The global surge in urbanization and population growth has led to a significant increase in municipal solid waste generation, posing a considerable challenge in identifying suitable landfill sites. This study proposes a novel framework that enhances landfill site monitoring and assessment by combining GIS-based hierarchical analytical processes with a fuzzy inference system (FIS). The study employs a systematic approach involving phases such as feature selection, spatial analysis, criteria weighting, FIS building, and a case study conducted in São Paulo State, Brazil. The proposed framework effectively assesses landfill suitability and offers practical recommendations for landfill management and future site selection. This framework provides actionable recommendations for landfill monitoring and assessment, supporting landfill management while minimizing environmental and social impacts. It offers a comprehensive approach to landfill assessment, enhancing the sustainability of waste management practices. Further research can improve the proposed framework by refining feature selection and incorporating real-time data for continuous monitoring. Additionally, exploring the integration of emerging technologies, such as remote sensing and artificial intelligence, can further enhance landfill site monitoring and assessment.

Climate change is a critical concern that has been greatly affected by human activities, resulting in a rise in greenhouse gas emissions. Its effects have far-reaching impacts on both living and non-living components of ecosystems, leading to alarming outcomes such as a surge in the frequency and severity of fires. This paper presents a data-driven framework that unifies time series of remote sensing images, statistical modeling, and unsupervised classification for mapping fire-damaged areas. To validate the proposed methodology, multiple remote sensing images acquired by the Sentinel-1 satellite between August and October 2021 were collected and analyzed in two case studies comprising Brazilian biomes affected by burns. Our results demonstrate that the proposed approach outperforms another method evaluated in terms of precision metrics and visual adherence. Our methodology achieves the highest overall accuracy of 58.15% and the highest F1 score of 0.72, both of which are higher than the other method. These findings suggest that our approach is more effective in detecting burned areas and may have practical applications in other environmental issues such as landslides, flooding, and deforestation.

Climate change is a phenomenon directly attributed to human activity due to greenhouse gas emissions and changes in land use and land cover. In this way, climate change has and will have direct and indirect impacts on the components of the climate systems, including the hydrological cycle, through an increase in the frequency and intensity of extreme events, such as heavy rains or droughts, which impact natural systems, groups, and human systems, as well as on economic activity, such as the energy, agricultural and water sectors that are directly dependent on the variables of the hydrological cycle. The southeast region of Brazil (São Paulo, Minas Gerais, Rio de Janeiro, and Espírito Santo) produces 60\% of the national wealth and much of the country's electricity, in addition to housing 80 million people. Extreme weather events have hit this region in recent decades, causing an increasing number of natural disasters. Good examples are the 2014/15 water crisis and the occurrence of heavy rains, particularly in the summers. Given the importance of this theme, the present study aims to analyze the spatial and temporal variability of precipitation over southeast Brazil through the Generalized Extreme Values (GEV) distribution. To this end, seasonal variability and changes in the patterns of precipitation extremes in the period 1981-2020 were analyzed, based on data obtained from the CPC (Climate Predict Center). This research revealed that the frequency and intensity of extreme rainfall events have been changing, notably decreasing during winter.