Inadequate pasture management causes land degradation through reduction of grass, increased presence of invasive plants or pests, compaction, erosion, and nutrient deficiency. The recognition of pasture degradation is therefore essential. Remote sensing satellite systems allow us to do so at regional-to global scales. A struggle is in progress nowadays is to improve detection accuracy and implement high-resolution surveys at farm scales using low-cost unmanned aerial vehicles (UAVs). The pasture imagery can be translated into maps of degraded pasture using the popular NDVI as diagnostic parameter, but their generation using a UAV requires a high-cost NIR sensor, while the struggle is to use low-cost UAVs equipped with RGB cameras. The first step to recognize degraded pastures using RGB cameras is to define a suitable vegetation index. Thus, the purpose of this study was to present the total brightness quotient of red (TBQR), green (TBQG), and blue (TBQB) bands. The test to the index resorted to LANDSAT-8 satellite images captured over the environmental protection area of Uberaba River basin (Minas Gerais, Brazil) in the 2017–2019 period. The images were not captured by a UAV because the equipment was not then available. The results were promising given the large detection accuracy (88.63%) of the TBQG and the high (0.965) correlation between TBQG and NDVI. Besides, the TBQ-based areas of degraded pasture (17,486.3–25,180.1 hectares) were larger than the NDVI counterparts (12,066.9 hectares). This is an additional reason to oversight degraded pastures based on the TBQs, as they seek for improved environmental compliance and economic development

Urban Heat Island (UHI) is an effect that corroborates to the increase of temperature in urban settlements when compared to the surrounding vegetated areas, especially after sunset. This research aimed to understand the Surface Urban Heat Island (SUHI), a sub-classification correlated to the UHI effect, in the city of Bragança (Portugal), at 23 points classified in different Local Climate Zone (LCZ), between 2003 and 2022, using Remote Sensing (RS) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The data were obtained at four passage times: 11am, 1pm, 10pm and 2am and analysed separately for summer and winter. Qualitative and quantitative analyses were applied, using an average of 1,337 Land Surface Temperature (LST) data, processed in the Google Earth Engine (GEE) platform. The computation of the SUHI intensity (SUHIint) for each year was obtained from the differences in LST between each of the LCZ and the average values from Rural Areas (RCD), considering both summer and winter campaigns. The boxplots showed similar medians in all LCZs at 11am and 1pm. At 10pm and 2am, only slight differences were found among the median values. The similarity in results may be associated with the low spatial resolution of the sensor and the difficulty in distinguishing between LCZs. The SUHIint was positive in most of the results (about 71%). Of the 23 points analysed, ten were not located in unique pixels, which compromised the analysis of the results in the different LCZs. This condition reinforces the need for use higher spatial resolution data to allow for a differentiation among LCZs data.

This study aimed to analyze the changes in use and vegetation cover in the municipality of Vigia and to evaluate the Normalized Difference Vegetation Index-NDVI in a transition area of ​​cerrado and secondary vegetation in the same municipality. Presenting two different scales, one at the municipal level and the other analysis was at the local level. The Grassland class showed an increase, from 107 km² in 1985 to 108 km² in 2018, losing area to the Urban and Secondary Vegetation classes, 9.85 km² and 60.43 km², respectively. The Secondary Vegetation Class showed an increase of 32.92 km², from 161.13 km² in 1985 to 194.05 km² in 2018, with the main contribution of the pasture class with 60.43 km². The Cerrado class showed a significant decrease, in 1985 the municipality of Vigia had 9.85 km², decreasing to 4042 km² in 2018, loss of 5.42 km² of Cerrado area, losing to urban classes, pasture and secondary vegetation. Concluding that the monitoring of land use and land cover becomes important for areas considered to be non-forest like the Cerrado, especially in the Amazon biome, due to the peculiar characteristics of transition between Cerrado and other types of vegetation, because with monitoring it is possible detect degradation and deforestation, thus contributing to the management of public policies aimed at preserving the biodiversity of these areas.

O geoprocessamento é uma ferramenta essencial na Engenharia Civil, desempenhando um papel crucial na coleta, análise e interpretação de dados geoespaciais para o planeamento e execução de projetos. O uso dessa ferramenta também pode contribuir para estudos de desmatação. O ano de 2022 teve um aumento significativo de 22,3% no número de alertas de desmatação no Brasil em comparação com o ano anterior, resultando na identificação, validação e refinados de um total de 76.193 alertas. Este aumento alarmante destaca a urgência de um monitoramento ambiental eficaz e proactivo para promover ações concretas no controle da desmatação, preservando os recursos naturais preciosos do país. Nesse contexto, o presente trabalho almeja demonstrar o potencial do Google Earth Engine como uma ferramenta fundamental para a coleta e análise de dados relacionados a desmatação. Foi desenvolvido um script, por meio da plataforma do Google Earth Engine, com o intuito de extrair imagens provenientes do satélite Landsat 8 e realizar a classificação supervisionada de áreas suscetíveis a desmatação na cidade de Lábrea, situada no estado do Amazonas. Os resultados derivados da classificação supervisionada foram submetidos a uma análise e comparados com os dados provenientes da plataforma Mapbiomas Alertas. Essa abordagem permitiu constatar que o Google Earth Engine, empregando em grande parte algoritmos de Inteligência Artificial, oferece resultados confiáveis, mesmo mostrando diferenças em sua comparação com o Mapbiomas Alertas.