Construction of Urban Black and Odorous Water Remote Sensing Recognition Model Based on Feature Band
DOI:
https://doi.org/10.54097/99bxgp07Keywords:
Black and odorous water; Characteristic band; Spectral information; Threshold method.Abstract
In recent years, with the rapid development of urban and rural areas in China, the discharge of industrial wastewater and household sewage has increased greatly, which has been directly discharged into pits and rivers, causing serious pollution of these bodies of water.In this study, the sampling points were first divided into black and odorous water bodies and general water bodies. Combined with remote sensing images, the best threshold value of the most effective feature bands in the classification of the study area was explored through the threshold method, and the accuracy performance of different feature bands in the classification process was comprehensively evaluated.In the algorithm of black and odorous water body identification based on characteristic bands, the thresholds of the single-band method, the difference method of black and odorous water body and the slope index of black and odorous water body in the experimental area of Nanning are 0.0186, 0.004 and 0.007 respectively. These three algorithms are used as thresholds to distinguish black and odorous water bodies from ordinary water bodies. The normalized black and odorous water index model determines that the range of black and odorous water is 0.007-0.137.
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References
Issued by the central people's government of the People's Republic of China, the State Council on water pollution prevention plan of action to inform [EB/OL]. [2015-4-16]. http://www.gov.cn/zhengce/content/2015-04/16/content_9613.htm.
Wen Shuang. Urban Black and odorous water body recognition by remote sensing based on GF-2 image [D]. Nanjing: Nanjing Normal University, 2018.
Yao Y. Research on urban black and odorous water body recognition model based on GF multispectral image [D]. Lanzhou: Lanzhou Jiaotong University,2018.
Li Jiaqi, Li Jiaguo, Zhu Li, et al. Identification and ground verification of black and odorous water by Remote Sensing in Taiyuan [J]. Journal of Remote Sensing,2019, 23 (4) : 773-784.
Zhang Xue, Lai Ji Bao, Li Jiaguo, et al. Remote sensing identification of black and odorous water bodies in Shenzhen based on Gaofen-1 image [J]. Science Technology and Engineering, 2019, 19(04): 268-274.
Yao Huanmei, LU Yannan, GONG Zhuqing. Research on black and odorous water body identification method based on PlanetScope image in Qinzhou, Guangxi [J]. Environmental Engineering, 2019, 37(10): 35-43.
Seven Ke Ke, Shen Qian, Luo Xiaojun, et al. Remote sensing classification of black and odorous water bodies in Shenyang based on GF-2 image [J]. Remote Sensing Technology and Application, 2020, 35(2): 424-434.
Gao Li, Zhang Lulu, Leaf Vein, et al. Remote sensing identification of black and odorous water bodies in built-up areas of Guangzhou based on GF-6 image [J]. Environmental Ecology, 2019,3(05):13-18.
Han Wencong, Zhang Xiaoyu, Chen Jiaxing, et al. Urban black and odorous water body monitoring by remote sensing based on Gaofen-2 image [J]. Environmental Ecology, 2019, 3(01):63-71. (in Chinese)
Phiri D, Simwanda M, Salekin S, et al. Sentinel-2 data for land cover/use mapping: A review[J]. Remote Sensing, 2020, 12(14): 2291.
Drusch M, Del Bello U, Carlier S, et al. Sentinel-2: ESA's optical high-resolution mission for GMES operational services[J]. Remote Sensing of Environment, 2012, 120: 25-36.
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