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@Article{PereiraPLOWMMC:2017:BuArMa,
               author = "Pereira, Allan A. and Pereira, Jos{\'e} M. C. and Libonati, 
                         Renata and Oom, Duarte and Waingort, Setzer Alberto and Morelli, 
                         Fabiano and Machado-Silva, Fausto and Carvalho, Luis Marcelo 
                         Tavares de",
          affiliation = "{Instituto Federal de Ci{\^e}ncia e Tecnologia do Sul de Minas 
                         Gerais} and {Universidade de Lisboa} and {Universidade Federal do 
                         Rio de Janeiro (UFRJ)} and {Universidade de Lisboa} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade Federal do Rio de 
                         Janeiro (UFRJ)} and {Universidade Federal de Lavras (UFLA)}",
                title = "Burned area mapping in the brazilian savanna using a one-class 
                         support vector machine trained by active fires",
              journal = "Remote Sensing",
                 year = "2017",
               volume = "9",
                pages = "1--21",
             keywords = "support vector machine one class, burned area, active fire, 
                         Cerrado, PROBA-V, VIIRS.",
             abstract = "We used the Visible Infrared Imaging Radiometer Suite (VIIRS) 
                         active fire data (375 m spatial resolution) to automatically 
                         extract multispectral samples and train a One-Class Support Vector 
                         Machine for burned area mapping, and applied the resulting 
                         classification algorithm to 300-m spatial resolution imagery from 
                         the Project for On-Board Autonomy-Vegetation (PROBA-V). The active 
                         fire data were screened to prevent extraction of unrepresentative 
                         burned area samples and combined with surface reflectance 
                         bi-weekly composites to produce burned area maps. The procedure 
                         was applied over the Brazilian Cerrado savanna, validated with 
                         reference maps obtained from Landsat images and compared with the 
                         Collection 6 Moderate Resolution Imaging Spectrometer (MODIS) 
                         Burned Area product (MCD64A1) Results show that the algorithm 
                         developed improved the detection of small-sized scars and 
                         displayed results more similar to the reference data than MCD64A1. 
                         Unlike active fire-based region growing algorithms, the proposed 
                         approach allows for the detection and mapping of burn scars 
                         without active fires, thus eliminating a potential source of 
                         omission error. The burned area mapping approach presented here 
                         should facilitate the development of operational-automated burned 
                         area algorithms, and is very straightforward for implementation 
                         with other sensors.",
                  doi = "10.3390/rs9111161",
                  url = "http://dx.doi.org/10.3390/rs9111161",
                 issn = "2072-4292",
             language = "en",
           targetfile = "pereira_burned.pdf",
        urlaccessdate = "05 dez. 2020"
}


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