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. |

If you want an export of this model, leave a comment below, I might just release it to the public! |

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