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The physical phenomena derived from an analysis of remotely sensed imagery provide a clearer understanding of the spectral variations of a large number of land use and cover (LUC) classes. The creation of LUC maps have corroborated this view by enabling the scientific community to estimate the parameter heterogeneity of the Earth’s surface. Along with descriptions of features and statistics for aggregating spatio-temporal information, the government programs have disseminated thematic maps to further the implementation of effective public policies and foster sustainable development. In Brazil, PRODES and DETER have shown that they are committed to monitoring the mapping areas of large-scale deforestation systematically and by means of data quality assurance. However, these programs are so complex that they require the designing, implementation and deployment of a spatial data infrastructure based on extensive data analytics features so that users who lack a necessary understanding of standard spatial interfaces can still carry out research on them. With this in mind, the Brazilian National Institute for Space Research (INPE) has designed TerraBrasilis, a spatial data analytics infrastructure that provides interfaces that are not only found within traditional geographic information systems but also in data analytics environments with complex algorithms. To ensure it achieved its best performance, we leveraged a micro-service architecture with virtualized computer resources to enable high availability, lower size, simplicity to produce an increment, reliable to change and fault tolerance in unstable computer network scenarios. In addition, we tuned and optimized our databases both to adjust to the input format of complex algorithms and speed up the loading of the web application so that it was faster than other systems.

This paper presents a dataset of yearly land use and land cover classification maps for Mato Grosso State, Brazil, from 2001 to 2017. Mato Grosso is one of the world’s fast moving agricultural frontiers. To ensure multi-year compatibility, the work uses MODIS sensor analysis-ready products and an innovative method that applies machine learning techniques to classify satellite image time series. The maps provide information about crop and pasture expansion over natural vegetation, as well as spatially explicit estimates of increases in agricultural productivity and trade-offs between crop and pasture expansion. Therefore, the dataset provides new and relevant information to understand the impact of environmental policies on the expansion of tropical agriculture in Brazil. Using such results, researchers can make informed assessments of the interplay between production and protection within Amazon, Cerrado, and Pantanal biomes. Measurement(s) land • land use Technology Type(s) computational modeling technique Factor Type(s) year • geographic location • land use and cover class Sample Characteristic - Environment land Sample Characteristic - Location Mato Grosso State Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.11440461

Many of the world’s agricultural frontiers are located in the tropics. Crop and cattle expansion in these regions has a strong environmental impact. This paper examines land use and land cover transformations in Brazil, where large swaths of natural vegetation are being removed to make way for agricultural production. In Brazil, the land use dynamics are of great interest regarding the country’s sustainable development and climate mitigation actions, leading to the formulation and implantation of public policies and supply chain interventions to reduce deforestation. This paper uses temporal trajectory analysis to discuss the patterns of agricultural practices change in the different biomes of Mato Grosso State, one of Brazil’s agricultural frontiers. Taking yearly land use and cover classified images from 2001 to 2017, we identified, quantified, and spatialized areas of stability, intensification, reduction, interchange, and expansion of single and double cropping. The LUC Calculus was used as a tool to extract information about trajectories and trajectories of change. Over two decades, the land use change trajectories uncover the interplay between forest removal, cattle raising, grain production, and secondary vegetation regrowth. We observed a direct relationship between the conversion of forest areas to pasture and of pasture to agriculture areas in the Amazon portion of the Mato Grosso State in different periods. Our results enable a better understanding of trends in agricultural practices.

Demand for agricultural exports in Brazil has stimulated the expansion of crop production and cattle raising, which has caused environmental impacts. In response, Brazil developed public policies such as the new Forest Code (FC) and supply chain arrangements such the Soy and the Cattle Moratoriums. This paper analyzes the effectiveness of these policies, considering the trajectories of agricultural expansion in the state of Mato Grosso in three years: 2005 (pre-moratorium and before the new FC), 2010 (post-moratorium and before the new FC) and 2017 (post-moratorium and post-new FC). Our analysis uses a detailed land use change data for both the Amazon and Cerrado biomes in Mato Grosso. In all the years considered, soybean expansion occurred in consolidated production areas and by conversion of pastures. Pasture expansion is influenced by existence of pastures nearby, by areas of secondary vegetation and deforestation. Our data and models show the effectiveness of public policies and private arrangements to reduce direct conversion from forests to crop production. However, our results also provide evidence that soybean expansion has caused indirect impacts by replacing pasture areas and causing pasture expansion elsewhere. Evidence from our work indicates that Brazil needs broader-ranging land use policies than what was done in the 2010s to be able to reach the land use goals stated in its Nationally Determined Contribution (NDC) to the Paris Agreement.