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I once wanted to generalize a high resolution raster and instead of MEAN i accidentally selected the default SUM technique, using a cell factor 5 (Lidar DEM is 1m resolution). When I used this result as input to the hillshade funciton what I got was what I can only describe a highly exaggerated hillshade. I deleted the results and recreated the generalized DEM but now I have the need to show more exaggerated terrain characteristics in a topographic map and it seems to produce the results I want albeit generalized. Is this a common way to do this or should I be using a different technique? How would I exaggerate a DEM without having to aggregate the raster?

Hillshade created from generalized (Mean, Cell factor 5) LiDAR DEM enter image description here

Hillshade created from generalized (SUMm Cell factor 5) LiDAR DEM enter image description here

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Since hillshade is just an overlay, you can do it anyway that gives you the desired result. However, the usual way to emphasise the terrain is to apply a vertical exaggeration. In ArcGIS you use the 'Z-factor' in the hillshade tool to do this as per the documentation here. You multiply the z conversion factor by the exageration factor. If your z-units are the same as your distance units then it will default to 1 and you can then treat it just as an exageration factor.

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  • Thanks. That works pretty well. I have to use a high exaggeration factor (10 or more) to get similar results. Thanks for pointing it out, I overlooked that feature. I have to say though the the SUM technique yields a more dramatic result but perhaps too dramatic...
    – Jakub Sisak GeoGraphics
    Commented May 1, 2012 at 15:40
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    Jakub, Summing with a 5 by 5 square neighborhood is algebraically the same as multiplying the mean by 25, that's all. The Z-factor does exactly the same thing: it multiplies all values in the DEM before computing the hillshade. One implication is that the effect of using a sum instead of a mean depends on the neighborhood size.
    – whuber
    Commented May 1, 2012 at 16:15

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