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.
Titolo: | Adaptive spatio-temporal models for satellite ecological data |
Autori: | |
Data di pubblicazione: | 2004 |
Rivista: | |
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. |
Handle: | http://hdl.handle.net/11578/56096 |
Appare nelle tipologie: | 1.1 Articolo su Rivista |