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  • This land cover classification refers to a study area in Goiás state, in the Cerrado biome. For this map, the CBERS-4/AWFI monthly data cube was used, with a spatial resolution of 64 meters, using the best pixel composition function (Stack). This experiment uses the time series of an agricultural calendar year, from September 2018 to August 2019, extracted from the CBERS-4/AWFI data cube. The classification was made using 701 samples (Annual Crop: 299; Natural: 202; Pasture: 200) and the following data cube bands: red, green, blue, and near-infrared (NIR) along with the NDVI and EVI indices. We trained a multi-layer perceptron for a deep learning classification network to classify the data cube using sits R package. To access this resource in GeoTIFF format it is necessary to have an access key provided by the BDC-OAuth service.

  • This land cover classification refers to a study area in Mato Grosso state, in the Cerrado biome. For this map, the CBERS-4/AWFI monthly data cube was used, with a spatial resolution of 64 meters, using the best pixel composition function (Stack). This experiment uses the time series of an agricultural calendar year, from September 2018 to August 2019, extracted from the CBERS-4/AWFI data cube. The classification was made using 852 samples (Annual Crop: 257; Natural Vegetation: 245; Pasture: 216; Semi-Perennial Crop: 134) and the following data cube bands: bands red, green, blue, and near-infrared along with the EVI, NDVI, GEMI, GNDVI, NDWI2, PVR indices applying the random forest algorithm. The classification quality assessment using 5-fold cross-validation (Wiens et al., 2008) of the training samples showed an overall accuracy of 97.0% and a Kappa index of 0.96. For more information see the paper "CBERS DATA CUBE: A POWERFUL TECHNOLOGY FOR MAPPING AND MONITORING BRAZILIAN BIOMES". To access this resource in GeoTIFF format it is necessary to have an access key provided by the BDC-OAuth service.

  • This land cover classification refers to a study area in Bahia state, in the Cerrado biome. For this map, the Landsat-8 monthly data cube was used, with a spatial resolution of 30 meters, using the best pixel composition function (Stack). This experiment uses the time series of an agricultural calendar year, from September 2018 to August 2019, extracted from the Sentinel-2 data cube. The classification was made using 922 samples (Pasture: 258; Agriculture: 242; Natural Vegetation: 422) and the following data cube bands: red, green, blue, and near-infrared (NIR) along with the NDVI and EVI indices. We trained a multi-layer perceptron for a deep learning classification network to classify the data cube using sits R package. Validation was done using good practice guidelines by Olofsson. The validation was done independently for each map using the PRODES Cerrado data of 2019. This data obtained overall accuracy (OA) 0.90. For more information see the paper "Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products". To access this resource in GeoTIFF format it is necessary to have an access key provided by the BDC-OAuth service.

  • This land cover classification refers to a study area in Mato Grosso state, in the Cerrado biome. For this map, the CBERS-4/AWFI monthly data cube was used, with a spatial resolution of 64 meters, using the best pixel composition function (Stack). This experiment uses the time series of an agricultural calendar year, from September 2018 to August 2019, extracted from the CBERS-4/AWFI data cube. The classification was made using 852 samples (Annual Crop: 257; Natural Vegetation: 245; Pasture: 216; Semi-Perennial Crop: 134) and the following data cube bands: bands red, green, blue, and near-infrared along with the EVI, NDVI, GEMI, GNDVI, NDWI2, PVR indices. We trained a multi-layer perceptron for a deep learning classification network to classify the data cube using sits R package. To access this resource in GeoTIFF format it is necessary to have an access key provided by the BDC-OAuth service.

  • The CBERS-4 WFI Brazil Mosaic covers the entire Brazilian territory. The mosaic uses surface reflectance images from the CBERS satellite, a WFI imaging camera (or sensor) with 64 meters of spatial resolution. It is a composition of images from April to June 2020, selecting the best pixel within the period. The final product is an RGB color composite with the red (B15), NIR (B16), and blue (B13) bands.

