Spatio-temporal data processing and visualization with GRASS GIS

_{Verónica Andreo¹, Martin Landa², Ondřej Pešek³, Markus Neteler⁴, Luca Delucchi⁵ & Moritz Lennert⁶}

GRASS GIS is a general purpose Free and Open Source GIS that offers raster, 3D raster and vector data processing support. GRASS GIS has also incorporated a powerful support for time series, the so called TGRASS. Through this, it became the first open source temporal GIS with comprehensive spatio-temporal analysis, processing and visualization capabilities. The temporal functionality makes it easy to manage, analyse and visualize climatic data, vegetation index time series, harvest data or land use changes over time. Raster, vector and 3D raster time series are handled through a special data type called space time data sets (STDS) which are used as input in TGRASS modules. TGRASS incredibly simplifies the processing and analysis of large time series of hundreds of thousands of maps. For example, users can aggregate a daily time series into a monthly time series in just a single line; they can retrieve the date per year in which a certain value is reached; select maps from a time series in time periods in which a different time series reaches a certain value; perform different temporal as well as spatial operations among time series, and so much more.

In this 4-hours hands-on workshop we will present and exemplify the use of a subset of the more than 45 temporal modules in combination with other GRASS GIS modules and Add-ons in a workflow starting from the download of remote sensing data to the creation of a simple model and visualization of results. We will first learn how go to create STDS and assign time stamps to maps. In addition, we will learn different methods to gap-fill incomplete data, temporal algebra operations, temporal aggregation, queries and retrieval of basic and zonal statistics for time series. All along the session, we ll see different visualization options available in GRASS GIS. Moreover, we will show how this workflow might be included in python scripts and executed from outside GRASS GIS.

Author's affiliations:

_{¹ Instituto Nacional de Medicina Tropical (INMeT) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Puerto Iguazú, Argentina.
² Czech Technical University in Prague. Czech Republic.
³ JRC - Joint Research Centre. Ispra, Italy.
⁴ mundialis GmbH & Co. KG. Bonn, Germany.
⁵ Fondazione Edmund Mach. San Michele all’Adige, Italy.
⁶ Université Libre de Bruxelles. Belgium.}

Presentations

Software

We will use GRASS GIS 7.6.1 (current stable version). It can be installed either through standalone installers/binaries or through OSGeo-Live (which includes all OSGeo software and packages). See this Installation guide for details (Follow only the GRASS GIS part).

Standalone installers for different OS:

MS Windows

There are two different options:

Standalone installer: 32-bit version | 64-bit version
OSGeo4W package (network installer): 32-bit version | 64-bit version

For Windows users, we strongly recommend installing GRASS GIS through the OSGeo4W package (second option), since it allows to install all OSGeo software. If you choose this option, make sure you select GRASS GIS and msys. The latter one will allow the use of loops, back ticks, autocomplete, history and other nice bash shell features.

Ubuntu Linux

Install GRASS GIS 7.6.1 from the "unstable" package repository:

sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable
sudo apt-get update
sudo apt-get install grass grass-gui grass-dev

Fedora, openSuSe Linux

For other Linux distributions including Fedora and openSuSe, simply install GRASS GIS with the respective package manager. See also here

OSGeo-live:

OSGeo-live is a self-contained bootable DVD, USB thumb drive or Virtual Machine based on Lubuntu, that allows you to try a wide variety of open source geospatial software without installing anything. There are different options to run OSGeo-live:

For a quick-start guide, see: https://live.osgeo.org/en/quickstart/osgeolive_quickstart.html

GRASS GIS Add-ons that will be used during the course

i.modis: Toolset to download and process MODIS products. It requires pyModis library.
v.strds.stats: Zonal statistics from given space-time raster datasets based on a polygons vector map

Install with g.extension extension=name_of_addon

Data

North Carolina location (full dataset, 150Mb): download and unzip within $HOME/grassdata.
modis_lst mapset (2Mb): download and unzip within the North Carolina location in $HOME/grassdata/nc_spm_08_grass7.

Your grassdata folder should look like this:

  grassdata/
   ├── nc_spm_08_grass7
     ├── landsat
     ├── modis_lst
     ├── PERMANENT
     └── user1

References

Neteler, M. and Mitasova, H. (2008): Open Source GIS: A GRASS GIS Approach. Third edition. ed. Springer, New York. Book site
Neteler, M., Bowman, M.H., Landa, M. and Metz, M. (2012): GRASS GIS: a multi-purpose Open Source GIS. Environmental Modelling & Software, 31: 124-130 DOI
Gebbert, S. and Pebesma, E. (2014). A temporal GIS for field based environmental modeling. Environmental Modelling & Software, 53, 1-12. DOI
Gebbert, S. and Pebesma, E. (2017). The GRASS GIS temporal framework. International Journal of Geographical Information Science, 31, 1273-1292. DOI
Gebbert, S., Leppelt, T. and Pebesma, E. (2019). A Topology Based Spatio-Temporal Map Algebra for Big Data Analysis. Data, 4, 86. DOI

License

All the course material:

Creative Commons Attribution-ShareAlike 4.0 International License

Presentations were created with gitpitch:

MIT License

landam / tgrass-foss4g2019