August 15, 2023

How Many Raster Model Types Can be Created From a Single Point Cloud in Global Mapper Pro?

Written by: Amanda Lind

How many raster model types can be created from the analysis of a single point cloud? It’s almost a trick question. Just about any lidar or numeric vector attribute can be interpolated into a raster in Global Mapper. For most vector layers, it’s as easy as changing the default attribute from the Elevation Tab in Vector Options. Rasterizing different attributes from a point cloud, on the other hand, is a little easier as the elevation options are built into the grid creation tool. For now, let’s focus on point clouds and what the more commonly gridded attributes look like.

Elevation Values and Grid Methods

Elevation values are the most common grid type, the default, and the standard. Based on the grid method settings, elevation values can be used to create digital terrain models indicating the lowest points of elevation in each area (which can be further refined based on ground classification) or digital surface models indicating the highest elevation in each area. For examples of how these can be used, see this blog on Canopy Height Models.

One grid method option that isn’t often discussed is Variance, where each grid cell represents the rate of change in the area. For example, when the variance method is applied to elevation values, grid cells depicting a cliff side or the flat wall of a building will have a higher variance than the grid cells that define the flat ground because of the larger difference between the highest and lowest values in each grid cell.

A largely monocolor grid highlights irregular ground measurements — This variance grid created from ground points in a forest highlights areas of the ground beneath dense patches of trees where lidar could not penetrate reliably, causing irregular ground measurements that are good to be aware of in future analysis of this data.

Intensity values

Intensity is a measure of reflectance, of how strongly the beam of light bounced off an object and returned to the sensor during data collection. Objects that don’t reflect much light, such as tar black roofs, will have a lower intensity value as compared to cars and other shiny structures. In segmentation, intensity is a great attribute to use when differentiating objects based on their materials, such as transmission towers in vegetation. Intensity is represented using a black-and-white gradient, and gridding by intensity creates a black-and-white image. This is a great way to create a high-resolution base map, especially with lidar that doesn’t include color (RBG) attributes.

Black and white lidar image of Augusta, ME — Most man-made surfaces have different intensity values, making it a useful attribute for visualizing urban areas.

Height Above Ground

Height Above Ground measures each point not by elevation/sea level but by how far it is above the ground. This attribute does not exist natively in most lidar files, but it can be calculated by Global Mapper with one click. Simply change the Color Lidar by dropdown menu to Height Above Ground to add this attribute to your data. In the split second it takes to recolor the lidar display, Global Mapper will calculate this value for each individual point. If ground points have not yet been classified, Global Mapper will run a quick temporary classification based on default values, so be sure to run the ground classification tool first for the most accurate results. Gridding by height above ground can provide a greater understanding of the forest canopy by showing areas that have dense understory, or highlighting the tallest structures in an area. This layer can also be compared against other gridded datasets to measure change over time, etc.

A surface layer representing the tallest buildings in downtown Portland ME — With the data measured by height above ground, the values represent the heights of the buildings themselves rather than their elevation.

Color (RBG)

Newer lidar collection hardware are sometimes able to capture color data simultaneously with lidar. Each lidar point is assigned an RBG value as an attribute. Creating a grid from these values is an easy way to create a color image of the data. This is useful for creating high-resolution base maps and to more clearly visualize smaller details in the data when zoomed in.

Swipe to see how interpolating this dataset into a raster made it easier to zoom in and see details in the data as compared to the distinct lidar points.

Point Density

In lidar, ideally, point density is typically very uniform across the dataset. A sensor methodically sends out light and reads it back in, but sometimes a lack of overlap can cause issues between flight lines, or other technical difficulties can result in areas with a low point density. Point density is an important attribute to asses during lidar QA as areas of significantly low point density can represent low accuracy. Even more so, photogrammetric point clouds are built from overlapping images and are prone to variations in point density based on image arrangement and quality. The edges of the dataset, and areas not visible in many images, such as the side of buildings, are all areas where point density can be low. Gridding this data allows you to store the image as a smaller file for reference in the future.

A surface layer that displays the stripes of low survey overlap — Here in this dataset of a recently discovered Mayan temple, we can see slight dips in point density (red) between the flight lines.

Global Mapper Pro is packed with tools for cleaning, analyzing, and visualizing your elevation data. Try Global Mapper Pro for yourself with a free 14-day trial today! If you have any questions, please contact us!