Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

Since the 1980s, mangrove cover mapping has become a common scientific task. However, the systematic and continuous identification of vegetation cover, whether on a global or regional scale, demands large storage and processing capacities. This manuscript presents a Google Earth Engine (GEE)-managed pipeline to compute the annual status of Brazilian mangroves from 1985 to 2018, along with a new spectral index, the Modular Mangrove Recognition Index (MMRI), which has been specifically designed to better discriminate mangrove forests from the surrounding vegetation. If compared separately, the periods from 1985 to 1998 and 1999 to 2018 show distinct mangrove area trends. The first period, from 1985 to 1998, shows an upward trend, which seems to be related more to the uneven distribution of Landsat data than to a regeneration of Brazilian mangroves. In the second period, from 1999 to 2018, a trend of mangrove area loss was registered, reaching up to 2% of the mangrove forest. On a regional scale, ~85% of Brazil’s mangrove cover is in the states of Maranhão, Pará, Amapá and Bahia. In terms of persistence, ~75% of the Brazilian mangroves remained unchanged for two decades or more.

De acordo com o Atlas de Manguezais, este ecossistema representa 8% de toda a linha de costa do planeta e um quarto da linha de costa da zona tropical, perfazendo um total de 181.077 km². Este trabalho objetiva quantificar a extensão dos manguezais de macromaré da costa nordeste do Pará e noroeste do Maranhão, aqui denominada de Costa de Manguezais de Macromaré da Amazônia (CMMA). O reconhecimento das áreas de manguezais e sua quantificação foram realizados a partir da utilização de imagens Landsat-7 ETM+, adquiridas em 1999 e 2000 e de um sistema de informações geográficas (SIG). A integração dos dados de sensores remotos, dados geológicos e oceanográficos permitiu o reconhecimento de cinco setores geomorfológicos, que abrangem uma superfície total de 7.591 km² de manguezais. Esta área representa a maior faixa de manguezais contínuos do planeta e corresponde a 56,6% dos manguezais do Brasil. Medidas prioritárias de conservação dos manguezais da Amazônia devem ser tomadas e pesquisas que busquem a melhor compreensão deste complexo e importante ecossistema devem ser financiadas e desenvolvidas.