Example scripts and programs from the book

Demo examples to easily explore GRASS and the NC data set

Installation of NC data set as user in your HOME directory (see book chapter 3 for more details):

# for Unix based systems (winGRASS native comes with data set installer)

# copy the package 'nc_spm_XX_YYYY_ZZZZZ.tar.gz' into your HOME directory, 
# then go into it, just to be sure:
cd $HOME

# create new directory to keep all your GRASS data inside:
mkdir grassdata

# go into the newly created folder:
cd grassdata

# expand the package within this 'grassdata/' folder:
tar xvfz $HOME/nc_spm_XX_YYYY_ZZZZZ.tar.gz
cd $HOME

# now launch GRASS and
#  - select as 'GIS Data Directory' the 'grassdata' folder in your HOME
#  - select as 'Location' the NC location 'nc_spm_XX'
#  - select as 'Mapset' either 'user1' or create a new mapset using the user interface
grass63

• demo raster
• demo vector
• demo interpolation
• demo image processing (coming soon)

Additional scripts

CHAPTER 6: Various code snippets

CHAPTER 6: Script to perform cross validation in GRASS/RST splines interpolation

• grass_crossvalidation_rst.sh 4.0K
  
• grass_crossvalidation_vol.sh 4.0K

Answer:
Not mean - that should be close to zero (positive and negative differences should cancel out) - if it is not, interpolate the crossvalidation differences to a raster map and overlay it with your input points to see whether you may have an isolated point(s) with value significantly below or above the rest of your data that is causing the bias (in that area you need more samples to support that extreme value).

Use mean of absolute values of differences as a measure of predictive interpolation accuracy - so you chose the parameters that give you the lowest mean of absolute values (you need to use v.univar.sh) as the most optimal. There are some papers that explain why for this particular case mean of absolute values is better than RMSE. Reference:

• Hofierka J., Cebecauer T., Suri M., 2007. Optimisation of Interpolation Parameters Using Cross-validation. In: Peckham R.J., Jordan G. (eds.) Digital Terrain Modelling, Development and Applications in a Policy Support Environment, Series: Lecture Notes in Geoinformation and Cartography, Springer, ISBN: 3-540-36730-6 [PDF]

CHAPTER 8: Scripts to bulk import LANDSAT-TM5/LANDSAT-TM7 scenes from GLCF Maryland into GRASS

• glcf_landsat7_for_NC_SPM_WAKE_process.sh (reproject, spatial subset with GDAL)
• glcf_landsat5_for_NC_SPM_WAKE_import.sh (import into GRASS)
• glcf_landsat7_2000_for_NC_SPM_WAKE_import.sh (import into GRASS)
• glcf_landsat7_2002_for_NC_SPM_WAKE_import.sh (import into GRASS)