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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. [ABSTRACT FROM AUTHOR]

Important public and private initiatives to map agricultural lands and natural resources have been carried out in Brazil to support land use planning. Some studies indicate that Brazil still has up to 109.7 million hectares of cultivated pastures with some level of degradation, representing around 60% of the total pasturelands, estimated at 177 million hectares. This study aimed to gather, process, and analyze publicly available databases to generate quantitative and spatial information about the potential of Brazilian degraded pastures for agricultural expansion. We considered data related to the natural agricultural potential, restrictions imposed by special areas (indigenous lands and Afro-Brazilian "quilombola" settlements), areas with high biodiversity conservation priorities, infrastructure such as distance between major highways and availability of warehouses, current agricultural areas, and the information made available by Agricultural Climate Risk Zoning. The results indicated the existence of approximately 28 million hectares of planted pastures with intermediate and severe levels of degradation that show high potential for agricultural crops. These areas could increase the planted areas with grains in Brazil by approximately 35% in relation to the total area used in the 2022/23 crop season. [ABSTRACT FROM AUTHOR]

Farmers in the Brazilian Cerrado are increasing grain production by cultivating second crops during the same crop growing season. The release of PlanetScope (PS) satellite images represents an innovative opportunity to monitor double cropping production. In this study, we analyzed the potential of six PS monthly mosaics from the 2021/2022 crop growing season to discriminate double cropping areas in the municipality of Goiatuba, Goiás State, Brazil. The four multispectral bands of the PS images were converted into normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), green–red normalized difference index (GRNDI), and textural features derived from the gray-level co-occurrence matrix (GLCM). The ten most important combinations of these attributes were used to map double cropping systems and other land use and land cover classes (cultivated pasture, sugarcane, and native vegetation) of the municipality through the Random Forest classifier. Training and validation samples were obtained from field campaigns conducted in October 2021 and April 2022. PS mosaic from February 2022 was the most relevant data. The overall accuracy and Kappa index of the final map were 92.2% and 0.892, respectively, with an accuracy confidence of 81%. This approach can be expanded for mapping and monitoring other agricultural frontiers in the Cerrado biome. [ABSTRACT FROM AUTHOR]