Abstract
Water body extraction can help eco-environmental policymakers to intuitively grasp surface water resources. Remote sensing technology can accurately and quickly extract surface water information, which is of great significance for monitoring surface water changes. Fengyun satellite images have the advantages of high time resolution and multispectral bands. This provides important image data suitable for high-frequency surface water monitoring. Based on Fengyun 3 medium resolution spectral imager (FY-3/MERSI) data, 7 methods were applied in this study, which include single-band threshold method, water body index method, knowledge decision tree classification method, supervised classification method, unsupervised classification method, spectral matching based on discrete particle swarm optimization (SMDPSO), and improved spectral matching based on discrete particle swarm optimization with linear feature enhancement (SMDPSO+LFE). These methods were used to extract the land surface water of Poyang Lake, check the samples from the Landsat image with similar times to the FY-3 images, and calculate the classification accuracy via the confusion matrix. The results showed that the overall classification accuracy (OA) of the SMDPSO+LFE is 97.64%, and the Kappa coefficient is 0.95. To analyze the stability of the surface water extracted by SMDPSO+LFE in different regions, this paper selected eight test sites with different surface water types, landscapes, and terrains to extract surface water. Based on an analysis of the land surface water results at the eight test sites, every OA in the eight sites was higher than 94.5%, the Kappa coefficient was greater than 0.88. In conclusion, the SMDPSO+LFE is found to be the most suitable method among the 7 methods and effectively distinguish between different surface water bodies and backgrounds with good stability.
Highlights
Water body extraction plays a significant role in ecological environment monitoring, the information it provides can help in the utilization and protection of water resources, the evaluation and prevention of natural disasters such as floods, and the guidance of agricultural production around the waters [1]
Jiang et al used a multilayer perceptron (MLP) neural network for surface water accurate identification [6]; 2019, Hong et al claimed that automatic sub-pixel coastline extraction method (ASPCE) can accurately distinguish land and water area [7]; 2017, Yang and Du used principal component analysis (PCA) and modified normalized difference water index (MNDWI) to improve the accuracy of water extraction [8]; Normalized difference water index (NDWI) and Sentinel-2 Water Index (SWI) method were used by Jiang et al at 2020 to reach higher spatial and spectral resolution [9]
A confusion matrix analysis on the verification samples selected by Landsat 8 operational land imager (OLI) was conducted to compare with the seven classification methods
Summary
Water body extraction plays a significant role in ecological environment monitoring, the information it provides can help in the utilization and protection of water resources, the evaluation and prevention of natural disasters such as floods, and the guidance of agricultural production around the waters [1]. Many studies [2,3,4,5] have successfully used surface water extraction based on remote sensing images and multi-source remote sensing data such as moderate resolution imaging spectroradiometer (MODIS), small satellite constellation (HJ—1 a/B), multispectral scanner system (MSS), thematic mapper (TM), and enhanced thematic plotter (ETM +) to extract data on epicontinental water bodies. Researchers continue to explore more advanced methods for water body extraction by using remote sensing technology. Improving the accuracy of information extraction and imaging has become the focus of research. In 2018, Wang and Qin pointed out that the combination of remote sensing spectroscopy with the physical and chemical properties of water is an important method currently studied to improve the accuracy of water extraction [4]. Jiang et al used a multilayer perceptron (MLP) neural network for surface water accurate identification [6]; 2019, Hong et al claimed that automatic sub-pixel coastline extraction method (ASPCE) can accurately distinguish land and water area [7]; 2017, Yang and Du used principal component analysis (PCA) and modified normalized difference water index (MNDWI) to improve the accuracy of water extraction [8]; Normalized difference water index (NDWI) and Sentinel-2 Water Index (SWI) method were used by Jiang et al at 2020 to reach higher spatial and spectral resolution [9]
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