Recently, remote sensing image time series analysis has being widely used to investigate the dynamics of environments over time. Many studies have combined image time series analysis with machine learning methods to improve land use and cover change mapping. In order to support image time series analysis, analysis-ready data (ARD) image collections have been modeled and organized as multidimensional data cubes. Data cubes can be defined as sets of time series associated with spatially aligned pixels. Based on lessons learned in the research project e-Sensing, related to national demands for land use and cover monitoring and related to state-of-the-art studies on relevant topics, we define the requirements to build Earth observation data cubes for Brazil. This paper presents the methodology to generate ARD and multidimensional data cubes from remote sensing images for Brazil. We describe the computational infrastructure that we are developing in the Brazil Data Cube project, composed of software applications and Web services to create, integrate, discover, access, and process the data sets. We also present how we are producing land use and cover maps from data cubes using image time series analysis and machine learning techniques.

The agricultural frontier expansion in the Cerrado biome made Brazil a leader in commodity exports and is changing its landscape. Hence, efforts to accurate land use and land cover (LULC) monitoring in this region are strategic, due to its role in Brazil’s food, environmental, and economic security policy. Thinking on planning and technical sovereignty in the spatial sector, the China–Brazil Earth Resources Satellite (CBERS) Program was launched to provide useful data for decision-makers to manage the Brazilian territory independently of external policies. Their data, especially from CBERS-4 Wide-Field Imager (CBERS-4/WFI), are largely applied in deforestation monitoring by remote sensing specialists but less applied than data from other image providers for machine learning-based LULC mapping due to the small number of spectral bands and limitations related to clouds and shadows detection. However, with advances in orbital data analysis, data cubes enabled storing and accessing large spatio-temporal analysis-ready data. Within this scope, the Brazil Data Cube Project (BDC) creates multidimensional data cubes from orbital sensors’ data for all Brazilian territory. We applied BDC CBERS-4/WFI data cubes to generate LULC classifications for the Extremo Oeste Baiano agricultural belt correspondent to the 2017/2018 and 2019/2020 harvest periods, at two levels of detail: broad and crop type, incorporating ground truth samples, crop calendar knowledge, and vegetation indices to a dense time series analysis approach. Overall Accuracies were equal to 0.87 and 0.89 for broad, and 0.91 and 0.94 for crop type classifications. The results indicate CBERS-4/WFI data cubes as a useful tool for improving crop monitoring in the Cerrado biome based on machine learning.

The effects of global climate change on biodiversity and ecosystem functioning are unevenly distributed in the geographic space. Identifying sites more suitable to sustain biodiversity in a changing climate is essential to both species conservation and restoration strategies at different scales. Here, we map terrestrial climate‐resilient sites for biodiversity across Brazil to identify sites with greater chances of providing suitable conditions for species to persist under regional climate change. Our mapping combines spatial metrics based on landscape heterogeneity, a proxy for microclimatic variability, and local connectedness, a measure of connectivity between habitats, to determine landscape resilience, assuming that resilience to climate change will be greater the more heterogeneous the characteristics of local habitats are and the more connected they are in the landscape. Our results show that within each biome, medium to high resilient sites are mostly found in the Amazon (40% of the biome) and Pantanal (38%). Low resilience, conversely, is concentrated in the Atlantic Forest (41% of the biome), followed by Cerrado (37%), Pampa (36%), and Caatinga (34%). Landscape resilience information has the potential to be used to effectively guide decision‐making and public policy on strategies for conservation, restoration, and sustainable use practices. Priority for conservation should be on high resilience sites as they have the potential to sustain biodiversity in face of undergoing and future climate change. Other approaches could be used in situations of medium to low resilience also, such as: conservation of current corridors in sites with high local connectedness, but low landscape heterogeneity; restoration of natural vegetation on sites that show high landscape heterogeneity, but low local connectedness; and sustainable practices in areas of low resilience. Our study provides an updated method to pinpoint climate‐resilient sites for biodiversity which was applied to a megadiverse country but is applicable to any ecosystem around the globe.