GEO 580

• To introduce students to the use of surfaces in GIS, including issues such as:
  • data structures for elevation and other continuous surface data,
  • creation of DEMs and TINs,
  • accuracy and resolution,
  • effective display of elevation and other '2 1/2D' information,
  • uses of DTMs.
• You will also become familiar with the use of data frames in ArcGIS.

     The term Digital Terrain Model (DTM) is a generalized term for any digital representation of a topographic surface.  Three such representations are DEMs, TINs, and contour coverages:

• The term Digital Elevation Model (DEM) refers (almost always) to rasters or grids with regularly spaced height values.
• Triangular Irregular Networks (TINs) are irregularly spaced points with elevation values that are the vertices of triangles that represent the surface.
• In ArcGIS 9, a contour map can be stored as a coverage, with each line in the contour being attached to a height attribute.

     This lab will focus on topography, but keep in mind that many of the issues and techniques discussed will be applicable to these other kinds of continuous geographic information.

Basic information on USGS DEMs

(From the Rocky Mountain Mapping Center)

     The US Geological Survey has created a national database of DEMs.  The source data for this project includes contour maps and stereo pairs of air photos.  Several methods have been used to create DEMs, which you can read about on the USGS page on DEMs.  These methods can be manual (e.g., having a human estimate elevation along a regular grid) or more-or-less automated (interpolating a digitized contour map into a DEM).  Many of these techniques leave artifacts (artificially introduced patterns in the data) in the dataset, however.

     The USGS produces DEMs at a variety of scales.  Much of this is currently free to download in SDTS (Spatial Data Transfer Standard) format. The EROS Data Center hosts the USGS DEMs and DLGs at the USGS Geographic Data Download site.
 

The Future of DEMs: SRTM??

     In February 2000, the Shuttle Endeavor flew the Shuttle Radar Topography Mission (SRTM), and in ten days it mapped 80% of the Earth's land surface -- 94.59% of that twice.  SRTM should provide almost complete DEM coverage of areas between 60 N and 60 S, at a high resolution of about 30 m.  This is equivalent to the large scale 7.5-minute DEMs (also about 30 m resolution), but with uniform coverage both inside and outside of the US and without the artifacts characteristic of DEMs generated in other ways.  The 30 m resolution is also equivalent to Landsat TM resolution and thus provides rich opportunities for combining data.  The data is currently being processed distributed. This data is now available (as mentioned elsewhere) at seamless.usgs.gov. It is not always the best decision to use the SRTM data when performing analysis. It contains numerous missing pixels (assigned a value of NoData), and in heavily forested mountinous terrain may have incorrect pixel values.

SRTM Update 2009: Currently, the SRTM data for the entire US (except parts of Alaska) are available from the USGS. Although the joint agreement between NASA and NIMA called for the production of a worldwide 30-m coverage (as described above), post 9/11 security changes have led to a watered down version with a 90-m resolution being made available. These 90-m resolution data are now available globally for all locations between 60 degrees N and 56 degrees S latitude. Many scientists skirt these security concerns by using datasets covering smaller areas produced by processing stereo ASTER images to determine relative elevations. These DEM's tend to have limited coverage, but do have 30m spatial resolution, and accuracy comparable to that of the SRTM data.

• Here is an image of Mount Oyama, Japan, generated by ASTER data (Advanced Spaceborne Thermal Emission and Reflection Radiometer).

Download the data here (17.5 Mb) into your local work folder. Your data sets should include these layers:

• hood_dem -- A DEM of Mt. Hood - not from SRTM, and reprojected.
• hood_landcov -- A grid of landcover derived from Landsat TM data by the USGS
• hoodcov.met.html -- metadata for the hood_landcov layer.
• or_gap-clip -- Gap Landcover clipped from the oregon Gap coverage for the Mt. Hood area
• Hood_names -- Names of places from ORGNIS (ORregon Geographic Names Information System)

6.4.1. Methods of DEM display

• To begin, add hood_dem into ArcMap (Data Frame 1 is fine for now).

