Here are weblinks to provide additional resources so that you can complete the below exercises:

Click on the following links, which will open in a new window.

Geographic Resources Analysis Support System (GRASS) GIS, like QGIS, is a free and open source collection of software tools designed for the visualization, management, and analysis of geospatial data and comes with some very powerful analytic modules. Originally developed by a research branch of the US Army Corps of Engineers, GRASS has been embraced by the open source community and is now an official project of the Open Source Geospatial Foundation. While some consider GRASS to have a steeper learning curve than QGIS, GRASS has an active developer community and a wide variety of tutorials and forums that, when coupled with the comprehensive collection of available tools, make it another good choice for those starting out with GIS.
 

Exercise 1: Download and install GRASS and begin a new project

GRASS GIS website - Here you will find useful information for newcomers to GRASS, along with abundant documentation and tutorials, a gallery of screenshots, and more.

There is also a software bundle called OSGeo4W (http://trac.osgeo.org/osgeo4w/) for Windows that allows you to install GRASS, QGIS, and many other open source GIS software packages with one installer.
 

Exercise 2: Working with vector data

A complete list of GRASS vector tools can be found at https://grass.osgeo.org/grass70/manuals/vector.html
 

Exercise 3: Working with raster data

A complete list of GRASS raster tools can be found at https://grass.osgeo.org/grass70/manuals/raster.html
 

Exercise 4: 3D raster visualization

For more on 3D raster visualization in GRASS, go to https://grass.osgeo.org/grass70/manuals/raster3dintro.html
 

Exercise 5: Temporal data analysis

Github site for a North Carolina State University GRASS workshop
 

Exercise 6: Using GRASS tools in QGIS to work with existing mapset

For more on integrating GRASS and QGIS, go to https://grasswiki.osgeo.org/wiki/QGIS_GRASS_Cookbook
 

Exercise 7: Importing data to create a new mapset

For this exercise you can return to the Chapter 11 dataset resources at https://study.sagepub.com/abernathy for a digital elevation model (DEM) of an individual county in North Carolina from the NC Department of Transportation’s elevation data page with a cell size of 20 feet by 20 feet. This is a high-resolution DEM created as part of a statewide flood mapping program using a technology called “light detection and ranging” (LIDAR).
 

Exercise 8: Data conversion

For more on converting from the raster data model to the vector data model, see the documentation on the r.to.vect tool at https://grass.osgeo.org/grass64/manuals/r.to.vect.html