The use of remotely sensed data to estimate terrestrial properties usually involves the acquisition of ground data. Remotely sensed data are being applied to ever larger areas and the acquisition and use
of ground data, being so expensive, requires optimization. This paper investigates a sampling strategy that has already been used to acquire ground data in support of National Oceanic and Atmospheric Administration
Advanced Very High Resolution Radiometer (NOAA AVHRR) imagery of approximately 18 000 km2 of Cameroonian forest and attempts to validate both the strategy and the use of the ground data in regression
modelling. Specifically, a geostatistical approach was used to quantify the variability in the scene, the precision of the ground data, the benefits of twostage sampling and the errors associated with regression
modelling and prediction.
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Document Type: Research Article
Department of Geography, University of Southampton, Highfield, Southampton SO17 1BJ, England, UK
CEESR, School of Geography, Kingston University, Kingston-Upon-Thames, Surrey KT1 2EE, England, UK
Publication date: September 10, 2000
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