基于PlanetScope影像的广西钦州市黑臭水体识别方法研究
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摘要
城市黑臭水体实质上是由环境保护和城市建设两者发展不平衡产生的,也是我国当下生态环境保护工作的重点之一。针对城市黑臭水体监测评价这一热点难点展开研究,以钦州市主城区为研究区域,以高频次重访的高空间分辨PlanetScope遥感影像为数据源,结合两期的实地采样数据,分析黑臭水体表观光学特性,利用黑臭水体与一般水体光谱曲线差异特征,提出近红外(NIR)单波段阈值法、HI指数法、EHI指数法和NDBWI指数法以及基于色度法的饱和度识别算法。城市黑臭水体与一般水体在蓝、绿和红波段(455～670 nm)的相同点是反射率偏低,不同点在于一般水体在455～670 nm处的光谱曲线斜率高于黑臭水体,在红波段处反射率达到极大值,在红波段和近红外波段开始下降,而黑臭水体此波段范围内反射率开始大幅升高。识别结果表明,NIR单波段阈值法识别准确率较低,存在较大偏差;HI指数识别准确率为57. 14%; EHI指数和饱和度法对黑臭水体的识别准确率均为78. 57%; NDBWI指数的识别准确率最高,达90%以上。
Urban black and odorous water body is usually caused by the imbalance development between environment protection and city construction,and it has been one of the focuses of ecological environmental protection work in China recently. This paper focuses on monitoring and evaluation methodology of urban black and odorous water body based on remote sensing technology,and carried out field work in central downtown in Qinzhou,Guangxi. In this paper,5 arithmetic,which are NIR reflectance threshold method,HI arithmetic,EHI arithmetic,NDBWI arithmetic and saturation index based on CIE method,for recognizing black and odorous water body are proposed. The results are as follow: 1) the similarity of normal water and black-odorous water body is low reflectance in blue,green and red bands( 455 ～ 670 nm). The difference is that the slope of the spectral curve in 455 ～ 670 nm bands of the general water body is higher than the black-odorous water. The high peak reflectance of normal water appears at red band and go down in range of red and NIR bands,while the spectrum of blackodorous water appears a high peak at red band and goes up in the range of red and NIR bands. 2) The recognition results show that the NIR reflectance threshold method has low recognition accuracy and large deviation,and the HI index recognition accuracy is 57. 14%. The recognition accuracy of EHI index and saturation method for black odor water are 78. 57%. The NDBWI index has the highest recognition accuracy rate of 90% above.
Urban black and odorous water body is usually caused by the imbalance development between environment protection and city construction,and it has been one of the focuses of ecological environmental protection work in China recently. This paper focuses on monitoring and evaluation methodology of urban black and odorous water body based on remote sensing technology,and carried out field work in central downtown in Qinzhou,Guangxi. In this paper,5 arithmetic,which are NIR reflectance threshold method,HI arithmetic,EHI arithmetic,NDBWI arithmetic and saturation index based on CIE method,for recognizing black and odorous water body are proposed. The results are as follow: 1) the similarity of normal water and black-odorous water body is low reflectance in blue,green and red bands( 455 ～ 670 nm). The difference is that the slope of the spectral curve in 455 ～ 670 nm bands of the general water body is higher than the black-odorous water. The high peak reflectance of normal water appears at red band and go down in range of red and NIR bands,while the spectrum of blackodorous water appears a high peak at red band and goes up in the range of red and NIR bands. 2) The recognition results show that the NIR reflectance threshold method has low recognition accuracy and large deviation,and the HI index recognition accuracy is 57. 14%. The recognition accuracy of EHI index and saturation method for black odor water are 78. 57%. The NDBWI index has the highest recognition accuracy rate of 90% above.
引文
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[2] NICHOL J E. Remote sensing of tropical blackwater rivers:a method for environmental water quality analysis[J]. Applied Geography,1993,13(2):153-68.
[3] Kutser T,Paavel B,Verpoorter C,et al. Remote sensing of black lakes and using 810 nm reflectance peak for retrieving water quality parameters of optically complex waters[J]. Remote Sensing,2016,8(6):497.
[4] Tiit K,Pierson Donald C,Kallio Kari Y,et al. Mapping lake CDOM by satellite remote sensing[J]. Remote Sensing of Environment,2005,94(4):535-540.
[5]姚俊，曾祥福，益建芳．遥感技术在上海苏州河水污染监测中的应用[J]．影像技术，2003(2):3-7.
[6]万风年，纪晓亮，朱元励，等．应用遥感监测城市水体水质研究[J]．浙江农业科学，2013,1(3):349-355.
[7]王云鹏，闵育顺，傅家谟，等．水体污染的遥感方法及在珠江广州河段水污染监测中的应用[J]．遥感学报，2001,5(6):460-465.
[8]温爽，王桥，李云梅，等．基于高分影像的城市黑臭水体遥感识别:以南京为例[J]．环境科学，2018,39(1):57-67.
[9]曹红业．中国典型城市黑臭水体光学特性分析及遥感识别模型研究[D]．成都:西南交通大学，2017.
[10] Ahola R,Chenier R,Faucher M A,et al. Satellite derived bathymetry for arctic charting:a review of sensors and techniques for operational implementation within the Canadian Hydrographic Service[M]. Remote Sensing of the Ocean,Sea Ice,Coastal Waters,and Large Water Regions,2018.
[11] Tamondong A,Cruz C,Quides R R,et al. Remote sensing-based estimation of seagrass percent cover and LAI for above ground carbon sequestration mapping[M]. Remote Sensing of the Open and Coastal Ocean and Inland Waters,2018.
[12] Ichikawa D,Wakamori K. The integrated use of landsat,sentinel-2and Planet Scope satellite data for crop monitoring[M]. Igarss2018—2018 Ieee International Geoscience and Remote Sensing Symposium,2018:7707-7710.
[13] Possa E M,Maillard P,Gomes M F,et al. On water surface delineation in rivers using Landsat-8,Sentinel-1 and Sentinel-2data[M]. Remote Sensing for Agriculture, Ecosystems, and Hydrology,2018.
[14] Shendryk I,Rist Y,Lucas R,et al. Deep Learning-a New Approach for Multi-Label Scene Classification in Planetscope and Sentinel-2 Imagery[M]. Igarss 2018—2018 Ieee International Geoscience and Remote Sensing Symposium,2018:1116-1119.
[15] Pozdnyakov D V,Kondratyev K Y,Bukata R P,et al. Numerical modelling of natural water colour:implications for remote sensing and limnological studies[J]. International Journal of Remote Sensing,1998,19(10):1913-1932.
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