Combining Lidar Data
With the recent addition of the Swath Separation tool for visualizing alignment between point clouds, I thought this would be a good opportunity to review the different tools in Global Mapper that can be used to measure, adjust, and combine point clouds.
One of my favorite things about Global Mapper is how simple it is to combine multiple vector layers into one. First, select all of the point cloud features to be combined, then use your keyboard commands to copy and paste them into a newly created layer. This simple step does not require you to export or otherwise complicate your file management. While this does technically create a single dataset from the selected point clouds, it does not correct them for accuracy. If there are any discrepancies or conflicts between the datasets, they will still be present in the new combined cloud. So, before creating that final point cloud, you can run them through a few different tools in Global Mapper to measure these differences, and correct for them.
The Swath Separation tool meets the USGS Lidar Base Specifications for creating a Swath Separation Image (SSI). The generated 8-bit image uses a red, green, and yellow scale to represent how well the swaths match against one another. The lidar must have a Point Source ID attribute. If it doesn’t have this, don’t worry. You can run this tool by manually saving each swath as a separate layer prior to any combining you may do.
Measuring swath to swath accuracy is a good estimation of Relative Accuracy, that is, the internal quality of the point cloud. This doesn’t take ground measurements into account, so it isn’t measuring Absolute Accuracy, but it is a way to visually assess strengths in the individual swaths. For example, this image of the Maine Capitol building in Augusta, Maine, United States, shows that the bare ground points between the two swaths are very similar. They are displayed with green pixels. We do see some red/yellow discrepancies around the edges of the buildings and vegetation. This is caused by the differences in angles between the two flight lines/swaths compared to the building location. The southern swath (red point cloud) captured the southern face of the building, where the northern swath (blue) could not see. While interesting, and a good demonstration of how the tool works, the real measurement of relative accuracy here is the ground that’s equally visible in both swaths.
Copy and pasting two point clouds into one will not adjust the point clouds to be sure they fit where they overlap, as mentioned earlier. Global Mapper has a specific tool for this: it’s aptly named the Fit Point Clouds tool. This tool automatically aligns one point cloud with another, minimizing the X, Y, and Z differences between the point clouds. You can compare both point clouds to each other or to Ground Control Points. This is useful when you have two flight lines that do not line up exactly or to adjust a photogrammetric point cloud to lidar.
Photogrammetric point clouds, in general, are known to be a little less accurate than lidar due to the nature of how the clouds are generated from imagery, but it’s a more cost-effective method for generating dense point clouds. Here is an example of a photogrammetrically generated point cloud (color/RGB) compared to a USGS point cloud (teal/green). Slide the image to see how the Fit Point Clouds tool was used to adjust the dense point cloud to the sparse lidar. It’s a dynamic adjustment; the flat grass was about .5m apart, while the vegetation was significantly higher. Each area was adjusted using the ICP method, you can read more about that in the Knowledge Base.
To measure Real World, or Absolute vertical accuracy, you will need control data to compare and/or adjust the point cloud against. You can use Ground Control Points (GCPs) that were collected in the field using a higher accuracy GPS unit to represent the ground surface. The Lidar QC tool will allow you to measure the difference in elevation between the GCPs and the point cloud, and then give the option to adjust the cloud to fit the GCPs.
Before applying any changes to the point cloud, Global Mapper displays the distance and error measurements between the GCPs and the cloud. This includes the root mean square error, the elevation of the GCPs, the number of lidar points used for comparison, and the difference between the GCPs elevation and the lidar. From this window, the Fit Lidar to Control points button will act as described to adjust the point cloud.
These adjustments will be maintained when the data is copied and combined into a new layer, or exported out of Global Mapper. By default, all of the edtis done to your point cloud(s) reside only in the workspace. The original file is left preserved on your machine. To maintain these edits you can of course save the workspace (.gmw) file, send it to a colleague with a package file (.gmp), or export the file for use in another software.