Abstract 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. semiarid semi Brazilian Semiarid Semi . SAB) However decades emerged SABs s agrienvironmental agri environmental management 19702000 1970 2000 (1970-2000 20612080 2061 2080 (2061-2080 IPCC (IPCC prepared Pathways 585 585) century KruskalWallis Kruskal Wallis pvalue p value 0.05, 005 0.05 , 0 05 0.05) 56500 56 500 56,50 km 10% 10 (10% 12 140400 140 400 140,40 24% 24 (24% Consequently 622,400 622400 622 (622,40 706300 706 300 706,30 expand 4400 4 4,40 21700 21 700 21,70 1970200 197 200 (1970-200 2061208 206 208 (2061-208 58 00 0.0 5650 5 50 56,5 1 (10 14040 14 40 140,4 2 (24 622,40 62240 62 (622,4 70630 70 30 706,3 440 4,4 2170 21,7 197020 19 20 (1970-20 206120 (2061-20 0. 565 56, (1 1404 140, (2 622,4 6224 6 (622, 7063 7 3 706, 44 4, 217 21, 19702 (1970-2 20612 (2061-2 ( 622, (622 (1970- (2061- (62 (1970 (2061 (6 (197 (206 (19 (20

Resumo O Brasil possui a região semiárida mais populosa e de maior biodiversidade do mundo (Semiárido Brasileiro - SAB). No entanto, nas últimas décadas, núcleos de desertificação têm surgido, um problema que pode intensificar a partir de mudanças climáticas. O objetivo desse estudo foi elaborar a distribuição espacial das áreas suscetíveis à desertificação climática no SAB, considerando cenários futuros de mudanças do clima. O entendimento dessa dinâmica é algo essencial para a gestão agroambiental do SAB. Foram elaborados índices de aridez e proposição de classes climáticas para condição atual (1970-2000) e cenários futuros (2061-2080) do Painel Intergovernamental sobre Mudanças Climáticas (IPCC), levando em conta cenários do Caminhos Socioeconômicos Compartilhados: otimistas (SSP 126) e pessimistas (SSP 585). Os resultados indicam que até o final do século, o clima no SAB deverá tornar-se significativamente mais seco (Kruskal-Wallis = p-value < 0,05), com intensificação do índice de aridez no SSP 585. Nos cenários, a expansão de áreas mais áridas sobre climas úmidos pode alcançar 56.500 km2 (10%) no SSP 126 e 140,400 km2 (24%) no SSP 585. Consequentemente, espera-se expansão das áreas com alta (622,400 km2 a 706,300 km2) e muito alta (4,400 a 21,700 km2) suscetibilidade à desertificação climática no SAB, respectivamente nos cenários SSPs 126 e 585. Confirmando essas projeções, implicaria em riscos socioeconômicos e ecológicos no SAB. Semiárido . SAB) entanto décadas surgido 19702000 1970 2000 (1970-2000 20612080 2061 2080 (2061-2080 IPCC, IPCC , (IPCC) Compartilhados 585 585) século tornarse tornar se KruskalWallis Kruskal Wallis pvalue p value 0,05, 005 0,05 0 05 0,05) 56500 56 500 56.50 km 10% 10 (10% 12 140400 140 400 140,40 24% 24 (24% Consequentemente esperase espera 622,400 622400 622 (622,40 706300 706 300 706,30 4,400 4400 4 (4,40 21700 21 700 21,70 projeções 1970200 197 200 (1970-200 2061208 206 208 (2061-208 (IPCC 58 00 0,0 5650 5 50 56.5 1 (10 14040 14 40 140,4 2 (24 622,40 62240 62 (622,4 70630 70 30 706,3 4,40 440 (4,4 2170 21,7 197020 19 20 (1970-20 206120 (2061-20 0, 565 56. (1 1404 140, (2 622,4 6224 6 (622, 7063 7 3 706, 4,4 44 (4, 217 21, 19702 (1970-2 20612 (2061-2 ( 622, (622 4, (4 (1970- (2061- (62 (1970 (2061 (6 (197 (206 (19 (20

Resumen El avance de los modelos predictivos mediante Machine Learning Algorithms (ML) asociados a datos ambientales permite mejorar los modelos de fragilidad ambiental, que son herramientas fundamentales para la toma de decisiones. Este estudio tuvo como objetivo derivar una predicción de la fragilidad ambiental mediante la prueba de ML asociado con covariables ambientales en el estado de Minas Gerais. Se utilizaron variables físico-ambientales (suelo, geología, clima, relieve) con peso de fragilidad para los atributos y cálculo de la media para obtener un modelo de Fragilidad Ambiental Potencial (PEF). Posteriormente, extrajimos los valores de PEF a una cuadrícula de 4800 puntos, que se utilizó para generar una nueva predicción de ML, llamada PEFML. Esta predicción se basó en la prueba de cinco algoritmos y un conjunto de 105 covariables ambientales. Los resultados indicaron que la predicción PEFML con mejor desempeño fue el modelo Random Forest (R2 0.59 y RMSE 0.47), indicando un predominio del bajo nivel de fragilidad ambiental. Los modelos PEF y PEFML muestran fuertes correlaciones (0,7 Pearson); sin embargo, PEFML tiene correlaciones más fuertes con otros datos ambientales. Por lo tanto, la predicción PEFML es un modelo robusto que captura información de covariables y tiene patrones espaciales coherentes. (ML decisiones Gerais físicoambientales físico suelo, suelo (suelo geología clima relieve PEF. . (PEF) Posteriormente 480 puntos 10 R2 R (R 059 0 59 0.5 0.47, 047 0.47 , 47 0.47) 0,7 07 7 (0, Pearson Pearson) embargo tanto coherentes (PEF 48 1 05 5 0. 04 0.4 4 0, (0 (

