Coastal lagoons are highly productive ecosystems that offer a range of natural services for the environment and society. However, anthropic pressure in their basins cause water quality loss and endangers their ecosystem. Monitoring water quality constituent by remote sensing can provide spatial and high temporal resolution analysis. Not recognizing how depth influences on the signal received by a sensor causes on significant errors in estimating water constituent’s concentrations. Using a bathymetry survey and Landsat-5 thematic mapper (TM) images, different patterns of depth influence on reflectance were recognized. By means of a statistical analysis, it was able to classify and analyze three different regions of homogeneous spectral behavior for the closest image from the bathymetric survey and for a median image from 2001 to 2011. Nonparametric Kruskal–Wallis test and Dunn’s post hoc test provided great values in differentiating all groups generated for the coastal lagoon to both closest and median image. The methodology proposed here, being based on statistical methods, can be applied with the use of other images to assist studies in coastal lagoons across the globe.

中文翻译：

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利用卫星图像和水深测量确定沿海泻湖均匀反射率区域的统计分析

沿海泻湖是高产的生态系统，可为环境和社会提供一系列自然服务。然而，它们流域的人为压力会导致水质下降并危及它们的生态系统。通过遥感监测水质成分可以提供空间和高时间分辨率分析。没有认识到深度对传感器接收到的信号的影响如何导致估计水成分浓度的重大错误。使用水深测量和 Landsat-5 专题制图 (TM) 图像，识别出深度对反射率的不同影响模式。通过统计分析，它能够对来自水深测量的最接近图像和 2001 年至 2011 年的中值图像进行分类和分析三个不同的均匀光谱行为区域。非参数 Kruskal-Wallis 检验和 Dunn 的事后检验在区分为沿海泻湖生成的所有组与最近图像和中值图像之间提供了很好的价值。此处提出的方法基于统计方法，可与其他图像一起应用，以协助对全球沿海泻湖的研究。