The determination of land cover changes (LCCs) and their association to biophysical and socioeconomic factors is vital to support government policies toward the sustainable use of natural resources. The present study aimed to quantify deforestation, forest recovery and net cover change in tropical dry forests (TDFs) in Brazil from 2007 to 2016, and investigate how they are associated to biophysical and socioeconomic factors. We also assessed the effects of LCC variables in human welfare indicators. For this purpose, we used MODIS imagery to calculate TDF gross loss (deforestation), gross gain (forest recovery) and net cover change (the balance between deforestation and forest recovery) for 294 counties in three Brazilian states (Minas Gerais, Bahia, and Piauí). We obtained seven factors potentially associated to LCC at the county level: total county area, road density, humidity index, slope, elevation, and % change in human population and in cattle density. From 2007 to 2016, TDF cover increased from 76,693 to 80,964 km2 (+5.6%). This positive net change resulted from a remarkable forest recovery of 19,018 km2 (24.8%), offsetting a large deforested area (14,748 km2; 19.2%). Practically all these cover changes were a consequence of transitions from TDF to pastures and vice-versa, highlighting the importance of developing sustainable policies for cattle raising in TDF regions. Each LCC variable was associated to different set of factors, but two biophysical variables were significantly associated both to TDF area gained and lost per county: county area (positively) and slope (negatively), indicating that large and flat counties have very dynamic LCCs. The TDF net area change was only associated (negatively) to the humidity index, reflecting an increase in TDF cover in more arid counties. The net increase in Brazilian TDF area is likely a result from an interplay of biophysical and socioeconomic factors that reduced deforestation and caused pasture abandonment. Although the ecological integrity and permanence of secondary TDFs need further investigation, the recovery of this semi-arid ecosystem must be valued and accounted for in the national forest restoration programs, as it would significantly help achieving the goals established in the Bonn agreement and the Atlantic Rain Forest pact.

