author = "Mello, M{\'a}rcio Pupin de and Vieira, Carlos Ant{\^o}nio 
                         Oliveira and Aguiar, Daniel Alves de and Rudorff, Bernardo 
                         Friedrich Theodor",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais - INPE} and 
                         {Universidade Federal de Vi{\c{c}}osa - UFV} and {Instituto 
                         Nacional de Pesquisas Espaciais - INPE} and {Instituto Nacional de 
                         Pesquisas Espaciais - INPE}",
                title = "Classifica{\c{c}}{\~a}o da colheita da 
                         cana-de-a{\c{c}}{\'u}car por meio de imagens de sat{\'e}lite 
                         utilizando superf{\'{\i}}cies de resposta espectro-temporais",
            booktitle = "Anais...",
                 year = "2009",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                pages = "279--286",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 14. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "remote sensing, multitemporal image classification, accuracy 
                         assessment, automatization, sensoriamento remoto, 
                         classifica{\c{c}}{\~a}o multitemporal de imagens, 
                         avalia{\c{c}}{\~a}o da exatid{\~a}o, 
             abstract = "Environmental impacts related to sugarcane crop cultivation are 
                         becoming a worldwide issue due to the great potential that ethanol 
                         has to mitigate the emission of green house gases. However, the 
                         sugarcane straw burning prior to harvest is still a critical 
                         environmental problem that needs special attention. S{\~a}o Paulo 
                         State represents more than 60% of the Brazilian sugarcane 
                         production with 4.9 millions ha of cultivated area. The State 
                         government together with the private sugarcane production sector 
                         established in 2007 a protocol to gradually stop the sugarcane 
                         straw burning up to 2014. Remote sensing images have the potential 
                         to monitor the harvest management procedure identifying the fields 
                         that were harvested with and without straw burning prior to 
                         harvest. Currently, this identification and classification is 
                         carried out using visual interpretation which provides high 
                         quality results but is extremely tedious and time consuming. The 
                         present work has the objective to propose an automated 
                         classification procedure based on Spectral Temporal Response 
                         Surfaces (STRS) to classify the recent harvested sugarcane into 
                         burned and non-burned fields. This procedure is based on a 
                         pixel-by-pixel classification considering the spectral-temporal 
                         reflectance of each image pixel generating a thematic map. A 
                         visual interpreted reference map was used to assess the automated 
                         classification map accuracy which showed an overall index of 
                         87.3%. The STRS classification procedure showed to be a promising 
                         alternative to automate the generation of thematic maps of 
                         harvested sugarcane with and without straw burning based on 
                         spectral-temporal remote sensing images.",
      accessionnumber = "0",
  conference-location = "Natal",
      conference-year = "25-30 abr. 2009",
                 isbn = "978-85-17-00044-7",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2008/",
                  url = "http://urlib.net/rep/dpi.inpe.br/sbsr@80/2008/",
           targetfile = "279-286.pdf",
                 type = "Agricultura",
        urlaccessdate = "19 jan. 2021"