Modeling of Suspended Particulate Matter Concentration in an Extremely Turbid River Based on Multispectral Remote Sensing from an Unmanned Aerial Vehicle (UAV)

被引:2
作者
Zhai, Yinghui [1 ]
Zhong, Pu [1 ]
Duan, Hongtao [1 ,2 ,3 ]
Zhang, Dan [1 ]
Chen, Xin [4 ]
Guo, Xingjian [1 ,2 ]
机构
[1] Northwest Univ, Coll Urban & Environm Sci, Xian 710127, Peoples R China
[2] Shaanxi Key Lab Earth Surface Syst Carrying Capac, Xian 710127, Peoples R China
[3] Chinese Acad Sci, Key Lab Watershed Geog Sci, Nanjing Inst Geog & Limnol, Nanjing 210008, Peoples R China
[4] Shimadzu China Co Ltd, Analyt Applicat Ctr, Xian 710127, Peoples R China
基金
中国国家自然科学基金;
关键词
DJI P4 multispectral UAV; multispectral imagery; the Wuding River; suspended particulate matter concentration; inversion models; ATMOSPHERIC CORRECTION; YELLOW-RIVER; HYPERSPECTRAL DATA; CHLOROPHYLL-A; COASTAL; TRIBUTARIES; IMPACTS; HUANGHE;
D O I
10.3390/rs15225398
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Following consecutive years of governance efforts, there has been a substantial reduction in sediment transport in the Yellow River, resulting in significant changes in its water-sediment dynamics. This necessitates precise monitoring of sediment-bearing tributary inflows, a crucial requirement for effective governance strategies on the Loess Plateau's current developmental stage. While satellite remote sensing technology has been widely used to estimate suspended particulate matter concentration (CSPM) in open water bodies like oceans and lakes, its application in narrow rivers presents challenges related to hybrid pixel and proximity effects. As a result, the effectiveness and competence of satellite remote sensing in monitoring CSPM in such confined river environments are reduced. This study attempted to use unmanned aerial vehicle (UAV) remote sensing with multispectral technology to invert CSPM in the Wuding River, a sediment-bearing Yellow River tributary. A novel CSPM concentration inversion model was introduced for highly turbid river settings. The results showed that the accuracy of the new band ratio model in this study is significantly improved compared with the existing models. The validation dataset had a coefficient of determination (R-2) of 0.83, a root mean square error (RMSE) of 3.73 g/L, and a mean absolute percentage error (MAPE) of 44.95% (MAPE is 40.68% at 1-20 g/L, and 12.37% at >20 g/L). On this basis, the UAV also monitored the impacts of heavy rainfall on the CSPM, resulting in a rapid rise and fall in CSPM over a period of ten hours. This study demonstrated the potential of UAV remote sensing for CSPM monitoring in extremely turbid narrow rivers (tens to tens of meters), especially before and after rainfall sediment production events, which can provide technical support for accurate sediment management and source identification in the main tributaries of the Yellow River and help realize the goal of high-quality development of the Yellow River Basin.
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页数:20
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