Monday, March 21, 2011

Strike and Dip: GIS and LIDAR Data

For the last two quarters, I have had to extract strike and dips directly from LIDAR point cloud data using LidarViewer.  It was awful. Today I had some time to think about how one might do this in a GIS.  I also implemented it.

Essentially, LidarViewer fits a plane to select points in the cloud.  The problem is, it is difficult to repeat selections and extract location information for the fitted strike and dip (plane attitude).  Today, I devised a method (model) in ArcMap that uses an elevation grid, masks, and zonal statistics to find the mean dip direction and mean dip of bedding.  Of course, the dip is nothing more than the slope of topography.  Keeping that in mind, only features that are large enough relative to the scale of the LIDAR data can be measured.

I first created a polygon mask of small areas that surround previous strike and dip spatial data.  Each polygon has a unique ID.  The shapes were defined by referring to slope, aspect, and orthophoto rasters. I outlined areas with generally constant slope and aspect*.

I then derived aspects and the slopes of 0.5 m interpolated LIDAR DEM of Rainbow Basin, CA.  Using the mask, I extracted all the raster values from both derived rasters.  The polygon mask can then be used to generate a table of zonal raster statistics for both aspect and slope within each polygon.  This provides the following statistics: Minimum, Maximum, Range, Mean, Sum, Area, and Standard Deviation.  A few of these can be thrown out, such as area and sum.

So I generated two new tables without that extraneous information.  I then joined the tables together and joined the resultant table to the polygon mask using the unique IDs.  Finally, I created a point feature class by taking the centroids of each polygon within the mask.

The resultant point feature class has three important attributes: mean dip, mean dip direction (the aspect is the geographic azimuth of maximum slope), and the standard deviation.  The standard deviation is a useful measure of dispersion and may indicate an adjustment of polygons may be needed.  One can do this by referring back to the slope, aspect, and orthophoto rasters.

Here's my result.  Red are data collected using LidarViewer.  Black are strike and dips created using my model.

Polygons with blue cast are GIS sampled regions.  Notice two symbols coincide within a few degrees within strike.  A few are off by ~10 degrees in strike.  The dips are close enough, given the uncertainty in either method of extracting attitudes!  LIDAR data from OpenTopography.org, Blackwater Region, NSF EarthScope.
Here's my likely overcomplicated model:
If you want an export of this model, leave a comment below, I might just release it to the public!
I think it is fair to say, that given large enough scale (in the cartographic sense) elevation data, strike and dips can be extracted all at once very easily using a mask with multiple polygons.  The trick is comparing slope, aspect maps, and orthophotos to find areas that may have measurable attitudes.  But nothing will beat field measurements!

* I must confess that only today did I realize the value of aspect for remote mapping of geology!

5 comments:

  1. Have you checked out TNT Mips and its script for strike and dips from DEM's? It is quite straight forward, and we use it here all the time!

    Great blog, by the way.

    Jorge

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    1. Hi Jorge
      Is it possible to extract dip and strike information using DEM images?

      Could you please elaborate more on this?

      Regards
      Yask

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  2. Jorge: Nope, never heard of it. ESRI has a strangle-hold on the Universities and industry in the US. But I'm glad to hear of it! Thanks for the heads-up!

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  3. Hi Ryan I am interested to try this tool. Can you post it somehow?

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  4. Also interested in this tool. Posting soon? Pretty please? This looks awesome!

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