吴世红^1,
1.交通运输部天津水运工程科学研究院，天津 300000
基金项目: 交通运输部天津水运工程科学研究所科研创新基金资助项目TKS180401交通运输部天津水运工程科学研究所科研创新基金资助项目(TKS180401)

Research progress of remote sensing monitoring key technologies for urban black and odorous water bodies

WU Shihong^1,
1.Tianjin Research Institute for Water Transport Engineering, Ministry of Transport, Tianjin 300000, China

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摘要:城市黑臭水体泛滥是公众极为关切的城市环境问题，对其进行实时监测更是《水污染防治行动计划》的国家战略需求。遥感技术在生态环境监测领域表现出了不可替代的优势，可实现业务化大面积监测，而目前的研究主要集中在基于物理化学过程的黑臭水体监测技术方面，利用遥感技术监测黑臭水体的研究相对较少。在分析黑臭水体的实测光学性质和影像表观特征的基础上，重点归纳了黑臭水体遥感识别模型构建的研究进展，主要包括光学阈值法、基于典型遥感水质指标的识别法和色度法，并对未来黑臭水体遥感研究趋势进行了展望。
关键词: 黑臭/
城市水体/
遥感/
光学识别模型

Abstract:The frequent occurrence of black and odorous water bodies in urban city is the environmental problem of great public concern, and its real-time monitoring is also a national strategic requirement of the Water Pollution Control Action Plan. Remote sensing technology has shown irreplaceable advantages in the field of ecological environment monitoring and it can realize large area monitoring. However, the current researches mainly focused on the black and odorous water monitoring technologies based on physical and chemical processes, while there are relatively few researches on monitoring black and odorous water using remote sensing technology. Based on the analysis of the measured optical properties and the image apparent features of black and odorous water bodies, the research progress on the remote sensing identification model construction for black and odorous water bodies has been significantly summarized, including the optical threshold method, water quality indicators identification method based on typical remote sensing and chromaticity method. The future research trends on black and the remote sensing of odorous water bodies were proposed.
Key words:black and odorous/
urban water body/
remote sensing/
optical recognition model.

