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Brazil is home to the largest tracts of tropical vegetation in the world, harbouring high levels of biodiversity and carbon. Several biomass maps have been produced for Brazil, using different approaches and methods, and for different purposes. These maps have been used to estimate historic, recent, and future carbon emissions from land use change (LUC). It can be difficult to determine which map to use for what purpose. The implications of using an unsuitable map can be significant, since the maps have large differences, both in terms of total carbon storage and its spatial distribution. This paper presents comparisons of Brazil's new ‘official’ carbon map; that is, the map used in the third national communication to the UNFCCC in 2016, with the former official map, and four carbon maps from the scientific literature. General strengths and weaknesses of the different maps are identified, including their suitability for different types of studies. No carbon map was found suitable for studies concerned with existing land use/cover (LULC) and LUC outside of existing forests, partly because they do not represent the current LULC sufficiently well, and partly because they generally overestimate carbon values for agricultural land. A new map of aboveground carbon is presented, which was created based on data from existing maps and an up‐to‐date LULC map. This new map reflects current LULC, has high accuracy and resolution (50 m), and a national coverage. It can be a useful alternative for scientific studies and policy initiatives concerned with existing LULC and LUC outside of existing forests, especially at local scales when high resolution is necessary, and/or outside the Amazon biome. We identify five ongoing climate policy initiatives in Brazil that can benefit from using this map.
This dataset was produced using Landsat 8 Operational Land Imager and Landsat 7 Enhanced Thematic Mapper Plus surface reflectance data spanning 2013–2018 and Spectral Mixture Analysis for the identification of patterns of forest loss for each year. High-resolution Planet Dove (3m) and RapidEye (5m) imagery were used to validate the forest loss map. Overall Accuracy obtained for the forest loss map was 96%. Publication: https://doi.org/10.1088/1748-9326/ab57c3 Google Earth Engine code: https://code.earthengine.google.com/024b42f8eb3ab0c5fa8e0ad8fba86f36 For more information on SERVIR, visit http://www.servirglobal.net
Project Foresight was launched, in early 2019, as a continuous effort to develop machine-learning based deforestation and forest fire risk assessment for tropical forests, using increasing higher resolution satellite data and official country data on anthropogenic activity. Version 1.0 included maps of the Peruvian and Colombian Amazon using 18 years of official deforestation data and the open source release of Maxent, as the machine-learning algorithm. Newer versions have been developed using mutli-model ensembles in R and Google Earth Engine.