MODIStsp: a new “R” package for MODIS Land Products preprocessing

(This article was first published on Posts on Lorenzo Busetto Website & Blog, and kindly contributed to R-bloggers)

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/lbusett/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/lbusett/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.

To leave a comment for the author, please follow the link and comment on their blog: Posts on Lorenzo Busetto Website & Blog.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)