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. 2018 Jan 9;18(1):159.
doi: 10.3390/s18010159.

Proposal of a Method to Determine the Correlation between Total Suspended Solids and Dissolved Organic Matter in Water Bodies from Spectral Imaging and Artificial Neural Networks

Maurício R Veronez  1   2   3 , Lucas S Kupssinskü  4 , Tainá T Guimarães  5 , Emilie C Koste  6 , Juarez M da Silva  7 , Laís V de Souza  8 , William F M Oliverio  9 , Rogélio S Jardim  10 , Ismael É Koch  11 , Jonas G de Souza  12 , Luiz Gonzaga  13   14 , Frederico F Mauad  15 , Leonardo C Inocencio  16 , Fabiane Bordin  17
Affiliations

Proposal of a Method to Determine the Correlation between Total Suspended Solids and Dissolved Organic Matter in Water Bodies from Spectral Imaging and Artificial Neural Networks

Maurício R Veronez et al. Sensors (Basel). .

Abstract

Water quality monitoring through remote sensing with UAVs is best conducted using multispectral sensors; however, these sensors are expensive. We aimed to predict multispectral bands from a low-cost sensor (R, G, B bands) using artificial neural networks (ANN). We studied a lake located on the campus of Unisinos University, Brazil, using a low-cost sensor mounted on a UAV. Simultaneously, we collected water samples during the UAV flight to determine total suspended solids (TSS) and dissolved organic matter (DOM). We correlated the three bands predicted with TSS and DOM. The results show that the ANN validation process predicted the three bands of the multispectral sensor using the three bands of the low-cost sensor with a low average error of 19%. The correlations with TSS and DOM resulted in R² values of greater than 0.60, consistent with literature values.

Keywords: artificial neural networks; correlation; spectral imaging; unmanned aerial vehicles; water quality monitoring.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of the proposed method.
Figure 2
Figure 2
Study area.
Figure 3
Figure 3
Positions of the sampling points.
Figure 4
Figure 4
Hexacopter used for lake mapping.
Figure 5
Figure 5
Images generated by the compositions of the spectral bands obtained by ANN: (A) NDVI image; and (B) NDWI image.
Figure 6
Figure 6
Scatter plot of SST and DOM known and simulated considering the b4 and b5 bands in obtaining the NDVI and NDWI indices.
Figure 7
Figure 7
Images generated by the compositions of the spectral band b9 instead of the band b5 obtained by ANN: (A) NDVI image; and (B) NDWI image.
Figure 8
Figure 8
Scatter plot of SST and DOM known and simulated considering the b4 and b9 bands in obtaining the NDVI and NDWI indices.
Figure 9
Figure 9
Comparison between the correlation results.
See this image and copyright information in PMC

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