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

机译：利用地统计技术和遥感技术，优化估算湖泊平均水质的采样策略

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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.
机译：在计划采样方案时，理想的是采样过程应包括给定样本大小的最小估计误差或给定精度的最小采样工作量。可以使用两种方法来使采样工作与准确性相匹配：一种经典方法，它忽略了观测值之间的空间依赖性，而使用了一种随机方案；以及地统计学方法，该方法利用了空间依赖性，并使用了系统的方案。处理了两个英国湖泊的四个机载专题地图图像，以提供叶绿素指数，反映了叶绿素-a浓度的变化。使用变异函数来表征空间结构，并在Kriging中使用建模的变异函数来规划用于估计平均叶绿素的采样方案。对于给定的样本量，系统方案比随机方案产生的错误更少；对于给定的误差，系统方案比随机方案需要更小的样本量。与随机抽样方法相比，系统方法的相对优势随着样本量的增加和空间相关数据的方差比例的增加而增加。本文表明，必须根据所研究的湖泊的空间动态对采样方式进行校准，并建议遥感是确定此类动态的理想手段。

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