@InProceedings{XaudSantMartXaud:2017:PrImSA,
               author = "Xaud, Haron Abrahim Magalh{\~a}es and Santos, Jo{\~a}o Roberto 
                         dos and Martins, Flora da Silva Ramos Vieira and Xaud, Maristela 
                         Ramalho",
                title = "Processamento de imagem SAR (Banda L) para detec{\c{c}}{\~a}o 
                         hist{\'o}rica de {\'a}reas florestais degradadas por 
                         inc{\^e}ndios recorrentes em Roraima",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "7216--7223",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "This paper aims to analyse the historical images of ALOS/PALSAR 
                         data (L Band) as an alternative to monitoring degradation of 
                         tropical forests affected by fires in the Northernmost Amazon 
                         Region. The sites of this study are located in the Apia{\'u} 
                         Region, State of Roraima, Brazil. The study area was burned 
                         irregularly in 1998, 2003 and 2007 fires. The post-fire image 
                         (Jan.2008) was obtained in HH polarization. We ortoretified the 
                         PALSAR data and generated Amplitude and Intensity images. 
                         Additionally it were generated 13 textural data based on 
                         occurrence and co-occurrence matrix. Using Object-Based Image 
                         Analysis (OBIA) we segmented a 2007 Landsat TM image (as 
                         reference) to obtain objects that were described by 15 attributes 
                         derived from SAR images plus the standard deviation (SD) of each 
                         one, totalizing 30 attributes per object. We selected training and 
                         reference samples divided into 5 classes: (FN) unburned forests; 
                         (FQ1B) forests affected by 1 fire-low intensity; (FQ1A) forests 
                         affected by 1 fire-high intensity; (FQ2) forests affected by 2 
                         fires; (FQ3) forests affected by 3 fires. We optimized the 
                         selection of PALSAR attributes to obtain the best separability 
                         among classes using a feature space optimization tool in OBIA 
                         based on Nearest Neighbor classifier. From the 30 attributes 
                         derived from PALSAR image, the results highlighted the best 
                         attributes (images) to detect degraded areas by recurrent fires; 
                         eight of them obtained from SD of textures and amplitude images.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59365",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMFB5",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMFB5",
           targetfile = "59365.pdf",
                 type = "Degrada{\c{c}}{\~a}o de florestas",
        urlaccessdate = "27 abr. 2024"
}