[1]	王旭, 王永刚, 孙长虹, 等. 城市黑臭水体形成机理与评价方法研究进展[J]. 应用生态学报, 2016, 27(4): 1331-1340.
[2]	国务院. 水污染防治行动计划[EB/OL]. (2017-09-28)[2019-03-19]. http: //www. gov. cn/zhengce/content/2015-04/16/content_9613. htm.
[3]	KNOP E. Design studies for the emscher mouth treatment plant[J]. Journal of Water Pollution Control Federation, 1966, 38(7): 1194-1207.
[4]	李相力, 张鹏程, 于洪存. 沈阳市卫工河黑臭现象分析[J]. 环境保护科学, 2003, 29(5): 27-28.
[5]	LAZARO T R. Urban hydrology: A multidisciplinary perspective[J]. Geographical Journal, 1979, 147(3): 364-365.
[6]	住房城乡建设部. 城市黑臭水体整治工作指南[EB/OL]. (2015-08-28)[2019-03-22]. https://max.book118.com/html/2017/1114/140061925.shtm.
[7]	李伟杰, 汪永辉. 铁离子在水体中价态的转化及其与河道黑臭的关系[J]. 净水技术, 2007, 26(2): 35-37.
[8]	李真, 黄民生, 何岩, 等. 铁和硫的形态转化与水体黑臭的关系[J]. 环境科学与技术, 2010, 33(S1): 1-3.
[9]	KUTOVAYA O A, WATSON S B. Development and application of a molecular assay to detect and monitor geosmin-producing cyanobacteria and actinomycetes in the Great Lakes[J]. Journal of Great Lakes Research, 2014, 40(2): 404-414.
[10]	SUGIURA N, UTSUMI M, WEI B, et al. Assessment for the complicated occurrence of nuisance odours from phytoplankton and environmental factors in a eutrophic lake[J]. Lakes & Reservoirs Research & Management, 2010, 9(3/4): 195-201.
[11]	俞欣, 陈天安. 河道黑臭污染简易评价方法研究[J]. 环境科学与管理, 2015, 40(3): 176-179.
[12]	赵越, 姚瑞华, 徐敏, 等. 我国城市黑臭水体治理实践及思路探讨[J]. 环境保护, 2015, 43(13): 27-29.
[13]	崔伟, 张勇, 黄民生. 复合垂直流人工湿地脲酶和磷酸酶活性与黑臭河水净化效果[J]. 安徽农业科学, 2011, 39(13): 8016-8018.
[14]	申茜, 朱利, 曹红业. 城市黑臭水体遥感监测与筛查研究进展[J]. 应用生态学报, 2017, 28(10): 3433-3439.
[15]	丁潇蕾, 李云梅, 吕恒, 等. 城市黑臭水体的吸收特性分析[J]. 环境科学, 2018, 39(10): 129-139.
[16]	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.
[17]	姚月, 申茜, 朱利, 等. 高分二号的沈阳市黑臭水体遥感识别[J]. 遥感学报, 2017, 23(2): 1-12.
[18]	纪刚. 基于遥感的黑臭水体识别方法研究及应用[D]. 兰州: 兰州交通大学, 2017.
[19]	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.
[20]	胡淼, 张宁, 王罗娟, 等. 多源数据对黑臭水体整治的遥感监测[J]. 环境与发展, 2017, 29(9): 159-161.
[21]	李佳琦, 戴华阳, 李家国, 等. 城区重度污染水体遥感识别研究[J]. 测绘通报, 2018, 1(5): 54-58.
[22]	靳海霞, 潘健. 基于高分二号卫星融合数据的城镇黑臭水体遥感监测研究[J]. 国土资源科技管理, 2017, 34(4): 107-117.
[23]	DUAN H, MA R, LOISELLE S A, et al. Optical characterization of black water blooms in eutrophic waters[J]. Science of the Total Environment, 2014, 482-483(3): 174-183.
[24]	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): 497-498.
[25]	ZHANG Y, SHI K, LIU J, et al. Meteorological and hydrological conditions driving the formation and disappearance of black blooms, an ecological disaster phenomena of eutrophication and algal blooms[J]. Science of the Total Environment, 2016, 569-570(1): 1517-1529.
[26]	CHEN J, XIE P, MA Z, et al. A systematic study on spatial and seasonal patterns of eight taste and odor compounds with relation to various biotic and abiotic parameters in Gonghu Bay of Lake Taihu, China[J]. Science of the Total Environment, 2010, 409(2): 314-325.
[27]	卢信, 冯紫艳, 商景阁, 等. 不同有机基质诱发的水体黑臭及主要致臭物(VOSCs)产生机制研究[J]. 环境科学, 2012, 33(9): 3152-3159.
[28]	PARINET J, RODRIGUEZ M J, SERODES J. Influence of water quality on the presence of off-flavour compounds (geosmin and 2-methylisoborneol)[J]. Water Research, 2010, 44(20): 5847-5856.
[29]	BRIUCAUD A, MOREL A, PRIEUR L. Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains[J]. Limnology and Oceanography, 1981, 26(1): 43-53.
[30]	ZHOU C, CHEN S B, ZHANG Y Z, et al. Evaluating metal effects on the reflectance spectra of plant leaves during different seasons in post-mining areas, China[J]. Remote Sensing, 2018, 10(8): 1211-1212.
[31]	ZHOU C, CHEN S B, ZHAO J H, et al. Detection of alone stress and combined stress by Cu and Ni in wheat using visible to near-infrared spectroscopy[C]//Institute of Electrical and Electronics Engineers(IEEE). International Conference on Agro-Geoinformatics, Hangzhou, 2018: 1-6.
[32]	范宪创, 陈圣波, 周超, 等. 基于MODIS数据的海洋叶绿素反演算法研究[J]. 科学技术与工程, 2017, 17(10): 74-80.

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刊出日期:2019-06-18

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城市黑臭水体遥感监测关键技术研究进展

吴世红^1,
1.交通运输部天津水运工程科学研究院，天津 300000
基金项目: 交通运输部天津水运工程科学研究所科研创新基金资助项目TKS180401交通运输部天津水运工程科学研究所科研创新基金资助项目(TKS180401)
关键词: 黑臭/
城市水体/
遥感/
光学识别模型
摘要:城市黑臭水体泛滥是公众极为关切的城市环境问题，对其进行实时监测更是《水污染防治行动计划》的国家战略需求。遥感技术在生态环境监测领域表现出了不可替代的优势，可实现业务化大面积监测，而目前的研究主要集中在基于物理化学过程的黑臭水体监测技术方面，利用遥感技术监测黑臭水体的研究相对较少。在分析黑臭水体的实测光学性质和影像表观特征的基础上，重点归纳了黑臭水体遥感识别模型构建的研究进展，主要包括光学阈值法、基于典型遥感水质指标的识别法和色度法，并对未来黑臭水体遥感研究趋势进行了展望。

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