<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Journal Article">
		<site>mtc-m21c.sid.inpe.br 806</site>
		<holdercode>{isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}</holdercode>
		<identifier>8JMKD3MGP3W34R/3SMPMRB</identifier>
		<repository>sid.inpe.br/mtc-m21c/2019/02.07.15.51</repository>
		<lastupdate>2019:02.07.15.51.06 urlib.net/www/2017/11.22.19.04 simone</lastupdate>
		<metadatarepository>sid.inpe.br/mtc-m21c/2019/02.07.15.51.06</metadatarepository>
		<metadatalastupdate>2020:01.06.11.42.09 urlib.net/www/2017/11.22.19.04 administrator {D 2019}</metadatalastupdate>
		<doi>10.18637/jss.v088.i05</doi>
		<issn>1548-7660</issn>
		<citationkey>MausCamaAppePebe:2019:TiDyTi</citationkey>
		<title>dtwSat: time-weighted dynamic time warping for satellite image time series analysis in R</title>
		<year>2019</year>
		<month>Jan.</month>
		<typeofwork>journal article</typeofwork>
		<numberoffiles>1</numberoffiles>
		<size>1066 KiB</size>
		<author>Maus, Victor Wegner,</author>
		<author>Camara, Gilberto,</author>
		<author>Appel, Marius,</author>
		<author>Pebesma, Edzer,</author>
		<resumeid></resumeid>
		<resumeid>8JMKD3MGP5W/3C9JHB8</resumeid>
		<group></group>
		<group>DIDPI-CGOBT-INPE-MCTIC-GOV-BR</group>
		<affiliation>University of Münster</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>University of Münster</affiliation>
		<affiliation>University of Münster</affiliation>
		<electronicmailaddress>vwmaus1@gmail.com</electronicmailaddress>
		<electronicmailaddress>gilberto.camara@inpe.br</electronicmailaddress>
		<journal>Journal of Statistical Software</journal>
		<volume>88</volume>
		<number>5</number>
		<pages>1-31</pages>
		<secondarytype>PRE PI</secondarytype>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<versiontype>publisher</versiontype>
		<keywords>dynamic programming, MODIS time series, land cover changes, crop monitoring.</keywords>
		<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.</abstract>
		<area>SRE</area>
		<language>en</language>
		<targetfile>maus_dtwsat.pdf</targetfile>
		<usergroup>simone</usergroup>
		<readergroup>administrator</readergroup>
		<readergroup>simone</readergroup>
		<visibility>shown</visibility>
		<readpermission>deny from all and allow from 150.163</readpermission>
		<documentstage>not transferred</documentstage>
		<nexthigherunit>8JMKD3MGPCW/3EQCCU5</nexthigherunit>
		<dissemination>WEBSCI; PORTALCAPES; SCOPUS.</dissemination>
		<hostcollection>urlib.net/www/2017/11.22.19.04</hostcollection>
		<username>simone</username>
		<agreement>agreement.html .htaccess .htaccess2</agreement>
		<lasthostcollection>urlib.net/www/2017/11.22.19.04</lasthostcollection>
		<url>http://mtc-m21c.sid.inpe.br/rep-/sid.inpe.br/mtc-m21c/2019/02.07.15.51</url>
	</metadata>
</metadatalist>