Remote sensing and geostatistics
Remote sensing and geostatistics
In geostatistics, spatial autocorrelation is utilized to estimate optimally local values from data sampled elsewhere. The powerful synergy between geostatistics and remote sensing went unrealized until the 1980s. Today geostatistics are used to explore and describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data; and to increase the accuracy with which remotely sensed data can be used to classify land cover or estimate continuous variables. This article introduces these applications and uses two examples to highlight characteristics that are common to them all. The article concludes with a discussion of conditional simulation as a novel geostatistical technique for use in remote sensing.
geostatistics, remote sensing, mapping, error, optimum sampling
61-78
Curran, P.J.
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Atkinson, P.M.
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1998
Curran, P.J.
3f5c1422-c154-4533-9c84-f2afb77df2de
Atkinson, P.M.
aaaa51e4-a713-424f-92b0-0568b198f425
Curran, P.J. and Atkinson, P.M.
(1998)
Remote sensing and geostatistics.
Progress in Physical Geography, 22 (1), .
Abstract
In geostatistics, spatial autocorrelation is utilized to estimate optimally local values from data sampled elsewhere. The powerful synergy between geostatistics and remote sensing went unrealized until the 1980s. Today geostatistics are used to explore and describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data; and to increase the accuracy with which remotely sensed data can be used to classify land cover or estimate continuous variables. This article introduces these applications and uses two examples to highlight characteristics that are common to them all. The article concludes with a discussion of conditional simulation as a novel geostatistical technique for use in remote sensing.
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Published date: 1998
Keywords:
geostatistics, remote sensing, mapping, error, optimum sampling
Identifiers
Local EPrints ID: 17341
URI: http://eprints.soton.ac.uk/id/eprint/17341
ISSN: 0309-1333
PURE UUID: d36361f3-7a35-40a9-a4bd-ba5cd2a2636b
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Date deposited: 24 Aug 2005
Last modified: 15 Mar 2024 05:58
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Author:
P.J. Curran
Author:
P.M. Atkinson
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