     You probably don't have any great love for the continuous grayscale that ArcMap puts on your DEM display as a default.  However, displaying DEM information clearly and attractively is tricky.  There are a number of options in ArcMap to improve the display of the DEM:
 

DEM Display Improvement #1: Color Ramps.     In the Layer Properties window (get there by right-clicking hood_dem, then Properties--> Symbology tab), click on Stretched. Change the default greyscale color ramp to another one that looks more exciting. Drag your Layer Properties window aside a bit so that you can see a good portion of the map. Click the Apply button instead of OK (so that your properties window stays open) and look at the resulting map. Try a few different color maps.  Having trouble finding one you like?


   

DEM Display Improvement #2: Style Manager.     ArcGIS's Style Manager gives you access to a number of very useful pre-defined symbols and color ramps.  You can also save styles that you create. Close your Layer Properties window. In ArcMap, go to Menu Bar --> Tools (not ArcToolbox!) --> Styles --> Style Manager. You should get a window like this.  It works much like Windows Explorer or ArcCatalog: ESRI.style contains the predefined styles, while the second folder contains styles designed or copied by the current user (in this case that is you and whoever else has been using the computer).  You can see that the styles are grouped for various uses -- Reference Systems, Area Patches, etc. Click on the  button.  You can see a large number of topics which ESRI has created predefined styles for.  You can click on them to add them for a particular project.  E.g., a person mapping vegetation might use something like this: The most useful color ramp styles are in ESRI.style.  Go to Color Ramps and then scroll down until you see Elevation #1: (you can move the line between Name and Category so you can see more of the Name) Highlight it.  Right-click and Copy.  Navigate to the Guest directory and then to the color ramp folder.  Right-click, Paste in the right pane of the window.  Close the Style Manager. Now, when you return to Properties-->Symbology for hood_dem, you should have the Elevation #1 color ramp available somewhere in your list of color ramps (probably at the top).  It won't be labeled; but it should look like this: For More Info on Styles:    ESRI's ArcUser article on Map Making with Styles.    Use this color scheme or Elevation #2, if you like.  These color schemes probably succeed because they use a variety of colors, instead of just shades of one or two colors; and because the colors are arranged in a way that parallels map conventions we are used to (white for mountain tops, etc.) Note: Our own Dr. Kimerling is in part responsible for the development of the continuous graduated color ramp for the display of elevation information.





DEM Display Improvement #3: Classes      Another way to improve clarity is to group the elevation values into classes and have the color change abruptly when a threshold is crossed; this allows the viewer to identify a region as being in a specific elevation class, rather than having to think about where that region's particular shade fits into a smooth continuum on the legend bar. Return to the Symbology tab for hood_dem. Click on Classified.  (Under "Show" on the left side of the window) Click on the  button and change the classification method to Equal interval.  Then click okay. The Elevation color ramps that you copied from ESRI.style should still be available.  Select one of them. You can see that your elevation has been split into 5 classes with equal intervals. Change the number of classes, like so:  Do this a few times and hit . With equal interval classification, you will see that ArcMap divides the range of values evenly, which usually results in arbitrary-looking numbers and long fractions.  Far more useful would be to have the breaks fall on nice, even numbers.  In this case 200 m intervals seem reasonable.    Click .  You will see a window like this.  The graph shown is a histogram of the number of pixels (dependent variable) on the y-axis vs. elevation of pixel (independent variable) on the x-axis: There are two ways to do edit the breaks to do this: Method #1: You can change the breaks by dragging the vertical blue lines.  You will notice that when you do this, the classification "Method" changes to "Manual".  If your breaks are coming out with 6-digit decimals, check the box next to "Snap breaks to data values," i.e., .  This will round your breaks to the nearest data values. You can also Insert and Delete Breaks by right-clicking in the histogram.  You can examine portions of the histogram more closely by right-clicking and selecting Zoom In, or by clicking and dragging to create a zoom-in box. To type in specific values as breaks, click once on the break value in question, then click again to edit it:  Method #2: What we would really like is an easy way to set the breaks to occur every 200 m, starting at sea-level.  Click on the arrow to the right of classification "Method" to see your options:    Select "Defined Interval," and make the interval size 200.  Hit the keyboard TAB or click in the Break Values box to see the resulting breaks on the histogram (keyboard ENTER doesn't work).  Click OK, then click Apply or OK. The Labels may still show six decimal places. To change this, click the "Label" header, then "Format labels...." You can change the number of decimal places displayed under "Rounding."

