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.
Earth observation data cubes are increasingly used as a data structure to make large collections of satellite images easily accessible to scientists. They hide complexities in the data such that data users can concentrate on the analysis rather than on data management. However, the construction of data cubes is not trivial and involves decisions that must be taken with regard to any particular analyses. This paper proposes on-demand data cubes, which are constructed on the fly when data users process the data. We introduce the open-source C++ library and R package gdalcubes for the construction and processing of on-demand data cubes from satellite image collections, and show how it supports interactive method development workflows where data users can initially try methods on small subsamples before running analyses on high resolution and/or large areas. Two study cases, one on processing Sentinel-2 time series and the other on combining vegetation, land surface temperature, and precipitation data, demonstrate and evaluate this implementation. While results suggest that on-demand data cubes implemented in gdalcubes support interactivity and allow for combining multiple data products, the speed-up effect also strongly depends on how original data products are organized. The potential for cloud deployment is discussed.
Geographic data is growing in size and variety, which calls for big data management tools and analysis methods. To efficiently integrate information from high dimensional data, this paper explicitly proposes array-based modeling. A large portion of Earth observations and model simulations are naturally arrays once digitalized. This paper discusses the challenges in using arrays such as the discretization of continuous spatiotemporal phenomena, irregular dimensions, regridding, high-dimensional data analysis, and large-scale data management. We define categories and applications of typical array operations, compare their implementation in open-source software, and demonstrate dimension reduction and array regridding in study cases using Landsat and MODIS imagery. It turns out that arrays are a convenient data structure for representing and analysing many spatiotemporal phenomena. Although the array model simplifies data organization, array properties like the meaning of grid cell values are rarely being made explicit in practice.
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