@Article{SenaPaixFran:2021:ToDaAn,
               author = "Sena, Caio {\'A}tila Pereira and Paix{\~a}o, Jo{\~a}o 
                         Ant{\^o}nio Recio da and Fran{\c{c}}a, Jos{\'e} Ricardo de 
                         Almeida",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal do Rio de Janeiro (UFRJ)} and {Universidade 
                         Federal do Rio de Janeiro (UFRJ)}",
                title = "A Topological Data Analysis approach for retrieving Local Climate 
                         Zones patterns in satellite data",
              journal = "Environmental Challenges",
                 year = "2021",
               volume = "5",
                pages = "100359",
             keywords = "Topological Data Analysis, Local Climate Zones.",
             abstract = "In the context of geospatial studies, meaningful information may 
                         be hidden in the aspects of form and connectivity inscribed in the 
                         measurements. Therefore, here is proposed the use of H0 Persistent 
                         Homology (PH), a Topological Data Analysis tool to automatically 
                         summarize and quantify relevant spatial features in satellite 
                         data. With that aim, we extend the algebraic concepts of cubical 
                         complexes to the satellite data perspective and describe homology 
                         groups portrayal. As a proof by example, we present an inter-site 
                         comparison of Enhanced Vegetation Index from MODerate-resolution 
                         Imaging Spectroradiometer over fifteen regions worldwide. There, 
                         the Local Climate Zone (LCZ) framework is used to examine the 
                         outcomes of the PH filtration. Then, the features from every 
                         region that were encapsulated by the PH were compared against each 
                         other with the aid of the Bottleneck Distance metric. After that, 
                         it was performed a dimensionality reduction with a 
                         multi-dimensional scaling to build a 2-D geometry of the level of 
                         similarity among them. Thereby, the common aspects of the regions 
                         became explicit by their coordinates proximity in space. Then, 
                         with the use of the K-means algorithm, we were able to cluster 
                         those areas belonging to the same LCZ class. The results indicate 
                         that the proposed methods are robust to missing data in the 
                         satellite data and insensitive to a certain level of inhomogeneity 
                         in the spatial subsetting of data. Furthermore, the outcomes 
                         provide insights on several viable applications for future 
                         research.",
                  doi = "10.1016/j.envc.2021.100359",
                  url = "http://dx.doi.org/10.1016/j.envc.2021.100359",
                 issn = "2667-0100",
                label = "lattes: 3224723755240109 1 SenaPaixFran:2021:ToDaAn",
             language = "en",
           targetfile = "sena_topological.pdf",
        urlaccessdate = "20 maio 2024"
}