This article develops models for environmental data recorded by meteorological satellites. In general, such data are continuously available for suitable space and time units and are intrinsically nonstationary. Space-time auto-regression (STAR) is a class of models that can be used in monitoring and forecasting, but it must be adapted to nonstationary processes. A set of adaptive recursive estimators is then proposed to estimate STAR parameters that change both over space and time. An extensive application to the normalized difference vegetation index (NDVI), for a region of sub-Saharan Africa, illustrates and checks the approach.

Adaptive spatio-temporal models for satellite ecological data

GRILLENZONI, CARLO
2004

Abstract

This article develops models for environmental data recorded by meteorological satellites. In general, such data are continuously available for suitable space and time units and are intrinsically nonstationary. Space-time auto-regression (STAR) is a class of models that can be used in monitoring and forecasting, but it must be adapted to nonstationary processes. A set of adaptive recursive estimators is then proposed to estimate STAR parameters that change both over space and time. An extensive application to the normalized difference vegetation index (NDVI), for a region of sub-Saharan Africa, illustrates and checks the approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11578/56096
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