[GIS] Extracting raster DEM elevations to vector attribute tables in ArcGIS Desktop

arcgis-10.0arcgis-desktopdemfield-calculator

I have an ArcMap 10 project with multiple layer groups. One layer is a raster DEM, another layer group is a bunch of vector shapefiles (including points, lines and polygons). The client wants all vector data to contain Z values.

Populating the attribute tables for the vector data with X and Y values is easy enough but how do I populate the Z values?

Is there a way that I can use Field Calculator to "grab" the height values from the DEM layer for each vector feature?

I should add that we don't have a licence for the 3D Analyst or Spatial Analyst extensions.

Best Answer

In ArcGIS Desktop, I've only ever done this with point vectors—I'm not sure how you'd do it with polygons or line features. Unfortunately you do need Spatial Analyst, though. There's a tool at Spatial Analyst Tools\Extraction\Extract Multi Values to Points and Extract Values to Points. Extract Multi Values to Points is better because it lets you rename the z field name that you are extracting your values to—Extract Values to Points doesn't let you do this. I have trouble doing this in ArcMap (the tool crashes), but it works fine in ArcCatalog.

The (free) Hawths Analysis Tools extension for ArcMap had an Intersect Point Tool but development of the extension was discontinued a couple of years ago. It's been superseded by the Geospatial Modelling Environment, which I've never used.

SAGA-GIS (freeware standalone software) has two functions at Shapes - Grid\Add Grid Values to Points and Shapes - Grid\Add Grid Values to Shapes that can probably do what you want. This is the description of the Add Grid Values to Shapes tool:

Spatial Join: Retrieves information from the selected grids at the positions of the shapes of the selected shapes layer and adds it to the resulting shapes layer. For points this is similar to 'Add Grid Values to Points' module. For lines and polygons average values will be calculated from interfering grid cells. For polygons the 'Grid Statistics for Polygons' module offers more advanced options.

SAGA is your best bet, in my opinion.

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I wouldn't clamp the values, as suggested by the pseudocode, because the curvature artifacts do not necessarily correspond to high-curvature areas. Better cures for the problem include:

Consider smoothing the DEM before computing the curvature. This would slightly reduce most curvatures but might give reasonable curvature values at all locations. Smoothing the DEM can be done with local medians or means.
Find a robust way to estimate curvature. (You won't find this in any Arc* software, but with the availability of general-purpose tools like R, this is a feasible recommendation.) In general, curvature is estimated by fitting a quadratic (or some specialized low-order polynomial) in each neighborhood. The usual fitting method is least-squares, which can be sensitive to slight local variations. By expanding the neighborhood and using a fitting method that downweights outlying residuals (such as IWLS), you might be able to get representative and accurate curvature estimates everywhere.
Replace the extreme curvature values with NoData. This is still a draconian measure, but would be appropriate if the extreme values are really artifacts of the interpolation (and don't, therefore, reflect truly high curvatures).
Choose an interpolation method that produces smoother surfaces!

The 'map algebra' syntax for replacing values with NoData uses the SetNull command, as in

SetNull(Abs([Curvature grid]) > 10*Abs(m), [Curvature grid])

By the way, m should not be the median curvature: the median often will be close to zero and won't reflect typical large curvatures (positive or negative) A large multiple of the median absolute curvature might be a reasonable choice. Better yet, examine the histogram of curvatures to find where to cut off the values.

If the SetNull approach pokes too many NoData holes in the grid, think about whether the curvature grid should be smoothly varying. If so, and if those holes tend to be isolated (or in narrow stripes--which would be a strong indicator that a poor interpolator was used), you can fill them by computing a focal mean and pasting the mean values over the holes. The pasting is done with the conditional operator, as in

Con(IsNull([Modified curvature grid]), [Focal mean grid], [Modified curvature grid])

Note the use of IsNull to detect the NoData cells.

ArcGIS Desktop – Creating DEM from Point Data in CSV Files Using ArcGIS Desktop

Because these are regular points, you effectively already have a DEM; it's just in a different format than ArcGIS likes. This makes two different strategies available to you:

Convert the data into a format ArcGIS can handle. One way is to set up a raster extent and cellsize that (i) cover your DEM and (ii) situate each point near the middle of its cell. Import your DEM as a point layer and convert that directly, with no interpolation, into a grid format. (Each cell acquires the value of the unique point lying within it.) From now on, interpolation will be automatically performed whenever you resample the DEM, when aggregating, resizing, or reprojecting it. This gives you access to nearest-neighbor (not recommended), bilinear, and cubic convolution methods. You can create three separate grids this way: one for elevation, one for slope, one for aspect. For this to work, though, you must be using exactly the same coordinate system (including the projection) used to create the DEM in the first place, so that the points are truly evenly spaced and parallel to the coordinate axes.
Pretend the points are "irregular" and interpolate only the elevations using any method you can (splines, IDW, kriging, etc). Derive new slope and aspect DEMs from the interpolated elevations. Unless you are using a Topo2DEM approach and have additional information you can supply--such as stream beds, elevation contours, peaks, or such material--then it is likely this method will merely produce meaningless artifacts that reflect the interpolation method as much as they do the original data. It will also require much more computation than the first method (kriging may even be impossible because of this).

Best Answer

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