Generalizing Hillshades for small scale display
October 12 2009 |
3 comments
Categories:
Map Data
I have downloaded 30 m elevation data for all of the Middle East. How do I generalize the data so that it looks nice at smaller scales? I would like just major landforms highlighted at 1:1,000,000 and more detail appear as the user zooms in. This would be used for web applications.
Thanks
Mapping Center Answer:
It may be easier to use the SRTM 90 meter data for your area. I'm not sure what you mean by elevation data or "downgraded". We often get people calling a hillshade elevation, which is not quite true--a hillshade is a relief depiction that is derived from an elevation dataset. There's not much to be done for generalizing a hillshade--the elevation dataset, i.e. a DEM should instead be genearlized and then a new hillshade would ideally be produced from that.
So, let's assume you've got a downgraded DEM, maybe it's integer values instead of floating point values for elevation. Convert the DEM to floating point and then resample it, which will give you a more refined set of values in the result, even though the cell size will be larger--the idea is not to lose any more information than is already lost. Then you can produce a new hillshade.
That said, for your scale, you may have an easier time just downloading the SRTM 90 meter elevation data. It is always better to use data that has been captured at a given resolution than to generalized more refined data to that same resolution (generalizing will never give you the specificity or accuracy, often arbitrarily removing details). You can download that data from The National Map Seamless Server.
Let's say, for argument's sake, that you don't totally like the result of changing the pyramid resampling. If you end up resampling the DEM to produce new hillshades, consider setting the Z-factor for those hillshades to introduce a little more vertical exaggeration for each progressively smaller scale you produce a hillshade for. That will help enhance the terrain, and potentially let you get more out of each dataset.
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