MODIStsp: a new "R" package for MODIS Land Products preprocessing

In this post, we are introducing MODIStsp a new “R” package allowing to automatize the creation of time series of rasters derived from Land Products data derived from MODIS satellite data (; www.sciencedirect.com/science/article/pii/S0098300416303107).

Development of MODIStsp started from modifications of the ModisDownload “R” script by Thomas Hengl (spatial-analyst.net/wiki/index.php?title=Download_and_resampling_of_MODIS_images), and successive adaptations by Babak Naimi (r-gis.net/?q=ModisDownload). Their functionalities were gradually incremented with the aim of:

  1. Developing a standalone application allowing to perform several preprocessing steps (e.g., download, mosaicking, reprojection and resize) on all available MODIS land products by exploiting a powerful and user-friendly GUI front-end;
  2. Allowing the creation of time series of both MODIS original layers and additional Quality Indicators (e.g., data acquisition quality, cloud/snow presence, algorithm used for data production, etc. ) extracted from the aggregated bit-field QA layers
  3. Allowing the automatic calculation and creation of time series of several additional Spectral Indexes starting form MODIS surface reflectance products

Installation and usage

Detailed installation instructions and notes on use of the package, can be found in the main github page of the package (github.com/ropensci/MODIStsp) and in the package’s vignette.

Basic interactive usage

After installing and loading the package, launching the MODIStsp function without additional parameters opens a user-friendly GUI for the selection of processing options required for the creation of the desired MODIS time series (e.g., start and end dates, geographic extent, type of product and parameters of interest, etc.).

The main GUI of MODIStspThe main GUI of MODIStsp

After selecting the product, the user can select the MODIS original, QI and SI layers to be processed by pressing the Select Layers button, which opens a separate layers’ selection panel. Although some of the most common SIs available for computation by default users can add custom ones without modifying MODIStsp source code by clicking on the Add Custom Index button, which allows specifying the formula of the additional desired SI using a simple GUI interface.

Example of the GUI for selection of the layers to be processed for product M*D13Q1

Upon clicking the “Start” button in the main GUI, required MODIS HDF files are automatically downloaded from NASA servers and resized, reprojected, resampled and processed according to user’s choices.

Non-interactive execution and scheduled processing

Non-interactive execution exploiting a previously created Options File is also possible, as well as stand-alone execution outside an “R” environment. This allows to use scheduled execution of MODIStsp to automatically update time series related to a MODIS product and extent whenever a new image is available. For additional details see the main github page !

Output format

For each desired output layer, outputs are saved as single-band rasters corresponding to each acquisition date available for the selected MODIS product within the specified time period.

R RasterStack objects with temporal information as well as Virtual raster files (GDAL vrt and/or ENVI META files) facilitating access to the entire time series can be also created.

Accessing and analyzing the processed time series from R

Preprocessed MODIS data can be retrieved within R scripts either by accessing the single-date raster files, or by loading the saved RasterStack objects. This second option allows accessing the complete data stack and analyzing it using the functionalities for raster/raster time series analysis, extraction and plotting provided for example by the raster or rasterVis packages.

MODIStsp provides however also an efficient function (MODIStsp_extract()) for extracting time series data at specific locations. The function takes as input a rasterStack object with temporal information created by MODIStsp, the starting and ending dates for the extraction and a standard R Sp* object (or an ESRI shapefile name) specifying the locations (points, lines or polygons) of interest, and provides as output a R xts object or data.frame containing time series for those locations. As an example the following code:

#Set the input paths to raster and shape file
infile <- 'in_path/MOD13Q1_MYD13Q1_NDVI_49_2000_353_2015_RData.RData'
shp_name <- 'path_to_file/rois.shp'
#Set the start/end dates for extraction
start_date <- as.Date("2010-01-01")
end_date <- as.Date("2014-12-31")
#Load the RasterStack
inrts <- get(load(infile))

# Compute average and St.dev
dataavg <- MODIStsp_extract(inrts, shp_name, start_date, end_date, FUN = 'mean', na.rm = T)
datasd <- MODIStsp_extract(inrts, shp_name, start_date, end_date, FUN = 'sd', na.rm = T)
# Plot average time series for the polygons
plot.xts(dataavg)

, loads a RasterStack object containing 8-days 250 m resolution time series for the 2000-2015 period and extracts time series of average and standard deviation values over the different polygons of a user’s selected shapefile on the 2010-2014 period. The function exploits rasterization of the input Sp* object and fast summarization based on the use of _data.table _objects to greatly increase the speed of data extraction with respect to standard R functions.

Authors

The package is developed and maintained by Lorenzo Busetto and Luigi Ranghetti (Institute for Remote Sensing of Environment - National Research Council of Italy).

Problems and issues

Any problems/issues can be reported at: github.com/ropensci/MODIStsp/issues

Publication and citation

A paper on MODIStsp was recently published in the “Computers & Geosciences” journal www.sciencedirect.com/science/article/pii/S0098300416303107.To cite MODIStsp please use:

L. Busetto, L. Ranghetti (2016) MODIStsp: An R package for automatic preprocessing of MODIS Land Products time series, Computers & Geosciences, Volume 97, Pages 40-48, ISSN 0098-3004, http://dx.doi.org/10.1016/j.cageo.2016.08.020.

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