@Article{Muller-HansenCaDaDoHeKuTh:2017:MaClMe,
               author = "Muller-Hansen, Finn and Cardoso, Manoel Ferreira and Dalla Nora, 
                         El{\'o}i Lennon and Donges, Jonathan F. and Heitzig, Jobst and 
                         Kurths, J{\"u}rgen and Thonicke, Kirsten",
          affiliation = "{Potsdam Institute for Climate Impact Research} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Potsdam Institute for Climate 
                         Impact Research} and {Potsdam Institute for Climate Impact 
                         Research} and {Potsdam Institute for Climate Impact Research} and 
                         {Potsdam Institute for Climate Impact Research}",
                title = "A matrix clustering method to explore patterns of land-cover 
                         transitions in satellite-derived maps of the Brazilian Amazon",
              journal = "Nonlinear Processes in Geophysics",
                 year = "2017",
               volume = "24",
               number = "1",
                pages = "113--123",
                month = "Feb.",
             abstract = "Changes in land-use systems in tropical regions, including 
                         deforestation, are a key challenge for global sustainability 
                         because of their huge impacts on green-house gas emissions, local 
                         climate and biodiversity. However, the dynamics of land-use and 
                         land-cover change in regions of frontier expansion such as the 
                         Brazilian Amazon are not yet well understood because of the 
                         complex interplay of ecological and socioeconomic drivers. In this 
                         paper, we combine Markov chain analysis and complex network 
                         methods to identify regimes of land-cover dynamics from land-cover 
                         maps (TerraClass) derived from high-resolution (30 m) satellite 
                         imagery. We estimate regional transition probabilities between 
                         different land-cover types and use clustering analysis and 
                         community detection algorithms on similarity networks to explore 
                         patterns of dominant land- cover transitions. We find that land- 
                         cover transition probabilities in the Brazilian Amazon are 
                         heterogeneous in space, and adjacent subregions tend to be 
                         assigned to the same clusters. When focusing on transitions from 
                         single land- cover types, we uncover patterns that reflect major 
                         regional differences in land-cover dynamics. Our method is able to 
                         summarize regional patterns and thus complements studies performed 
                         at the local scale.",
                  doi = "10.5194/npg-24-113-2017",
                  url = "http://dx.doi.org/10.5194/npg-24-113-2017",
                 issn = "1023-5809",
             language = "en",
           targetfile = "muller_matrix.pdf",
        urlaccessdate = "27 abr. 2024"
}