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Estimating Chemical Oxygen Demand in estuarine urban rivers using unmanned aerial vehicle hyperspectral images

文献类型：期刊论文

作者Cai, Jiannan3,4; Meng, Ling1,2; Liu, Hailong1,2; Chen, Jun4; Xing, Qianguo1,2
刊名ECOLOGICAL INDICATORS
出版日期2022-06-01
卷号139页码:11
关键词Hyperspectral Chemical Oxygen Demand (COD) Urban river 1D-CNN UAV
ISSN号1470-160X
DOI10.1016/j.ecolind.2022.108936
通讯作者Chen, Jun(chenjun@xjtu.edu.cn) ; Xing, Qianguo(qgxing@yic.ac.cn)
英文摘要In this study, we combined ground-based hyperspectral data, unmanned aerial vehicles (UAVs) remotely sensed hyperspectral images, and 1D-CNN algorithms to quantitatively characterize and estimate the Chemical Oxygen Demand (COD) of estuarine urban rivers. The spectral response mechanism of COD is imprecise due to its complex composition; however, we found that hyperspectral remote sensing data could be used for COD monitoring because of the data's rich spectral information. The potential of hyperspectral sensors installed on UAVs to estimate and map the COD of urban rivers has not been thoroughly explored. We used in situ above water hyperspectral data from 498 sites and synchronous water samples in band ratio, SVM, and 1D-CNN algorithms to build retrieval models. We found that the 1D-CNN model performed the best with an R-2 of 0.78 and an RMSE of 5.22 when using the original reflectance data as input. The 1D-CNN model may also have a better ability to identify water samples with abnormally high concentrations. Our results revealed that transferring the ground-based derived 1D-CNN retrieval model for COD to the high-resolution hyperspectral images is a reliable method for determining COD from the images. We concluded that UAV remotely sensed hyperspectral images are valuable for COD concentration monitoring and mapping, critical to urban water quality management decision making.
WOS关键词SUPPORT VECTOR REGRESSION ; WATER-QUALITY PARAMETERS ; LAKE ; ABSORPTION ; CARBON
WOS研究方向Biodiversity & Conservation ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000804180400002
资助机构Chinese Academy of Science Strategic Priority Research Program: the Big Earth Data Science Engineering Project ; International Cooperation in Science and Technology Innovation Among Governments ; Instrument Developing Project of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; Science and Technology Program of Zhongshan
源URL[http://ir.yic.ac.cn/handle/133337/31128]  
专题烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
烟台海岸带研究所_海岸带信息集成与综合管理实验室
通讯作者Chen, Jun; Xing, Qianguo
作者单位1.Shandong Key Lab Coastal Environm Proc, Yantai 264003, Peoples R China
2.Chinese Acad Sci, Yantai Inst Coastal Zone Res, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China
3.Zhongshan Municipal Ecol Environm Bur, Zhongshan, Peoples R China
4.Xi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Xi'an, Peoples R China
推荐引用方式
GB/T 7714		 Cai, Jiannan,Meng, Ling,Liu, Hailong,et al. Estimating Chemical Oxygen Demand in estuarine urban rivers using unmanned aerial vehicle hyperspectral images[J]. ECOLOGICAL INDICATORS,2022,139:11.
APA Cai, Jiannan,Meng, Ling,Liu, Hailong,Chen, Jun,&Xing, Qianguo.(2022).Estimating Chemical Oxygen Demand in estuarine urban rivers using unmanned aerial vehicle hyperspectral images.ECOLOGICAL INDICATORS,139,11.
MLA Cai, Jiannan,et al."Estimating Chemical Oxygen Demand in estuarine urban rivers using unmanned aerial vehicle hyperspectral images".ECOLOGICAL INDICATORS 139(2022):11.

入库方式： OAI收割

来源：烟台海岸带研究所

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