author = "Curtarelli, Marcelo Pedroso and Ogashawara, Igor and Souza, Arley 
                         Ferreira de and Stech, Jos{\'e} Luiz",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Application of remote sensing data to monitor drinking water 
                         supply in large cities: the S{\~a}o Paulo Metropolitan Region 
                         study case",
            booktitle = "Posters",
                 year = "2015",
         organization = "Aslo 2015. Aquatic Sciences Meeting",
             abstract = "Along the last year the S{\~a}o Paulo Metropolitan Region (SPMR) 
                         experienced one of the worst water supply crises in his history. 
                         The Cantareira System (CS), the largest water supply system of 
                         SPMR formed by 5 reservoirs, reached critical operational levels 
                         leading to supply problems. We presented an application of 
                         Landsat-8/OLI images to monitor the dynamics of water supply 
                         systems. Different techniques to delineate the reservoirs 
                         superficial area (RSA) were tested. Then, a multi-temporal 
                         approach was applied to monitor the RSA dynamics. The results 
                         showed that the Modified Normalized Difference Water Index (MNDWI) 
                         was the method which presented the best results to delineate the 
                         RSA. The multi-temporal approach showed that between May 2013 and 
                         August 2014 the CS RSA decreased 27 km˛ (~37%). The Jaguari was 
                         the reservoir of CS which presented the highest decrease in the 
                         RSA (~ 58%). The RSA showed a high correlation with the water 
                         volume in the CS (R˛ > 0.9). In a future step, the time series of 
                         RSA and auxiliary data will be used to generate a model to predict 
                         the CS volume dynamic.",
             language = "en",
        urlaccessdate = "04 dez. 2020"