April 30, 2024

Identifying Change in Tree Height by Comparing Point Clouds to Terrain

Written by: Jeff Hatzel


The Compare Point Cloud tool was updated for the v25.1 release of Global Mapper Pro. This update allows users to compare their point clouds to a raster terrain layer. This tool is traditionally used to adjust ground offset between a point cloud and a terrain surface being used as a control. With a variety of output options, this tool has a multitude of other applications as well.

In this workflow, we’re going to attempt to identify trees which have changed by a certain amount. We have a fairly recent lidar scan of our area of interest. Unfortunately, the only historical data we have is a DSM terrain surface. Although this may not be ideal source data, it will allow us to accomplish a few things:

1) Identify trees which have changed more than a certain amount

2) Determine their location

3) Enable a field inspection of the trees in person. 

Tip: Are you interested in modeling tree height using one point cloud? See: Create Canopy Height Models from Lidar in Global Mapper Pro

In the above graphic, the point cloud shows the trees (colored green). On the terrain surface, they resemble isolated regions of higher elevation

Visually Comparing the Lidar and Terrain Layers

If we utilize the Path Profile tool, as shown below, we can gain a unique perspective on the change we are hoping to identify. Viewing the profile along a subset of trees, the change is fairly obvious. Our reference terrain surface (displayed in green) has peaks, representing trees. The point cloud points above those trees were obtained more recently. We can see that in each of these instances the trees have grown quite a bit. As it isn’t realistic to draw profiles across the entire point cloud, the question becomes: how can we automate this identification process?

Perpendicular perspective of lidar compared to DSM
The Path Profile tool gives us a clear visual of the offset between the trees in the point cloud, and how they are represented in the older terrain surface.

Using the Compare Point Cloud tool

The aforementioned updates to the Compare Point Cloud tool allow us to automatically compare point clouds to raster terrain layers. Having a pre-classified point cloud, and utilizing a bit of lidar filtering will help us analyze things even further.

In the Compare Point Cloud tool, I am going to make sure the settings compare my point cloud against my reference terrain surface. Since the point cloud was previously classified using the Automatic Point Cloud Analysis tool in Global Mapper Pro, I can filter my analysis to tree points only. Ignoring other data will help speed up the process quite a bit, and make sure I’m only analyzing the features of interest.

Tip: Most point cloud analysis tools have access to filtering options!

Settings for change detection between lidar and DSM
See the chosen tool and lidar filtering settings for this workflow.

In this situation, I am going to look for tree points which are offset from the terrain layer by more than 2 meters. The output will provide us with a new layer containing the identified points, which will also be automatically selected. If we take a look at the output in our profile view, the majority of point cloud’s points will be selected as points which meet our criteria. They tend to be those closest to the existing terrain data, and thus not farther than 2 meters away.

Notice that not all points are selected. Points closer to the terrain surface (within our 2 meter threshold) will not be identified as potential change points.

Automatically Creating One Point Per Tree 

At this stage, it’s time to get this data off to our users in the field so they can inspect these trees in person. The resulting lidar points were isolated to their own layer by the Compare Point Clouds tool. We could technically package them all and send them off to Global Mapper Mobile in a mobile package file. However, there’s no need for a field user to have every single point from the point cloud which represents a tree. The general location of the tree will be sufficient, so a better alternative would be to simply use one point to represent each tree. This will also make for much more manageable data. To do this, the Automatic Point Cloud Analysis tool’s Feature Extraction allows us to extract trees. The result will give us a layer of single points representing the location of all trees.

Lidar cloud with point per tree representation
The Feature extraction tool places one point feature at the highest elevation for each detected tree. Here they are symbolized with tree icons.

Sending the Data to Global Mapper Mobile

The tree points, and any other reference data, can now easily be exported to Global Mapper Mobile. Field users would benefit from utilizing the Show Distance and Bearing setting if they are not immediately near the features they need to inspect. For more information on sharing data with Global Mapper Mobile users, see:

Webinar: Managing data between Global Mapper and Global Mapper Mobile
Tool description: Mobile Data Management tool 

Interested in additional forestry based lidar workflows? Check out this list: Measuring Tree Height from Lidar

Try the Compare Point Cloud tool in Global Mapper Pro with a free 14-day trial! If you have any questions, please contact us.

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