Efficient Monitoring of Total Suspended Matter in Urban Water Based on UAV Multi-spectral Images | Water Resources Management Skip to main content

Advertisement

Springer Nature Link
Log in

Efficient Monitoring of Total Suspended Matter in Urban Water Based on UAV Multi-spectral Images

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Water is not only an indispensable part of the natural environment but also an important component and basic guarantee of the urban ecological environment. Total Suspended Matter (TSM) is an important parameter to measure urban water quality. Therefore, it is essential to carry out real-time and efficient monitoring of the total suspended matter of urban water bodies to ensure water quality safety of urban water bodies. In this paper, the inversion model of TSM concentration of urban water is constructed using Unmanned Aerial Vehicle (UAV) multi-spectral images and measured TSM concentration data. The results show that: (1) The NIR band of the UAV image is highly sensitive to TSM concentration. (2) When the sample size is small, it is found that the TSM regression model is more stable and explanatory than the machine learning model. (3) The overall water quality of urban water bodies is poor, especially in areas with intense human activities such as shipping and construction. The TSM concentration in the river reaches is high, and the TSM concentration in the wider river reaches is higher than that in the narrow river channels and Landscape Lakes, and the TSM concentration remains stable in a certain length of river reaches. It can lay a foundation for further realizing real-time and efficient water quality parameter inversion in the future, and also provide an important scientific reference value for other water quality parameter inversion research, and provide theoretical basis and technical support for the scientific management of water ecological environment in urban water bodies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Availability of Data and Materials

The data generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Alizadeh MJ, Jafari NE, Kalarestaghi N, Chau KW (2017) Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models. Environ Sci Pollut Res 24:28017–28025

    Article  Google Scholar 

  • Amin I, Fikrat F, Mammado E, Babayev M (2020) Soil organic carbon prediction by Vis-NIR spectroscopy: Case study the Kur-Aras Plain, Azerbaijan. Commun Soil Sci Plant Anal 51(6):726–734

    Article  Google Scholar 

  • Balasubramanian SV, Pahlevan N, Smith B, Binding C, Schalles J, Loisel H, Gurlin D, Greb S, Alikas K, Randla M et al (2020) Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters. Remote Sens Environ 246:111768

    Article  Google Scholar 

  • Bean TP, Greenwood N, Beckett R, Biermann L, Bignell JP, Brant JL, Copp GH, Declin MJ, Dye S, Feist SW et al (2017) A review of the tools used for marine monitoring in the UK: combining historic and contemporary methods with modeling and socioeconomics to fulfill legislative needs and scientific ambitions. Front Mar Sci 4:263

    Article  Google Scholar 

  • Belgiu M, Drăguţ L (2016) Random forest in remote sensing: A review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31

    Article  Google Scholar 

  • Cao Z, Ma R, Duan H, Pahlevan N, Melack J, Shen M, Xue K (2020) A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes. Remote Sens Environ 248:111974

    Article  Google Scholar 

  • Carpenter DJ, Carpenter SM (1983) Modeling inland water quality using Landsat data. Remote Sens Environ 13(4):345–352

    Article  Google Scholar 

  • Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 8:785–794

    Google Scholar 

  • Cheng KH, Jiao JJ, Luo X, Yu S (2022) Effective coastal Escherichia coli monitoring by unmanned aerial vehicles (UAV) thermal infrared images. Water Res 118900

  • Chicco D, Warrens MJ, Jurman G (2021) The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput Sci 7:e623

    Article  Google Scholar 

  • Fulazzaky MA (2009) Water quality evaluation system to assess the Brantas river water. Water Resour Manag 23:3019–3033

    Article  Google Scholar 

  • Gai Y, Yu D, Zhou Y, Yang L, Chen C, Chen J (2020) An improved model for chlorophyll-a concentration retrieval in coastal waters based on UAV-Borne hyperspectral imagery: A case study in Qingdao. China Water 12(10):2769

    Article  Google Scholar 

  • Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chim Acta 185:1–17

    Article  Google Scholar 

  • Giardino C, Bresciani M, Braga F, Cazzaniga I, De Keukelaere L, Knaeps E, Brando VE (2017) Bio-optical modeling of total suspended solids. Bio-optical modeling and remote sensing of inland waters. Elsevier 129–156

  • Guo F, Zhang J, Zhu AX, Zhang Z, Zhang H (2021) An assimilation simulation approach for the suspended sediment concentration in inland lakes using a hybrid perturbation generation method. Water Resour Manag 35:2007–2022

    Article  Google Scholar 

  • Klein GA (1993) A recognition-primed decision (RPD) model of rapid decision making. Decis Mak Act Models Methods 5(4):138–147

    Google Scholar 

  • Liu X, Zhang Z, Jiang T, Li X, Li Y (2021) Evaluation of the effectiveness of multiple machine learning methods in remote sensing quantitative retrieval of suspended matter concentrations: A case study of Nansi Lake in North China. J Spectrosc 2021:5957376

    Article  Google Scholar 

  • Liu YJ, Xia K, Feng HL, Fang YM (2019) Inversion of water quality elements in small and micro-size water region using multispectra image by UAV. Acta Sci Circum 39(04):1241–1249

    Google Scholar 

  • Lu H, Ma X (2020) Hybrid decision tree-based machine learning models for short-term water quality prediction. Chemosphere 249:126169

    Article  Google Scholar 

  • McKenzie J (2011) Mean absolute percentage error and bias in economic forecasting. Econ Lett 113(3):259–262

    Article  Google Scholar 

  • Mishra S, Mishra DR, Lee ZP (2013) Bio-optical inversion in highly turbid and cyanobacteria-dominated waters. IEEE Trans Geosci Remote Sens 52(1):375–388

    Article  Google Scholar 

  • National Standards of the People's Republic of China (1989) Water quality-Determination of suspended substance-Gravimetric method (GB11901–89). Beijing

  • Novo E, Godoy M (1989) Eutrophication assessment through remote sensing techniques. 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium, IEEE 5:2825–2828

    Article  Google Scholar 

  • Rigatti SJ (2017) Random forest. J Insur Med 47(1):31–39

    Article  Google Scholar 

  • Romero-Trigueros C, Nortes PA, Alarcón JJ, Hunink JE, Parra M, Contreras S, Droogers P, Nicolás E (2017) Effects of saline reclaimed waters and deficit irrigation on Citrus physiology assessed by UAV remote sensing. Agric Water Manag 183:60–69

    Article  Google Scholar 

  • Sun Z, Li J, Tian L, Cehn B, Hu X (2021) Spatial variation and risk assessment of arsenic and heavy metals in surface water and suspended particulate matter in tail reaches of the Yellow River, China. Chin Geogra Sci 31:181–196

    Article  Google Scholar 

  • Teixeira LC, Mariani PP, Pedrollo OC, dos Reis CNM, Sari V (2020) Artificial neural network and fuzzy inference system models for forecasting suspended sediment and turbidity in basins at different scales. Water Resour Manag 34(11):3709–3723

    Article  Google Scholar 

  • Wang S, Gao Y, Li Q, Gao J, Zhai S, Zhou Y, Cheng Y (2019) Long-term and inter-monthly dynamics of aquatic vegetation and its relation with environmental factors in Taihu Lake, China. Sci Total Environ 651:367–380

    Article  Google Scholar 

  • Wang Z, Kawamura K, Sakuno Y, Fan X, Gong Z, Lim J (2017) Retrieval of chlorophyll-a and total suspended solids using iterative stepwise elimination partial least squares (ISE-PLS) regression based on field hyperspectral measurements in irrigation ponds in Higashihiroshima, Japan. Remote Sens 9(3):264

    Article  Google Scholar 

  • Xia J, Zhang YY, Zhan C, Ye AZ (2011) Water quality management in China: the case of the Huai River Basin. Int J Water Resour Dev 27(1):167–180

    Article  Google Scholar 

  • Xiao Y, Guo Y, Yin G, Zhang X, Shi Y, Hao F, Fu Y (2022) UAV multispectral image-based urban river water quality monitoring using stacked ensemble machine learning algorithms—A case study of the Zhanghe River, China. Remote Sens 14(14):3272

    Article  Google Scholar 

  • Ye X, Li Y, Tong L, He L (2017) Remote sensing retrieval of suspended solids in Longquan Lake based on GA-SVM model. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE 5501–5504

  • Yuan J, Wang X, Yan CX, Wang SR, Ju XP, Li Y (2019) Soil moisture retrieval model for remote sensing using reflected hyperspectral information. Remote Sens 11(3):366

    Article  Google Scholar 

  • Zhu SD, Zhang F, Zhang ZY, Kung HT, Yushanjiang A (2019) Hydrogen and oxygen isotope composition and water quality evaluation for different water bodies in the Ebinur Lake watershed, Northwestern China. Water 11:2067

    Google Scholar 

  • Zhu W, Yu Q (2012) Inversion of chromophoric dissolved organic matter from EO-1 Hyperion imagery for turbid estuarine and coastal waters. IEEE Trans Geosci Remote Sens 51(6):3286–3298

    Article  Google Scholar 

Download references

Funding

This research was supported by grants awarded by Suzhou University of science and technology, the Youth Science Foundation Project of China (41801148).

Author information

Authors and Affiliations

  1. School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China

    Yi Tang, Yang Pan, Lei Zhang, Hongchen Yi, Yiping Gu & Weihao Sun

Authors
  1. Yi Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongchen Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yiping Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Weihao Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the research conception and design. Material preparation, data collection and analysis were performed by Lei Zhang, Hongchen Yi, Yiping Gu, and Weihao Sun. The first draft of the manuscript was written by Yi Tang and Yang Pan, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yi Tang or Yang Pan.

Ethics declarations

Ethics Approval

All authors kept the “Ethical Responsibilities of Authors”.

Consent to Participate

All authors gave explicit consent to participate in this study.

Consent for Publication

All authors gave explicit consent to publish this manuscript.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, Y., Pan, Y., Zhang, L. et al. Efficient Monitoring of Total Suspended Matter in Urban Water Based on UAV Multi-spectral Images. Water Resour Manage 37, 2143–2160 (2023). https://doi.org/10.1007/s11269-023-03484-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-023-03484-2

Keywords

Search

Navigation