| Autor: |
Fiedler NC; Universidade Federal do Espírito Santo/UFES, Departamento de Ciências Florestais e da Madeira, Avenida Governador Lindemberg, 316, 29550-000 Jerônimo Monteiro, ES, Brazil., Jesus RMM; Universidade Federal do Espírito Santo/UFES, Departamento de Ciências Florestais e da Madeira, Avenida Governador Lindemberg, 316, 29550-000 Jerônimo Monteiro, ES, Brazil., Moreira FZ; Universidade Federal do Espírito Santo/UFES, Departamento de Ciências Florestais e da Madeira, Avenida Governador Lindemberg, 316, 29550-000 Jerônimo Monteiro, ES, Brazil., Ramalho AHC; Universidade Federal do Sul e Sudeste do Pará/UNIFESSPA, Instituto de Estudos do Xingu, Faculdade de Ciências Agrárias, Loteamento Cidade Nova, Lote n° 1, quadra 15, setor 15, Avenida Norte Sul, s/n, 68380-000 São Félix do Xingu, PA, Brazil., Santos ARD; Universidade Federal do Espírito Santo/UFES, Departamento de Engenharia Rural, Alto Universitário, s/n, 29500-000 Alegre, ES, Brazil., Souza KB; Universidade Federal do Espírito Santo/UFES, Departamento de Ciências Florestais e da Madeira, Avenida Governador Lindemberg, 316, 29550-000 Jerônimo Monteiro, ES, Brazil. |
| Abstrakt: |
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. |
