Type of resources
Contact for the resource
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
[FROM: http://iridl.ldeo.columbia.edu/maproom/Fire/Regional/Amazonia/SST_Fire_Forecast.html] These graphs include July-September fire season anomaly hindcasts and forecasts in the Western Amazon. The incidence is on a standardized scale and is based on the north tropical Atlantic (NTA) sea surface temperature (SST) seasonal forecast issued in April, May and June. Positive values indicate an expected active fire season and negative values stand for a mild fire season. Lead 1 stands for the first trimester SST forecast and Lead-2 for the the second trimester SST forecast. For example, March Lead-1 forecast uses April-June SST forecast to calculate the NTA index and predict the following JAS fire season. March Lead-2 uses May-July SST forecast to calculate the NTA and predict JAS fire season and so on. As we advance in the seasons, the more accurate the forecast is expected to be. Use the drop-down menus at the top of the page to select the map field (Forecast or Observed) and the forecast issue month to display.
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.
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.
Dentre as diversas barreiras existentes para a implementação eficiente do Código Florestal no país está a falta de informações confiáveis sobre a localização e a extensão dos déficits ambientais, Idificultando o desenvolvimento de políticas públicas e privadas que possam dar suporte ao processo de regularização ambiental. Utilizando os dados recém-liberados pelo SFB, o projeto Atlas – A Geografia da Agropecuária Brasileira gerou pela primeira vez uma base fundiária nacional integrada, que possibilitou a realização de cálculos mais precisos sobre os déficits de APPs e RLs no nível de imóvel rural. Acreditamos que esses novos números podem servir de embasamento para o desenvolvimento dos PRAs estaduais, assim como de outras ações e políticas que interagem e sustentam a implementação da Lei 12.651/2012 no país.
Amazonia holds the largest continuous area of tropical forests with intense land use change dynamics inducing water, carbon, and energy feedbacks with regional and global impacts. Much of our knowledge of land use change in Amazonia comes from studies of the Brazilian Amazon, which accounts for two thirds of the region. Amazonia outside of Brazil has received less attention because of the difﬁculty of acquiring consistent data across countries. We present here an agricultural statistics database of the entire Amazonia region, with a harmonized description of crops and pastures in geospatial format, based on administrative boundary data at the municipality level. The spatial coverage includes countries within Amazonia and spans censuses and surveys from 1950 to 2012. Harmonized crop and pasture types are explored by grouping annual and perennial cropping systems, C3 and C4 photosynthetic pathways, planted and natural pastures, and main crops. Our analysis examined the spatial pattern of ratios between classes of the groups and their correlation with the agricultural extent of crops and pastures within administrative units of the Amazon, by country, and census/survey dates. Signiﬁcant correlations were found between all ratios and the fraction of agricultural lands of each administrative unit, with the exception of planted to natural pastures ratio and pasture lands extent. Brazil and Peru in most cases have signiﬁcant correlations for all ratios analyzed even for speciﬁc census and survey dates. Results suggested improvements, and potential applications of the database for carbon, water, climate, and land use change studies are discussed. The database presented here provides an Amazon-wide improved data set on agricultural dynamics with expanded temporal and spatial coverage.
The Sustainable Wetlands Adaptation and Mitigation Program (SWAMP - https://www2.cifor.org/swamp) provides this global data set categorizing 10 types of wetlands. The Amazonian Intterfluvial region in Brazil contains the largest wetland area in the world. For a full documentation and downloading see: https://www2.cifor.org/global-wetlands/