 

More on ArcGIS 9.x Extensions:      Much of the GIS functionality needed for scientific research comes from extensions to ArcGIS.  These are software add-ons that make new tools accessible.  When installed (they must be purchased from ESRI, of course) they will be accessible as new tools in ArcToolbox and/or as new toolbars in ArcMap (the latter are those drag-and-drop toolbars, such as the Layout, Draw, Effects, and Network Analysis toolbars, which you have accessed from ArcMap's Menu Bar --> View --> Toolbars).      You are probably familiar with ArcView extensions such as Spatial Analyst.  As ArcView is folded into ArcGIS 9, ESRI makes the functionality of these extensions available as ArcGIS extensions. More on ArcView extensions from ESRI's site    More importantly, you have the option of downloading free extensions and scripts with a tremendous variety of functionalities. Try going to the ESRI ArcScripts page, and search for extensions for ArcGis 9.x. (A really good one is XTools.) These are generally free to use, easy to install, and can often complete tasks with the click of a button that might be difficult or impossible otherwise.

 

     There are many other options for displaying DEMs, although using them in ArcGIS 9 will depend upon the availability of various extensions such as 3-D Analyst - available to users of the Digital Earth lab thanks to our OSU campus site license.  Some common extensions from ESRI that you will see: 3-D Analyst, Spatial Analyst, Geostatistical Analyst.

• Topographic Shading (hillshading): Here, the DEM is used to simulate the effects of sunlight reflecting off of topography with differing slope and aspect.  This effect is always seen in visible-infrared satellite imagery.  To run the simulation program, the user must usually choose a sun geometry -- i.e., how high is the sun in the sky and what direction is the sunlight coming from.  The simulation gives the viewer a sense of the relief, although not any quantitative elevation information.
  • Additional Note: Calculations similar to those used for topographic shading are also useful in other areas.  For example, one may wish to calculate the insolation(amount of light) that a slope receives over the course of a year, to study what influence this has on the vegetation that grows there.  Or one may wish to remove the influence of topographic shading from a remotely-sensed image.
  • We will see ArcGIS 9 display topographic shading when we view the TINs we create.  When TINs are created, each triangle has an attribute for slope and for aspect, as well as elevation.
• 3D Perspective: ESRI's 3-D Analyst and other software packages can display DEMs in 3-D -- e.g., "what would Mt. Shasta look like from the side?"  Animated "fly-throughs" can also be built from DEMs, if you have enough computing horsepower.  Perspective views and animations tend to be most useful for "gee-whiz" displays for general audiences rather than for analysis itself. ArcGIS Desktop has ArcGlobe, allowing the creation of 3-D scenes and fly-bys.
  • Check out  ESRI's ArcGlobe demos

Your map -- Data frame #1:       Use a classified map of the hood area elevations for your first data frame.

If you haven't already done so, create a hillshade of hood_dem(see above). Now set the transparency of the Hillshade by going to its properties-->Display tab and changing the transparency to 50%. Make sure the Hillshade is on top, and the DEM underneath. You should see something like the image below. This is an impressive and useful display technique when producing a cartographic product.

     Review the Introduction to this lab and the Lab 2 to make sure you have a clear idea of what data structures are and what the main kinds of DTMs are. 

