When we have a new product release like the version 19 of the LiDAR Module that comes with the Pixels-to-Points™ tool, it’s always exciting to see that feature in action for the first time outside of the Blue Marble office. Our South and Central American reseller Laurent Martin from EngeSat was quick to try the new Pixels-to-Points tool for himself using drone data collected by his peer Fabricio Pondian.
The new Pixels-to-Points tool uses the principles of photogrammetry, generating high-density point clouds from overlapping images. It’s a functionality that makes the LiDAR Module a must-have addition to the already powerful Global Mapper, especially for UAV experts.
Below, screenshots captured by Laurent illustrate the simple step-by-step process of creating a point cloud using the Pixels-to-Points tool and some basic point cloud editing using other LiDAR Module tools.
1. Loading drone images into the LiDAR Module
2. Calculating the point cloud from loaded images
3. Viewing the generated point cloud
4. Classifying the point cloud
5. Creating an elevation grid and contours from the point cloud
A quick and easy process
In just a few steps, Laurent was able to create a high-density point cloud from 192 images, reclassify the points, and create a Digital Terrain Model. It’s a prime example of how easy version 19 of the LiDAR Module and the new Pixels-to-Points tool are to use. Check out EngeSat’s full article on the release of LiDAR Module.
I recently purchased a house in Hallowell, Maine, where the Blue Marble Geographics office is located. Hallowell is a teeny tiny city with lots of historic homes that sit on a rather large hill overlooking the Kennebec River. One aspect of my historic fixer-upper property that needs some work is the drainage. I have decided to explore drainage solutions by estimating property modifications using Global Mapper and publicly available data.
Finding Data in Global Mapper
The first step is finding the right data. So, to start with, I use the search tool in Global Mapper to create a point feature at my address. I also change the projection to something that works for the area, such as the State Plane projection for Maine. Next, with the online data tool, I easily connect to the US NAIP high-resolution imagery.
The State of Maine GIS site, MEGIS, has a number of other helpful layers that can be added. Vector data can be downloaded as shapefiles using a web browser and can be loaded into Global Mapper by simply dragging the files into the software. Like a lot of states, Maine’s GIS site also offers web services that can be added to the list of online sources in the software. For my project, I need the outline of my individual property, so, I first download the property parcels layer for the entire city and drag the downloaded zip file onto the map to import it. I use the Digitizer to select my property and then use CTRL+C and CTRL+V to copy it to a new layer.
What I really need for this analysis is some high-resolution terrain data, and luckily my property is close enough to the coast to be included in the NOAA coastal LiDAR data. I use the online data source tool again to search the Digital Coast for data that matches my current map bounds.
Cleaning up LiDAR Data in Global Mapper
A quick look at the LiDAR data confirms that it contains preexisting point classifications, including a lot of points marked as noise that look fine to me.
My first task is to clean up and then improve the classification with the Automatic Classification tools. Using the Path Profile tool, which renders a lateral view of the point cloud data, I can clean the data up even more with some manual editing, since it is such a small area that I am interested in.
Applying Colors to a Point Cloud in Global Mapper
The Maine GIS site also provides 4-band ortho-imagery that was collected in a similar time frame to the publicly available LiDAR data. From that imagery, I apply the RGB color values to my point cloud using the Apply Color tool, which improves the point cloud analysis capability and creates an interesting visual perspective of the data. The imagery is leaf-off, so it does not match up perfectly with the point cloud, but it adds some detail that can help with identification and analysis.
Estimating Property Modifications with Global Mapper
After creating a terrain surface from the classified and filtered LiDAR data, I estimate the modifications that are needed to improve the drainage around the base of the house.
Using the new Breakline and Hydro-flattening tools, I create a flattened foundation by applying a height to the buildings in the terrain modeling process. Next, using the Watershed tool, I see the current drainage problem.
By using the digitizer tool and calculating the elevations, I create a line for a back drainage that would allow water to flow from start to finish. Then using buffering and site planning tools, I create a modified terrain surface that will calculate the necessary terrain modification.
Finally, I measure the volume of soil to be removed, and calculate the benching and terracing for the back retaining wall.
After the modification, the drainage from the back of the house to the road is much better. I am also glad to have some warning of just how much dirt removal a plan like this will involve.
I am still considering options for creating a small pond, ending with a tile drain, and many other possibilities. But thanks to freely available data and some quick calculating and visualization with Global Mapper, I have a much better sense of the scope of this project and what the final results might look like.
Katrina Schweikert is an Application Specialist at Blue Marble Geographics. She provides technical support, training, and software documentation. Katrina has over five years of professional experience in GIS, a GIS certificate from University of Wisconsin-Madison, and a degree in Geography from Middlebury College. She is happy to be working in technology back in her home state, as well as meeting GIS users across the globe.