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.

Forest degradation in the Brazilian Amazon due to selective logging and forest fires may greatly increase the human footprint beyond outright deforestation. We demonstrate a method to quantify annual deforestation and degradation simultaneously across the entire region for the years 2000-2010 using high-resolution Landsat satellite imagery. Combining spectral mixture analysis, normalized difference fraction index, and knowledge-based decision tree classification, we mapped and assessed the accuracy to quantify forest (0.97), deforestation (0.85) and forest degradation (0.82) with an overall accuracy of 0.92. We show that 169,074 km 2 of Amazonian forest was converted to human-dominated land uses, such as agriculture, from 2000 to 2010. In that same time frame, an additional 50,815 km 2 of forest was directly altered by timber harvesting and/or fire, equivalent to 30% of the area converted by deforestation. While average annual outright deforestation declined by 46%OPEN ACCESS

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This work presents the SEEG platform, a 46-year long dataset of greenhouse gas emissions (GHG) in Brazil (1970–2015) providing more than 2 million data records for the Agriculture, Energy, Industry, Waste and Land Use Change Sectors at national and subnational levels. The SEEG dataset was developed by the Climate Observatory, a Brazilian civil society initiative, based on the IPCC guidelines and Brazilian National Inventories embedded with country specific emission factors and processes, raw data from multiple official and non-official sources, and organized together with social and economic indicators. Once completed, the SEEG dataset was converted into a spreadsheet format and shared via web-platform that, by means of simple queries, allows users to search data by emission sources and country and state activities. Because of its effectiveness in producing and making available data on a consistent and accessible basis, SEEG may significantly increase the capacity of civil society, scientists and stakeholders to understand and anticipate trends related to GHG emissions as well as its implications to public policies in Brazil.

[1] Forest fragmentation due to deforestation is one of the major causes of forest degradation in the Amazon. Biomass collapse near forest edges, especially within 100 m, alters aboveground biomass and has potentially important implications for carbon emissions in the region. This phenomenon is tightly linked to spatial and temporal dynamics of forest edges in a landscape. However, the potential biomass loss and carbon emissions from forest edges and these spatiotemporal changes have never been estimated for actual landscapes in the Amazon. We conducted a deep temporal analysis of Rondônia, southwestern Brazilian Amazonia, using six Landsat path-row scenes covering the 1985-2008 time period to estimate annual biomass loss and associated carbon emissions within 100 m of forest edges. Annual edge biomass loss averaged 9.1% of the biomass loss from deforestation during the study period, whereas average annual edge-related carbon emissions from biomass loss were 6.0% of deforestation-derived carbon emissions. However, because many edges were subsequently deforested during the 24 year study period, actual unaccounted for edge-related carbon emissions during the 1985-2008 period, calculated from edges of all ages extant on the landscape in 2008, amounted to 3.6% of that attributed to all deforestation-derived carbon fluxes for this time interval. Biomass loss and carbon emissions are highly influenced by the extent and age of edge-affected forests. Large annual contributions of biomass loss and carbon emissions were found from active deforestation regions with young edges, whereas regions dominated by older edges had lower biomass loss and carbon emissions from edges.

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