@Article{SilveiraCuGaWiAcSc:2019:MoAbBi,
               author = "Silveira, Eduarda Martiniano de Oliveira and Cunha, Luiza Imbroisi 
                         Ferraz and Galv{\~a}o, L{\^e}nio Soares and Withey, Kieran 
                         Daniel and Acerbi J{\'u}nior, Fausto Weimar and Scolforo, 
                         Jos{\'e} Roberto Soares",
          affiliation = "{Universidade Federal de Lavras (UFLA)} and {Universidade Federal 
                         de Lavras (UFLA)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Lancaster University} and {Universidade Federal de 
                         Lavras (UFLA)} and {Universidade Federal de Lavras (UFLA)}",
                title = "Modelling aboveground biomass in forest remnants of the Brazilian 
                         Atlantic Forest using remote sensing, environmental and 
                         terrain-related data",
              journal = "Geocarto International",
                 year = "2019",
               volume = "34",
                pages = "1--17",
             keywords = "biomass, Random Forest, Remote Sensing.",
             abstract = "The Brazilian Atlantic Forest, one of the most threatened tropical 
                         regions in the world, exhibits high levels of terrestrial 
                         aboveground biomass (AGB). We propose a Random Forest (RF) 
                         approach to model, map and assess whether public lands provide 
                         protection for AGB in the Rio Doce watershed, one of the most 
                         important watercourses of the Atlantic Forest biome. We used 188 
                         field plots and individual and hybrid features from remote 
                         sensing, environmental and terrain-related data. The hybrid model 
                         improved the AGB prediction by reducing the root mean square error 
                         (RMSE) to 33.43 Mg/ha and increasing the coefficient of 
                         determination (R 2 ) to 0.57. The total estimated AGB was 
                         178,967,656.73 Mg, ranging from 20.40 to 167.72 Mg/ha following 
                         the seasonal precipitation pattern and anthropogenic disturbance 
                         effects. Only 5.76% of the total AGB was located on public 
                         protected lands, totalling 10,305,501 Mg, while most of the 
                         remaining AGB were located on private properties.",
                  doi = "10.1080/10106049.2019.1594394",
                  url = "http://dx.doi.org/10.1080/10106049.2019.1594394",
                 issn = "1010-6049",
                label = "lattes: 5507769922001047 3 
                         MartinianodeOliveiraSilveiraImGaDaWeSc:2019:MoAbBi",
             language = "en",
           targetfile = "silveira_modelling.pdf",
        urlaccessdate = "28 abr. 2024"
}