Although the semi-arid region of Brazil appears to be homogeneous due to drought conditions, there is a great deal of variability in climatic elements in the region, so that the definition of homogeneous regions will provide the deployment of measures appropriate for each locality. However, the limited information on climatic parameters in the region makes it difficult to define these regions. This problem can, however, be alleviated by the use of entropy theory. Therefore, this study aimed to investigate the potential of the theory to identify hydrologically homogeneous regions for conditions of the semi-arid region of Brazil. Entropy-based Disorder Index (DI) data were computed, based on monthly precipitation and monthly water balance (precipitationreference evapotranspiration) for 290 gauge stations. For defining homogeneous regions, cluster analysis was utilized, using the data on geographical information about rain gauges (latitude and longitude), annual precipitation, annual water balance, coefficient of skewness, coefficient of kurtosis and DI. The identification of homogeneous regions in the Brazilian semi-arid region was only possible when the grouping of stations was performed, based on DI for precipitation and latitude. Results showed the definition of seven homogeneous regions in the semiarid region of Brazil.

Global population growth drives the increase in demand for water and food. Consequently, there is a build‐up of pressure for land use for agricultural production, creating the necessity of sacrificing areas previously occupied by the native land cover to create production areas. However, globally, the possibilities of agricultural frontier expansion are limited, and agricultural expansion activities conducted without adequate planning can accelerate the erosive processes that decrease the potential land production capability. In an attempt to attenuate environmental imbalances, payment for ecosystem services (PES) programmes have been created, highlighting the possibility of their being applied in the agricultural sector. This study developed a methodology for PES that follows the basic principles of the land use capability classification system proposed by the United States Department of Agriculture. However, the study aimed to make the classification process operational in a way that notes the conditions in which a rural property maintains its production capability without jeopardizing its environmental role. In this way, it is possible to evaluate whether a rural property is suitable to receive PES. To detail the steps for applying the methodology, a case study was conducted on a rural property in the town of Itabira, Minas Gerais State, Brazil.

The current demand for food has been met through the exploitation of natural reserves. Brazil has 26% of its extension occupied by agricultural uses, 62% of which are pastures. Degraded pastures have greater land use intensity than well-managed pastures, leading to greater degradation of the environment. Land use classification systems consider that pastures are well managed, a misconception for the Brazilian reality. Based on this approach, it was aimed to develop a methodology for mapping the intensity of land use by pasture via remote sensing. The method of mapping was developed and validated in basins with different soil and climatic characteristics. Three calibrations were performed based on NDVI values to ascertain the influence on the results, being evaluated from the field campaigns and the kappa and weighted kappa indices. The kappa and weighted kappa indices presented reasonable and moderate agreement, respectively. The results were considered as satisfactory for the three calibrations, evidencing that the degree of degradation of the pastures can be estimated in a simple way by remote sensing. The Limoeiro River Basin has around 46.9% of pastures, at least, heavily degraded and 96.6% with some degree of degradation, which contributes to degradation of the natural resources and reduction of livestock farming and economic potential of the basin.

Regionalization is an important technique for estimating the flow of hydrographic sections with a lack of data. First, it is necessary to identify hydrologically homogeneous regions (HHRs), which are commonly validated via statistical analyses. Because this step is understood to be subjective, studies that contribute to a greater reliability in identifying regions are needed. In this context, the objective was to evaluate the inclusion of a physical analysis of the average regionalized flow rates as an aid to identify HHRs. The groupings were defined on the basis of geographical convenience methods and cluster analysis. For the assessment of regionalized flows, six statistical indices were used with a physical analysis that was performed via a comparison of the runoff coefficient to the spatial distribution of precipitation values. It was concluded that the physical analysis reduced the subjectivity in the identification of HHRs.

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In the world, the most significant change in the ecosystems structure is the conversion from natural land surface into cultivated systems. In 2018, 26.8% of the Brazilian territory was occupied by agricultural activities, from which 73% is pasture. Considering that the management adopted in Brazilian pastures is incipient and leads to degradation, there is a need to characterize the state of the pastures to diagnose the intensity of this use on the soil. However, the diagnosis of large areas using satellites with more detailed resolution is limited by cloud coverage and low temporal resolution. In this sense, the present work aims to diagnose the intensity of land use by pastures (ILUP) in large areas based on the mosaic of images from Landsat 8 (LS8), Landsat 7 (LS7), Sentinel-2 (S2), and MODIS. The methodology consists of harmonizing the NDVI from LS7 and S2 satellites with LS8. For MODIS, the harmonization was carried out based on ILUP obtained previously from NDVI LS8. The methodology was applied at the Doce river basin (DRB). The combination of different sensors allowed to overcome the cloud coverage limitation. DRB has 61.3% of its area occupied by pastures and 78.2% of them have some degree of degradation. ILUP was dependent on DRB’s pedological and climatic characteristics. This dependence is enhanced due to pasture management in the basin, mainly characterized by continuous grazing, which commonly leads to overgrazing scenarios. The areas with great rainfall seasonality and associated with Acrisols/Cambisols are the most susceptible to degradation.

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