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Optimising sampling strategies for estimating mean water quality in lakes using geostatistical techniques with remote sensing

Optimising sampling strategies for estimating mean water quality in lakes using geostatistical techniques with remote sensing
Optimising sampling strategies for estimating mean water quality in lakes using geostatistical techniques with remote sensing
In planning a sampling regime, it is desirable that the sampling procedure should involve minimum estimation error for a given sample size or minimum sampling effort for a given accuracy. Two approaches for matching sampling effort to accuracy may be used: a classical approach, which ignores spatial dependence between observations, and uses a random scheme; and a geostatistical approach, which exploits spatial dependence, and uses a systematic scheme. Four Airborne Thematic Mapper images of two British lakes were processed to provide a chlorophyll index, reflecting variations in chlorophyll-a concentration. Spatial structure was characterized using the variogram, and the modelled variogram was used in Kriging to plan sampling regimes for estimating the mean chlorophyll. For a given sample size, the systematic scheme incurred less error than the random scheme; and for a given error, the systematic scheme required smaller sample sizes than the random scheme. The relative advantage of the systematic approach over the random sampling approach increased with an increase in sample size and an increase in the proportion of variance in the data that was spatially dependent. This paper demonstrates that the sampling regime must be calibrated to the spatial dynamics of the lake under investigation, and suggests that remote sensing is the ideal means by which to determine such dynamics.
geostatistics, loch awe, loch ness, optimal sampling regimes, remote sensing
1320-5331
279-288
Hedger, R.
68efed4b-56cf-42a5-8033-02e854a4c9d1
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Malthus, T.J.
42dec45a-9314-4147-9cf6-029322bc1563
Hedger, R.
68efed4b-56cf-42a5-8033-02e854a4c9d1
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Malthus, T.J.
42dec45a-9314-4147-9cf6-029322bc1563

Hedger, R., Atkinson, P.M. and Malthus, T.J. (2001) Optimising sampling strategies for estimating mean water quality in lakes using geostatistical techniques with remote sensing. Lakes and Reservoirs: Research and Management, 6 (4), 279-288.

Record type: Article

Abstract

In planning a sampling regime, it is desirable that the sampling procedure should involve minimum estimation error for a given sample size or minimum sampling effort for a given accuracy. Two approaches for matching sampling effort to accuracy may be used: a classical approach, which ignores spatial dependence between observations, and uses a random scheme; and a geostatistical approach, which exploits spatial dependence, and uses a systematic scheme. Four Airborne Thematic Mapper images of two British lakes were processed to provide a chlorophyll index, reflecting variations in chlorophyll-a concentration. Spatial structure was characterized using the variogram, and the modelled variogram was used in Kriging to plan sampling regimes for estimating the mean chlorophyll. For a given sample size, the systematic scheme incurred less error than the random scheme; and for a given error, the systematic scheme required smaller sample sizes than the random scheme. The relative advantage of the systematic approach over the random sampling approach increased with an increase in sample size and an increase in the proportion of variance in the data that was spatially dependent. This paper demonstrates that the sampling regime must be calibrated to the spatial dynamics of the lake under investigation, and suggests that remote sensing is the ideal means by which to determine such dynamics.

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Published date: 2001
Keywords: geostatistics, loch awe, loch ness, optimal sampling regimes, remote sensing

Identifiers

Local EPrints ID: 15175
URI: http://eprints.soton.ac.uk/id/eprint/15175
ISSN: 1320-5331
PURE UUID: 3da74b26-771e-472f-a87c-c19490a38490
ORCID for P.M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

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Date deposited: 11 Apr 2005
Last modified: 16 Mar 2024 02:46

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Contributors

Author: R. Hedger
Author: P.M. Atkinson ORCID iD
Author: T.J. Malthus

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