Abstract The advancement of predictive models by Machine Learning Algorithms (ML) associated with environmental data enables the improvement of models of environmental fragility, which are essential tools for decision-making. This study aimed to derive a prediction of environmental fragility by testing ML associated with environmental covariates in the state of Minas Gerais. We use physical-environmental variables (soil, geology, climate, relief) with a weight of fragility for the attributes and calculation of the average to obtain a model of Potential Environmental Fragility (PEF). Subsequently, we extracted the PEF values to a 4,800-point grid, which was used to generate a new prediction by ML called PEFML. This prediction was based on testing five algorithms and a set of 105 environmental covariates. The results indicated that the best-performing PEFML prediction was the Random Forest model (R2 0.59 and RMSE 0.47), indicating a predominance of the low environmental fragility level. The PEF and PEFML models have strong correlations (0.7 Pearson); however, PEFML has stronger correlations with other environmental data. Therefore, the PEFML prediction is a robust model that captures information from covariates and has coherent spatial patterns. (ML decisionmaking. decisionmaking decision making. making decision-making Gerais physicalenvironmental physical soil, soil (soil geology climate relief PEF. . (PEF) Subsequently 4,800point 4800point point 4,800 4 800 grid 10 bestperforming best performing R2 R (R 059 0 59 0.5 0.47, 047 0.47 , 47 0.47) level 0.7 07 7 (0. Pearson Pearson) however Therefore patterns (PEF 800point 4800 4,80 80 1 05 5 0. 04 0.4 (0 480 4,8 8 ( 48 4,

Resumo O avanço de modelos preditivos por Algoritmos de Aprendizado de Máquina (ML) associados à dados ambientais possibilita aprimoramento de modelos de fragilidade ambiental, os quais são importantes ferramentas para tomada de decisão. O objetivo desse estudo foi derivar uma predição de fragilidade ambiental, testando ML associados a covariáveis ambientais no estado de Minas Gerais. Utilizamos variáveis físico-ambientais (solo, geologia, clima, relevo) com peso de fragilidade para os atributos e cálculo da média obtendo o modelo de Fragilidade Ambiental Potencial (PEF). Posteriormente, extraímos os valores de PEF para uma grade de 4.800 pontos e usadas para gerar uma nova predição por ML, denominada PEFML. A predição foi com teste de cinco algoritmos e conjunto de 105 covariáveis ambientais. Comparamos os dois modelos de fragilidade ambiental (PEF e PEFML), inclusive com outros dados de riscos/vulnerabilidade/fragilidade. Os resultados indicaram que a predição de PEFML de melhor desempenho foi o modelo Random Forest (R2 0.59 e RMSE 0.47), indicando predomínio do nível fragilidade baixa. Os modelos de fragilidade PEF e PEFML têm forte correlação (0.7 Pearson), porém, PEFML possui correlações mais fortes com outros dados ambientais. Portanto, a predição de PEFML é um modelo robusto que capta informações de covariáveis e possui padrões espaciais coerentes (ML decisão Gerais físicoambientais físico solo, solo (solo geologia clima relevo PEF. . (PEF) Posteriormente 4800 4 800 4.80 10 PEFML, , PEFML) riscosvulnerabilidadefragilidade riscos vulnerabilidade riscos/vulnerabilidade/fragilidade R2 R (R 059 0 59 0.5 0.47, 047 0.47 47 0.47) baixa 0.7 07 7 (0. Pearson, Pearson Pearson) porém Portanto 480 80 4.8 1 05 5 0. 04 0.4 (0 48 8 4. (

ABSTRACT 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.

