Assessing lidar coverage and sample density

One basic QA/QC process when you receive lidar data is to ensure the lidar points delivered by your data provider have the coverage and density expected. Checking any coverage and density problems and resolving them early in your lidar processing is important. The LAS dataset, along with two geoprocessing tools—Point File Information and LAS Point Statistics as Raster—is useful in this regard.

LAS dataset

Data for individual lidar projects is typically split into collections of tiled LAS files. This is done for the sake of processing convenience and to keep file sizes within reasonable limits. Unfortunately, this can turn into a hassle for someone working on one of these projects. The LAS dataset helps by allowing you to reference a project's collection of LAS files and operate on them as a whole. Use the Create LAS Dataset geoprocessing tool to make a LAS dataset.

The Create LAS Dataset tool provides an option to compute statistics. If you do not specify this option, the LAS dataset is made quickly because only the header records of the referenced LAS files are read. Having the statistics computed requires a complete scan of the referenced LAS files. The time it takes to compute statistics corresponds to the number of points in the LAS files, but yields much more information. Gathering statistics is recommended if you are not in a time-critical situation.

Once your LAS dataset is created, open the LAS Dataset Properties dialog box from the Catalog window. This dialog box reports information about the LAS dataset as a whole plus details about the specific LAS files that participate in the dataset. Go to the LAS Files tab of the dialog box.

Look for the Point Spacing column in the middle of the LAS Files tab. This is the estimated point (in other words, return) spacing for each file. It is reported in the x,y units of the source data. The estimate is more accurate if you calculate statistics when creating the LAS dataset.

Note:

The return spacing may be a little denser than the pulse spacing since an individual laser pulse can produce multiple returns.

To get graphic information of the LAS files, in addition to other statistical information, use the Point File Information geoprocessing tool.

Point File Information

The Point File Information geoprocessing tool reports basic statistics and graphic information about point data files. The primary purpose of this tool is to help you review your data and check it for correctness. LAS datasets, as well as stand-alone LAS and ASCII format files, are supported as input. When given a folder, the tool reads all files inside it that have the suffix you specify. When working with lidar data in LAS format, it is recommended that you create a LAS dataset and use that as input to the tool.

For each input point file, including each file in a LAS dataset, the tool outputs one polygon with accompanying attribution to a target feature class. The polygon graphically depicts the x,y extent, or bounding box, of the data in the file. Attributes include file name, point count, z-minimum, z-maximum, and point spacing.

The point spacing reported by Point File Information is an estimate and deserves some discussion. For LAS files without statistics and ASCII point data, 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, but can otherwise produce misleading results. In this situation, the reported point spacing is more meaningful as a summary when looking at trends for collections of files. For more reliable point spacing estimates when working with LAS format data, it is recommended that you create a LAS dataset with statistics and use that as input to the tool.

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. In the image below, the LAS extent polygons are symbolized using a color ramp based on the point spacing estimate. The polygons symbolized in red have a higher point density.

You can also examine the classification conducted on the lidar points. This important step is usually performed by the lidar data provider. The classification is maintained using class codes in the LAS files. These codes can be used to distinguish between ground and nonground returns. When you choose the Summarize by class code option on the Point File Information dialog box, the output attribute table contains summary statistics for each class code encountered in each LAS file. Use this option to examine the class codes in each LAS file and the corresponding point information associated with each class code. This option can be used, for example, to check the z-range specifically for class 2 (bare earth) points rather than all points lumped together, which, particularly if class 7 (noise) is involved, can mislead you into thinking something is wrong with the data.

Assuming the Point File Information review process determines everything to be acceptable, the next step is to use the LAS Point Statistics as Raster geoprocessing tool to get a more in-depth view of the point distribution.

LAS Point Statistics As Raster

The LAS Point Statistics as Raster geoprocessing tool lets you see the spatial distribution of different lidar point metrics. It does this by characterizing the points that fall into each cell of an output raster. You can choose to characterize the points in several ways:

• Pulse count
• Return count
• Predominant return count
• Maximum returns
• Predominant class
• Intensity range
• Z-range

For the sake of assessing point coverage and density, pulse count is a good option. It assigns the number of last returns falling in a cell as the cell value. First, input a LAS dataset and pick PULSE_COUNT as the method. For sampling, choose CELLSIZE. You might think the average point spacing is a good cell size for the output raster, but this typically results in too many empty, or NoData, cells because lidar points are not evenly spaced. Also the output raster could end up being unnecessarily large. Instead, it's better to go with a cell size that is 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, if your data is sampled at 1 meter and you set cell size to 4, you can expect, on average, to get 16 pulses 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. For example, looking at the pulse count for ground points can tell you how good your ground penetration is in vegetated areas. You can control what points are used by setting filtering criteria on the Filter tab of the LAS dataset layer. Point filter properties set on the layer are honored by the tool. If you use a dataset rather than a layer, there is no filtering.

Once your raster has been created, look at it in ArcMap. Use a color ramp renderer to display it so it is 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.

In the image below, the black represents areas where NoData exists, while red represents areas where the lidar sample density is higher.