@InProceedings{SantosDiSoDiAdMaGo:2013:IdMaFo,
               author = "Santos, Diogo Corr{\^e}a and Dias, M{\'{\i}}rian Corr{\^e}a 
                         and Souza, Arlesson Antonio de Almeida and Diniz, Cesar Guerreiro 
                         and Adami, Marcos and Maia, Jana{\'{\i}}na Sant'Ana and Gomes, 
                         Alessandra Rodrigues",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and {} 
                         and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Identification and mapping of forest degradation patterns on the 
                         Brazilian Amazon based on AWIFS sensor image",
            booktitle = "Proceedings...",
                 year = "2013",
         organization = "Semana Latino-Americana de Percepci{\'o}n Remota, (LARS).",
                 note = "Informa{\c{c}}{\~o}es Adicionais: The Near Real Time 
                         Deforestation Detection System (DETER) was developed by the 
                         National Institute for Space Research as an MODIS based alert 
                         system to support surveillance and control of deforestation, 
                         integrating the plan for prevention and control of deforestation 
                         in the Amazon and has been responsible for a fast and systematic 
                         deforestation survey since May 2004 in the Brazilian. In the last 
                         decade there was a reduction in the size of the deforestation 
                         patches. This reduction causes a major limitation to DETER mapping 
                         capabilities due to its reduced spatial resolution. This study 
                         aims to map and identify forest degradation patterns using the 
                         Advanced Wide Field Sensor (AWiFS) onboard of Resourcesat-I 
                         satellite testing its suitability to map and monitor deforestation 
                         in the Amazon rainforest. It was possible to identify the presence 
                         of six forest degradation patterns, within a mapped area of 2757.3 
                         ha as clear cut, 7620.91 ha for moderate degradation, 9295.42 ha 
                         as high degradation, 31.7810,38 ha as burnt scar, 9300.22 ha for 
                         regular selective cut and 928.77 ha for conventional selective 
                         cut. The highest percentage of mapped area (~ 91%) is associated 
                         with the burnt scar class. This result indicates that the AWiFS 
                         sensor with moderate spatial resolution, can be used in the 
                         monitoring and surveillance of the Brazilian Amazon, providing 
                         support for conservation measures. and {1. INTRODCTION} and The 
                         Brazilian Amazon has large biogeographic heterogeneity and had its 
                         human occupation processes resulting in a huge variety of spatial 
                         patterns that may be associated with different actors, history and 
                         types of occupation (Alves, 2002; Fearnside, 2008). It is 
                         estimated that in 1990 the rainforests covered and area between 
                         11.5 and 12.4 million km (Achard et al., 2002). The Brazilian 
                         Legal Amazon (BLA), has approximately 5 million km , represents 
                         about 30% of the rainforests being the largest contiguous 
                         rainforest in the planet and home a vast biodiversity (Foley et 
                         al., 2007). I.",
  conference-location = "Santiago - Chile",
      conference-year = "2013",
                label = "lattes: 8568657851459133 2 SantosDiSoDiAdMaGo:2013:IdMaFo",
             language = "en",
           targetfile = "santos_identification.pdf",
        urlaccessdate = "01 maio 2024"
}