中文关键词城市黑臭水体遥感分级决策树光学特性PlanetScope卫星影像 英文关键词urban black-odor water bodyremote sensing classificationdecision treeoptical characteristicsPlanetScope satellite image

作者	单位	E-mail
李玲玲	南京师范大学虚拟地理环境教育部重点实验室, 南京 210023	leell0408@foxmail.com
李云梅	南京师范大学虚拟地理环境教育部重点实验室, 南京 210023 江苏省地理信息资源开发与利用协同创新中心, 南京 210023	liyunmei@njnu.edu.cn
吕恒	南京师范大学虚拟地理环境教育部重点实验室, 南京 210023
徐杰	南京师范大学虚拟地理环境教育部重点实验室, 南京 210023
杨子谦	南京师范大学虚拟地理环境教育部重点实验室, 南京 210023
毕顺	南京师范大学虚拟地理环境教育部重点实验室, 南京 210023
许佳峰	南京师范大学虚拟地理环境教育部重点实验室, 南京 210023

中文摘要 水体黑臭程度遥感监测是了解城市水质现状和综合评价城市水环境治理效果的重要手段.以南京、常州、无锡和扬州为研究区，共采集171个样点，同步测量水质参数和光学参数，分析黑臭水体与一般水体的水色和光学特征，构建决策树模型进行重度黑臭水体、轻度黑臭水体和非黑臭水体（记为一般水体）识别.结果表明：①根据色度可将水体分为1~6类水体，其中，类型1~4为黑臭水体，分别为灰黑色、深灰色、灰色和浅灰色水体，类型5和类型6水体为一般水体，分别为绿色系和黄色系水体；②类型1水体的非色素颗粒物和有色可溶性有机物含量高，但色素颗粒物的吸收并不占主导，类型2和5水体的吸收以色素颗粒物吸收占主导，类型3、4和6水体的吸收以非色素颗粒物吸收占主导；③根据六类水体的反射光谱差异用黑臭水体差值指数（difference of black-odorous water index，DBWI）、三波段面积水体指数（green-red-nir area water index，G-R-NIR AWI）、绿光波段反射率和归一化黑臭水体指数（normalized difference black-odorous water index，NDBWI）构建的水体分类识别决策树，能够有效识别出重、轻度黑臭水体和一般水体；④将决策树模型应用于2019年4月9日扬州的PlanetScope影像上，并利用10个同步过境点进行验证，整体识别精度达到80.00%，K值达到0.67.通过水色分类后的城市水体分级模型方法，可推广应用于类似的水体，为黑臭水体监管提供技术方法. 英文摘要 Remote sensing monitoring of black-odor water is an important method for understanding the current status of urban water quality, and comprehensively evaluating the effect of urban water environment treatment. A total of 171 samples were collected in Nanjing, Changzhou, Wuxi, and Yangzhou cities and water quality parameters and optical parameters were measured simultaneously. Based on the analysis of the water color and optical characteristics of the black-odor water and non-black-odor water (denoted as general water), a decision tree was constructed to identify the severe, mild black-odor water, and general water as green and yellow water. The results found that:①According to the water color, the water bodies can be divided into six types. Among them, type 1 to 4 water bodies are black-odor water, which are gray black, dark gray, gray, and light gray water, respectively, and type 5 and 6 water bodies are general water, which are green and yellow water, respectively; ②Type 1 water body contains high contents of non-pigmented particulate matter and colored dissolved organic matter(CDOM), however, the absorption of pigmented particulate matter is not dominant. Type 2 and 5 water bodies are dominated by pigmented particulate matter. Type 3, 4, and 6 water bodies are dominated by non-pigmented particulate matter; ③After water color classification, and according to the differences of the reflection spectrums of the six types of water bodies, the difference of black-odorous water index (DBWI), green-red-nir area water index (G-R-NIR AWI), the green band reflectance and the normalized difference black-odorous water index (NDBWI) were used to construct a decision tree to identify the severe, mild black-odor water, and general water; ④The decision tree was applied to the PlanetScope satellite image of Yangzhou City on April 9, 2019, and 10 synchronous sampling points were used for verification. The overall recognition accuracy reached 80.00%, and the K value reached 0.67. The urban water classification model, after water color classification, can be applied to other similar water bodies, and provides a technical method for the supervision of black-odor water bodies.

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