研究目的
To propose a novel approach for assessing water quality of urban rivers by combining the Canadian Water Quality Index (CWQI) method with remote sensing techniques to overcome limitations of traditional methods and enable large-scale, cost-effective analysis.
研究成果
The proposed approach effectively combines CWQI and remote sensing to assess urban river water quality, showing high correlation between red band satellite data and CWQI scores. The models are validated with satisfactory results, enabling spatial and temporal analysis. Future work should involve more data collection and advanced algorithms to enhance the method's robustness and applicability to other regions.
研究不足
The water monitoring data in the study region is limited, resulting in fewer data points for model building. Linear regression modeling is somewhat naive and could be improved with advanced algorithms. The approach requires water quality monitoring data for validation in other regions, limiting its immediate adaptability.
1:Experimental Design and Method Selection:
The study uses the Canadian Water Quality Index (CWQI) for water quality assessment, combined with remote sensing (RS) techniques. Linear regression analysis is employed to model the relationship between satellite data (specifically red band data) and CWQI scores.
2:Sample Selection and Data Sources:
Water monitoring data from 2014 to 2017 for two urban rivers (Yi and Shu rivers) in the Linyi development area, China, is used, with 24 parameters measured. Satellite data from Landsat-8 is selected, focusing on cloudless or less cloudy images matching the water sampling dates.
3:List of Experimental Equipment and Materials:
Landsat-8 satellite data, water quality monitoring equipment for parameters like COD, BOD5, NH3-N, etc., and software such as ENVI (Environment for Visualizing Images) for image processing.
4:Experimental Procedures and Operational Workflow:
Pre-processing of satellite images includes conversion to spectral radiance and reflectance, calculation of NDVI for river masking, and application of factor analysis to select key parameters for CWQI calculation. Linear regression models are built using red band data and validated with testing datasets.
5:Data Analysis Methods:
Pearson's correlation coefficient (r) and coefficient of determination (r2) are used to evaluate model performance. Spatial and temporal analysis of CWQI distribution is conducted using ENVI software.
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