author = "Maus, Victor Wegner and Camara, Gilberto and Appel, Marius and 
                         Pebesma, Edzer",
          affiliation = "{University of M{\"u}nster} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {University of M{\"u}nster} and {University 
                         of M{\"u}nster}",
                title = "dtwSat: time-weighted dynamic time warping for satellite image 
                         time series analysis in R",
              journal = "Journal of Statistical Software",
                 year = "2019",
               volume = "88",
               number = "5",
                pages = "1--31",
                month = "Jan.",
             keywords = "dynamic programming, MODIS time series, land cover changes, crop 
             abstract = "The opening of large archives of satellite data such as LANDSAT, 
                         MODIS and the SENTINELs has given researchers unprecedented access 
                         to data, allowing them to better quantify and understand local and 
                         global land change. The need to analyze such large data sets has 
                         led to the development of automated and semi-automated methods for 
                         satellite image time series analysis. However, few of the proposed 
                         methods for remote sensing time series analysis are available as 
                         open source software. In this paper we present the R package 
                         dtwSat. This package provides an implementation of the 
                         time-weighted dynamic time warping method for land cover mapping 
                         using sequence of multi-band satellite images. Methods based on 
                         dynamic time warping are flexible to handle irregular sampling and 
                         out-of-phase time series, and they have achieved significant 
                         results in time series analysis. Package dtwSat is available from 
                         the Comprehensive R Archive Network (CRAN) and contributes to 
                         making methods for satellite time series analysis available to a 
                         larger audience. The package supports the full cycle of land cover 
                         classification using image time series, ranging from selecting 
                         temporal patterns to visualizing and assessing the results.",
                  doi = "10.18637/jss.v088.i05",
                  url = "http://dx.doi.org/10.18637/jss.v088.i05",
                 issn = "1548-7660",
             language = "en",
           targetfile = "maus_dtwsat.pdf",
        urlaccessdate = "23 abr. 2021"