Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Assessing the ground data requirements for regional-scale remote sensing of tropical forest biophysical properties
AU - Atkinson, Peter M.
AU - Foody, G. M.
AU - Curran, P. J.
AU - Boyd, Doreen Sandra
N1 - M1 - 13 & 14
PY - 2000
Y1 - 2000
N2 - 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.
AB - 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.
U2 - 10.1080/01431160050110188
DO - 10.1080/01431160050110188
M3 - Journal article
VL - 21
SP - 2571
EP - 2587
JO - International Journal of Remote Sensing
JF - International Journal of Remote Sensing
SN - 0143-1161
IS - 13-14
ER -