Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 510000, China; State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China.
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 510000, China; State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China; School of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, China.
Sci Total Environ. 2022 Dec 20;853:158374. doi: 10.1016/j.scitotenv.2022.158374. Epub 2022 Aug 28.
Terrestrial pollution has a great impact on the coastal ecological environment, and widely distributed coastal outfalls act as the final gate through which pollutants flow into rivers and oceans. Thus, effectively monitoring the water quality of coastal outfalls is the key to protecting the ecological environment. Satellite remote sensing provides an attractive way to monitor sewage discharge. Selecting the coastal areas of Zhejiang Province, China, as an example, this study proposes an innovative method for automatically detecting suspected sewage discharge from coastal outfalls based on high spatial resolution satellite imageries from Sentinel-2. According to the accumulated in situ observations, we established a training dataset of water spectra covering various optical water types from satellite-retrieved remote sensing reflectance (R). Based on the clustering results from unsupervised classification and different spectral indices, a random forest (RF) classification model was established for the optical water type classification and detection of suspected sewage. The final classification covers 14 optical water types, with type 12 and type 14 corresponding to the high eutrophication water type and suspected sewage water type, respectively. The classification result of model training datasets exhibited high accuracy with only one misclassified sample. This model was evaluated by historical sewage discharge events that were verified by on-site observations and demonstrated that it could successfully recognize sewage discharge from coastal outfalls. In addition, this model has been operationally applied to automatically detect suspected sewage discharge in the coastal area of Zhejiang Province, China, and shows broad application value for coastal pollution supervision, management, and source analysis.
陆地污染对沿海生态环境有重大影响，而广泛分布的沿海排污口则是污染物进入江河湖海的最后一道关卡。因此，有效监测排污口的水质是保护生态环境的关键。卫星遥感为监测污水排放提供了一种极具吸引力的方法。本研究以中国浙江省沿海地区为例，提出了一种基于 Sentinel-2 高空间分辨率卫星图像自动检测沿海排污口疑似污水排放的创新方法。根据积累的现场观测数据，我们建立了一个涵盖卫星遥感反射率（R）中各种光学水类型的水光谱训练数据集。基于无监督分类和不同光谱指数的聚类结果，建立了随机森林（RF）分类模型，用于光学水类型分类和疑似污水的检测。最终的分类涵盖了 14 种光学水类型，其中类型 12 和类型 14 分别对应于高富营养化水类型和疑似污水水类型。模型训练数据集的分类结果具有很高的准确性，只有一个样本被错误分类。该模型通过现场观测验证的历史污水排放事件进行了评估，表明它可以成功识别沿海排污口的污水排放。此外，该模型已在中国浙江省沿海地区投入业务应用，自动检测疑似污水排放，为沿海污染监管、管理和源分析提供了广阔的应用价值。
