@InProceedings{BalieiroGirãSilvSarc:2019:UsSeRe,
               author = "Balieiro, C{\'{\i}}ntia and Gir{\~a}o, Vanessa and Silva, 
                         Thamires and Sarcinelli, Tathiane",
          affiliation = "{The Nature Conservancy Brasil (TNC)} and {The Nature Conservancy 
                         Brasil (TNC)} and {The Nature Conservancy Brasil (TNC)} and 
                         {Fibria Celulose S.A (FIBRIA)}",
                title = "Uso de sensoriamento remoto para monitorar projetos de 
                         restaura{\c{c}}{\~a}o de vegeta{\c{c}}{\~a}o nativa do 
                         Brasil",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "1883--1886",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Restaura{\c{c}}{\~a}o, sensoriamento remoto, 
                         classifica{\c{c}}{\~a}o, drones, vegeta{\c{c}}{\~a}o nativa, 
                         Restoration, remote sensing, classification, drones, native 
                         vegetation.",
             abstract = "Um dos gargalos para o sucesso da restaura{\c{c}}{\~a}o de 
                         vegeta{\c{c}}{\~a}o nativa em larga escala {\'e} a dificuldade 
                         de obten{\c{c}}{\~a}o de dados otimizados e confi{\'a}veis, 
                         principalmente no monitoramento. Por outro lado, imagens obtidas 
                         por drones e sat{\'e}lite podem ser usadas para monitorar 
                         {\'a}reas de restaura{\c{c}}{\~a}o em tempo real. Este estudo 
                         tem como objetivo monitorar 4.203,00 ha de restaura{\c{c}}{\~a}o 
                         no nordeste e sudeste do Brasil, utilizando imagens de 
                         sat{\'e}lite (SPOT 6\&7 e Pl{\^e}iades). A 
                         classifica{\c{c}}{\~a}o semiautom{\'a}tica foi aplicada nas 
                         imagens de sat{\'e}lite para comparar a cobertura do dossel. O 
                         {\'{\i}}ndice Kappa indicou 90% de efici{\^e}ncia do 
                         classificador. Usando imagens SPOT, foram detectados 1.729 ha de 
                         cobertura de dossel, 15% a mais do que a {\'a}rea detectada 
                         usando Pl{\^e}iades (1.471 ha). Agora est{\'a} sendo avaliada a 
                         cobertura do dossel por drones, para validar {\'a}reas 
                         priorit{\'a}rias e estimar riqueza de esp{\'e}cies de 
                         {\'a}rvores e arbustos, plantadas ou regeneradas, ao longo do 
                         tempo. ABSTRACT: One of the bottlenecks to the success of 
                         large-scale native vegetation restoration is the difficulty of 
                         obtaining optimized and reliable data, especially in monitoring. 
                         On the other hand, images obtained by drones and satellite can be 
                         used to monitor restoration areas in real time. This study aims to 
                         monitor 4,203.00 ha of restoration in the northeast and southeast 
                         of Brazil, using satellite imagery (SPOT 6 \& 7 and Pleiades) and 
                         drones. The semi-automatic classification was applied to the 
                         satellite images to compare the canopy cover. The Kappa index 
                         indicated 90% efficiency of the classifier. Using SPOT images, 
                         1,729 ha of canopy cover were detected, 15% more than the area 
                         detected using Pleiades (1,471 ha). Now is being evaluated the 
                         canopy cover detected by drones to validate priority areas and to 
                         estimate tree and shrub richness, planted or regenerated, over 
                         time.",
  conference-location = "Santos",
      conference-year = "14-17 abril 2019",
                 isbn = "978-85-17-00097-3",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3UB3388",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3UB3388",
           targetfile = "98034.pdf",
                 type = "VANTs, videografia e alta resolu{\c{c}}{\~a}o",
        urlaccessdate = "16 jun. 2024"
}