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A vereda, típica formação no bioma Cerrado, é reconhecida por sua elevada capacidade de recarga hídrica, em função de uma topografia plana, solos hidromórficos e proximidade de matas de galeria/cursos hidrográficos. Estudos nesses ambientes ainda são escassos, especialmente aqueles voltados para o entendimento da dinâmica do balanço de radiação e energia, relacionado com processos ecológicos e climáticos. O objetivo do trabalho é analisar o comportamento do balanço de energia em ambientes de veredas no Norte de Minas Gerais, em área amostral específica, com técnicas avançadas de sensoriamento remoto, aplicando-se o algoritmo SEBAL (Surface Energy Balance Algorithms for Land). No contexto do balanço de energia, os seguintes valores médios foram obtidos para o inverno e verão, respectivamente: Rn 24 h de 98,71 Wm-2 e 230,95 Wm-2; G de 53,43 Wm-2 e 80,84 Wm-2; H de 26,93 Wm-2 e 88,52 Wm-2; LE de 77,78 Wm-2 e 142,34 Wm-2; e taxa de evapotranspiração de 2,53 mm.d-1 e 5,02 mm.d-1. As técnicas de sensoriamento remoto utilizadas neste estudo foram de grande utilidade para as análises em áreas de vereda, destacando com eficiência o comportamento do balanço de energia e evapotranspiração nesse importante ambiente no Cerrado. Palavras-chave: Savana. Recarga hídrica. Sazonalidade climática. SEBAL. Landsat 8.     ANALYSIS OF THE RADIATION AND ENERGY BALANCE IN VEREDAS AREAS IN NORTHERN MINAS GERAIS, CERRADO BIOME ABSTRACT The vereda, typical formation in the Cerrado biome, is recognized for its high-water recharge capacity, due the flat topography, hydromorphic soils and proximity to gallery forests / river courses. Studies in these environments are still scarce, especially those aimed at understanding the dynamics of the radiation and energy balance, related to ecological and climatic processes. The objective of the work is to analyze the energy balance behavior in vereda environments in Northern of Minas Gerais state, in a specific sample area, with advanced remote sensing techniques, applying the SEBAL algorithm (Surface Energy Balance Algorithms for Land). Among the results, it was observed that the behavior of the components of the balance of radiation and energy follow the meteorological and phenological dynamics expected for this phytophysiognomy. In the context of the energy balance, the following average values were obtained, in the winter and summer, respectively: Rn 24 h of 98.71 Wm-2 and 230.95 Wm-2; G of 53.43 Wm-2 and 80.84 Wm-2; H of 26.93 Wm-2 and 88.52 Wm-2; LE of 77.78 Wm-2 and 142.34 Wm-2; and evapotranspiration rate of 2.53 mm.d-1 and 5.02 mm.d-1. The remote sensing techniques used in this study were of great useful for analyzes in veredas, efficiently highlighting the behavior of energy balance and evapotranspiration in this important environment of Cerrado. Keywords: Savanna.  Water recharge.  Climatic seasonality.  SEBAL.  Landsat 8.    ANÁLISIS DE LA RADIACIÓN Y EL EQUILIBRIO ENERGÉTICO EN LAS ZONAS DE VEREDA EN EL NORTE DE MINAS GERAIS, CERRADO BIOME RESUMEN El Vereda, formación típica en el bioma Cerrado, es reconocido por su alta capacidad de recarga de agua, debido a su topografía plana, suelos hidromórficos y proximidad a bosques de galería / cursos hidrográficos. Los estudios en estos entornos aún son escasos, especialmente aquellos destinados a comprender la dinámica de la radiación y el equilibrio energético, relacionados con los procesos ecológicos y climáticos. El objetivo del trabajo es analizar el comportamiento del equilibrio energético en entornos de senderos en el norte de Minas Gerais, en un área de muestra específica, con técnicas avanzadas de teledetección, aplicando el algoritmo SEBAL (Algoritmos de equilibrio de energía de superficie para tierra).  En el contexto del balance energético, se obtuvieron los siguientes valores promedio para invierno y verano, respectivamente: Rn 24 h de 98.71 Wm-2 y 230.95 Wm-2; G de 53,43 Wm-2 y 80,84 Wm-2; H de 26.93 Wm-2 y 88.52 Wm-2; LE de 77.78 Wm-2 y 142.34 Wm-2; y tasa de evapotranspiración de 2.53 mm.d-1 y 5.02 mm.d-1. Las técnicas de detección remota utilizadas en este estudio fueron de gran utilidad para los análisis en áreas de senderos, destacando efectivamente el comportamiento del equilibrio energético y la evapotranspiración en este importante entorno en el Cerrado.  Palabras-clave: Savanna. Recarga de agua. Estacionalidad estacional. SEBAL. Landsat 8.

Brazil has the world's most populous semi-arid region and climate change represents significant ecological and socioeconomic challenges for this area. To better understand the impact of these changes, it is crucial to analyze the dynamics of climate variables and evapotranspiration (ETo), a critical climate variable. This study aimed to model ETo rates considering climate change scenarios in the Brazilian Semi-arid region (BSR). The modeling was based on tests of five machine learning algorithms: Bayesian Regularized Neural Networks (BRNN), Cubist, Earth, Linear Regression (LM), and Random Forest (RF). A dataset with 20 covariates was used to represent the current scenario. In the future prediction, covariates from two shared socio-economic pathways were used (SSPs 126 and 585). The best statistical performance was achieved by Cubist (R² = 0.98 and RMSE = 0.08 mm day-¹ in the holdout-test). The current daily average ETo is 4.77 mm day-¹, while in future scenarios, it can increase by 3.56% in SSP 126 and 15.51% in SSP 585. ETo rates are expected to expand territorially; ranges from > 0.60 mm day-¹ should increase 8% in SSP 126 and 40% in SSP 585. The applied model suggests that ETo may increase in future scenarios in the BSR, which could affect biodiversity levels and intensify social conflicts.

