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

Sensors

Fabiane Bordin¹  Laís V. de Souza¹  Lucas S. Kupssinskü¹  Juarez M. da Silva²  Maurício R. Veronez²  Leonardo C. Inocencio²  Jonas G. de Souza³  Frederico F. Mauad³  Ismael É. Koch³  Tainá T. Guimarães³  Luiz Gonzaga³  William F. M. Oliverio³  Emilie C. Koste⁴  Rogélio S. Jardim⁵ 

[1] Geoinformatics Lab—VizLab, Unisinos University, São Leopoldo 93022-750, Brazil;;Advanced Visualization &Graduate Programme in Applied Computing, Unisinos University, São Leopoldo 93022-750, Brazil;Graduate Programme in Environmental Engineering Sciences, São Carlos Engineering School, University of São Paulo, São Carlos 13566-590, Brazil;Graduate Programme in Geology, Unisinos University, São Leopoldo 93022-750, Brazil;

关键词: spectral imaging;    unmanned aerial vehicles;    correlation;    water quality monitoring;    artificial neural networks;   

DOI  :  10.3390/s18010159

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown