@Article{MausCamaAppePebe:2019:TiDyTi,
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
monitoring.",
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 = "27 abr. 2024"
}