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Mapping Turbidity in the Charles River, Boston Using a High-resolution Satellite

机译:使用高分辨率卫星绘制波士顿查尔斯河中的浊度

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The usability of high-resolution satellite imagery for estimating spatial water quality patterns in urban water bodies is evaluated using turbidity in the lower Charles River, Boston as a case study. Water turbidity was surveyed using a boat-mounted optical sensor (YSI) at 5 m spatial resolution, resulting in about 4,000 data points. The ground data were collected coincidently with a satellite imagery acquisition (IKO-NOS), which consists of multispectral (R, G, B) reflectance at 1 m resolution. The original correlation between the raw ground and satellite data was poor (R~2= 0.05). Ground data were processed by removing points affected by contamination (e.g., sensor encounters a particle floc), which were identified visually. Also, the ground data were corrected for the memory effect introduced by the sensor's protective casing using an analytical model. Satellite data were processed to remove pixels affected by permanent non-water features (e.g., shoreline). In addition, water pixels within a certain buffer distance from permanent non-waterrnfeatures were removed due to contamination by the adjacency effect. To determine the appropriate buffer distance, a procedure that explicitly considers the distance of pixels to the permanent non-water features was applied. Two automatic methods for removing the effect of temporary non-water features (e.g., boats) were investigated, including (1) creating a water-only mask based on an unsupervised classification and (2) removing (filling) all local maxima in reflectance. After the various processing steps, the correlation between the ground and satellite data was significantly better (R~2= 0.70). The correlation was applied to the satellite image to develop a map of turbidity in the lower Charles River, which reveals large-scale patterns in water clarity. However, the adjacency effect prevented the application of this method to near-shore areas, where high-resolution patterns were expected (e.g., outfall plumes).
机译:以波士顿下游查尔斯河的浊度为例,评估了高分辨率卫星图像在估算城市水体中空间水质模式方面的可用性。使用船用光学传感器(YSI)在5 m的空间分辨率下对水的浊度进行了测量,得出约4,000个数据点。地面数据是与卫星图像采集(IKO-NOS)同步采集的,卫星图像采集由1 m分辨率的多光谱(R,G,B)反射率组成。原始地面和卫星数据之间的原始相关性很差(R〜2 = 0.05)。通过删除受污染影响的点(例如,传感器遇到颗粒絮凝物)来处理地面数据,这些点可以通过视觉识别。此外,还使用分析模型对地面数据进行了校正,以补偿传感器保护套管引入的记忆效应。处理卫星数据以删除受永久性非水特征(例如海岸线)影响的像素。另外,由于邻接效应的污染,从永久非水特征起一定缓冲距离内的水象素也被去除了。为了确定适当的缓冲区距离,应用了明确考虑像素到永久非水特征的距离的过程。研究了两种自动方法来消除临时的非水特征(例如船)的影响,包括(1)基于无监督分类创建仅水的蒙版和(2)消除(填充)反射率中的所有局部最大值。经过各种处理步骤后,地面和卫星数据之间的相关性明显更好(R〜2 = 0.70)。将该相关性应用于卫星图像,以绘制查尔斯河下游的浊度图,该图揭示了水质清晰的大规模模式。但是,邻接效应使该方法无法应用于预计会有高分辨率模式(例如排烟口)的近岸地区。

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