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Bibliothèque Robust prediction of time-integrated NDVI

Robust prediction of time-integrated NDVI

Robust prediction of time-integrated NDVI

Resource information

Date of publication
Décembre 2013
Resource Language
ISBN / Resource ID
AGRIS:US201400182364
Pages
4791-4811

The time-integrated normalized difference vegetation index (iNDVI) provides key remote-sensing-derived information on the interactions between vegetation growth, climatic and soil conditions, and land use. Using a time-series of Landsat imagery obtained for Queensland, Australia, it has been demonstrated how robust geostatistics can be used to predict iNDVI. This approach is novel because it explicitly quantifies the uncertainty of prediction and uses Winsorizing, a data-censoring method, to minimize the distorting effects of outliers. Robust prediction of iNDVI, as opposed to non-robust prediction, was justifiable in 79% of the study area, highlighting the need for methods that deal with outliers in time-series analysis of remotely sensed imagery. There was a strong coarse-scale association between Queensland’s bioregions and iNDVI, and also between bioregion and the rain-induced difference in iNDVI through time (effects that were significant at p

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Authors and Publishers

Author(s), editor(s), contributor(s)

Pringle, M.J.

Publisher(s)
Data Provider
Geographical focus