The Brazilian Legal Amazon (BLA), the largest global rainforest on earth, contains nearly 30% of the rainforest on earth. Given the regional complexity and dynamics, there are large government investments focused on controlling and preventing deforestation. The National Institute for Space Research (INPE) is currently developing five complementary BLA monitoring systems, among which the near real-time deforestation detection system (DETER) excels. DETER employs MODIS 250 m imagery and almost daily revisit, enabling an early warning system to support surveillance and control of deforestation. The aim of this paper is to present the methodology and results of the DETER based on AWIFS data, called DETER-B. Supported by 56 m images, the new system is effective in detecting deforestation smaller than 25 ha, concentrating 80% of its total detections and 45% of the total mapped area in this range. It also presents higher detection capability in identifying areas between 25 and 100 ha. The area estimation per municipality is statistically equal to those of the official deforestation data (PRODES) and allows the identification of degradation and logging patterns not observed with the traditional DETER system.
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%.
Recebido em abril de 2018. Aprovado em novembro de 2018. RESUMOA Floresta Amazônica abrange 1/3 das florestas tropicais úmidas do planeta. Desta área, aproximadamente 62% encontra-se no território brasileiro sendo o mais extenso dos biomas brasileiros predominantemente florestais. Considerando as dimensões continentais da Amazônia brasileira e, portanto, todas as dificuldades que evidentemente se apresentam ao policiamento e fiscalização de toda a sua extensão, o monitoramento in loco de todas as áreas autuadas nas ações de fiscalização de combate ao desmatamento torna-se algo quase que inexecutável. O IBAMA é o órgão responsável por essa hercúlea tarefa, de verificar se as leis ambientais estão sendo obedecidas. Caso exista alguma infração, este instituto pode embargar e multar os proprietários. Entretanto, torna-se necessário avaliar se os embargos estão sendo respeitados. Portanto, este estudo teve o objetivo de verificar a efetividade dos embargos em áreas atuadas por desmatamento ilegal, no período de 2004 a 2016, no sudeste do Estado do Pará. Para isto desenvolveu-se uma aplicação web para o monitoramento dessas áreas, tendo como suporte técnicas de geoprocessamento e sensoriamento remoto. A análise destas áreas foi realizada pela interpretação do perfil temporal do NDVI-MODIS juntamente com imagens Landsat 5 e 8. Como resultado,
Apesar da grande riqueza de espécies vegetais presentes na Amazônia, há muito ainda de ser mais bem utilizado deste patrimônio, como o grande potencial de suas paisagens. Sendo assim, a Universidade Federal Rural da Amazônia (UFRA) em Belém, Pará, Brasil (1°27'22"S 48°26'14"W), é caracterizada neste estudo através de um amplo e detalhado trabalho de pesquisa científica, sobre as espécies vegetais que compõem o paisagismo da instituição. O objetivo do estudo foi identificar e mapear as espécies vegetais utilizadas no paisagismo do Campus de Belém da UFRA. Assim, foi realizado um levantamento florístico através de técnicas de coleta e identificação de espécimes vegetais, bem como análises de dados, ao final foram identificadas 187 espécies, 156 gêneros, distribuídos em 57 famílias. As cinco famílias com maior número de espécies são: Fabaceae, Asparagaceae, Arecaceae, Apocynaceae e Araceae. As seringueiras (Hevea spp.) e maxarimbés (Cenostigma tocantinum Ducke) são as espécies arbóreas ocorrentes na área com maiores quantidades de indivíduos, sendo ambas nativas do território brasileiro. No entanto, as espécies exóticas (53%) predominaram em relação às nativas (47%). Os resultados indicam a necessidade de valorizar o uso de espécies nativas no paisagismo da instituição. Bem como, a base de dados aqui apresentada é útil para a realização de demais estudos.
Abstract. PRODES and DETER project together turned 33 years-old with an undeniably contribution to the state-of-art in mapping and monitoring tropical deforestation in Brazil. Monitoring systems all over the world have taken advantage of big data repositories of remote sensing data as they are becoming freely available together with artificial intelligence. Thus, considering the advent of new generation remote sensing data hubs, online platforms of big data that can fill in spatial and temporal resolutions gaps in current deforestation mapping, this work aims to present recent innovations at INPE´s deforestation monitoring systems in Brazil and how they are gauging new realms of technological levels. Recent innovations at INPE´s monitoring systems are: 1) the development of TerraBrasilis platform of data access and analysis; 2) the adoption of new sensors and cloud detection strategies; 3) the complementary use of multi-sensor images; 4) the complementary adoption of SAR C-band images using cloud data to sample and process Sentinel-1. Future innovations are: 1) development of a Brazilian data cube to be used in deep learning techniques of image classification; 2) Routine uncertainty analysis of PRODES data. Automatization might fasten mapping process, but the real challenge is to succeed in automatization maintaining data quality and historical series. The hyper-availability of remote sensing data, the initiative of a Brazilian Data Cube and promising machine learning techniques applied to land cover change detection, allowed INPE to reinforce its central role in tropical forest monitoring.
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