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

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Optimising sampling strategies for estimating mean water quality in lakes using geostatistical techniques with remote sensing. / Hedger, R. D.; Atkinson, Peter M.; Malthus, T. J.
In: Lakes and Reservoirs: Research and Management, Vol. 6, No. 4, 12.2001, p. 279-288.

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Hedger RD, Atkinson PM, Malthus TJ. Optimising sampling strategies for estimating mean water quality in lakes using geostatistical techniques with remote sensing. Lakes and Reservoirs: Research and Management. 2001 Dec;6(4):279-288. doi: 10.1046/j.1440-1770.2001.00159.x

Author

Hedger, R. D. ; Atkinson, Peter M. ; Malthus, T. J. / Optimising sampling strategies for estimating mean water quality in lakes using geostatistical techniques with remote sensing. In: Lakes and Reservoirs: Research and Management. 2001 ; Vol. 6, No. 4. pp. 279-288.

Bibtex

@article{ecc31cab1c1b4677b8e5dce70b4de9ee,
title = "Optimising sampling strategies for estimating mean water quality in lakes using geostatistical techniques with remote sensing",
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.",
author = "Hedger, {R. D.} and Atkinson, {Peter M.} and Malthus, {T. J.}",
note = "M1 - 4",
year = "2001",
month = dec,
doi = "10.1046/j.1440-1770.2001.00159.x",
language = "English",
volume = "6",
pages = "279--288",
journal = "Lakes and Reservoirs: Research and Management",
issn = "1320-5331",
publisher = "Wiley-Blackwell",
number = "4",

}

RIS

TY - JOUR

T1 - Optimising sampling strategies for estimating mean water quality in lakes using geostatistical techniques with remote sensing

AU - Hedger, R. D.

AU - Atkinson, Peter M.

AU - Malthus, T. J.

N1 - M1 - 4

PY - 2001/12

Y1 - 2001/12

N2 - 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.

AB - 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.

U2 - 10.1046/j.1440-1770.2001.00159.x

DO - 10.1046/j.1440-1770.2001.00159.x

M3 - Journal article

VL - 6

SP - 279

EP - 288

JO - Lakes and Reservoirs: Research and Management

JF - Lakes and Reservoirs: Research and Management

SN - 1320-5331

IS - 4

ER -