Abstract
The key problem in the reasonable management of water is identifying the effective radius of surface water pollution. Remote sensing and three-dimensional fluorescence technologies were used to evaluate the effects of land use/cover on surface water pollution. The PARAFAC model and self-organizing map (SOM) neural network model were selected for this study. The results showed that four fluorescence components, microbial humic-like (C1), terrestrial humic-like organic (C2, C4), and protein-like organic (C3) substances, were successfully extracted by the PARAFAC factor analysis. Thirty water sampling points were selected to build 5 buffer zones. We found that the most significant relationships between land use and fluorescence components were within a 200 m buffer, and the maximum contributions to pollution were mainly from urban and salinized land sources. The clustering of land-use types and three-dimensional fluorescence peaks by the SOM neural network method demonstrated that the three-dimensional fluorescence peaks and land-use types could be grouped into 4 clusters. Principal factor analysis was selected to extract the two main fluorescence peaks from the four clustered fluorescence peaks; this study found that the relationships between salinized land, cropland and the fluorescence peaks of C1, W2, and W7 were significant by the stepwise multiple regression method.
Highlights
Water quality plays pivotal roles in habitat protection, agriculture, industry, and public health[1]
We studied the arid area of the Jinghe Oasis, Central Asia, using three-dimensional fluorescence spectral and Gao Fen-1 (GF-1) satellite images, and we combined the data with excitation-emission matrix (EEM)-parallel factor analysis (PARAFAC) methods and self-organizing feature map neural network (SOM) methods to determine the connection between land use/ cover and the fluorescence peaks
The Jinghe Oasis is located in the China-Kazakhstan border in the Xinjiang Uyghur Autonomous Region of China; we demonstrated the potential of integrated remote sensing and three-dimensional fluorescence technologies to investigate the effect of land use/cover types on surface water
Summary
Water quality plays pivotal roles in habitat protection, agriculture, industry, and public health[1]. Previous results have shown that land-use types are closely related to human activities, and there is a positive relationship between cropland and urban areas and water quality pollution indicators (e.g., nitrogen, phosphorus, ammonia) and a negative relationship between forests, sandy areas, and grasslands and water quality pollution indicators (e.g., nitrogen, phosphorus, ammonia)[9]. These previous analyses and approaches were all based on the assumption that relationships between water quality indicators and land-use patterns were constant over the entire study area. The objectives were: (1) identify the fluorescence peak of water bodies in the arid area; (2) analyze the relationship between land use/cover and the fluorescence peaks at multiple spatial scales; (3) examine the radius of action on the effect of land use/cover on surface water pollution; and (4) provide more information and references for the management and control of water pollution
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