Contours. Create a contour coverage from our DEM, hood_dem.  Space our contours every 200 vertical meters.

• Check to see if the projection is defined for your contours shapefile you created. If not, Define the projection of your new hood_contours shapefile based on hood_dem using the Define Projection tool in ArcToolbox or with its ArcCatalog properties.
• Add hood_contours to ArcMap in your second data frame..

TIN Creation. Now, we're going to create two TINs from our elevation data.
 
 

• Put the two TINs into ArcMap, Frame #3.  Classify the elevation symbology of both TINs with the default color ramp (TINs have Elevation #1 as their default color ramp), using defined intervals of 200 m as we did with hood_dem.  You may need to change the symbology of the edited TIN to display elevation. In the symbology tab, click "Add...", and select "Face elevation with graduated color ramp." Visually compare the two TINs.

Topographic Shading in TINs. You can see that your TINs display topographic shading by default:

 

Hint    You won't be able to see  in the Symbology window unless Elevation is highlighted, like this:

To turn off shading, go to Symbology (again) and uncheck this box: .  Hit  to see what happens. Change it back. If you wished to show shading on, say, our color-ramped DEM hood_dem (or other layers, for that matter), you could drape a semi-transparent, shaded TIN on top of it. To make a TIN semi-transparent, go to Properties-->Display and make the %Transparency something like 30%. You would also want to make the base color of the TIN uniform -- instead of separate classes by elevation, you should have one class colored white.

To Display Slope:  Copy and paste your hood_tin into your fourth data frame and rename it slope_hood. Go to Properties-->Symbology for this slope_hood. Highlight Elevation, like so: Uncheck Edge types, Elevation, and Show hillshade illumination so that they won't display.  Click the  button.  The Add Renderer comes up: Highlight the line "Face slope...", then , then . Pick a logical color ramp that communicates degree of slope (e.g., red for high slope, blue for flat).  You may want to return to the Style Manager to find or create a good one.  Precipitation, with the color order flipped, seems to work well. Note: a nifty trick is that you can Flip Symbols by right-clicking in the Symbology editing window (see graphic below). Decide on how many classes you want to group slope into, and classify the ranges manually if necessary (look under the  button. Make sure the  button is unchecked (it appears to re-check itself if you switch between Elevation and Slope displays in the Symbology tab).  Your final Symbology might look something like this: Click OK.

     First, we will conduct a viewshed analysis.  Viewshed analysis is useful for several purposes, such as determining the views available from roads, lookouts, and housing developments.  For example, logging or building may be restricted in National Scenic Areas or areas viewable from highways. This can be done with the 3-D analyst, but the output options are greater with ArcToolbox coverages.
 
 

First, we have to create a point feature shapefile from which to do the viewing. Create a point shapefile for the point from which you want to do the viewshed analysis.  Pick any location at a medium elevation. Remember that new features must be created in ArcCatalog, then transferred to ArcMap.  They are then edited to create the geometry. Knowing this, create the (empty) point shapefile in your lab6_data directory (Don't remember how? Hint: When in doubt, right-click) Name it viewpoint. Use hood_dem to define the projection. This will be a point feature (although one can perform viewshed analysis on a line feature such as a road or river, for example). Put viewpoint in ArcMap. (Note: If you get the warning like "One or more layers lacks spatial reference information" -- this means either that you didn't define a projection or datum for one of your layers, or a bug occurred and this information didn't get transferred with the layer.  Make sure the Editor toolbar is displayed and start editing viewpoint. Place a viewpoint in a familar location. Save your edits and stop editing.

 

Now use the Viewshed tool in ArcToolbox to create a visibility raster dataset..

Use hood_dem as the input grid, and viewpoint as the input point.  Name the output something logical (e.g., viewshed). 

