Generate Point Density Image for Spatial Distribution

The USGS LIDAR Base Specification refers to Spatial Distribution and Regularity. It describes an assessment that is done, in addition to general point density assessments, to determine the regularity with which the a LIDAR point data collection is executed. The density images that allow one to follow the USGS specification for making the assessment can be generated using LP360 and TerraScan. The details for LP360 for ArcGIS are below and we’ll expand on this article in the near future to provide more detail.

Create a Point Density Tiff Image

1. Click the Export LIDAR Data Command
   1. Set the parameters for Step 1 of the Export Wizard to the following settings:
      1. Export Type is Surface.
      2. Set the Filter
         1. Select Return Combinations
            1. Under the drop down menu pick First Return
         2. Select Scan Angle
            1. Filter based on Scan Angle to avoid overlap, or use the overlap flag
      3. Set the Surface Method as Point Insertion, with a cell size that is two times the Nominal Pulse Spacing.
      4. Under Attributes select Point Density.
      5. On the Density Tab set the Point Density value as the inverse of the cell size.
         1. For instance if Cell size is equal to 2.8 then Point Density is equal to 1 divided by 2.8, or 0.35
         2. This value is used to determine if there is at least one LIDAR point per pixel cell
      6. Set the Units to meter and pick an interval of 4
         1. If desired, change the color of the intervals
   2. Step 2 is used to define the extent of the exported surface based off the loaded LIDAR Data.
   3. Complete Step 3 of the Export Wizard.
   4. Check the box to Insert Output(s) to Map and click the Finish command and wait for the export to be completed.
   5. Select the generated TIFF file, right click and select Properties. Under Symbology change the Stretch Type to Percent Clip with no Gamma Stretch applied if the resulting image appears to be all one color for instance yellow and pink.

Determine if at least 90% of the cells within the image have good values marked, meaning that there is at least one LIDAR point per cell. This can be determined based upon the color of the cell and may be different for each user depending upon the color scale specified. In the April 2015 edition of GeoCue News we’ll detail a method using ArcGIS for a similar quantification process when determining Low Confidence Polygons that may be directly applicable here to turn the qualitative assessment into a more quantitative one.