Fuzzy logic applied to assess the environmental vulnerability of vegetation in the Atlantic Forest biome, State of Espírito Santo, Brazil
Name: ELAINE CORDEIRO DOS SANTOS
Publication date: 22/02/2024
Examining board:
Name |
Role |
|---|---|
| ALEXANDRE ROSA DOS SANTOS | Advisor |
Summary: The Atlantic Forest, a biome of remarkable biodiversity, faces conservation challenges due to intense anthropogenic pressure resulting in continuous loss of forest areas. In order to analyze the areas most susceptible to environmental vulnerability of vegetation in the Atlantic Forest biome of Espírito Santo, ten variables were selected, divided into two categories: environmental and anthropogenic, for the years 2012 and 2022. Data from MapBiomas for Land Use and Cover (LUC), Pasture Quality and Fire Scars, and from the Sistema Integrado de Bases Geoespaciais do estado do Espiríto Santo (GEOBASES) for Road Proximity were used. Fuzzy inference was applied in the elaboration of the environmental vulnerability map, with five classes: Very High, High, Moderate, Low, and Very Low. Subsequently, the environmental vulnerability map was compared with the phytophysiognomies of the Atlantic Forest biome, obtained from the Instituto Estadual de Meio Ambiente e Recursos Hídricos (IEMA). In the years analyzed, the Pasture class was the most representative in LUC area, with a reduction in area from 2012 (21,740.78 km²) to 2022 (19,752.54 km²) which is reflected in the reduction of area in the categories of Severely Degraded Pasture (970.492 km²), Moderately Degraded (96.092 km²), and Pasture without signs of Degradation (921.65 km²). In 2022, the area with Very High environmental vulnerability decreased compared to the year 2012. This reduction was less pronounced in the area occupied by the phytophysiognomy of Seasonal Semideciduous Forest (reduction of 0.39 km²). The results demonstrated the application of geotechnologies and fuzzy logic as strategic tools in spatial analysis and environmental data generation. The
integration of these techniques enabled the analysis of environmental vulnerability classes based on established variables, which can support the management and monitoring of the most vulnerable vegetation areas in the state of Espírito Santo.
Keywords: Artificial intelligence, Nature conservation, Atlantic Forest
