@InProceedings{ShimabukuroAraiSantJorg:2017:MoDeFo,
               author = "Shimabukuro, Yosio Edemir and Arai, Egidio and Santos, Erone 
                         Ghizoni dos and Jorge, Anderson",
          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 = "Monitoring deforestation and forest degradation using 
                         multi-temporal fraction images derived from landsat sensor data in 
                         the brazilian amazon",
            booktitle = "Anais...",
                 year = "2017",
         organization = "International Geoscience and remote Sensing Symposium",
                 note = "Informa{\c{c}}{\~o}es Adicionais: This work presents a 
                         semi-automated procedure for monitoring deforestation and forest 
                         degradation in the Brazilian Amazon using a multi-temporal dataset 
                         of Landsat TM images. Degradation in forest cover in the Brazilian 
                         Amazon region is mainly due to selective logging of 
                         intact/un-managed forests and to uncontrolled fires. For this 
                         study, part of a Landsat TM scene located in the State of Mato 
                         Grosso, in the ?deforestation arc? of the Brazilian Amazon was 
                         selected. Landsat TM images acquired in years 2005, 2006, 2007, 
                         2008, 2009, 2010 and 2011 and one RapidEye image acquired in 2013 
                         were used in this study. The results showed that the proposed 
                         approach can be used for monitoring deforestation and forest 
                         degradation activities by selective logging and fires. The current 
                         availability of high spatial resolution data such as Sentinel-2 is 
                         expected to allow improving the assessment of deforestation and 
                         forest degradation processes using the proposed method and, 
                         consequently, facilitating the implementation of actions of forest 
                         protection..",
             keywords = "Forest degradation, Landsat, Remote Sensing.",
  conference-location = "Fort Worth, Texas",
      conference-year = "23-28 July",
                label = "lattes: 8183796256304624 4 ShimabukuroAraiSantJorg:2017:MoDeFo",
             language = "pt",
           targetfile = "yosio_monitoring.pdf",
                  url = "http://www.igarss2017.org/",
        urlaccessdate = "28 abr. 2024"
}