ABSTRACT The term Post-Barreiras is a definition for sediments above the deposits of the Barreiras Formation, and the genesis of soils in these environments must be related to sedimentary deposition. Our objective was to apply multi-technique analyses to characterize the sediments and soils to understand pedogenesis in these environments. We analysed sedimentological parameters and the geochronology of sediments. Morphological, chemical, and mineralogical analyses allowed the characterization of the soil. Also, these data supported the analysis of lithological discontinuity. We considered the contents of Ti and Zr, uniformity value, the fraction of organic material, morphology, and palynological analysis. The age of Post-Barreiras sediments is from the Pleistocene, and they have a more significant variation of sedimentological parameters concerning Barreiras Formation layers. In general, the soils are sandy, acidic, have a low level of exchangeable cations. Mineralogy has a predominance of quartz and kaolinite minerals. In the region, there are soils with low morphological variation, classified as Quartzipsamments. In other cases, there are soils with apparent spodic morphology, which is conditioned by four aspects: (i) Podzolization in Post-Barreiras sandy sediments without evidence of lithological discontinuity, forming Bs horizon (Spodosols); (ii) contact zones (Post-Barreiras/Barreiras Formation) with physical, chemical, and morphological evidence of discontinuity, forming Quartzipsamments or Ultisols; (iii) layers of the Barreiras Formation buried by Post-Barreiras sediments and the subsequent podzolization process, forming Bhm horizon (Spodosols); and (iv) destruction of Ultisols clay, forming Bs horizon (Spodosols). The sedimentary association (Post-Barreiras/Barreiras Formation) favors the development of different soils. The contact zones generate a morphological aspect similar to the Spodosols, associated or not with podzolization processes.

Resumo Dentre as várias características que o território brasileiro dispõe, uma precisamente se sobressai: o país contempla a segunda maior reserva florestal do planeta, respondendo por aproximadamente 10% do cômputo total das formações florestais globais. Nesse Cenário, as Florestas Estacionais Deciduais (FED) constituem o segundo tipo florestal menos expressivo no Brasil, estando situadas predominantemente em biomas não-florestados, tal como Cerrado e Caatinga. Dessa Forma, sua conservação apoia-se fundamentalmente na sua correta identificação, o qual se torna difícil, uma vez que as áreas de FED são geralmente classificadas como outros tipos de vegetação. Logo, a pesquisa corrente objetivou realizar o monitoramento do Uso e Cobertura do Solo para os anos de 2007 e 2016 da faixa contínua Norte de Minas Gerais - Sul do Piauí, com finalidade de diagnosticar a situação atual das florestas deciduais brasileiras e verificar os principais agentes que afetam seu desmatamento e regeneração. Como resultante, o estudo constatou que o incremento significativo das áreas de cultivos e a mobilidade espacial dos pastos contribuíram determinantemente para as alterações apresentadas pelas formações vegetais. No Entanto, tais drivers desempenharam papeis diferenciados nas perdas/ganhos. Em especial, concluiu-se que as mudanças a qual as florestas deciduais perpassaram foram explicadas particularmente pela pastagem. Os demais tipos de vegetação foram impactados igualmente por esta classe, mas com participação mais incisiva das culturas.

Abstract Among the various characteristics of the Brazilian territory, one is foremost: the country has the second largest forest reserve on the planet, accounting for approximately 10% of the total recorded global forest formations. In this scenario, seasonally dry tropical forests (SDTF) are the second smallest forest type in Brazil, located predominantly in non-forested biomes, such as the Cerrado and Caatinga. Consequently, correct identification is fundamental to their conservation, which is hampered as SDTF areas are generally classified as other types of vegetation. Therefore, this research aimed to monitor the Land Use and Coverage in 2007 and 2016 in the continuous strip from the North of Minas Gerais to the South of Piauí, to diagnose the current situation of Brazilian deciduous forests and verify the chief agents that affect its deforestation and regeneration. Our findings were that the significant increase in cultivated areas and the spatial mobility of pastures contributed decisively to the changes presented by plant formations. However, these drivers played different roles in the losses/gains. In particular, it was concluded that the changes occurring to deciduous forests are particularly explained by pastured areas. The other vegetation types were equally impacted by this class, but with a more incisive participation of cultivation.

Resumen Entre las diversas características que el territorio brasileño dispone, una precisamente se destaca: el país contempla la segunda mayor reserva forestal del planeta, respondiendo por aproximadamente el 10% del cómputo total de las formaciones forestales globales. En este escenario, los Bosques Estacionales Deciduales (BED) constituyen el segundo tipo forestal menos expresivo en Brasil, estando situadas predominantemente en biomas no florestados, tal como Cerrado y Caatinga. Sendo así, su conservación debe apoyarse fundamentalmente en su correcta identificación, lo cual se vuelve difícil, ya que las áreas de BED son generalmente clasificadas como otros tipos de vegetación. Logo, la investigación actual tuvo como objetivo realizar el seguimiento del uso y cobertura de la tierra para los años 2007 y 2016 de la banda continua Norte de Minas Gerais - Sur del Piauí, con el fin de diagnosticar la situación actual de los bosques deciduales brasileños y verificar los principales agentes que afectan su deforestación y regeneración. El estudio constató que el incremento significativo de las áreas de cultivos y la movilidad espacial de los pastos contribuyeron determinantemente a las alteraciones presentadas por las formaciones vegetales. Sin embargo, tales controladores desempeñaron papeles diferenciados en las pérdidas/ganancias. Especialmente, se concluyó que los cambios en los bosques han sido explicados particularmente por los pastos. Los demás tipos de vegetaciones fueron impactados igualmente por esta clase, pero con participación más incisiva de las culturas.