One basic QA/QC process is to ensure the Lidar points delivered by your data provider have the coverage and density expected. You want to catch problems with this early on and have them resolved before continuing. Two geoprocessing tools are useful in this regard: Point File Information found in the 3D Analyst toolbox and Point To Raster located in core Conversion Tools.
Point File Information
The Point File Information tool reports basic statistics about one or more point data files on disk. The tool’s primary purpose is to help you review and summarize the data before loading it into your geodatabase. LAS (the industry standard format for Lidar data) and ASCII format files are supported as input. Since Lidar projects often utilize collections of data files, sometimes in the hundreds or even thousands, the tool lets you specify folder names in addition to individual files. When given a folder, it reads all files inside it that have the suffix you specify.
For each input point file it outputs one polygon with accompanying attribution to a target feature class. The polygon graphically depicts the xy extent, or bounding box, of the data in the file. Attributes include file name, point count, z-min, z-max, and point spacing.
The point spacing reported by Point File Information is not exact and deserves some discussion. For the sake of performance it uses a rough estimate that simply compares the area of the file’s bounding box with the point count. It’s most accurate when the rectangular extent of the file being examined is filled with data. So, files with significant numbers of points excluded over large water bodies or on the perimeter of a study area, only partially occupied with data, will not have accurate estimates. Therefore, the reported point spacing is more meaningful as a summary when looking at trends for collections of files. Something useful to do with the output feature class is to display it in ArcMap, open its attribute table, and sort the point spacing field in ascending order. You can also symbolize on the point spacing field using a graduated color ramp.
Point File Information works quickly on LAS files because it only needs to scan their headers to obtain the information it’s looking for. It takes significantly longer with ASCII files because with them the tool actually has to read all the data.
Assuming everything checks out OK, the next thing to do is load your Lidar points into a multipoint feature class with the LAS To Multipoint or ASCII 3D To Feature Class tools. Put this feature class in a feature dataset if you intend to build a terrain dataset from the points. While you have the choice between using LAS or ASCII format files, LAS is generally a better way to go. They contain more information and, being binary, can be read by the importer more efficiently.
Once the points are loaded into a multipoint feature class you can use the Point to Raster tool to get a more in-depth view of the point distribution.
Point to Raster
The Point to Raster tool creates rasters from points and it also supports multipoints. It’s a generic tool with many options and uses. For the sake of evaluating Lidar point density the tool’s COUNT option is the thing to go for. This uses the number of points falling in a raster cell as the cell value. Being able to look at this graphically over the extent of the project area is revealing.
There’re a couple parameters on the Point to Raster tool whose values for this exercise aren’t obvious. First, is the Value Field parameter. It doesn’t matter what this is set to. That’s because the Value Field is ignored when the Cell Assignment type is set to COUNT. Then there’s the cellsize. You might think the average point spacing is good but this typically results in too many empty, or NoData, cells because Lidar points just aren’t that evenly spaced. Also, the output raster could end up being unnecessarily large. Instead, it’s better to go with a cellsize that’s several times larger than the average point spacing but small enough to identify gaps or voids that warrant further investigation. A reasonable size is four times the point spacing. As an example, let’s say your data is sampled at 1 meter. If you set the cellsize to 4 then you can expect, on average, to get 16 points in a cell.
You can also evaluate the density for different types of points. While most of the time you’ll probably just check the density for all returns it can be useful to look at those that fall in a certain class like ‘ground’. For example, this can give you an idea of how good your ground penetration is in vegetated areas. The Point to Raster tool doesn’t know how to make the distinction between point types though. So, you control what points get used by how you go about creating the multipoint feature class with the LAS To Multipoint tool. It provides options for loading points by class code and return number.
Once your raster has been created have a look at it in ArcMap. Use a color ramp renderer to display it so it’s easy to distinguish between cells with high counts and those with low. You can also set the NoData color to something that stands out. Look for variance in density and data voids. Have your vendor explain anything that doesn’t look right.
Hopefully, you’ll find your data meets specifications and lacks surprises. It’s worth the effort to check.
That’s it for this installment of Lidar Solutions in ArcGIS. Subscribe to this blog or check back in a couple weeks for a discussion on the creation of raster DEMs/DSMs from Lidar.