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.
Highlights
Remote sensing images are the most widely used data source for measuring land use and land cover change (LUCC)
In a tropical forest area, the method has achieved a high accuracy for mapping a classes of single cropping, double cropping, forest, and pasture (Maus et al 2016). n We chose R because it is an open source software that offers a large number of reliable packages. io The dtwSat package builds upon on a number of graphical and statistical tools in R: dtw it (Giorgino 2009), proxy (Meyer and Buchta 2015), zoo (Zeileis and Grothendieck 2005), mgcv (Wood 2000, 2003, 2004, 2006, 2011), sp (Pebesma and Bivand 2005; Bivand et al 2013), raster d (Hijmans 2015), caret (Kuhn et al 2016), and ggplot2 (Wickham 2009)
In the dtwSat package, we focus on the specific case of satellite image time series analysis
Summary
Remote sensing images are the most widely used data source for measuring land use and land cover change (LUCC). The need to analyse such large data sets has lead to the development of automated and semi-automated methods for satellite image time series analysis. These methods include multi-image compositing (Griffiths et al 2013), detecting forest disturbance dtwSat: Time-Weighted Dynamic Time Warping and recovery (Kennedy et al 2010; Zhu et al 2012; DeVries et al 2015), crop classification (Xiao et al 2005; Wardlow et al 2007; Petitjean et al 2012; Maus et al 2016), planted forest mapping (le Maire et al 2014), crop expansion and intensification (Galford et al 2008; Sakamoto et al 2009), detecting trend and seasonal changes (Lunetta et al 2006; Verbesselt et al 2010a,b, 2012), and extracting seasonality metrics from satellite time series (Jonsson and Eklundh 2002, 2004).
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