Abstract In March 2020, the World Health Organization declared the Covid-19 a pandemic. It was considered that social isolation measures adopted to slow the spread of the virus, could have reduced the level of pollutants such as nitrogen dioxide (NO2) and AOD (Aerosol Optical Depth). Therefore, this research identified changes in atmospheric air pollution in Northeast Brazil through MODIS Terra / Aqua AOD and Sentinel 5P-TROPOMI images to investigate changes in AOD and NO2, respectively, during the first half of 2020 compared to the same period in 2019. The result revealed that NO2 in almost the entire Territory decreased in the second two months of 2020 (-4. 49%), which corresponds to the beginning of social blocks. The third bimester showed an increase of 2.26%. Regarding AOD, despite the slowdowns in social interaction, there was no significant drop. Pollutant levels were also checked in urban, industrial areas and around water reservoirs and the results showed similarity between the analyzes carried out for the Northeast and these areas.

The environmental preservation areas in the State of Pará provide with the lack of scientific studies on the effects of changes in land use and the temperature of the earth's surface. In this context, this study aims to analyze the Vegetation Index by Normalized Difference (NDVI) and to verify the behavior of the surface temperature of the land in the Environmental Protection Area (APA) Triunfo do Xingu, in the state of Pará, between the years 2000 and 2019 using a Google Earth Engine (GEE) platform. The results sought that the terrestrial surface in the study area, varied in average of 26.79º C for the year 2000 to 28.90º C for the year 2019. The NDVIs attenuated to represent the vegetation between the 20 years studied indicating changes occurred in an area of ??approximately 828 km² (4.93% of the total area of ??the APA). The results induced that despite the changes identified in the study region, much of the vegetation cover was conserved.

In recent years, the process of deforestation in the Amazon Forest has intensified. Among the main causes, we highlight the encouragement of policies of territorial occupation, timber exploitation and increase of areas destined for livestock and agricultural crops. In Brazil, the Forest involves nine states that are part of the Amazon Basin, each of them having portions of the Amazon Forest. Among these states, Pará is the one that most contributed to the increase in deforestation. In this sense, this work aims to analyze the dynamics of the vegetation cover of Pará between the years 1989 and 2019 through the processing of satellite images on the Google Earth Engine (GEE) platform. For the execution of this work, the Normalized Difference Vegetation Index (NDVI) was calculated using JavaScript language programming in GEE. Images from the Landsat-5 / TM and Landsat-8 / OLI-TIRS satellites were used. The results showed a significant reduction in vegetation cover between 1989 and 2019 mainly in the eastern region of the state. The importance of GHG and NDVI in environmental monitoring and control is highlighted because through the results obtained it was possible to verify changes in the vegetation cover that the Forest has been suffering over the years.

The use of RPAs (Remotely Piloted Aircraft) popularly known as Drones, has stood out as one of the techniques with wide potential for contribution in several scientific fields. The possibility of generating cartographic products and Digital Terrain Models quickly compared to traditional topography techniques as a total station has proved to be quite applicable in the diagnosis and prevention of geological and geotechnical accidents. This article evaluated the accuracy of the volume calculation, carried out by means of Digital Terrain Models (MDTs), generated from the technologies RPAs, LiDAR (Light Detecting And Ranging) and GNSS (Global Navigation Satellite System) in Alto da Sé in Olinda. The results were classified according to the PEC (Cartographic Accuracy Standard), obtaining a planimetric error of less than 0.5 mm, obtaining classification A on a scale of 1/2000. The RPA survey showed better accuracy in calculating the volume.Keywords: Photogrammetric survey. Calculation of volume. Data modeling.