Class Meetings: Thursday evenings, 6-9 PM. Townsend GIS/CAD lab.

Grading: any 5 lab assignments (16% each):

1. Landscape Change Analysis
2. Habitat Analysis
3. Creating and Analyzing DEM's
4. TIGER Data
5. Management of Non-Point Pollution
6. Intro to Image Processing
7. Spatial Regression UNDER REVISION
8. Geostatistics (TBA)

Final Project (20%; your own topic; obtain instructor approval)

All assignments must be submitted as web pages, including maps saved as GIF or JPEG format images.  For a quick introduction to HTML, check out Mad Dog's HTML Reference.

Prerequisites: Undergraduates must have completed a prior GIS course. Prior familiarity with UNIX is essential.

Texts, Etc.: There is no required textbook to buy. Students without strong UNIX backgrounds should buy the CNS handbook Introduction to UNIX at the University Bookstore. All students are encouraged to buy Linux in a Nutshell (O'Reilly, 1999), available at Borders or through Amazon.com.  Class notes and extensive GRASS documentation are available on-line.  Students may download (right mouse click) and print their own copies of the GRASS documentation listed below. Please run large print jobs in duplex (qpr -D -q printername), and preferably overnight (with the at command) to avoid tying up the printer during peak-load times.

The GRASS User Manual (over 600 pages) contains complete documentation on all modules in the GRASS 5.0beta release. These are sectioned by category. To print these, use acroread or print from your web browser.

• Display modules
• General Utility modules
• Image Processing modules
• Data Management modules
• Paint, Print and Photo modules
• Raster modules
• Shell scripts
• Sites modules
• Vector Modules

 
• the GRASS Programmer Guide (340 pages). documents GRASS data structures, C library functions and source code compilation procedures.  Not necessary unless you want to write your own new modules.
• a tutorial on r.mapcalc
• a tutorial on image processing
• a tutorial on importing TIGER vector data
• the r.le landscape evaluation suite of modules.

• intro: course organization, objectives, grading; basic GIS concepts: what a GIS does; geographic features, raster vs. vector data
• SPATLAB cluster account set-up; logins; intro to X

• UNIX basics: basic commands, man pages, file and directory maintenance, text editors
• Getting Started: GRASS setup; map displays and queries
• data structure basics: locations, mapsets and regions; raster, vector and site files
• regions, masks, etc.

• GRASS data structures--the Gory Details
• reclass maps
• report generation
• raster analytics: buffers, clumps and neighbor filters
• r.mapcalc: map algebra
• miscellaneous display tools: d.grid, d.scale, d.label, d.text, d.colormode, r.colors, d.colors, d.geodesic

• intro to shell programming
• uses and abuses of hardcopy; graphics formats
• basic hardcopy: local devices; Sun OpenWindows snapshot, PBM filters, XView

• profiles, volumes, etc.: r.mapcalc, d.profile, r.profile, r.transect, r.cross, r.random, r.volume
• report generation: r.stats, r.average, r.median, r.mode, r.covar, r.coin
• basic AWK filters
• GRASS hardcopy utilities: the CELL monitor and ps.map

• 3-D visualization: d.3d
• ASCII import/export: r/v.in/out.ascii, s.menu
• miscellaneous vector tools: v.spag, v.cutter, v.mkgrid, v.mkquads, v.patch

• digitizing and vector topology: v.digit, r.digit, v.support
• scanning as an alternative to digitizing
• raster-vector-site conversions: s.menu, r.poly, v.to.rast, v.to.sites, s.to.rast

8: April 6: digital elevation models

• Boolean vs. fuzzy
• digital elevation models: uses; data sources; DLG formats
• DMA 1-degree DEM's: ftp data sources; m.dmaUSGS.read; m.rot90; r.in.ll;
• creating a DEM from hypsography data: v.import; v.to.rast; r.reclass; r.surf.contour
• terrain analyses: r.slope.aspect, r.watershed, r.mapcalc, r.drain, r.los

• overview of TIGER, STF1A, STF1B, STF3A, STF3B
• DLG hydrography, transportation, etc.: v.import, v.to.rast, r.reclass
• TIGER import and thematic mapping of Census data: AWK filters; v.in.tig.basic, v.apply.census

• data sources: SPOT, LANDSAT, aerial photos
• data import from digital media or scanner
• band-ratios; vegetation indices
• band stretches, histogram equalization, saturation, de-striping, etc.: d.histogram; r.colors; r.mapcalc
• color composites, filters: d.rgb; i.colors; i.composite, i.fft; r.mfilter
• satellite imagery import/rectification: i.tape tools, i.group, i.target, i.points, i.vpoints, i.rectify
• colorspace transformations: i.rgb.his/i.his.rgb
• Anderson categories
• unsupervised classification: i.cluster, i.maxlik
• supervised classification: i.class, i.gensig, i.maxlik
• alternative classification procedures: i.smap, neural networks, etc.
• other tools: i.cca, i.pca

• projections, geoids and datums: v.proj, v.transform, m.ll2u, m.u2ll, m.datum.shift, m.region.ll
• creating a new GRASS location
• animation techniques: r.out.ppm, using the CELL monitor, shell scripting a fly-by, ImageMagick animate

• interpolation tools: r.surf.contour, r.surf.idw, s.surf.tps
• model validation and error surfaces
• surface sampling: r.what, r.random
• intro to spatial statistics

• taxonomy and consequences of spatial autocorrelation pathologies
• diagnostics for regression models
• EGLS, maximum likelihood and bootstrap regression methods

• other GIS's
• other Web resources
• compilation of user-contributed GRASS code
• course wrap-up and evaluations

 

 

Spatlab home page
UD College of Agricultural Sciences home page