The aim of this study was to evaluate the impact of the creation of protected areas (PAs) to avoid deforestation using Matching methods, focusing on the north of the Minas Gerais state, Brazil. Using GIS (Geographic Information System) techniques, we randomly distributed several 3 ha plots across the study region. The deforestation rates in plots inside and outside PAs were compared using Matching analysis. Our results indicate that the PAs created in the north of Minas Gerais in the 1990s were effective in reducing deforestation, though avoided deforestation rates have been relatively low (1.62% from 1996 to 2009). Geographical location was a key factor for PAs effectiveness and plots inside PAs in flat regions or close to large cities and roads had a greater impact on avoided deforestation. The results of this study corroborate a global trend of establishment of PAs in remote areas under low deforestation pressure.

Climate change is expected to alter the environmental suitability of land use and land cover (LULC) classes globally. In this study, we investigated the potential impacts of climate change on the environmental suitability of the most representative LULC classes in the southern Brazilian semiarid region. We employed the Random Forest algorithm trained with climatic, soil, and topographic data to project future LULC suitability under the Representative Concentration Pathway RCP 2.6 (optimistic) and 8.5 (pessimistic) scenarios. The climate data included the mean annual air temperature and precipitation from the WorldClim2 platform for historical (1970–2000) and future (2061–2080) scenarios. Soil data were obtained from the SoilGrids 2.1 digital soil mapping platform, while topographic data were produced by NASA’s Shuttle Radar Topography Mission (SRTM). Our model achieved an overall accuracy of 60%. Under the worst-case scenario (RCP 8.5), croplands may lose approximately 8% of their suitable area, while pastures are expected to expand by up to 30%. Areas suitable for savannas are expected to increase under both RCP scenarios, potentially expanding into lands historically occupied by forests, grasslands, and eucalyptus plantations. These projected changes may lead to biodiversity loss and socioeconomic disruptions in the study area.

American Tegumentary Leishmaniasis (ATL) is endemic in the municipality of Montes Claros, Minas Gerais State. The use of geotechnology such as spatial statistics and remote sensing has contributed to a better understanding of the eco-epidemiology of diseases, and consequently a better definition of control strategies. This study aimed to analyze the spatial distribution of probable sites of cases of ATL infection (2007-2011) in the municipality of Montes Claros and to identify related socio-environmental factors. Data on ATL cases notification were obtained from the Municipal Health Department of Montes Claros. The annual incidence of ATL in the municipality was calculated and the probable sites of infection were georeferenced. Crude Rate and the Local Empirical Bayesian Rate were calculated with census sectors considered as the unit of analysis. The Normalized Difference Vegetation Index (NDVI) was calculated from LANDSAT 5 TM images. The spatial association between the crude rate of ATL and the NDVI of the census tracts was evaluated using the Local Bivariate of Moran I. The socio-environmental aspects of household structures were assessed based on a structured questionnaire. The incidence of ATL in the evaluated period ranged from 6.2 to 16.6 cases/100,000 inhabitants. The highest rates of ATL occurrence were found in the census sectors located in the rural area and in the peripheral census sectors in the city. Through the Empirical Bayes Smoothed Rate map, it was found that in the peripheral areas of the city, the rates of ATL occurrence were lower than in the rural area and their values decreased as they approach the city center. Local Bivariate of Moran I showed a positive correlation between NDVI and crude ATL rates, with significant high-high clusters observed in the rural area and in the census sectors in the Western peripheral area of the city that have experienced an urban expansion concomitant to the period investigated. In most homes of people affected by the disease, there were domestic animals and organic matter in the peridomicile. In addition, a high percentage of individuals affected by ATL reported the presence of rodents circulating near their homes. In conclusion, it is possible that the disorderly expansion process in the city of Montes Claros favored the establishment of the ATL periurban and urban transmission cycle. These regions deserve special attention from health surveillance to combat this zoonosis.

