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@Article{BispoSaVaGrBaFrBi:2016:PrMoPr,
               author = "Bispo, Polyanna da Concei{\c{c}}{\~a}o and Santos, Jo{\~a}o 
                         Roberto dos and Valeriano, M{\'a}rcio de Morisson and 
                         Gra{\c{c}}a, Paulo Maur{\'{\i}}cio Lima de Alencastro and 
                         Baltzer, Heiko and Fran{\c{c}}a, Helena and Bispo, Pit{\'a}goras 
                         da Concei{\c{c}}{\~a}o",
          affiliation = "{Universidade Federal do ABC (UFABC)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas da 
                         Amaz{\^o}nia (INPA)} and {University of Leicester} and 
                         {Universidade Federal do ABC (UFABC)} and {Universidade Estadual 
                         Paulista (UNESP)}",
                title = "Predictive models of primary tropical forest structure from 
                         geomorphometric variables based on SRTM in the Tapajo's region, 
                         Brazilian Amazon",
              journal = "Plos One",
                 year = "2016",
               volume = "11",
               number = "4",
                pages = "e0152009",
                month = "Apr.",
             keywords = "altitude, Brazil, canopy, forest structure, genetic polymorphism, 
                         height, model, multiple linear regression analysis, tropical rain 
                         forest, uncertainty, validation process, vegetation.",
             abstract = "Surveying primary tropical forest over large regions is 
                         challenging. Indirect methods of relating terrain information or 
                         other external spatial datasets to forest biophysical parameters 
                         can provide forest structural maps at large scales but the 
                         inherent uncertainties need to be evaluated fully. The goal of the 
                         present study was to evaluate relief characteristics, measured 
                         through geomorphometric variables, as predictors of forest 
                         structural characteristics such as average tree basal area (BA) 
                         and height (H) and average percentage canopy openness (CO). Our 
                         hypothesis is that geomorphometric variables are good predictors 
                         of the structure of primary tropical forest, even in areas, with 
                         low altitude variation. The study was performed at the Tapajo's 
                         National Forest, located in the Western State of Par{\'a}, 
                         Brazil. Forty-three plots were sampled. Predictive models for BA, 
                         H and CO were parameterized based on geomorphometric variables 
                         using multiple linear regression. Validation of the models with 
                         nine independent sample plots revealed a Root Mean Square Error 
                         (RMSE) of 3.73 m2/ha (20%) for BA, 1.70 m (12%) for H, and 1.78% 
                         (21%) for CO. The coefficient of determination between observed 
                         and predicted values were r2 = 0.32 for CO, r2 = 0.26 for H and r2 
                         = 0.52 for BA. The models obtained were able to adequately 
                         estimate BA and CO. In summary, it can be concluded that relief 
                         variables are good predictors of vegetation structure and enable 
                         the creation of forest structure maps in primary tropical 
                         rainforest with an acceptable uncertainty.  2016 Bispo et al. 
                         This is an open access article distributed under the terms of the 
                         Creative Commons Attribution License, which permits unrestricted 
                         use, distribution, and reproduction in any medium, provided the 
                         original author and source are credited.",
                  doi = "10.1371/journal.pone.0152009",
                  url = "http://dx.doi.org/10.1371/journal.pone.0152009",
                 issn = "1932-6203",
             language = "en",
           targetfile = "bispo_predictive.PDF",
        urlaccessdate = "04 dez. 2020"
}


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