Defining an optimal size of support for remote sensing investigations
Defining an optimal size of support for remote sensing investigations
The support is a geostatistical term used to describe the size, geometry and orientation of the space on which an observation is defined. In remote sensing, the size of support is equivalent to the spatial resolution. The relation of size of support with the precision of estimating the mean of several properties is evaluated by kriging. The authors chose three examples; estimating the dry biomass of pasture on May 6, 1988, and estimating the percentage cover of clover in the pasture and its NDVI (measured using a ground-based radiometer) on Aug. 6, 1988. The modelled experimental variograms of these properties were deregularized to estimate the punctual variograms and these functions regularized to new sizes of support. The regularized variograms were then used to estimate the kriging variances attainable by sampling on a square grid. The kriging variances were plotted against grid spacing for each new size of support and the optimal sampling strategy read from the graph. In each case, there were several optimal sampling strategies, and the final choice depended on the cost of measurement. In some cases increasing the size of support was more efficient than increasing the sampling intensity
768-776
Atkinson, P.M.
aaaa51e4-a713-424f-92b0-0568b198f425
Curran, P.J.
3f5c1422-c154-4533-9c84-f2afb77df2de
1995
Atkinson, P.M.
aaaa51e4-a713-424f-92b0-0568b198f425
Curran, P.J.
3f5c1422-c154-4533-9c84-f2afb77df2de
Atkinson, P.M. and Curran, P.J.
(1995)
Defining an optimal size of support for remote sensing investigations.
IEEE Transactions on Geoscience and Remote Sensing, 33 (3), .
(doi:10.1109/36.387592).
Abstract
The support is a geostatistical term used to describe the size, geometry and orientation of the space on which an observation is defined. In remote sensing, the size of support is equivalent to the spatial resolution. The relation of size of support with the precision of estimating the mean of several properties is evaluated by kriging. The authors chose three examples; estimating the dry biomass of pasture on May 6, 1988, and estimating the percentage cover of clover in the pasture and its NDVI (measured using a ground-based radiometer) on Aug. 6, 1988. The modelled experimental variograms of these properties were deregularized to estimate the punctual variograms and these functions regularized to new sizes of support. The regularized variograms were then used to estimate the kriging variances attainable by sampling on a square grid. The kriging variances were plotted against grid spacing for each new size of support and the optimal sampling strategy read from the graph. In each case, there were several optimal sampling strategies, and the final choice depended on the cost of measurement. In some cases increasing the size of support was more efficient than increasing the sampling intensity
Text
Atkinson_&_Curran_TGRS_1995.pdf
- Other
Restricted to Registered users only
More information
Published date: 1995
Identifiers
Local EPrints ID: 17431
URI: http://eprints.soton.ac.uk/id/eprint/17431
ISSN: 0196-2892
PURE UUID: eae96c4c-ab71-4f39-8fb8-1dc27cc55a01
Catalogue record
Date deposited: 16 Sep 2005
Last modified: 15 Mar 2024 05:59
Export record
Altmetrics
Contributors
Author:
P.M. Atkinson
Author:
P.J. Curran
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics