Blooms of phytoplankton are associated with increased levels of nutrients in aquatic environments from external sources of pollution. Lakes ponds and reservoirs, including those used for human consumption, are susceptible to the development of algae species potentially toxic, increasing the risk for population and aquatic ecosystem.The activity of phytoplankton in the water can be monitored by quantifying the levels of chlorophyll-a, a photosynthetic active pigment present in all phytoplankton, which shows specific features of absorption and scattering of electromagnetic radiation. In this context, the purpose of this study is performing the spatial inference of chlorophyll-a concentration, by using different interpolation methods that allow representing its variability in early-stage phytoplankton bloom. The study was conducted in two different experimental areas and the measurements of chlorophyll-a concentration was performed using in vivo fluorometry, based on the fluorescent properties of the pigment. For the spatial inference of chlorophyll-a were considered two approaches: Geostatistics, using Ordinary Kriging to perform the spatial inference in non-sampled locations, and Artificial Neural Network (ANN), extracting input data of a WorldView-2 multispectral image. For both experimental areas were obtained small values of chlorophyll-a fluorescence and, consequently, low concentrations of pigment, indicating little photosynthetic activity. Both ordinary kriging as the trained ANN generated values representing the concentrations measured in situ, showing its effectiveness in capturing the characteristics of the phenomenon and also its small spatial variability.Pages: 6799-680
