Remotely sensed-based analysis about climatic and landscape change effects on phytoplankton bloom in Barra Bonita Reservoir (São Paulo State, Brazil)

• Autor(a) principal: Araújo, Bruno Munhoz [UNESP]
• Data de Publicação: 2023
• Outros Autores: Negri, Rogério Galante [UNESP], Moraes Ananias, Pedro Henrique [UNESP], Bressane, Adriano [UNESP], Rodgher, Suzelei [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1117/1.JRS.17.014509; http://hdl.handle.net/11449/247119
• topic: climatic variables; Google Earth Engine; land cover change; phytoplankton bloom; remote sensing; spectral index

The constant land use and land cover (LULC) changes combined with climatic factors are frequently assigned to anthropogenic eutrophication, one of the main ecological imbalances in aquatic systems characterized by dense phytoplankton proliferation. Beyond the degradation of freshwater ecosystems, some cyanobacterial and algae species produce toxins harmful to living beings. Distinct studies in the literature, usually supported by in situ data, have discussed the influence of LULC and climatic changes on phytoplankton bloom events. In this context, motivated by the importance of understanding the environmental mechanisms assigned to phytoplankton bloom events and considering the difficulties imposed by field data collection, our study focuses on analyzing the mentioned issue only using remotely sensed time series data. For this purpose, we performed a temporal analysis between 1985 and 2018 over a portion of the Barra Bonita Hydroelectric Reservoir, Brazil. Initially, we obtained the landscape occupation, precipitation, and temperature information from the MapBiomas, FLDAS, and CHIRPS projects, respectively. A fully automatic algorithm fed by Landsat image series and supported by Google Earth Engine functions was developed and employed to identify and quantify phytoplankton bloom events. Then, the obtained data were inspected by distinct statistical procedures, including correlation and trend analysis. Although there was an absence of a relationship between the climatic components and the emergence of phytoplankton blooms, it was identified using linear regression models (R2 ≥ 78 %) an intensification of blooms after the increase in nonnatural forestry areas, reduction of pastures, and advance of agricultural areas. Furthermore, machine learning methods were employed to obtain nonlinear regression models (R2 ≥ 73 %), making evident that the landscape changes are mainly responsible for the phytoplankton insurgences in the analyzed region. This result agrees with other studies found in the literature and highlights the possibility of investigating anthropogenic eutrophication only using remotely sensed data and automatic algorithms.