Lubia Vinhas

A summary of the principles and actions that have emerged from the Brazilian Computing Society's global climate change task force, focusing on the importance of computing in addressing sustainability challenges.

O governo brasileiro possui três sistemas oficiais de monitoramento por satélite da supressão da vegetação nativa dos biomas brasileiros, concebidos e executados pelo Instituto Nacional de Pesquisas Espaciais (INPE), por meio do Programa de Monitoramento dos Biomas Brasileiros (BiomasBR): 1) o Prodes – Monitoramento Anual da Supressão de Vegetação Nativa; 2) o Deter - Monitoramento Diário da Supressão e Degradação de Vegetação Nativa; e 3) o TerraClass - Monitoramento Sistêmico da Cobertura e Uso da Terra, este em colaboração com a Empresa Brasileira de Agropecuária (EMBRAPA). Neste artigo são apresentadas as principais características de cada um destes sistemas, os resultados mais recentes do monitoramento da vegetação nativa brasileira, as principais políticas públicas baseadas no BiomasBR e o papel do programa no cumprimento dos compromissos climáticos internacionais assumidos pelo país.

The Amazon biome has experienced significant changes in its landscape, similar to other tropical forests, primarily due to changes in land use in the area. Despite previously established strategies to contain the advance of deforestation, we have observed an increase in deforestation rates recently. Our objective was to analyze deforestation in the biome using clustering and spatial analysis methods to understand deforestation patterns and spatio-temporal dynamics. Regions 1 and 2 stood out as priority areas for monitoring and combating deforestation. Human settlements, agricultural land, and undesignated public forests characterize these areas. Deforestation in these areas presents a significant level of seasonality in its mode and dynamics. The results emphasize that annual deforestation changes require strategies addressing territorial differences. Therefore, prevention and intervention measures must be adapted to seasonal variations and the specific characteristics of each region to combat deforestation in the Amazon biome effectively.

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.

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 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.

Earth observation images are a powerful source of data about changes in our planet. Given the magnitude of global environmental changes taking place, it is important that Earth Science researchers have access to spatiotemporal reasoning tools. One area of particular interest is land-use change. Using data obtained from images, researchers would like to express abstractions such as ‘land abandonment’, ‘forest regrowth’, and ‘agricultural intensification’. These abstractions are specific types of land-use trajectories, defined as multi-year paths from one land cover into another. Given this need, this paper introduces a spatiotemporal calculus for reasoning about land-use trajectories. Using Allen’s interval logic as a basis, we introduce new predicates that express cases of recurrence, conversion and evolution in land-use change. The proposed predicates are sufficient and necessary to express different kinds of land-use trajectories. Users can build expressions that describe how humans modify Earth’s terrestrial surface. In this way, scientists can better understand the environmental and economic effects of land-use change.

After a thorough review of the past editions of the Brazilian Symposium on GeoInformatics (GEOINFO), from 1999 to 2015, which included more than 300 articles, we have identified some topics pointing to trends in the area of GeoInformatics for the next years of research. As expected, the topics Data/Information, Geographic Information System (GIS), Time and Space were included as the most relevant terms. From this perspective, we understand that research in GeoInformatics for the next years will follow these trends, namely big data, spatiotemporal data analysis, geographical applications for mobile devices, new architectures of Spatial Data Infrastructure (SDI), and spatiotemporal visualisation methods guiding exploratory data analysis.

The aim of geospatial web services is to enable users to share and process geospatial data on the Web, no matter what platform or protocol is used. In this paper, we investigate what the design decisions needed to implement web services for big Earth observation (EO) data are. The focus of the work is discussing what is unique about big EO data and why current standards are unsuitable for big EO data analytics. Instead, simpler APIs can be more eff ective for accessing big EO data than generic services such as WMS and WCS, especially for data analytics. We support this viewpoint by describing the WTSS - Web Time Series Service - that off ers time series of remote sensing data using a simple API and is suited for big data analytics.

The temporal dimension of spatial data has been the subject of discussion in the literature for a long time. While there are numerous Database Management System (DBMS) solutions for spatial dimension, we do not observe the same situation for spatiotemporal data. Considering this gap, our purpose is to design and implement an extension to PostgreSQL DBMS that is based on a formal spatiotemporal algebra in order to incorporate representations of spatiotemporal data within the DBMS. The proposed extension can be used in a large range of applications. We intend this extension to be a reasonable framework to store and handle observational remote sensing data usually present in applications like animal migration researches, wildfi res monitoring, vessel tracking for monitoring fi shing, and other similar applications. In this work, we show how to apply it in a case study based on spatiotemporal data collected from drifting buoys belonging to the NOOA's Global Drifter Program.