Abstract
Abstract The accuracy of NOAA AVHRR NDVI data can be poor because of interference from several sources, including cloud cover. A parameter of the variogram model can be used to estimate the contribution of noise from the total variation in an image. However, remotely sensed information over large areas incorporates non-stationary (regional) trend and directional effects, resulting in violation of the assumptions for noise estimation. These assumptions were investigated at five sites across Africa for a range of ecological environments over several seasons. An unsupervised spectral classification of multi-temporal NDVI data partially resolved the problem of non-stationarity. Quadratic polynomials removed the remaining regional trend and directional effects. Isotropic variograms were used to estimate the noise contributing variation to the image. Standardized estimates of noise ranged from a minimum of 18.5% in west Zambia to 68.2% in northern Congo. Cloud cover and atmospheric particulates (e.g. dust) expl...
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