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首页> 外文期刊>Continental Shelf Research: A Companion Journal to Deep-Sea Research and Progress in Oceanography >Integration of multi-source data for water quality classification in the Pearl River estuary and its adjacent coastal waters of Hong Kong
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Integration of multi-source data for water quality classification in the Pearl River estuary and its adjacent coastal waters of Hong Kong

机译：整合多源数据以进行珠江口及其邻近海域水质分类

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摘要

The spatial patterns of water quality were studied by integrating a Landsat TM image, 58 in situ water quality datasets and 30 samples from two concentration maps of water quality parameters derived from SeaWiFS and NOAA/ AVHRR images in the Pearl River estuary and the adjacent coastal waters of Hong Kong. The reflectance of TM bands 1-4 was derived by using the COST method. The normalized difference water index (NDWI) was extracted from the raw image and the threshold segmentation was used to retrieve the water pixels of spectral reflectance. In order to study the spectral reflectance categories related to water quality, a dataset comprising 88 sampling points from four spectral bands of a Landsat TM image was used. The samples were positioned according to the availability of water quality parameters in the study area, and five reflectance classes could be finally distinguished by using the cluster analysis. Three supervised classifiers, maximum likelihood (MLH), neural network (NN) and support vector machine (SVM), were employed to recognize the spatial patterns of ocean color. All the 88 samples were divided into two data sets: 65 in the training data set and 23 in the testing data set. The classification results using the three methods showed similar spatial patterns of spectral reflectance, although the classification accuracies were different. In order to verify our assumption that the spatial patterns of water quality in the coastal waters could be indirectly detected by ocean color classification using the Landsat TM image, five optically active water quality parameters: turbidity (TURB), suspended sediments (SS), total volatile solid (TVS), chlorophyll-a (Chl-a) and phaeo-pigment (PHAE), were selected to implement the analysis of variance (ANOVA). The analysis showed that a statistically significant difference in water quality clearly existed among the five classes of spectral reflectance. It was concluded that the five classes classified by reflectance showed distinct water quality characteristics. Therefore, the ocean color classification based on landsat TM images and regular measurements of water quality may provide a reasonable spatial distribution for the coastal water quality. This may serve as an effective tool for the rapid detection of changes in coastal water quality and subsequent management. (C) 2004 Elsevier Ltd. All rights reserved.
机译：通过整合Landsat TM图像，58个原位水质数据集和30个来自两个水质参数浓度图的样本，研究了水质的空间格局，这两个水质参数来自于珠江口及邻近沿海水域的SeaWiFS和NOAA / AVHRR图像香港TM带1-4的反射率通过使用COST方法获得。从原始图像中提取归一化差异水指数（NDWI），并使用阈值分割检索光谱反射率的水像素。为了研究与水质有关的光谱反射率类别，使用了一个包含Landsat TM图像四个光谱带中88个采样点的数据集。根据研究区域内水质参数的可用性对样本进行定位，并通过聚类分析最终区分出五个反射率类别。利用三个监督分类器，最大似然（MLH），神经网络（NN）和支持向量机（SVM）来识别海洋颜色的空间模式。 88个样本全部分为两个数据集：训练数据集中的65个数据和测试数据集中的23个数据。尽管分类准确性不同，但使用这三种方法的分类结果显示出相似的光谱反射率空间格局。为了验证我们的假设，即可以使用Landsat TM图像通过海洋颜色分类，五个光学活性水质参数：浊度（TURB），悬浮沉积物（SS），总和来间接检测沿海水域水质的空间格局选择挥发性固体（TVS），叶绿素a（Chl-a）和相颜料（PHAE）进行方差分析（ANOVA）。分析表明，五类光谱反射率之间明显存在水质的统计学显着差异。结论是，按反射率分类的五个类别显示出明显的水质特征。因此，基于Landat TM影像和定期测量水质的海洋颜色分类可以为沿海水质提供合理的空间分布。这可以作为快速检测沿海水质变化和后续管理的有效工具。 （C）2004 Elsevier Ltd.保留所有权利。

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