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---
title: "Using Satellite Data for Species Distribution Modeling with GRASS GIS and R"
author: "Verónica Andreo"
date: '`r Sys.Date()`'
---
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, however, if you want to run it locally
afterwards, here are the requirements.
## Software
### GRASS GIS
We will use **GRASS GIS 8.2+**. It can be installed either
through standalone installers/binaries or through
[OSGeo-Live](https://live.osgeo.org/en/index.html)
(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](https://grass.osgeo.org/grass82/binary/mswindows/native/WinGRASS-8.2.1-1-Setup.exe)
2. [OSGeo4W 64-bit](http://download.osgeo.org/osgeo4w/v2/osgeo4w-setup.exe)
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:
```bash
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](https://grass.osgeo.org/download/linux/)
##### Mac OS
Find GRASS GIS binaries on <http://grassmac.wikidot.com/> or install the
latest available version from [MacPorts](https://ports.macports.org/port/grass/).
#### GRASS GIS Add-ons
* [r.bioclim](https://grass.osgeo.org/grass-stable/manuals/addons/r.bioclim.html): Calculates bioclimatic indices as those in [WorldClim](https://www.worldclim.org/bioclim).
Install with `g.extension extension=name_of_addon`
### R packages
The following R packages should be installed beforehand:
```r
install.packages(c("rgrass","terra","raster","sf","mapview","biomod2","dismo","usdm","SDMtune","zeallot"))
```
### Python libraries
The following Python libraries should be installed beforehand:
```bash
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_new_2017) for Northern Italy. This dataset is courtesy of
[mundialis GmbH & Co. KG](mundialis.de/en/).
* [Northern Italy (1.7 Gb)](https://drive.google.com/file/d/1z1b2NLC4Z6yzz_57RddTdRRK_gUkd7fU/view?usp=sharing)
We will also use a points vector map representing *Aedes albopictus* presence data:
* [Mosquito occurrence data](data/aedes_albopictus.gpkg)
## References
:::{#refs}
:::
<!-- - https://github.com/veroandreo/foss4g2022_grass4rs -->
<!-- - https://github.com/veroandreo/grass_opengeohub2021 -->
<!-- - Neteler, M. and Mitasova, H. (2008): *Open Source GIS: A GRASS GIS Approach*. Third edition. ed. Springer, New York. [Book site](https://grassbook.org/) -->
<!-- - 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](http://dx.doi.org/10.1016/j.envsoft.2011.11.014) -->
<!-- - Gebbert, S. and Pebesma, E. (2014). *A temporal GIS for field based environmental modeling*. Environmental Modelling & Software, 53, 1-12. [DOI](https://doi.org/10.1016/j.envsoft.2013.11.001) -->
<!-- - Gebbert, S. and Pebesma, E. (2017). *The GRASS GIS temporal framework*. International Journal of Geographical Information Science, 31, 1273-1292. [DOI](http://dx.doi.org/10.1080/13658816.2017.1306862) -->
<!-- - Gebbert, S., Leppelt, T. and Pebesma, E. (2019). *A Topology Based Spatio-Temporal Map Algebra for Big Data Analysis*. Data, 4, 86. [DOI](https://doi.org/10.3390/data4020086) -->