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.

Continuous monitoring of forest disturbance on tropical forests is a fundamental tool to support proactive preservation actions and to stop further destruction of native vegetation. Currently most of the monitoring systems in operation are based on optical imagery, and thus are flaw-prone on areas with frequent cloud cover. As this, several Synthetic Aperture Radar (SAR)-based systems have been developed recently, aiming all-weather disturbance detection. This article presents the main aspects and the results of the first year of operation of the SAR based Near Real-Time Deforestation Detection System (DETER-R), an automated deforestation detection system focused on the Brazilian Amazon. DETER-R uses the Google Earth Engine platform to preprocess and analyze Sentinel-1 SAR time series. New images are treated and analyzed daily. After the automated analysis, the system vectorizes clusters of deforested pixels and sends the corresponding polygons to the environmental enforcement agency. After 12 months of operational life, the system has produced 88,572 forest disturbance warnings. Human validation of the warning polygons showed a extremely low rate of misdetections, with less than 0.2% of the detected area corresponding to false positives. During the first year of operation, DETER-R provided 33,234 warnings of interest to national monitoring agencies which were not detected by its optical counterpart DETER in the same period, corresponding to an area of 105,238.5 ha, or approximately 5% of the total detections. During the rainy season, the rate of additional detections increased as expected, reaching 8.1%.

The objective of this work was to analyze land use dynamics in the Brazilian Cerrado region from 2002 to 2013. This analysis was based on the interpretation of Landsat satellite images carried out by the projects Projeto de Conservação e Utilização Sustentável da Diversidade Biológica Brasileira (Probio) and TerraClass Cerrado 2013, both coordinated by Ministério do Meio Ambiente. In 2002, 38.9% of the Cerrado was covered by some type of anthropic activity. In 2013, this percentage increased to 43.4%. One of the main highlights is the emergence of a new agricultural frontier in the northern region of the study area, known as Matopiba.

O Instituto Nacional de Pesquisas Espaciais (INPE) opera desde 1988 o Projeto de Monitoramento do Desmatamento na Amazônia por Satélites (PRODES), cujo principal objetivo é fornecer taxa anual de desmatamento florestal da Amazônia Legal Brasileira utilizando imagens de satélite de sensoriamento remoto. Esse levantamento envolve o mapeamento de mais de quatro milhões de km2 e os resultados obtidos são utilizados pelo governo brasileiro no estabelecimento e acompanhamento das políticas públicas relativas ao controle e combate ao desmatamento. Este dado tem sido produzido e divulgado em base anual desde 1988, porém, uma avaliação sobre a qualidade dos dados nunca foi estabelecida. Assim, o objetivo deste trabalho foi desenvolver uma metodologia para estimar índices de exatidão do mapeamento das áreas desmatadas apontadas pelo PRODES para o ano de 2014, a partir de uma amostragem estratificada de padrões de desmatamento mapeados em células de 50 x 50 km. O mapeamento desses padrões foi realizado a partir do estabelecimento e uso de uma tipologia de padrões de desmatamento, métricas de paisagem e técnica de mineração de dados. Pontos amostrais representativos de cada padrão de desmatamento foram sorteados aleatoriamente e avaliados visualmente por especialistas independentes. Como resultado dessa avaliação foi possível estabelecer o nível exatidão global do mapeamento em questão, estimado em 93% e com índices de omissão e de inclusão estimados em 7% e 1,5%, respectivamente. Padrões como espinha de peixe, multidirecional e consolidado, considerados mais complexos, apresentaram menores índices de acerto, mostrando coerência e indicando que maior atenção deve ser dada ao seu mapeamento. Os resultados apresentados, de uma forma geral, se mostraram consistentes, indicando que a metodologia desenvolvida pode ser replicada em mapeamentos similares.

The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic’s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data.

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.