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Progress in Physical Geography, Vol. 22, No. 1, 61-78 (1998)
DOI: 10.1177/030913339802200103

Geostatistics and remote sensing

Paul J. Curran

Department of Geography, University of Southampton, Highfield, Southampton SO17 1BJ, UK

Peter M. Atkinson

Department of Geography, University of Southampton, Highfield, Southampton SO17 1BJ, UK

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.

Key Words: Geostatistics • remote sensing • mapping • error • optimum sampling


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