@InProceedings{RodriguesEsca:2019:FaAsFo,
               author = "Rodrigues, Danilo Avancini and Escada, Maria Isabel Sobral",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Factors associated with forest degradation on an Amazonian logging 
                         frontier area in southwestern Par{\'a}, Brazil",
                 year = "2019",
         organization = "Congresso Mundial da IUFRO",
             abstract = "Forest degradation is a long-term process that reduces forests 
                         biodiversity and impoverishes the ecosystems. Understanding the 
                         factors that generates and intensifies forest degradation, and its 
                         consequences, allows directing public policies to prevent this 
                         process. This study performed a spatial regression analysis 
                         between forest degradation intensity (dependent variable), 
                         environmental, political and economic variables in Novo Progresso 
                         (PA), a logging frontier expansion area in the Amazon Forest, from 
                         2009 to 2011. The independent variables were related to fire 
                         occurrence, deforestation, conservation units, indigenous lands, 
                         logging poles and roads. The relationship between the dependent 
                         and independent variables (R2 and p-value) was individually 
                         tested, and the variables with the highest relationship were 
                         included in the regression model. Then, the variables with 
                         multicollinearity were excluded from the model with the stepwise 
                         backward technique. It was used the Moran test to detect spatial 
                         dependency on the data. Spatial dependency was detected with 
                         statistical significance (I = 0,2434; p-valor = 0 e z-score = 
                         9,91), justifying the use of a spatial regression model. The 
                         Lagrange Multiplier test pointed out Spatial Lag Model as the best 
                         model adjusted to the data. The variables area of deforestation 
                         and total edge area explained 40% (R2 -adj: 0,4092) of the forest 
                         degradation intensity, indicating that fragmented forest and the 
                         forest areas closest to deforested areas are more likely to suffer 
                         higher degradation. For further studies, the R2 of the model can 
                         be raised by adding variables related to pasture expansion, forest 
                         management, colonization projects and updating the roads data 
                         yearly.",
  conference-location = "Curitiba, PR",
      conference-year = "29 set. - 05 out.",
             language = "en",
        urlaccessdate = "01 maio 2024"
}