dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R

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.

Forest-Type Classification Using Time-Weighted Dynamic Time Warping Analysis in Mountain Areas: A Case Study in Southern China

“…TWDTW was developed by adding a time constraint to DTW to classify various land-cover types and has significantly improved the accuracy of DTW. The TWDTW method was implemented in the R (v3.4.4) package dtwSat v0.2.5 (São José dos Campos, Brazil) [40,52].…”

“…The temporal patterns of forest type had been extracted by using the pixel-based stack of NDVI datasets obtained from the time-series images and training samples. The function dtwSat::createPatterns [52] in the package was used to define the temporal patterns,…”

“…The temporal patterns of forest type had been extracted by using the pixel-based stack of NDVI datasets obtained from the time-series images and training samples. The function dtwSat::createPatterns [52] in the package was used to define the temporal patterns, which can create a smooth temporal pattern of the NDVI using the Generalized Additive Model (GAM) method [53]. The sampling frequency of the output patterns was set to 5 days.…”

“…Time-series image classification. A logistic TWDTW was implemented based on the R package "dtwSat" [52], which has a low penalty for small time warps and significant costs for large time warps [40,51]. Time warps are the inconsistency between the NDVI temporal pattern of the forest types to be classified and the created NDVI temporal pattern [40].…”

Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

“…Since it can "fill-in" temporal gaps in the remote sensing time series (e.g., cloudy images), it has been successfully applied in satellite imagery time-series analysis [17,18]. However, the distinctive phenological cycle of each LULC class requires an equilibrium between shape matching and temporal alignment [19,20], which is why Maus et al [21] improved the DTW algorithm. Maus et al [21] proposed the Time-Weighted Dynamic Time Warping (TWDTW) method that includes time-weighting to account for seasonality.…”