Tropical Geography ›› 2020, Vol. 40 ›› Issue (4): 675-683.

### Application of GF-2 Satellite Data for Monitoring Organic Pollution Delivered to Water Bodies in the Guangdong-Hong Kong-Macao Greater Bay Area

Di Wu1,2(), Wenjin Yu1,2, Tao Xie1,2

1. 1.School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.School of Remote Sensing and Surveying Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
• Received:2019-10-14 Revised:2020-04-08 Online:2020-07-31 Published:2020-08-11

Abstract:

Remote sensing technology for monitoring of water pollution has the advantages of wide monitoring range, fast monitoring speed, low cost, and long-term dynamic monitoring. To explore the applicability of the GF-2 earth observation satellite in the monitoring of organic pollution delivered to water bodies, this study extracted water body information from the normalized difference water index based on GF-2 earth observation multi-spectral data and used the Ratio Vegetation Index (RVI) to obtain the water quality classification and organic pollution distribution of six major rivers in the Guangdong-Hong Kong-Macao Greater Bay Area in March 2019. To reflect the water quality classification and water pollution distribution more clearly and intuitively, this study used ArcGIS software to visualize the water pollution information. According to their different degrees of organic pollution, the water bodies were categorized into the four levels of pollution-free, light pollution, moderate pollution, and heavy pollution, respectively, corresponding to the blue, green, yellow, and red colors in the figure, and the classification results of each river section were developed using area statistics. Finally, the water quality indexes of measured data were classified and evaluated according to the single water-quality parameter evaluation standard of surface water. The classification map of organic pollution was verified by comparing it with the remote sensing analysis results. The results of the study are as follows.: 1) The water quality of the study area is generally good. The Jitiemen and Jiaomen watercourses mainly have light pollution, whereas the Modaomen watercourse, Dongjiangnan tributary, Hengmen watercourse, and Hongqili watercourse are mainly pollution-free, with generally good water quality. 2) The distribution of organic pollution shows a spatial pattern. The main channel of the river is mainly a pollution-free water body; the two sides of the river have mainly light pollution, and the closed water body away from the main river has mainly medium pollution and heavy pollution. Timely and effective water pollution prevention and control measures need to be taken according to local conditions. 3) The extraction of organic pollution information from GF-2 multispectral data is feasible. The water bodies of JiTimen bridge, MoDaomen bridge, HongQili, and Jiao Men belong to the pollution-free class of water bodies. The ShatianShisheng is a water body of four types, with dissolved oxygen and ammonia nitrogen parameters exceeding the limit, and the ZhongShan port wharf is a water body of three types, with ammonia nitrogen parameters exceeding the limit. Both of these are mildly polluted water bodies with respect to organic pollution. The pollution levels of the six monitoring sections analyzed by remote sensing are consistent with the evaluation results of the actual monitoring sites in March 2019. The study concluded that GF-2 earth observation satellite multi-spectral data are accurate and reliable as a remote sensing data source for water pollution monitoring. These data can provide auxiliary information for decision-making pertaining to water pollution prevention and control in China. The research method of water body classification using the RVI implemented in this study is a semi-quantitative analysis method, which cannot be used to analyze specific water quality parameters quantitatively. The next step is to establish a chlorophyll concentration inversion model, which would allow the specific chlorophyll concentration data of each detected water body to be obtained quickly.

CLC Number:

• TP79