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Metadados

@InProceedings{BispoRZMCFBSARBPGRLBWSOB:2020:EsAbGr,
               author = "Bispo, Polyanna da Concei{\c{c}}{\~a}o and Rodriguez-Veiga, 
                         Pedro and Zimbres, Barbara and Miranda, Sabrina de Couto and 
                         Cezare, Cassio Henrique Giusti and Fleming, Sam and Baldacchino, 
                         Francesca and Shimbo, Julia Zanin and Alencar, Ane Auxiliadora 
                         Costa and Roitman, Iris and Bustamante, Mercedes and 
                         Pacheco-Pascagaza, Ana Maria and Gou, Yaqing and Roberts, John and 
                         Louis, Valentin and Barret, Kirsten and Woodhouse, Iain and Sousa 
                         Neto, Er{\'a}clito Rodrigues de and Ometto, Jean Pierre Henry 
                         Balbaud and Balzter, Heiko",
          affiliation = "{University of Manchester} and {University of Leicester} and 
                         {Instituto de Pesquisa Ambiental da Amaz{\^o}nia (IPAM)} and 
                         {Universidade Estadual de Goi{\'a}s (UEG)} and {Universidade 
                         Federal de Goi{\'a}s (UFG)} and {Carbomap Ltd.} and {Carbomap 
                         Ltd.} and {Instituto de Pesquisa Ambiental da Amaz{\^o}nia 
                         (IPAM)} and {Instituto de Pesquisa Ambiental da Amaz{\^o}nia 
                         (IPAM)} and {Universidade de Bras{\'{\i}}lia (UnB)} and 
                         {Universidade de Bras{\'{\i}}lia (UnB)} and {University of 
                         Leicester} and {University of Leicester} and {University of 
                         Leicester} and {University of Leicester} and {University of 
                         Leicester} and {Carbomap Ltd} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {University of Leicester}",
                title = "Estimating the Above Ground Biomass of Brazilian Savanna using 
                         multi-sensor approach",
                 year = "2020",
         organization = "EGU General Assembly",
             abstract = "The Brazilian Savanna, known as Cerrado (Cerrado sensu lato 
                         (s.l.)), is the second largest biome in South America. It 
                         comprises different physiognomies due to variations of soil, 
                         topography and human impacts. The gradients of tree density, tree 
                         height, above ground biomass (AGB) and wood species cover vary 
                         according to the Cerrado formation, ranging from different 
                         grassland formations (Campo limpo, campo sujo), savanna 
                         intermediary formations (Campo cerrado and Cerrado sensu stricto - 
                         s.s) and forest formations (Cerrad{\~a}o, Mata ciliar, Mata de 
                         galeria and Mata Seca). Although the carbon stock in Cerrado is 
                         lower than in the Brazilian Amazon, the conversion of this biome 
                         to other types of land use is occurring much faster. In the last 
                         ten years, the degradation of Cerrado forest was the second 
                         largest source of carbon emissions in Brazil. Therefore, effective 
                         methods for assessing and monitoring aboveground woody biomass and 
                         carbon stocks are needed. A multi-sensor Earth observation 
                         approach and machine learning techniques have shown potential for 
                         the large-scale characterization of Cerrado forest structure.The 
                         aim of this study is to present a method to estimate the AGB of an 
                         area of the Brazilian Cerrado using ALOS-PALSAR (L-band SAR), 
                         Landsat, LIDAR (LIght Detection And Ranging) and field datasets. 
                         Field data consisted of 15 plots of 1 ha area located in Rio 
                         Vermelho watershed in Goi{\'a}s-State (Brazil). We used a 2-step 
                         AGB estimation (i) from the field AGB using LIDAR metrics and (ii) 
                         from LIDAR-AGB to satellite Earth Observation scales following a 
                         Random Forest regression algorithm. The methodology to estimate 
                         ABG of Cerrado Stricto Sensu vegetation is part of the Forests 
                         2020 project which is the largest investment by the UK Space 
                         Agency, as part of the International Partnerships Programme (IPP), 
                         to support in the improvement of the forest monitoring in six 
                         partner countries through advanced uses of satellite data.",
  conference-location = "Online",
      conference-year = "04-08 may",
           targetfile = "bispo_estimating.pdf",
        urlaccessdate = "22 abr. 2021"
}


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