@InProceedings{BispoRMCFBSZAFRBGRBPSWEOB:2019:EsAbGr,
               author = "Bispo, Polyanna da Concei{\c{c}}{\~a}o and Rodriguez-Veiga, 
                         Pedro and Miranda, Sabrina do Couto de and Cezare, Cassio Henrique 
                         Giusti and Feming, Sam and Baldacchino, Francesca and Shimbo, 
                         Julia and Zimbres, B{\'a}rbara and Alencar, Ane and Ferreira, 
                         Laerte and Roitman, Iris and Bustamante, Mercedes and Gou, Yaqing 
                         and Roberts, John and Barrett, Kirsten M. and Pascagaza, Ana Maria 
                         Pacheco and Sousa Neto, Eraclito Rodrigues de and Woodhouse, Ian 
                         and Esp{\'{\i}}rito Santo, Fernando and Ometto, Jean Pierre 
                         Henry Balbaud and Balzter, Heiko",
          affiliation = "{University of Leicester} and {University of Leicester} and 
                         {Universidade do Estado 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 {Instituto de Pesquisa Ambiental da Amaz{\^o}nia 
                         (IPAM)} and {Universidade Federal de Goi{\'a}s (UFG)} 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 {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Carbomap Ltd.} and {University of 
                         Leicester} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {University of Leicester}",
                title = "Estimating the Above Ground Biomass of Brazilian Savannah 
                         (Cerrado) using multi-sensor approach",
                 year = "2019",
         organization = "AGU Fall Meeting",
             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), savannah 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 the 
                         Brazilian Cerrado using ALOS-PALSAR (L-band SAR), Sentinel-1 
                         (C-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 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 = "San Francisco, CA",
      conference-year = "09-13 dec.",
             language = "en",
           targetfile = "bispo_estimating.pdf",
        urlaccessdate = "24 abr. 2024"
}