Brazil has the most populous and biodiverse semi-arid region in the world (Brazilian Semi-arid - SAB). However, in recent decades, clusters of desertification have emerged, a problem that could intensify from climate change. The objective of this study was to elaborate on the spatial distribution of areas susceptible to climatic desertification in the SAB, considering future climate change scenarios. Understanding this dynamic is essential for SAB's agri-environmental management. Aridity indices and proposition of climate classes for current condition (1970-2000) and future scenarios (2061-2080) of the Intergovernmental Panel on Climate Change (IPCC) were prepared, considering scenarios from Shared Socioeconomic Pathways: Optimistic (SSP 126) and pessimists (SSP 585). The results indicate that by the end of the century, the climate in the SAB should become significantly drier (Kruskal-Wallis = p-value < 0.05), with an intensification of the aridity index in SSP 585. In the scenarios, the expansion of more arid areas over humid climates could reach 56,500 km² (10%) in SSP 126 and 140,400 km² (24%) in SSP 585. Consequently, areas with high (622,400 km² to 706,300 km²) and very high (622,400 km² to 706,300 km²) are expected to expand. 4,400 to 21,700 km²) susceptibility to climate desertification in the SAB, respectively in scenarios SSPs 126 and 585. Confirming these projections would imply socioeconomic and ecological risks in the SAB.

Several studies have already demonstrated the relationship between the loss of natural habitats and the incidence of diseases caused by vectors, such as dengue, which is an acute fever disease that is considered a serious public health problem. The aim of the present study was to investigate the relationship between the number of dengue cases and land use and cover changes (LUCC) and socioeconomic and climatic factors by municipality, using the state of Minas Gerais as a case study. For this, secondary data obtained from openly available sources were used. Natural vegetation cover data were obtained from the MAPBiomas platform and dengue occurrence data from the Ministry of Health, in addition to eight socioeconomic parameters from the Brazilian Institute for Geography and Statistics and precipitation data from the Brazilian Agricultural Research Corporation (EMBRAPA, Sete Lagoas, Minas Gerais). Between 2015 and 2019, 1,255,731 cases of dengue were recorded throughout the state of Minas Gerais, ranging from 0 to 227 per thousand inhabitants between municipalities. The occurrence of dengue was distributed throughout all regions of Minas Gerais and was associated with LUCC and socioeconomic factors. In general, municipalities with a net loss of natural vegetation, predominantly located in the Cerrado biome, had the highest number of dengue cases in the studied period. Additionally, the occurrence of dengue was associated to three socioeconomic parameters: population density, human development index (both positively), and Gini inequality index (negatively). These results indicate that, contrary to expectations, municipalities with better social conditions had more dengue cases. Our study indicates that natural vegetation is, directly or indirectly, involved in the ecosystem service of dengue control, despite the occurrence of this disease being affected by multiple factors that interact in a complex way. Thus, policies towards reducing deforestation must be complemented by a continuous investment in public health policies and a reduction in social inequalities to efficiently control dengue fever.

The spatiotemporal dynamic of rainfall erosivity is essential for environmental studies and guidance to control erosion. The purpose of this study is to assess rainfall erosivity (monthly and annual), testing machine learning algorithms aided by a covariate bank for spatial prediction of rainfall erosivity in Southeastern Brazil. The modeling tested Random Forest-RF, Cubist, Support Vector Machine, Earth, and Linear Model, associated with 154 covariates (topographic, climatic, and vegetation data). However, we apply the cut-off correlation function (findcorrelation) and feature selection algorithm (Recursive Feature Elimination—RFE) to select strong covariates. Our results show that the RF algorithm was more efficient in modeling (R² values between 0.29 and 0.82), whit the best metrics in the low rainfall period (winter). The modeling showed fluidity by selecting only 43 significant covariates due to the findcorrelation and RFE functions. The most important and frequent covariates in spatial modeling were coordinates, water deficit, topographical, and climatic data. In general, the spatial results show that the dynamics of rainfall erosivity is strongly affected by factors of air mass circulation, relief, and geographic position. Our approach is promising as it is a method capable of estimating rainfall erosivity in unsampled areas, capturing information from significant spatial covariates.