• If the layer was not automatically added, use Properties --> Symbology to make the attribute VALUE display (you should be able to figure this out without help at this point).  '1' in the viewshed's VALUE column means: "This cell is visible from point viewpoint."  Zero (0) means that the cell is not visible. You may want to change the labels to reflect these meanings.
• We don't need to display the non-visible information, so remove that row from the Symbology display (Forgot how?  Remember: when in doubt, Right-click).
• As one of your four frames (3), make a map clearly showing elevation, your viewpoint, and the viewshed.

Save your project so far. Close down and Open a new blank map document.    

Our second analysis will examine the relationship between elevation and vegetation.  Drag org_gap-clip into ArcMap.  We will use the skills we have learned for displaying and converting data and then investigate the questions by visual inspection.  You will have to decide how best to display the vegetation and hood_dem to answer the questions below.  Note that there is an orgap_lut (look-up table) file in the directory. You can use your skills to import this file into ArcMap, and join it with the GAP shapefile. You should also add the Hood_names file.
   

Adding Textfiles to Tables Now load the hood_landcov coverage. Examine its attributes. Do you know what the Values field in this grid represents??? It is common that files are delivered with text describing their attributes, either in a seperate LUT (look-up table), a text file, or in the metadata. This can be a problem if the text is extensive, requiring some effort to format for use. It is possible to create tables using a number of different methods. This method may not be the easiest, especially with ESRI's efforts to become fully compliant with Microsoft products. For example a Spreadsheet or Database program could be used to export the text file. Using a web browser, open the hood_landcov.met.html. Near the end is a list of the values, and their text meanings. We don't want to have to type these all in and create a legend by hand. Normally, you would clip the data, add it to a new text file, and format it. However, to speed things along, the text has been removed and formatted for you in a file called hood_landcov.txt. The trick is to convert the text file into a table, and join it with the appropriate coverage (or in this case, grid). Open the text file from windows explorer, and examine it in NOTEPAD, WORDPAD, or the editor of your choice. Notice the formatting of the text... commas to indicate new fields, apostrophes around text fields, and carriage returns to indicate when to start a new row. This is a fairly standard format called comma seperated text; often these files have a .csv extension. Open the the hood_lancov.txt in Excel. It is comma delimited with a text qualifier of '. Insert a row before row 1 to create field names. The first will be value and can be an integer with a column width of 8. The second, landcov, should be of type Text, with a width of 40. Load your new table into ArcMap Join it to the hood_landcov grid (you might have to use N1 for your field if it replaced Value, also, hit ok if it says anything about a problem) Change your symbology to Unique values, and use the hood_landcov.landcov field.

Question #9 : Compare the hood_landcov grid and the or_gap-clip and discuss when it might be most appropriate to use each one. Limit your discussion to one paragraph with the major Pros and Cons of each.

6.5 Extrusion Visualization of Geospatial Data

6.5.1 Introduction and Background

3-D visualization is useful for much more than representing real-world features that have height. A third dimension provides a means of visualizing multiple facets of a dataset simultaneously. An example of this is the technique known as extrusion. Extrusion involves creating a 3-D data display by "pulling" or "raising" a two-dimensional polygon up along a Z-value height to make columns. The resulting image resembles a cityscape of high-rise buildings. In terms of maps, this can be used either to 1) give extra emphasis to features on a map, but not display additional data; or 2) display two dataset attributes at the same time, one represented by the color scheme of the polygons and another by the extruded columns.

In this exercise we will be using extrusion to compare socioeconomic data from the U.S. Census with national voting patterns. We will be using ArcGlobe to give you some practice with an additional (and fun!) ArcGIS module.

6.5.2 Data

The dataset we will use in this exercise, the "2004_election_counties" shapefile, can be found in your "Lab6" folder that you created previously. The shapefile contains voting data at the county level for the 2004 U.S. Presidential election, covering the lower 48 states.

3.3 Procedure

This exercise will use ArcGlobe, ESRI's module for global data display. Take a few minutes to explore ArcGlobe before we begin working with the data.

