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@Article{BradleyRPAABBCCHSE:2016:SiMuMa,
               author = "Bradley, A. V. and Rosa, I. M. D. and Pontius Junior, R. G. and 
                         Ahmed, S. E. and Ara{\'u}jo, M. B. and Brown, D. G. and 
                         Brand{\~a}o J{\'u}nior, A. and C{\^a}mara, Gilberto and 
                         Carneiro, T. G. S. and Hartley, A. J. and Smith, M. J. and Ewers, 
                         R. M.",
          affiliation = "{Imperial College of London} and {Imperial College of London} and 
                         {Clark University} and {Computational Science Laboratory} and 
                         {Imperial College of London} and {University of Michigan} and 
                         Imazon and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Ouro Preto (UFOP)} and {Met Office Hadley 
                         Centre} and {Microsoft Research} and {Imperial College of 
                         London}",
                title = "SimiVal, a multi-criteria map comparison tool for land-change 
                         model projections",
              journal = "Environmental Modelling and Software",
                 year = "2016",
               volume = "82",
                pages = "229--240",
                month = "Aug.",
             keywords = "Land-cover change, Land-cover modelling, Landscape metrics, Model 
                         similarity, Quantity allocation, Validation.",
             abstract = "The multiple uses of land-cover models have led to validation with 
                         choice metrics or an ad hoc choice of the validation metrics 
                         available. To address this, we have identified the major 
                         dimensions of land-cover maps that ought to be evaluated and 
                         devised a Similarity Validation (SimiVal) tool. SimiVal uses a 
                         linear regression to test a modelled projection against benchmark 
                         cases of, perfect, observed and systematic-bias, calculated by 
                         rescaling the metrics from a random case relative to the observed, 
                         perfect case. The most informative regression coefficients, 
                         p-value and slope, are plot on a ternary graph of 'similarity 
                         space' whose extremes are the three benchmark cases. SimiVal is 
                         tested on projections of two deliberately contrasting land-cover 
                         models to show the similarity between intra- and inter-model 
                         parameterisations. We find metrics of landscape structure are 
                         important in distinguishing between different projections of the 
                         same model. Predictive and exploratory models can benefit from the 
                         tool.",
                  doi = "10.1016/j.envsoft.2016.04.016",
                  url = "http://dx.doi.org/10.1016/j.envsoft.2016.04.016",
                 issn = "1364-8152",
             language = "en",
           targetfile = "bradley_simival.pdf",
        urlaccessdate = "03 dez. 2020"
}


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