An Alternative Method of Spatial Autocorrelation for Chlorophyll Detection in Water Bodies Using Remote Sensing

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 and Frederico F. Mauad
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Tainá T. Guimarães: Advanced Visualization & Geoinformatics Lab—VizLab, Unisinos University, São Leopoldo 93022-750, Brazil
Maurício R. Veronez: Advanced Visualization & Geoinformatics Lab—VizLab, Unisinos University, São Leopoldo 93022-750, Brazil
Emilie C. Koste: Advanced Visualization & Geoinformatics Lab—VizLab, Unisinos University, São Leopoldo 93022-750, Brazil
Luiz Gonzaga: Advanced Visualization & Geoinformatics Lab—VizLab, Unisinos University, São Leopoldo 93022-750, Brazil
Fabiane Bordin: Advanced Visualization & Geoinformatics Lab—VizLab, Unisinos University, São Leopoldo 93022-750, Brazil
Leonardo C. Inocencio: Advanced Visualization & Geoinformatics Lab—VizLab, Unisinos University, São Leopoldo 93022-750, Brazil
Ana Paula C. Larocca: Graduate Programme in Transportation Engineering, São Carlos Engineering School, University of São Paulo, São Paulo 93022-750, Brazil
Marcelo Z. De Oliveira: Advanced Visualization & Geoinformatics Lab—VizLab, Unisinos University, São Leopoldo 93022-750, Brazil
Dalva C. Vitti: Graduate Programme in Environmental Engineering Sciences, São Carlos Engineering School, University of São Paulo, São Leopoldo 93022-750, Brazil
Frederico F. Mauad: Graduate Programme in Environmental Engineering Sciences, São Carlos Engineering School, University of São Paulo, São Leopoldo 93022-750, Brazil

Sustainability, 2017, vol. 9, issue 3, 1-14

Abstract: Additional measures of in situ water quality monitoring in natural environments can be obtained through remote sensing because certain elements in water modify its spectral behavior. One of the indicators of water quality is the presence of algae, and the aim of this study was to propose an alternative method for the quantification of chlorophyll in water by correlating spectral data, infrared images, and limnology data. The object of study was an artificial lake located at Unisinos University, São Leopoldo/RS, Brazil. The area has been mapped with a modified NGB (near infrared (N), green (G) and blue (B)) camera coupled to an unmanned aerial vehicle (UAV). From the orthorectified and georeferenced images, a modified normalized difference vegetation index (NDVImod) image has been generated. Additionally, 20 sampling points have been established on the lake. At these points, in situ spectral analysis with a spectroradiometer has been performed, and water samples have been collected for laboratory determination of chlorophyll concentrations. The correlation resulted in two models. The first model, based on the multivariate analysis of spectral data, and the second model, based on polynomial equations from NDVI, had coefficients of determination (R2) of 0.86 and 0.51, respectively. This study confirmed the applicability of remote sensing for water resource management using UAVs, which can be characterized as a quick and easy methodology.

Keywords: lake; chlorophyll; normalized difference vegetation index (NDVI); spectral reflectance; space autocorrelation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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