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Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images

Author

Listed:
  • Tainá T. Guimarães

    (Graduate Programme in Environmental Engineering Sciences, São Carlos Engineering School, University of São Paulo, São Carlos 13566-590, Brazil)

  • Maurício R. Veronez

    (Advanced Visualization & Geoinformatics Lab—VizLab, Unisinos University, São Leopoldo 93022-750, Brazil
    Graduate Programme in Applied Computing, Unisinos University, São Leopoldo 93022-750, Brazil
    Graduate Programme in Biology, Unisinos University, São Leopoldo 93022-750, Brazil)

  • Emilie C. Koste

    (Advanced Visualization & Geoinformatics Lab—VizLab, Unisinos University, São Leopoldo 93022-750, Brazil)

  • Eniuce M. Souza

    (Advanced Visualization & Geoinformatics Lab—VizLab, Unisinos University, São Leopoldo 93022-750, Brazil
    Graduate Programme in Applied Computing, Unisinos University, São Leopoldo 93022-750, Brazil)

  • Diego Brum

    (Advanced Visualization & Geoinformatics Lab—VizLab, Unisinos University, São Leopoldo 93022-750, Brazil
    Graduate Programme in Applied Computing, Unisinos University, São Leopoldo 93022-750, Brazil)

  • Luiz Gonzaga

    (Advanced Visualization & Geoinformatics Lab—VizLab, Unisinos University, São Leopoldo 93022-750, Brazil
    Graduate Programme in Applied Computing, Unisinos University, São Leopoldo 93022-750, Brazil)

  • Frederico F. Mauad

    (Graduate Programme in Environmental Engineering Sciences, São Carlos Engineering School, University of São Paulo, São Carlos 13566-590, Brazil)

Abstract

The concentration of suspended solids in water is one of the quality parameters that can be recovered using remote sensing data. This paper investigates the data obtained using a sensor coupled to an unmanned aerial vehicle (UAV) in order to estimate the concentration of suspended solids in a lake in southern Brazil based on the relation of spectral images and limnological data. The water samples underwent laboratory analysis to determine the concentration of total suspended solids (TSS). The images obtained using the UAV were orthorectified and georeferenced so that the values referring to the near, green, and blue infrared channels were collected at each sampling point to relate with the laboratory data. The prediction of the TSS concentration was performed using regression analysis and artificial neural networks. The obtained results were important for two main reasons. First, although regression methods have been used in remote sensing applications, they may not be adequate to capture the linear and/or non-linear relationships of interest. Second, results show that the integration of UAV in the mapping of water bodies together with the application of neural networks in the data analysis is a promising approach to predict TSS as well as their temporal and spatial variations.

Suggested Citation

  • Tainá T. Guimarães & Maurício R. Veronez & Emilie C. Koste & Eniuce M. Souza & Diego Brum & Luiz Gonzaga & Frederico F. Mauad, 2019. "Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images," Sustainability, MDPI, vol. 11(9), pages 1-13, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:9:p:2580-:d:228284
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    References listed on IDEAS

    as
    1. Tainá T. Guimarães & Maurício R. Veronez & Emilie C. Koste & Luiz Gonzaga & Fabiane Bordin & Leonardo C. Inocencio & Ana Paula C. Larocca & Marcelo Z. De Oliveira & Dalva C. Vitti & Frederico F. Mauad, 2017. "An Alternative Method of Spatial Autocorrelation for Chlorophyll Detection in Water Bodies Using Remote Sensing," Sustainability, MDPI, vol. 9(3), pages 1-14, March.
    2. Qiaozhen Guo & Xiaoxu Wu & Qixuan Bing & Yingyang Pan & Zhiheng Wang & Ying Fu & Dongchuan Wang & Jianing Liu, 2016. "Study on Retrieval of Chlorophyll-a Concentration Based on Landsat OLI Imagery in the Haihe River, China," Sustainability, MDPI, vol. 8(8), pages 1-15, August.
    3. Gago, J. & Douthe, C. & Coopman, R.E. & Gallego, P.P. & Ribas-Carbo, M. & Flexas, J. & Escalona, J. & Medrano, H., 2015. "UAVs challenge to assess water stress for sustainable agriculture," Agricultural Water Management, Elsevier, vol. 153(C), pages 9-19.
    4. Jamil Amanollahi & Shahram Kaboodvandpour & Hiva Majidi, 2017. "Evaluating the accuracy of ANN and LR models to estimate the water quality in Zarivar International Wetland, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(3), pages 1511-1527, February.
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    Cited by:

    1. Benjamin T. Fraser & Christine L. Bunyon & Sarah Reny & Isabelle Sophia Lopez & Russell G. Congalton, 2022. "Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review," Geographies, MDPI, vol. 2(2), pages 1-38, June.

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