Assessing the ground data requirements for regional scale remote sensing of tropical forest biophysical properties

The use of remotely sensed data to estimate terrestrial properties usually involves the acquisition of ground data. Remotely sensed data are being app lied to ever larger areas and the acquisition and use of ground data, being so expensive, requires optimization. This paper investigates a sampling st rategy that has already been used to acquire ground data in support of Nati onal Oceanic and Atmospheric Administration Advanced Very High Resolution R adiometer (NOAA AVHRR) imagery of approximately 18 000 km(2) of Cameroonian forest and attempts to validate both the strategy and the use of the groun d 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 two-stage sampling and the errors associated with re gression modelling and prediction.