Wang Honghao, Liu Chun, Li Lei, Kong Yuanhang, Akbar Akram, Zhou Xiaoteng
College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, PR China.
College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, PR China.
Water Res. 2025 Jun 15;278:123378. doi: 10.1016/j.watres.2025.123378. Epub 2025 Feb 23.
With the ongoing process of urbanization, it poses challenges to the monitoring of water quality in urban rivers. The mainstream methods for remote sensing water quality monitoring rely on the optical characteristics of water to achieve water quality inversion, while overlooking the correlation between water quality and riparian zones. The spatial arrangement and scale fluctuation of the riparian zones exert a substantial influence on water quality as it serves as an intermediary region connecting riverine and terrestrial ecosystems. Therefore, this study firstly employed unmanned aerial vehicle (UAV)-borne multispectral remote sensing technology to capture the subtle variations in urban river water quality and obtain detailed spatial information of the riparian zone. The Liang-Kleeman information flow was subsequently employed to quantitatively assess the causal responses of the spatial composition of riparian zone to water quality parameters across various spatial scales. Finally, we developed a hierarchical ensemble learning model for water quality assessment by integrating the spatial characteristics of the riparian zone with the spectral properties of the water body. The result demonstrates that this model accurately delineated water quality grades for three key parameters: ammonia nitrogen (NHN), chemical oxygen demand (COD), and total phosphorus (TP), achieving accuracies of 94.87 %, 92.31 %, and 89.74 %, respectively. Our study presents a water quality inversion method for urban rivers, which holds significant guidance for the monitoring and management of urban rivers and contributes to further promoting the sustainable development of cities.
随着城市化进程的不断推进，城市河流的水质监测面临挑战。主流的遥感水质监测方法依赖于水体的光学特性来实现水质反演，却忽视了水质与河岸带之间的相关性。河岸带作为连接河流生态系统和陆地生态系统的中间区域，其空间布局和尺度波动对水质有着重大影响。因此，本研究首先采用无人机搭载多光谱遥感技术来捕捉城市河流水质的细微变化，并获取河岸带的详细空间信息。随后运用梁 - 克里曼信息流对不同空间尺度上河岸带空间组成对水质参数的因果响应进行定量评估。最后，通过将河岸带的空间特征与水体光谱特性相结合，构建了用于水质评估的分层集成学习模型。结果表明，该模型能够准确划分氨氮（NHN）、化学需氧量（COD）和总磷（TP）这三个关键参数的水质等级，准确率分别达到94.87%、92.31%和89.74%。本研究提出了一种城市河流水质反演方法，对城市河流的监测与管理具有重要指导意义，有助于进一步推动城市的可持续发展。
