逆路径距离加权法在高密度海岸带水质空间分布中的应用
Application of Inverse Path Distance Weighting for high-density spatial mapping of coastal water quality patterns
摘要：
沿海水质监测的主要目标之一是表征空间变化。一般来说，这种监测是在数量有限的固定取样点进行的。本文探讨的替代采样方法包括从机载流通水分析系统（Dataflow）进行高密度采样。数据流有可能提供更好的水质特征空间分辨率，因为它产生许多近距离（< 10米）的测量。无论测量技术如何，必须从附近的测量点对未采样位置处的参数值进行插值，以便生成空间变化的综合图像。在沿海地区，标准的欧几里德插值往往会产生不准确的结果，因为它们会穿过地形中的障碍物，如半岛、岛屿和淹没的河岸。我们最近开发了一种方法，非欧几里德插值的逆路径距离加权（IPDW），以考虑这些障碍。这些算法是作为R包的一部分实现的，并可从R库中获得。IPDW与Dataflow的结合提供了相对于欧几里得逆距离加权（IDW）更准确的盐度模式估计。在强烈的空间梯度存在下，IPDW明显比IDW更准确。
英文摘要：
One of the primary goals of coastal water quality monitoring is to characterize spatial variation. Generally, this monitoring takes place at a limited number of fixed sampling points. The alternative sampling methodology explored in this paper involves high density sampling from an onboard flow-through water analysis system (Dataflow). Dataflow has the potential to provide better spatial resolution of water quality features because it generates many closely spaced (< 10 m) measurements. Regardless of the measurement technique, parameter values at unsampled locations must be interpolated from nearby measurement points in order to generate a comprehensive picture of spatial variations. Standard Euclidean interpolations in coastal settings tend to yield inaccurate results because they extend through barriers in the landscape such as peninsulas, islands, and submerged banks. We recently developed a method for non-Euclidean interpolation by inverse path distance weighting (IPDW) in order to account for these barriers. The algorithms were implemented as part of an R package and made available from R repositories. The combination of IPDW with Dataflow provided more accurate estimates of salinity patterning relative to Euclidean inverse distance weighting (IDW). IPDW was notably more accurate than IDW in the presence of intense spatial gradients.
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