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@Article{LucianoPiRoDuLaLeLe:2019:GeSpOB,
               author = "Luciano, Ana Cl{\'a}udia dos Santos and Picoli, Michelle Cristina 
                         Ara{\'u}jo and Rocha, Jansle Vieira and Duft, Daniel Garbellini 
                         and Lamparelli, Rubens Augusto Camargo and Leal, Manoel Regis Lima 
                         Verde and Le Maire, Guerric",
          affiliation = "{Centro Naconal de Pesquisa em Energia e Materiais (CNPEM)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Estadual de Campinas (UNICAMP)} and {Centro Naconal 
                         de Pesquisa em Energia e Materiais (CNPEM)} and {Universidade 
                         Estadual de Campinas (UNICAMP)} and {Centro Naconal de Pesquisa em 
                         Energia e Materiais (CNPEM)} and CIRAD, UMR Eco\&So",
                title = "A generalized space-time OBIA classification scheme to map 
                         sugarcane areas at regional scale, using Landsat images 
                         time-series and the random forest algorithm",
              journal = "International Journal of Applied Earth Observation and 
                         Geoinformation",
                 year = "2019",
               volume = "80",
                pages = "127--136",
                month = "Aug.",
             keywords = "Classifier extension, Data mining, Machine learning, Sugarcane 
                         mapping.",
             abstract = "The monitoring of sugarcane areas is important for sustainable 
                         planning and management of the sugarcane industry in Brazil. We 
                         developed an operational Object-Based Image Analysis (OBIA) 
                         classification scheme, with generalized space-time classifier, for 
                         mapping sugarcane areas at the regional scale in Sao Paulo State 
                         (SP). Binary random forest (RF) classification models were 
                         calibrated using multi-temporal data from Landsat images, at 10 
                         sites located across SP. Space and time generalization were tested 
                         and compared for three approaches: a local calibration and 
                         application; a cross-site spatial generalization test with the RF 
                         model calibrated on a site and applied on other sites; and a 
                         unique space-time classifier calibrated with all sites together on 
                         years 2009-2014 and applied to the entire SP region on 2015. The 
                         local RF models Dice Coefficient (DC) accuracies at sites 1 to 8 
                         were between 0.83 and 0.92 with an average of 0.89. The cross-site 
                         classification accuracy showed an average DC of 0.85, and the 
                         unique RF model had a DC of 0.89 when compared with a reference 
                         map of 2015. The results demonstrated a good relationship between 
                         sugarcane prediction and the reference map for each municipality 
                         in SP, with R-2 = 0.99 and only 5.8% error for the total sugarcane 
                         area in SP, and compared with the area inventory from the 
                         Brazilian Institute of Geography and Statistics, with R-2 = 0.95 
                         and -1% error for the total sugarcane area in SP. The final unique 
                         RF model allowed monitoring sugarcane plantations at the regional 
                         scale on independent year, with efficiency, low-cost, limited 
                         resources and a precision approximating that of a 
                         photointerpretation.",
                  doi = "10.1016/j.jag.2019.04.013",
                  url = "http://dx.doi.org/10.1016/j.jag.2019.04.013",
                 issn = "0303-2434",
             language = "en",
           targetfile = "1-s2.0-S0303243418311917-main.pdf",
        urlaccessdate = "29 nov. 2020"
}


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