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

doi:10.1016/j.ecolind.2022.108936

	Estimating Chemical Oxygen Demand in estuarine urban rivers using unmanned aerial vehicle hyperspectral images
	Cai, Jiannan 1,2; Meng, Ling 3,4; Liu, Hailong 3,4; Chen, Jun 1; Xing, Qianguo3,4
发表期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
	2022-06-01
卷号	139 页码:11
关键词	Hyperspectral Chemical Oxygen Demand (COD) Urban river 1D-CNN UAV
DOI	10.1016/j.ecolind.2022.108936
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.
收录类别	SCI
语种	英语
关键词[WOS]	SUPPORT VECTOR REGRESSION ; WATER-QUALITY PARAMETERS ; LAKE ; ABSORPTION ; CARBON
研究领域[WOS]	Biodiversity & Conservation ; Environmental Sciences & Ecology
WOS记录号	WOS:000804180400002
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/31129
专题	中国科学院海岸带环境过程与生态修复重点实验室中国科学院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
通讯作者	Chen, Jun; Xing, Qianguo
作者单位	1.Xi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Xi'an, Peoples R China 2.Zhongshan Municipal Ecol Environm Bur, Zhongshan, Peoples R China 3.Chinese Acad Sci, Yantai Inst Coastal Zone Res, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China 4.Shandong Key Lab Coastal Environm Proc, Yantai 264003, 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.