December 2013, but the model can be applied to many other variables, countries or regions with different resolutions. View Full-Text
Keywords: kriging; spatial statistics; stochastic modelling kriging; spatial statistics; stochastic modelling

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Militino, A.F.; Ugarte, M.D.; Pérez-Goya, U. Stochastic Spatio-Temporal Models for Analysing NDVI Distribution of GIMMS NDVI3g Images. Remote Sens. 2017, 9, 76.

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