Using Satellite Data for Species Distribution Modeling with GRASS GIS and R

Author

Verónica Andreo

Published

June 2, 2023

Traditionally, species distribution models (SDM) use climatic data as predictors of habitat suitability for the target species. In this studio, we will explore the use of satellite data to derive relevant predictors. The satellite data processing, from download to analysis, will be performed using GRASS GIS software functionality. Then, we’ll read our predictors within R and perform SDM, visualize and analyze results there, to then exemplify how to write the output distribution maps back into GRASS.

Getting ready

We’ll run this session online within the Whole Tale platform. Whole Tale is an NSF-funded Data Infrastructure Building Block (DIBBS) initiative to build a scalable, open source, web-based, multi-user platform for reproducible research. It enables the creation, publication, and execution of tales - executable research objects that capture data, code, and the complete software environment used to produce research findings. It’s also great for teaching, as participants do not need to install all software packages required. They only need to register with institutional or personal email and they are ready to go!

Run the session online: Satellite data for SDM with GRASS GIS and R

Software

If you still want to run the workshop locally, you’ll find instructions and requirements below.

GRASS GIS

We will use GRASS GIS 8.2+. It can be installed either through standalone installers/binaries or through OSGeo-Live (a linux based virtual machine which includes all OSGeo software and packages).

MS Windows

There are two different options to install GRASS GIS in MS Windows:

  1. Standalone installer 64-bit
  2. OSGeo4W 64-bit

For Windows users, we strongly recommend installing GRASS GIS through the OSGeo4W package (second option), since it allows to install all OSGeo software and resolves dependencies.

Ubuntu Linux

Install GRASS GIS 8.2+ 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

Mac OS

Find GRASS GIS binaries on http://grassmac.wikidot.com/ or install the latest available version from MacPorts.

GRASS GIS Add-ons

Install with g.extension extension=name_of_addon

R packages

The following R packages should be installed beforehand:

  install.packages(c("rgrass","terra","raster","sf","mapview","biomod2","dismo","usdm","SDMtune","zeallot","rJava","ggpubr"))

Python libraries

The following Python libraries should be installed beforehand:

  pip install folium

Other software

We will use the software MaxEnt to model habitat suitability. It can be downloaded from: https://biodiversityinformatics.amnh.org/open_source/maxent/

Data

Download the following ready to use location with reconstructed daily LST averages (Metz et al. (2017)) for Northern Italy. This dataset is courtesy of mundialis GmbH & Co. KG.

We will also use a points vector map representing Aedes albopictus presence data:

References

Metz, M., Andreo, V., and Neteler, M. (2017), “A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data,” Remote Sensing, 9, 1333. https://doi.org/10.3390/rs9121333.