Lidar point clouds and point clouds derived from photogrammetry look and function similarly in Global Mapper Pro, but they have distinct differences.
They can both be classified, features can be extracted, and they’re equally compatible with the vast lidar toolset. Without looking at the attributes, it can be difficult to discern between them. The differences between the two stem from how they are created. Knowledge of each data type’s strengths and limitations can help you better use your data to its full potential with Global Mapper.
Lidar is traditionally the more accurate method for measuring terrain. Unlike photogrammetry, lidar can often penetrate vegetation and see the ground below to create a more detailed picture of the terrain and surface. A lidar sensor works by sending beams of light to the surface and back, calculating distance by measuring the time it takes for the beam of light to return. Sometimes the beam of light can fracture, where part of the beam hits a surface and returns to the sensor while the rest continues to the next surface. This creates multiple returns from the same beam of light. Return numbers are saved as an attribute that can be used in the analysis.
This process makes lidar data ideal for creating digital terrain models in densely vegetated areas or when working with smaller and less visible objects or structures.
The image below is a side profile of lidar data that has been classified. Notice that even in a densely forested area, the brown ground points are visible beneath the green trees.
Photogrammetric Point Clouds
While lidar is a direct measurement of the landscape, photogrammetry is an indirect measurement, as it derives locational and elevation data (XYZ) by triangulating from overlapping aerial imagery. Learn more about the specifics of how Global Mapper’s Pixels to Points tool uses photogrammetry to create points from imagery here. What’s important is that it can only map what it can see in the images. Continuing with our forest example, this means that in areas of dense forests where the canopy obstructs the camera’s view of the ground, no ground points can be created. This is photogrammetry’s most outstanding limitation.
Photogrammetry has the advantage over lidar in the ease of data collection. Despite the development of more portable lidar apparatuses, drone imagery is still the more affordable and therefore very popular method of data collection. This accessibility means users can measure areas more frequently, collecting more time-relevant data. Users can take advantage of this in Global Mapper to measure changes in the terrain over time, like erosion or habitat fragmentation. The Compare Point Clouds tool can be used to measure differences or detect changes between point clouds. While this is possible for lidar point clouds as well, photogrammetric point clouds are easier to gather at higher frequencies.
Another advantage is because photogrammetric point clouds are derived from imagery, they automatically include the color (RGB value) of the surface being measured. Colored point clouds are easier to visually interpret, classify, and they make for a more visually appealing product. Color can be manually added to lidar from an image, but those colors are not necessarily representative of actual ground color at the time of lidar collection.
Eventually, technological advancements will make drone-mounted and color-compatible lidar sensors more affordable and accessible, but for today, photogrammetry fills this data void.
Both point cloud types are great tools for mapping terrain and surface data, no matter what purpose you wield it for. When used together, they can create a complete view of the landscape. Understanding the differences between the two will help you make the best of your data and create higher-quality results. Learn more about processing lidar and drone-collected data in Global Mapper Pro by downloading a free 14-day trial.