Environmental vulnerability evolution in the Brazilian Amazon.

Decision making and environmental policies are mainly based on propensity level to impact in the area. The propensity level can be determined through artificial intelligence techniques included in geotechnological universe. Thus, this study aimed to determine the areas of greatest vulnerability to human activities, in Amazon biome, through MODIS images of Land use and land cover (LULC) from the 2001 and 2013. Remote sensing, Euclidean distance, Fuzzy logic, AHP method and analysis of net variations were applied to specialize the classes of vulnerability in the states belonging to the Amazon Biome. From the results, it can be seen that the class that most evolved in a positive net gain during the evaluated period was "very high" and the one that most reduced was "high", showing that there was a transition from "high" to "very high" risk areas. The states with the largest areas under "very high" risk class were Mato Grosso (101,100.10 km2) and Pará (81,010.30 km2). It is concluded that the application of remote sensing techniques allows the determination and assessment of the environmental vulnerability evolution. Mitigation measures urgently need to be implemented in the Amazon biome. The methodology can be extended to any other area of the planet.

Use of machine learning as a tool for determining fire management units in the brazilian atlantic forest.

Geoprocessing techniques are generally applied in natural disaster risk management due to their ability to integrate and visualize different sets of geographic data. The objective of this study was to evaluate the capacity of classification and regression tree (CART) to assess fire risk. MCD45A1 product of the burnt area, relative to a 16-year period (2000-2015) was used to obtain a fire occurrence map, from center points of the raster, using a kernel density approach. The resulting map was then used as a response variable for CART analysis with fire influence variables used as predictors. A total of 12 predictors were determined from several databases, including environmental, physical, and socioeconomic aspects. Rules generated by the regression process allowed to of define different risk levels, expressed in 35 management units, and used to produce a fire prediction map. Results of the regression process (r = 0.94 and r² = 0.88) demonstrate the capability of the CART algorithm in highlighting hierarchical relationships among predictors, while the model's easy interpretability provides a solid basis for decision making. This methodology can be expanded in other environmental risk analysis studies and applied to any area of the globe on a regional scale.

Gis and fuzzy logic applied to modelling forest fire risk.

Fire risk mapping is a basic planning and protection element. This study presents the application of fuzzy logic in a geographic information system (GIS) as an alternative multi-criteria analysis for determining the areas of highest risk of forest fire in natural forest remnants in the Brazil. In the decision-making process, a set of factors that are relevant to fire safety were identified in the study area. For each input variable chosen for the model, a pertinence function was defined that best described its influence on fire risk. Subsequently, the variables were combined for the presentation of the final fire risk map. Concluded in the study that an increased risk of fire occurs at the wildland - urban interface. A strong relationship was observed between the fire ignition points and proximity to roads and urban areas. The proposed model was efficient to integrate the variables and determine areas of greatest risk.

Bioclimatic distribution and prevalence maps for Fasciola hepatica in Espírito Santo State, Brazil

Fasciolosis affects different ruminant species and leads to great economic losses for cattle farmers worldwide. Thus, the current study aimed to evaluate bovine fasciolosis prevalence in the state of Espírito Santo, Brazil, using slaughter maps provided by slaughterhouses and verifying the origin of cattle. : A map was created based on analysis of epidemiological data. The ArcGIS/ArcINFO 10.1 software was employed in order to elaborate updated bioclimatic maps that displayed the fasciolosis prevalence within the state – per city– between 2009 and 2011.

Bioclimatic distribution and prevalence maps for Fasciola hepatica in Espírito Santo State, Brazil

Fasciolosis affects different ruminant species and leads to great economic losses for cattle farmers worldwide. Thus, the current study aimed to evaluate bovine fasciolosis prevalence in the state of Espírito Santo, Brazil, using slaughter maps provided by slaughterhouses and verifying the origin of cattle. : A map was created based on analysis of epidemiological data. The ArcGIS/ArcINFO 10.1 software was employed in order to elaborate updated bioclimatic maps that displayed the fasciolosis prevalence within the state – per city– between 2009 and 2011.