Assessing the ground data requirements for regional-scale remote sensing of tropical forest biophysical properties
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 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.
2571-2587
Atkinson, P.M.
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Foody, G.M.
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Curran, P.J.
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Boyd, D.
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2000
Atkinson, P.M.
aaaa51e4-a713-424f-92b0-0568b198f425
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37
Curran, P.J.
3f5c1422-c154-4533-9c84-f2afb77df2de
Boyd, D.
a9a18d6b-8f33-4025-9ba0-9de91b4924bd
Atkinson, P.M., Foody, G.M., Curran, P.J. and Boyd, D.
(2000)
Assessing the ground data requirements for regional-scale remote sensing of tropical forest biophysical properties.
International Journal of Remote Sensing, 21 (13 & 14), .
(doi:10.1080/01431160050110188).
Abstract
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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Published date: 2000
Identifiers
Local EPrints ID: 17309
URI: http://eprints.soton.ac.uk/id/eprint/17309
ISSN: 0143-1161
PURE UUID: 14d5874e-21aa-413e-aeee-fd3adb331dad
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Date deposited: 23 Aug 2005
Last modified: 15 Mar 2024 05:57
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Author:
P.M. Atkinson
Author:
G.M. Foody
Author:
P.J. Curran
Author:
D. Boyd
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