The great majority of the world population resides nowadays in urban areas. Understanding their physical and social structure, and especially their urban land use pattern dynamics throughout time, becomes crucial for successful, effective management of such areas. This study is committed to simulate and predict urban land use change in a pilot city belonging to the São Paulo Metropolitan Region, southeast of Brazil, by means of a cellular automata model associated with the Markov chain. This model is driven by data derived from orbital and airborne remotely sensed images and is parameterized by the Bayesian weights of evidence method. Several layers related to infrastructure and biophysical aspects of the pilot city, São Caetano do Sul, were used as evidence in the simulation process. Alternative non-stationary scenarios were generated for the short-run, and the results obtained from past simulations were statistically validated using a multiresolution “goodness-of-fit” metric relying on fuzzy logic. The best simulations reached fuzzy similarity indices around 0.25–0.58 for small neighborhood windows when an exponential decay approach was employed for the analysis, and approximately 0.65–0.95 when a constant decay and larger windows were considered. The adopted Bayesian inference method proved to be a good parameterization approach for simulating processes of urban land use change involving multiple classes and transitions.

In recent years, sugarcane production areas in Brazil have experienced a slower evolution in productivity, and one of the reasons for this is related to the increase in phytosanitary problems, such as the presence of pests. Nevertheless, limited attention has been paid to the development of tools for simulating the spatial dynamics of pests, including their impact on production. This study aims to simulate the potential population growth and dispersal of Diatraea saccharalis (sugarcane borer) in sugarcane crop fields and its estimated impacts on this crop production and to simulate biological control strategies. We developed an agent-based model to simulate the pest population and its dispersal in a one-hectare sugarcane crop field in Pederneiras, São Paulo, Brazil, delimited with the aid of satellite imagery, considering two scenarios: the first without biological control and the second with biological control using the parasites Trichogramma galloi and Cotesia flavipes. The model was developed using the NetLogo 6.3.0 software. The results indicate that the model accurately reproduced the infestation rates reported in the literature. Additionally, it provided insights into expected pest dispersal, potential production losses, and how the use of T. galloi in association with C. flavipes could mitigate production losses. We believe that the model can be used to simulate different biological control strategies and the implementation of integrated pest management (IPM) to achieve adequate control levels and greater productivity in sugarcane production.

Climate change in Brazil has led to an increase in temperature and changes in the distribution of climatic extremes, such as droughts, droughts, floods and floods. This is potentialized when it comes to the semi-arid Northeast. Environmental studies related to this theme are in constant development. For this, the geotechnologies are of great importance for the environmental studies, since they allow the acquisition and analysis of information of use of the ground, vegetal cover and phenomena of the nature. In the present work, the Vegetation Cover Index was studied using remote sensing and geoprocessing techniques through the digital processing of images acquired in the years 2000 to 2015 of the LANDSAT satellites of three municipalities of Pernambuco to observe the vegetation cover in the said temporal space with the degraded areas possibly resulting from the desertification process in the region. Thus, it was found that the municipalities of Betânia and Itacuruba have very high vulnerability (0.75 and 1, respectively). On the other hand, the municipality of Floresta presented a high vulnerability of ICV (0.5), as the vegetation deficit influences hydrological dynamics, soil protection, climatic stability and thermal influence, corroborating the desertification process.

The creation of the Guadeloupe EPA aimed to protect natural ecosystems and promote sustainable development in atlantic forest fragments existing in Pernambuco, Brazil. However, even with the State Decree formalizing these objectives, the ecosystems of the Atlantic Forest Biome present at the site suffered serious data by anthropic actions. For the study of these ecosystems, spectral analysis was an excellent channel of connection between geotechnologies and phytogeography of the site, allowing to study the location and type of vegetation in the extension of the Conservation Unit. From this perspective, the present research aimed to differentiate the atlantic forest ecosystems through spectral analysis. The results of spectral analysis allowed to indicate that in the identification of the Dense Ombrófila Forest the best specific combination is Atmospheric Penetration (PA) in its Pixel (PI) α. Mangrove in turn can be better identified in Infrared Vegetation (IVV) - Ranging from 150 to 220 Ash Levels (NC) - in pi α and also in PI β and PI γ varying in values very close to 50NC. Regarding restinga it is estimated that it can be accurately identified in Agriculture (AG), especially in pi γ, and also in Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI), in all its Pixels.