  • This land cover classification refers to a study area in Bahia state, in the Cerrado biome. For this map, the CBERS-4/AWFI monthly data cube was used, with a spatial resolution of 64 meters, using the best pixel composition function (Stack). This experiment uses the time series of an agricultural calendar year, from September 2018 to August 2019, extracted from the CBERS-4/AWFI data cube. The classification was made using 922 samples (Pasture: 258; Agriculture: 242; Natural Vegetation: 422) and the following data cube bands: red, green, blue, and near-infrared (NIR) along with the NDVI and EVI indices. We trained a multi-layer perceptron for a deep learning classification network to classify the data cube using sits R package. Validation was done using good practice guidelines by Olofsson. The validation was done independently for each map using the PRODES Cerrado data of 2019. This data obtained overall accuracy (OA) 0.74. For more information see the paper "Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products". To access this resource in GeoTIFF format it is necessary to have an access key provided by the BDC-OAuth service.

  • The Paraiba State Mosaic was generated with technologies under development in the Brazil Data Cube project, using the best pixel (free of cloud and cloud shadows) for three months (April, May, and June 2020). This mosaic was generated using CBERS-4A (55 meters).

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    A infraestrutura rodoviária é um dos componentes do sistema terrestre expostos às ameaças climáticas como a altas temperaturas. Por convenção, o termo ameaça climática no AdaptaBrasil MCTI será utilizado para denotar fatores externos (especificamente climáticos) que interagem com a infraestrutura rodoviária analisada e que possuem capacidade de impactar de forma significativa a infraestrutura, seja de forma lenta ou repentina. O Índice de Ameaça Climática de altas temperaturas é resultante da duração de ondas de calor (em dias) (HWDI), e a média de temperatura dos 7 (sete) dias consecutivos mais quentes do ano (Tx7day). Fontes: Ministério dos Transportes. P5 – Análise de Risco Climático. Rio de Janeiro: Ministério dos Transportes, 2022. 117p. GALLOPÍN, G. C.. Box 1: A systemic synthesis of the relations between vulnerability, hazard, exposure and impact, aimed at policy identification. In: Economic Commission for Latin American and the Caribbean (ECLAC). Handbook for Estimating the Socio-Economic and Environmental Effects of Disasters. Mexico, D.F.: ECLAC, LC/MEX/G.S., p. 2-5, 2003. INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE - IPCC. Climate Change 2014: Synthesis Report. Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.

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    Indicador de capacidade adaptativa considerando a temática capacidade socioeconômica familiar, obtido pela proporção do número de domicílios que recebem bolsa família, multiplicado pelo peso atribuído pelos especialistas considerando a relevância regional do mesmo à ameaça de chuvas intensas. A abrangência do programa foi calculada a partir da razão entre o número médio de beneficiários do Bolsa Família para 2012 a 2016, obtidos no Portal da Transparência disponibilizado pela Controladoria Geral da União (CGU), e média da quantidade estimada de domicílios com até 2 salários mínimos (classe de renda “E”) para 2011 a 2015, a partir dos dados do Censo Demográfico 2010 e das classes de rendimento mensal domiciliar (dados estaduais obtidos na Pesquisa Nacional por Amostra de Domicílios (PNAD)), ambos disponibilizados pelo Instituto Brasileiro de Geografia e Estatística (IBGE). Fonte: Sistema de Informações e Análises sobre Impactos das Mudanças Climáticas - AdaptaBrasil MCTI.

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    Os movimentos migratórios, em geral, estão associados ao aumento do fluxo de pessoas de distintas áreas e com distintos perfis epidemiológicos. Neste sentido, a chegada de um número considerável de pessoas oriundas de outro município pode indicar uma ocupação territorial recente, onde as alterações socioambientais do território podem propiciar maior número de criadores do vetor e a exposição da população local ao vetor, além da introdução de pessoas infectadas em um município onde não havia registro endêmico do desfecho. Para estimar este indicador, a proporção de pessoas que tinham menos de 1 ano ininterrupto de residência no município é multiplicada pelo peso atribuído por especialistas considerando sua importância relativa à Leishmaniose Tegumentar Americana para os biomas brasileiros. Os dados do percentual de pessoas que tinham menos de 1 ano ininterrupto de residência no município são obtidos no Censo Demográfico disponibilizado pelo Instituto Brasileiro de Geografia e Estatística (IBGE) para 2010. Fonte: Sistema de Informações e Análises sobre Impactos das Mudanças Climáticas - AdaptaBrasil MCTI.