Open ArcGlobe via the Start menu under Programs -> ArcGIS -> ArcGlobe.

Step 1: Add & symbolize data

First, turn off the Countries layer to simplify the display a bit.

Use the Center on Target tool from the toolbar to center the display on the middle of the US, and zoom in if appropriate.

Add the "2004_election_counties" shapefile to the ArcGlobe display. (Click OK if there is a projection-related warning.) Before adding the layer to the display, ArcGlobe will ask you about setting an appropriate display scale for the visualization. You can simply accept the given defaults.

Take a moment to look through the "2004_election_counties" attribute table. You will see that the attributes are a combination of Census data followed by various categories of election results (see the excel table “2004_election_counties_obmeta” for metadata information on each attribute). The election data include total vote counts and vote percentages for George Bush and John Kerry in each county. The fields “Bush_pct” is the attribute we want to display on the map.

Right-click on "2004_election_counties" in the Table of Contents and choose Properties, then click on the Symbology tab. In the left-hand column, choose “Quantities” -> “graduated colors” as the display type. Change the drop-down Value menu to "Bush_pct". In the “Classification” box on the right, click on the “classify” button and change the interval method to “equal intervals”. Click on the “labels” button, and change the number of decimal places to 0. Finally, choose a color scheme that makes sense to you (i.e. light to dark colors representing high to low percentage of Bush voters).

Step 2: Extrusion

We will now do an extrusion of a second attribute in the dataset. Right-click on "2004election_county" in the Table of Contents and choose Properties, then click on the Globe Extrusion tab. Check the box labeled "Extrude features in layer". The panel below it labeled "Extrude value or expression:" determines which attribute (and any modifiers of it you wish to add) will be used for the extrusion. Note that the extrusion units will be meters. Click on the Expression Builder button next to this panel to bring up the Expression Builder window. Scroll through the attributes listed in the Fields panel on the left for "POP00SQMIL" (2000 population density) and double-click on it. The attribute will be added to the Expression panel. To make sure the extrusion columns will be tall enough to easily view (remember, the extrusion units are meters) add a multiplication by 100 to the expression. Click OK to apply the expression, then OK again. You may need to turn off the "World Image" layer in order to see counties with low population densities.

You now have a visualization of the population density of each county, colored according to the percentage of Bush votes in it. Explore the display and look for any revealing patterns.

Step 3: Exploratory analysis

Now that you have learned the extrusion process, it is time to use it for some exploratory analysis on your own. Look through the dataset attributes and try to come up with another socioeconomic factor (i.e. other than population density) that you think you might partially explain the voting pattern depicted on the map. Visualize this factor as you just did for population density above. Be sure to normalize the attribute if appropriate, and give it a scaling factor in the extrusion equation if needed for greater visibility. (Note: Area may not necessarily be the appropriate factor to use for normalizing your attribute!)

    In this lab, you learned about the basic data structures used for surfaces in ArcGIS.  You can see that each has advantages and disadvantages in terms of conveying information, in conducting various analyses, and in accurately representing the real world.  You should also have an appreciation for the uses and hazards of converting surface data between structures.  Surface modeling is a key part of many GIS analyses, but the results attainable will depend upon the data and software tools available.  Most important, however, is the intelligent use of the data and tools by the GIS user.

• The question sheet, with typed answers (Word document).
• A one-page map with four maps in four separate frames (as a separate PDF document).

Weibul, R., and Heller, M.  "Digital terrain modelling."  In Geographical Information Systems: Principles and Applications, edited by Maguire, David J., Goodchild, Michael F., and Rhind, David W.  London: Longman Scientific and Technical, 1991, pp. 269-297.
 

Modified by Jeremiah Knoche, Mark Bernard, Tracy Kugler, and Dawn Wright, OSU Geosciences
http://dusk.geo.orst.edu/buffgis/Arc9Labs/Lab6/lab6.html