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城市黑臭河流污染源类型的无人机高光谱判别

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过量的溶解有机物质DOM(Dissolved Organic Matter)是城市黑臭水体形成的重要原因,而荧光光谱技术可以识别DOM的荧光特征和组成结构,从而指示黑臭水体污染来源.为了实现基于无人机高光谱影像的城市黑臭河流污染源类型识别,为黑臭水体治理和监管提供技术支持,本研究以城市黑臭水体中溶解有机质的荧光特征和光学特性为研究对象,探究它们之间的内在联系,构建可识别污染源类型的模型.结果表明:(1)荧光峰积分比IA∶IT(A峰和T峰的荧光强度比值)比其他荧光指数更适合作为城市河流DOM组分动态变化的追踪因子;(2)确定了 IA∶IT的阈值,将城市重度黑臭水体污染源划分为生活污水、工业废水和混合废水;(3)构建了基于遥感反射率-aCDOM(275)-IA∶IT的城市重度黑臭水体污染源遥感判别模型,并应用于南京市屯粮河河段无人机高光谱影像中,结果表明该河段污染源类型与实际调查一致.
Discrimination method of unmanned aerial vehicle hyperspectral for the types of pollution sources of black-odor rivers in cities
The research presented in this paper explores a method for identifying pollution sources in urban black-odor rivers using UAV hyperspectral imaging technology.Excessive Dissolved Organic Matter(DOM)is a primary cause of black-odor water bodies,and fluorescence spectroscopy can identify the fluorescence characteristics and composition of DOM,providing insights into pollution sources.The study focuses on developing a model based on the fluorescence characteristics and optical properties of DOM to distinguish between different types of pollution sources,such as domestic sewage,industrial wastewater,and mixed waste water.Key findings of the research include the identification of the fluorescence peak integral ratio(IA∶IT)as a reliable indicator for tracking dynamic changes in DOM components in urban rivers.This ratio proved to be more effective than other fluorescence indices.By determining the threshold values for IA∶IT,the study categorizes pollution sources in heavily polluted water bodies into three types.The developed model,which incorporates remote sensing reflectance and the absorption coefficient of CDOM at 275 nm(aCDOM(275)),is validated using UAV hyperspectral images from the Tunliang River in Nanjing.The results show that the identified pollution sources in the river section are consistent with actual field investigations.The methodology involves extensive data collection from several industrialized cities in Jiangsu Province,China,including Nanjing,Wuxi,Changzhou,and Yangzhou.Parameters such as dissolved oxygen,oxidation-reduction potential,and remote sensing reflectance were measured.UAV hyperspectral data was collected using a hyperspectral imager with a spectral range of 400-1000 nm and 270 spectral channels.The study employed three-dimensional fluorescence spectroscopy to analyze the fluorescence characteristics of DOM and used Parallel Factor Analysis(PARAFAC)to decompose the fluorescence data into individual components.Statistical methods were utilized to establish relationships between fluorescence indices and water optical properties.Linear models were developed to predict IA:IT based on the absorption coefficient aCDOM(275)and remote sensing reflectance ratios.The models were validated and applied to UAV hyperspectral imagery to classify sections of the river based on their primary pollution sources.In conclusion,the study demonstrates the effectiveness of combining UAV hyperspectral imagery with fluorescence spectroscopy to identify pollution sources in urban black-odor rivers.The developed models provide a robust method for monitoring and managing water quality in urban environments,offering a promising approach for pollution source identification and water body management.The findings emphasize the potential of this technology to aid in the effective management and remediation of polluted urban waterways,highlighting the importance of accurate pollution source identification for sustainable urban development and water quality improvement.

urban black-odor water bodiesfluorescence indexoptical featurespollution source identificationUAV hyperspectral imageryindustrial sewageabsorption coefficient of CDOM

成鑫、徐杰、李云梅、张玉、朱雨新、蔡小兰、吕恒

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南京师范大学江苏省地理信息资源开发与利用协同创新中心,南京 210023

无锡市惠山区钱桥街道综合服务中心,无锡 214151

生态环境部长江流域生态环境监督管理局生态环境监测与科学研究中心,武汉 430010

武汉润江生态科技有限公司,武汉 430010

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城市黑臭水体 荧光指数 光学特征 污染源类型识别 无人机高光谱影像 工业废水 CDOM吸收系数

国家自然科学基金云南省重点研发计划

42071299202203AC100001-01

2024

10.11834/jrs.20243306

遥感学报
中国地理学会环境遥感分会 中国科学院遥感应用研究所

遥感学报

CSTPCD北大核心
影响因子：2.921
ISSN：1007-4619
年,卷(期)：2024.28(8)