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@Article{LuPebeSancVerb:2016:DeDeMO,
               author = "Lu, Meng and Pebesma, Edzer and Sanchez Ipia, Alber Hamersson and 
                         Verbesselt, Jan",
          affiliation = "{Westf{\"a}lische Wilhelms-Universitt M{\"u}nster} and 
                         {Westf{\"a}lische Wilhelms-Universitt M{\"u}nster} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and Wageningen 
                         University, Droevendaalsesteeg",
                title = "Spatio-temporal change detection from multidimensional arrays: 
                         Detecting deforestation from MODIS time series",
              journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
                 year = "2016",
               volume = "117",
                pages = "227--236",
                month = "July",
             keywords = "BFAST, Time series analysis, Spatial correlation, Temporal 
                         correlation, Array database, Spatio-temporal change modeling.",
             abstract = "Growing availability of long-term satellite imagery enables change 
                         modeling with advanced spatio-temporal statistical methods. 
                         Multidimensional arrays naturally match the structure of 
                         spatio-temporal satellite data and can provide a clean modeling 
                         process for complex spatio-temporal analysis over large datasets. 
                         Our study case illustrates the detection of breakpoints in MODIS 
                         imagery time series for land cover change in the Brazilian Amazon 
                         using the BFAST (Breaks For Additive Season and Trend) change 
                         detection framework. BFAST includes an Empirical Fluctuation 
                         Process (EFP) to alarm the change and a change point time locating 
                         process. We extend the EFP to account for the spatial 
                         autocorrelation between spatial neighbors and assess the effects 
                         of spatial correlation when applying BFAST on satellite image time 
                         series. In addition, we evaluate how sensitive EFP is to the 
                         assumption that its time series residuals are temporally 
                         uncorrelated, by modeling it as an autoregressive process. We use 
                         arrays as a unified data structure for the modeling process, R to 
                         execute the analysis, and an array database management system to 
                         scale computation. Our results point to BFAST as a robust approach 
                         against mild temporal and spatial correlation, to the use of 
                         arrays to ease the modeling process of spatio-temporal change, and 
                         towards communicable and scalable analysis.",
                  doi = "10.1016/j.isprsjprs.2016.03.007",
                  url = "http://dx.doi.org/10.1016/j.isprsjprs.2016.03.007",
                 issn = "0924-2716",
             language = "en",
           targetfile = "lu_spatio.pdf",
        urlaccessdate = "22 jan. 2021"
}


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