Protected areas were created essentially for the conservation of fauna and flora. Analyzing its
socio-environmental dynamics becomes a challenge, and at the same time contributes to the
understanding of the landscape. The present study aims to model future scenarios from remote
sensing and geoprocessing techniques in the National Forest (FLONA) of Jamanxim-PA,
based on the land use classification of the years 2013 and 2020. Analyze the independent
variables through of Artificial Intelligence. Apply the DINAMICA EGO model using the
transition method to simulate deforestation trajectories up to 2030, based on the dependent
variables (land cover and use 2013 and 2020) and independent variables (altimetry, slope,
distance to roads, distance to settlements and distance to hydrography. The high rates of
deforestation within the limits of conservation units, lead to large environmental losses over
time. According to INPE, the state of Pará presented the highest forest loss of the Brazilian
Amazon states in 2019, a total of 3,862 km2 , with an increase rate of 41% when compared to
2018. Through the mathematical model it is possible to analyze “Where” will be deforested;
“When” will it be deforested and what deforestation rates will be; and “How”, what will be
the spatial representation of the new areas of modification, that is, how the deforestation
process will be based on the dynamic cover and use of the land and the elements that make up
the landscape, such as the variations independent activities, it is possible to carry out future
projections of deforestation in the FLONA do Jamanxim. Theories of authors representing
different currents of Geography are approached to conceptualize space, landscape and
dynamic modeling. In Physical Geography, one starts from Bertrand's concepts. For
Quantitative Geography, Waldo Tobler was used as a basis. The discussion of Critical
Geography is based on the works of Milton Santos. And Soares-Filho for spatial dynamic
modeling. The methodology was divided into three main phases: 1- Processing of satellite
images, using the supervised classification method through the Maximum Likelihood
algorithm; 2- Processing of independent variables; 3- Stage considered the main part of the
study, which consists of spatial modeling in DINAMICA EGO. As a result of the analysis of
land cover and use, it was observed that there was a reduction in the area of 112.51 km²
(0.87%) of primary forest, and an increase in the mosaic class of occupations (deforestation)
with an area of 393.53 km², equivalent to 3% of deforested area. The main activities observed
were: forest exploitation and mining. There is also a deforestation pattern classified as
geometric and regular, with economic activities, such as agriculture, and mainly medium to
large-scale grain and livestock monocultures, and an intermediate stage of occupation. The
independent variables assume Bertrand's GTP model to observe landscape dynamics. It was
observed that 0.28% of the primary forest was converted to deforestation. That is, from 2013
to 2020 deforestation is occurring at a net rate of 28% per year. And there is a high probability
of transition from primary forest to a mosaic of occupations, and from forest exploitation to a
mosaic of occupations to the north and south of the FLONA do Jamanxim, areas that may be
associated with the implementation of roads (BR-163), and to the PDS Brasília and Vale do
Jamanxim Project settlements, which consequently can impact the resilience of the landscape.
Based on modeling and analysis of future scenarios, it appears that there may be a loss of
198.79 km² (1.52%) of primary forest, and a considerable increase in deforestation of 155.20
km² by 2030. The mapping of this study, it can support public policy actions by analyzing the
impact of laws and identifying priority areas for government action in FLONA do Jamanxim.
Based on the spatial modeling, together with the command, control and monitoring plans, it is
possible to guide the socio-environmental, economic and cultural development in this UC, for
the maintenance and conservation of natural assets.
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