• Brief intro to GRASS GIS - Joint tutorial with Markus Neteler
• TGRASS for environmental monitoring: Plenary and Workshop
• TGRASS and R for disease ecology applications: Plenary and Workshop

• Brief intro to GRASS GIS - Tuesday
• TGRASS for environmental monitoring 1: LST - Wednesday
• GRASS and R for disease ecology applications - Thursday
• TGRASS for environmental monitoring 2: NDVI - Friday

We will use GRASS GIS 7.6+. 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).

There are two different options:

1. Standalone installer: 32-bit version | 64-bit version
2. 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.

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

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

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:

• Run OSGeo-live in a Virtual Machine
• Run OSGeo-live from a bootable USB flash drive

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

• i.modis: Toolset to download and process MODIS products. It requires pyModis library.
• v.strds.stats: Estimates zonal statistics from space-time raster datasets based on a polygon vector map.
• r.hants: Gap filling with Harmonic Analysis of Time Series (HANTS) method.
• r.seasons: Extracts seasons from a time series.
• r.regression.series: Calculates linear regression parameters between two time series.
• v.in.pygbif: Searches and imports GBIF species distribution data. It requires pygbif library.
• r.bioclim: Calculates bioclimatic indices as those in WorldClim.

Install with g.extension extension=name_of_addon

We will use the software MaxEnt for the tutorial related to disease ecology. The software can be downloaded from: https://biodiversityinformatics.amnh.org/open_source/maxent/

Please, create a folder in your $HOME directory, or under Documents if in Windows, and name it grassdata. Then, download the following ready to use locations and unzip them within grassdata:

• North Carolina sample dataset (70 Mb)
• Northern Italy Land Surface Temperature 1km daily Celsius gap-filled dataset (1 Gb)

In the end, your grassdata folder should look like this:

  grassdata/
  ├── eu_laea
  │   ├── italy_LST_daily
  │   └── PERMANENT
  └── nc_basic_ogh_2019
      ├── modis_lst
      ├── modis_ndvi
      ├── PERMANENT
      └── user1

Verónica Andreo is a researcher for CONICET working at the Argentinean Space Agency (CONAE) in Córdoba, Argentina. Her main interests are remote sensing and GIS tools for disease ecology research fields and applications. Vero is an OSGeo Charter member and a FOSS4G enthusiast and advocate. She is part of the GRASS GIS Development team and she also teaches introductory and advanced courses and workshops, especially on GRASS GIS time series modules and their applications.

• 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

All the course material:

Creative Commons Attribution-ShareAlike 4.0 International License

Presentations were created with gitpitch:

• MIT License