O objetivo deste trabalho foi analisar por pontos amostrais o saldo de radiação instantâneo em diferentes usos da Terra na Área de Proteção Ambiental (APA) Rio Pandeiros no Norte de Minas Gerais em período seco e chuvoso (08/08/2016 e 27/10/2016). A escolha dessa área de estudo se deu pelo fato de ser uma APA, e, observa-se avanços significativos de ações antrópicasem seu território. Para auxiliar este trabalho, foram utilizadas técnicas de sensoriamento remoto e imagens do Landsat – 8 (OLI/TIRS). O saldo de radiação mostrou ter variação de acordo com cada uso da Terra estabelecido para análise, tendo maiores valores para corpos hídricos e em áreas de vegetação nativa. Destaca-se as áreas de Veredas com os maiores valores, tendo relação com suas características biológicas.  A B S T R A C TThe objective this work was analyze by sample point the instantaneous net radiation in diferente land uses in the Environment Pretection Area (APA) River Pandeiros in the North Minas Gerais in period dry and rainy (08/08/2016 and 27/10/2016). The choice this study area by the fact of being a APA observe significant advances of anthropic actions in the distribuition territories. For help this work, was basidestechical of remote sensing and images of Landsat – 8 (OLI/TIRS). The net radiation showed varied accord in every land use establish for analyze, haved values high for body water and native vegetation.  Pointed veredas areas in values high, haved relationship in yours biologicals characteristic.Keywords: Cerrado, APA River Pandeiros, Net radiationand Land uses.

O presente trabalho tem por objetivo compreender o comportamento e a influência do albedo e da temperatura de superfície no balanço de radiação, considerando diferentes usos e coberturas da terra em áreas de Cerrado no Norte de Minas Gerais. Para realização da pesquisa, foram utilizados produtos orbitais do satélite Landsat8 (OLI/TIRS), para órbita/ponto 219/070. Para a mensuração dos componentes do balanço de radiação, utilizou-se o algoritmo SEBAL adaptado para o satélite Landsat8. No que toca aos resultados, observou-se influência do albedo e da temperatura de superfície no balanço de radiação, sobretudo considerando a sazonalidade climática imposta na área de estudo. Do ponto de vista operacional, o presente trabalho foi realizado com dados de fácil acesso e manipulação, sendo atrativos para novos estudos no âmbito da ciência geográfica. Quanto à validação científica dos dados, é importante destacar que eles se mostraram consistentes, com pequenas diferenças (somente com valores destoantes para dois produtos) ao comparar com os dados de referência em superfície, neste caso, os dados do INMET.

Agricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pressure for new land clearing. Due to the growing demand for more sustainable practices, more accurate information on geospatial monitoring is required. Remote sensing products and artificial intelligence models for pixel-by-pixel classification have great potential. Therefore, we developed a methodological framework with spectral indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil-Adjusted Vegetation Index (SAVI)) derived from the Harmonized Landsat Sentinel-2 (HLS) and machine learning algorithms (Random Forest (RF), Artificial Neural Networks (ANNs), and Extreme Gradient Boosting (XGBoost)) to map agricultural intensification considering three hierarchical levels, i.e., temporary crops (level 1), the number of crop cycles (level 2), and the crop types from the second season in double-crop systems (level 3) in the 2021–2022 crop growing season in the municipality of Sorriso, Mato Grosso State, Brazil. All models were statistically similar, with an overall accuracy between 85 and 99%. The NDVI was the most suitable index for discriminating cultures at all hierarchical levels. The RF-NDVI combination mapped best at level 1, while at levels 2 and 3, the best model was XGBoost-NDVI. Our results indicate the great potential of combining HLS data and machine learning to provide accurate geospatial information for decision-makers in monitoring agricultural intensification, with an aim toward the sustainable development of agriculture.

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.