Analyses and climate forecasts indicate significant changes in climate elements, particularly the global mean temperature, and variations in rainfall patterns, which can have profound effects on ecosystems and agriculture. This study aims to assess the impacts of climate change on the Brazilian territory using the Köppen-Geiger (1936) climate classification. Climate data were analyzed at 4,942 locations, encompassing municipalities in Brazil from 1989 to 2019. These data were obtained from the NASA/POWER platform and complemented with monthly temperature and rainfall projections from the BCC-CSM1-1 model, part of the CMIP5 (Coupled Model Intercomparison Project Phase 5), under four emission scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) for the periods 2041-2060 and 2061-2080. The findings reveal a temperature increase across all scenarios, with RCP 8.5 indicating the most significant rise, reaching 4.30 and 5.42 °C for the periods 2041-2060 and 2061-2080, respectively. Additionally, the least rainy month of the year exhibits precipitation values exceeding 60 mm, leading to the dominance of the tropical climate typology “A” in 82.94% of the current climate assessment. In contrast, under climate change scenarios, reductions in areas with typical temperate climate “C” and expansions in arid climate “B” and tropical climate classes were observed compared to the present climate pattern. Notably, the BSh class has a prevalence of 6.09% and 8.16% for the periods 2041-2060 and 2061-2080, respectively. The observed climate changes signal potential challenges for the preservation of species in Brazil, as higher temperatures may hinder their adaptability to drier and warmer conditions. As a result, careful measures and strategies are needed to address the implications of these changes in the coming decades.

Methods using remote sensing associated with artificial intelligence to forecast corn yield at the management zone level can help farmers understand the spatial variability of yield before harvesting. Here, spectral bands, topographic wetness index, and topographic position index were integrated to predict corn yield at the management zone using machine learning approaches (e.g., extremely randomized trees, gradient boosting machine, XGBoost algorithms, and stacked ensemble models). We tested four approaches: only spectral bands, spectral bands + topographic position index, spectral bands + topographic wetness index, and spectral bands + topographic position index + topographic wetness index. We also explored two approaches for model calibration: the whole-field approach and the site-specific model at the management zone level. The model’s performance was evaluated in terms of accuracy (mean absolute error) and tendency (estimated mean error). The results showed that it is possible to predict corn yield with reasonable accuracy using spectral crop information associated with the topographic wetness index and topographic position index during the flowering growth stage. Site-specific models increase the accuracy and reduce the tendency of corn yield forecasting on management zones with high, low, and intermediate yields.

The rupture of a tailings dam causes several social, economic, and environmental impacts because people can die, the devastation caused by the debris and mud waves is expressive and the released substances may be toxic to the ecosystem and humans. There were two major dam failures in the Minas Gerais state, Brazil, in the last decade. The first was in 2015 in the city of Mariana and the second was in 2019 in the municipality of Brumadinho. The extent of land use and cover changes derived from those collapses were an expression of their impacts. Thus, knowing the changes to land use and cover after these disasters is essential to help repair or mitigate environmental degradation. This study aimed to diagnose the changes to land cover that occurred after the failure of dam B1 in Brumadinho that affected the Ferro-Carvão stream watershed. In addition to the environmental objective, there was the intention of investigating the impact of image preparation, as well as the spatial and spectral resolution on the classification’s accuracy. To accomplish the goals, visible and near-infrared bands from Landsat (30 m), Sentinel-2 (10 m), and PlanetScope Dove (4.77 m) images collected between 2018 and 2021 were processed on the Google Earth Engine platform. The Pixel Reduction to Median tool was used to prepare the record of images, and then the random forest algorithm was used to detect the changes in land cover caused by the tailings dam failure under the different spatial and spectral resolutions and to provide the corresponding measures of accuracy. The results showed that the spatial resolution of the images affects the accuracy, but also that the selected algorithm and images were all capable of accurately classifying land use and cover in the Ferro-Carvão watershed and their changes over time. After the failure, mining/tailings areas increased in the impacted zone of the Ferro-Carvão stream, while native forest, pasture, and agricultural lands declined, exposing the environmental deterioration. The environment recovered in subsequent years (2020–2021) due to tailings removal and mobilization.