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遥感技术与应用  2020, Vol. 35 Issue (2): 424-434    DOI: 10.11873/j.issn.1004-0323.2020.2.0424
遥感应用     
基于GF-2影像的沈阳市黑臭水体遥感分级识别
七珂珂1,2(),申茜2(),罗小军1,李家国2,姚月2,杨崇1
1.西南交通大学地球科学与环境工程学院,四川 成都 611756
2.中国科学院遥感与数字地球研究所,北京 100094
Remote Sensing Classification and Recognition of Black and Odorous Water in Shenyang based on GF-2 Image
Keke Qi1,2(),Qian Shen2(),Xiaojun Luo1,Jiaguo Li2,Yue Yao2,Chong Yang1
1.Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
2.Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
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摘要:

城市黑臭水体遥感分级识别对于黑臭水体的监管及治理具有重要作用。针对目前黑臭水体遥感识别算法无法对河流黑臭程度分级这一问题,在沈阳市建成区内开展野外实验,对比分析一般水体、轻度黑臭水体和重度黑臭水体的反射率光谱差异,利用绿波段反射率的基线差值与红波段反射率之比,提出了一种城市黑臭水体遥感分级指数BOCI(Black and Odorous water Classification Index)模型。首先采用实测光谱数据对BOCI模型检验,并将其与改进后归一化比值模型进行对比,结果表明,BOCI模型具有更高的黑臭水体识别精度,且可以将重度黑臭水体与轻度黑臭水体区分开,解决了现有模型无法对黑臭水体污染程度分级的问题;然后将BOCI模型应用于沈阳市同步GF-2影像进一步检验,同样取得了较高的识别精度;最后将该模型应用于2015~2018年4景GF-2影像,对研究区内黑臭水体进行动态监测,结果显示,新开河、南运河和满堂河黑臭现象逐步得到改善,辉山明渠黑臭现象依然很严峻。

关键词: 城市黑臭水体黑臭水体分级指数光谱特征高分二号沈阳市    
Abstract:

The classification and recognition of urban black-odor water by remote sensing plays an important role in the supervision and treatment of the black-odor water. Aiming at the problem that the current remote sensing recognition algorithm of black-odor water cannot classify the pollution degree of black-odor water, we conducted field experiments in Shenyang built-up area. The reflectance spectra and water quality parameters of general water, mild and heavy black-odor water were measured. According to the spectral characteristics of different water, based on the ratio of baseline difference of green band reflectance to red band reflectance, a remote sensing classification index BOCI (Black and Odorous water Classification Index) model is proposed. Firstly, BOCI is checked by the measured data on the ground, compared with improved normalized ratio model. The results show that BOCI has higher recognition accuracy. Moreover, BOCI can distinguish between mild and heavy black-odor water, which solves the problem that the existing model cannot classify the pollution degree of the black-odor water. Then, BOCI is applied to the synchronous GF-2 image of Shenyang for further tested, and the recognition accuracy is also high. Finally, BOCI is applied to the four GF-2 images of Shenyang from 2015 to 2018 to monitor the dynamic changes of black-odor water. The results show that the black-odor phenomena of Xinkai River, Nanyun River and Mantang River are gradually improved, but the black-odor phenomena of Huishan Canal are still very serious.

Key words: Urban black-odor water    Black and Odorous water Classification Index (BOCI)    Spectral characteristics    GF-2    Shenyang
收稿日期: 2018-12-19 出版日期: 2020-07-10
ZTFLH:  TP701  
基金资助: 中国科学院战略性先导科技专项资金(XDA19040302);国家重点研发项目(2017YFB0503902);国家水体污染控制与治理科技重大专项(2017ZX07302?003);城市黑臭水体遥感监管关键技术先期研究(2016SZXHC)
通讯作者: 申茜     E-mail: swjtu_qkk@163. com;shenqian@radi.ac.cn
作者简介: 七珂珂(1991-),女,河南商丘人,硕士研究生,主要从事水环境遥感监测研究。E?mail: swjtu_qkk@163. com
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引用本文:

七珂珂,申茜,罗小军,李家国,姚月,杨崇. 基于GF-2影像的沈阳市黑臭水体遥感分级识别[J]. 遥感技术与应用, 2020, 35(2): 424-434.

Keke Qi,Qian Shen,Xiaojun Luo,Jiaguo Li,Yue Yao,Chong Yang. Remote Sensing Classification and Recognition of Black and Odorous Water in Shenyang based on GF-2 Image. Remote Sensing Technology and Application, 2020, 35(2): 424-434.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.2.0424        http://www.rsta.ac.cn/CN/Y2020/V35/I2/424

图1  沈阳市野外采样样点分布图
特征指标(单位)轻度黑臭重度黑臭
透明度(cm)10*~25<10
溶解氧(mg/L)0.2~2.0<0.2
氧化还原电位(mV)-200~50<-200
氨氮(mg/L)8.0~15>15
表1  城市黑臭水体判别指标
图2  大气校正评价结果
图3  4种类型水体遥感反射率光谱、均值及GF-2 PMS 模拟结果
图7  沈阳市黑臭水体遥感分级识别时序分析
调研结果BOCI结果一般水体轻度黑臭水体重度黑臭水体行总数用户精度/%
一般水体17011894.44
轻度黑臭水体063966.67
重度黑臭水体01111291.67
列总数1771539
生产者精度/%10085.7173.33
Kappa0.80
总体精度/%87.18
表2  基于实测光谱等效数据的BOCI模型的混淆矩阵精度评价
图4  BOCI指数与改进后归一化比值指数阈值确定及对比
图5  BOCI指数与改进后归一化比值指数精度验证及对比
图6  2016年9月19日和10月9日沈阳市GF-2同步验证结果
调研结果BOCI结果一般水体轻度黑臭水体重度黑臭水体行总数用户精度/%
一般水体180018100
轻度黑臭水体01151668.8
重度黑臭水体0191090
列总数18121444
生产者精度/%10091.764.3
Kappa0.79
总体精度/%86.4
表3  基于同步GF-2影像的BOCI模型的混淆矩阵精度评价
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