The maintenance of biodiversity is a global concern in economic, social and environmental terms. Thus, conservation units for the protection of natural environments were created. Despite the importance of these areas, forest fires have caused immeasurable and constant damage to Conservation Units. In view of this, the objective of this study was to determine the risk areas of forest fire occurrence through Fuzzy logic modeling in the Córrego Grande Biological Reserve, located in the Mata Atlântica Brazilian biome. In order to prospect for areas at risk of forest fires, the following variables were inserted in the model: land use, road network, slope and relief orientation. Finally, the model was validated by comparing the location of fire occurrences between the years 2008 and 2018, and a layout of the risk classes in the study area. In doing so, it was found that 65.87% of the area is between the 'moderate' and 'very high' range of fire risk classes, and that 70.22% of the fires which occurred in the studied period also occurred in that class range. The study concluded that the most influential variable on the risk level of fire occurrence is the forest road network. In this way, the proposed methodology can be applied to any other areas and types of land cover.

Forest fires are responsible for the destruction of millions of hectares of forest worldwide, and they lead to diverse economic, social, and landscape damage. Thus, the development of techniques to combat them has become increasingly necessary. In this context, this study aims to evaluate the efficiency of different fire retardants at different concentrations in fighting forest fires, considering the burning times and intensities of forest fuel. The study was conducted inside Eucalyptus spp. stands using three fire retardants (Silv-Ex, F-500, and HoldFire) at three concentrations (1%, 1.5%, and 2%), in addition to a water-only control. A completely randomized design was used, and the statistical analysis was completed based on experimental arrangements (factorial 3x3). Variables evaluated during the burning process were as follows: burning times (the time required for the flames to consume all forest fuel within the sample, with and without the retardant) and intensity of burning. Results regarding the time and intensity of burning in relation to the concentrations indicated a decreasing trend as the latter were increased, classifying the highest dose (2%) as the most efficient. For the retardants, all were observed to be efficient, with Silv-Ex being the most appropriate as it significantly reduced the burning intensity and increased the burning time of the forest fuel.

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Flooding occurrence is one of the most common phenomena that impact urban areas, and this intensifies during heavy rainfall periods. Knowing the areas with the greatest vulnerability is of paramount importance as it allows mitigating actions to be implemented in order to minimize the generated impacts. In this context, this study aimed to use Geographic Information System (GIS) tools to identify the areas with greater flooding vulnerability in Espírito Santo state, Brazil. The study was based on the following methodological steps: (1) a Digital Elevation Model (DEM) acquisition and watersheds delimitation; (2) maximum and accumulated rainfall intensity calculations for the three studied periods using meteorological data; (3) a land use and occupation map reclassification regarding flood vulnerability and fuzzy logic application; (4) an application of Euclidean distance and fuzzy logic in hydrography and water mass vector variables; (5) a flood vulnerability model generation. Based on the found results, it was observed that the metropolitan and coastal regions presented as greater flood vulnerability areas during the dry season, as in these regions, almost all of the 9.18% of the state’s area was classified as highly vulnerable, while during rainy season, the most vulnerable areas were concentrated in Caparaó and in the coastal and immigration and metropolitan regions, as in these regions, almost all of the 12.72% of the state’s area was classified as highly vulnerable. In general, by annually distributing the rainfall rates, a greater flood vulnerability was observed in the metropolitan and coastal and immigration regions, as in these areas, almost all of the 7.72% of the state’s area was classified as highly vulnerable. According to the study, Espírito Santo state was mostly classified as a low (29.15%) and medium (28.06%) flood vulnerability area considering the annual period, while its metropolitan region has a very high flood vulnerability risk. Finally, GIS modeling is important to assist in decision making regarding public management and the employed methodology presents worldwide application potential.