Metodologia para monitoramento da qualidade da água de reservatórios utilizando sensoriamento remoto

Abstract

Water quality is one of the greatest challenges of the twenty-first century man, keep fresh water available, clean and safe water is one way to ensure good quality of life. Obtain means of monitoring the entry of pollutants, pesticides, industrial and domestic sewage, solid waste and sediments in reservoirs and other aquatic environments are essential to the operation of hydroelectric plants and to reduce costs in the stages of water treatment. Remote sensing is a proven technology for spatial analysis and can be used for surveys of vegetation, soil, rock, urban occupation and to study the water. From the need to constantly monitor the aquatic environment and accessibility of technology, which makes the remote sensing of a usual tool for planners and executors of environmental projects, this paper proposes a simple and affordable methodology for monitoring the water quality of reservoirs, through remote sensing technology. This research used data points from the literature, about the quality of water in the reservoir of San Simon, located in the lower course of Paraíba River, where water samples were collected and tested in the years 2002, 2003 and 2004. In this context, we analyzed three parameters of water quality: transparency, chlorophyll and turbidity. These data were compared with images from the satellite Land and Land-Sat 5-Mon 7 of the reservoir area in the same months of collections. The images were manipulated in GIS programs, selecting the bands of the electromagnetic spectrum more suitable for the analysis of each parameter. Within these bands were established bands of gray levels which were correlated with the analysis of water. The results were used to establish a rating scale of images, which was used to monitor changes in water transparency, presence of chlorophyll and turbidity in the reservoir of San Simon in 2009. As an extension of the methodology, the correlation between gray levels of images and data from the literature was used to predict the spatial distribution of parameters along the reservoir area. The results show that the proposed methodology is an interesting tool and inexpensive to monitor spatial and temporal variations of transparency, chlorophyll and turbidity in reservoirs.