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Estimating chlorophyll-a concentration through OLI Landsat-8 images in Bonito River, Baixo Tietê watershed, SP

Grant number: 21/04466-0
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Effective date (Start): July 01, 2021
Effective date (End): January 31, 2022
Field of knowledge:Physical Sciences and Mathematics - Geosciences - Geophysics
Principal Investigator:Fernanda Sayuri Yoshino Watanabe
Grantee:Lucas Lima Ladeira
Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil


Reservoirs are aquatic ecosystems formed by the construction of dams in rivers. The increase in the retention time and the consequent increase in the time available for nutrients leads to eutrophication of the environment and algal blooms. Some species of phytoplankton can produce toxic metabolites for animals and humans. Within this context, the monitoring of phytoplankton concentrations and photosynthetic pigments in inland water bodies has a great environmental and health importance for the management of water resources. Due to its large areas, the reservoirs become environments that are difficult to monitor water quality through traditional methods, such as collecting water samples for analysis. Thus, the use of remote sensing data is a useful tool for assessing water quality, in terms of optically active constituents, i.e, those that interact with electromagnetic radiation. Several bio-optical models have been developed to estimate optically active constituents from remote sensing data. These models are being used in different approaches, such as empirical, semi-analytical and based on machine learning. In this context, the objective of the proposed project is to estimate the concentration of chlorophyll-a in the Bonito River, a tributary of Tietê River, in the corresponding stretch to the Nova Avanhandava reservoir, from the application of bio-optical models in Operational Land Imager sensor (OLI) Landsat-8. The least squares statistical method and the algorithm based on machine learning will be tested. From the proposed project, it is expected to obtain suitable models for estimating the concentration of chlorophyll-a, in addition to contributing to the use of technologies and tools that estimate water quality, assisting in environmental planning and water resources management. (AU)

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