Abstract 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. universe Thus activities biome LULC (LULC 200 2013 distance logic Biome results very high high, , “high 101,100.10 10110010 101 100 10 (101,100.1 km2 km 81,010.30 8101030 81 010 30 (81,010.3 km2. . evolution planet 20 201 101,100.1 1011001 1 (101,100. 81,010.3 810103 8 01 3 (81,010. 2 101,100. 101100 (101,100 81,010. 81010 0 (81,010 101,100 10110 (101,10 81,010 8101 (81,01 101,10 1011 (101,1 81,01 810 (81,0 101,1 (101, 81,0 (81, 101, (101 81, (81 (10 (8 (1 (