Measuring Vegetation Encroachment with Point Clouds

Overgrown vegetation can cause more problems than just an eyesore. The encroachment of trees on power lines or other utilities increases the risk of vegetation fires and power outages. As a preventative measure, vegetation mitigation is a standard part of utility and risk management operations. Measuring vegetation encroachment on assets such as buildings, road intersections, and power lines through remote methods can save time and energy. The suite of point cloud editing and processing tools in Global Mapper® is perfect for identifying these encroachments before they become hazardous.

From classifying point clouds to identifying hazardous encroachments, this blog will walk you through the risk management process. Powerlines and buildings are used as examples for encroachment, but these same tools can be applied on other features such as poles or roads.

1. Classify the Point Cloud

Automatically locate and label the features to be measured in the point cloud by applying a classification. Global Mapper’s Automatic Point Cloud Classification tools detect features in the point cloud based on their structure and attributes.

In general, it’s a good idea to classify noise and ground points first. Noise points must be excluded from processing, and the ground points will provide the basis of Height Above Ground settings during other classifications. You may need to adjust the classification settings to best fit your data. It is recommended to try the default settings first, then adjust them, including resolution, to capture missed areas. More information on point cloud classification can be found in the Knowledge Base.

Powerline Classification

If measuring encroachment on powerlines, be sure the point cloud is of high enough resolution to capture smaller structures such as powerlines and individual tree branches. These detailed representations are pertinent to a precise analysis of the infrastructure and landscape.

After powerline classification, if the powerlines are too sparse for proper vegetation detection, they can be extracted as vector line features. These line features inherit the attribute information from the point cloud, such as elevation. Use these vector line features in place of the classified points in the next step: Detect Encroaching Vegetation.

Vegetation Classification

The Max Likelihood classification tools in Global Mapper provide the ability to separate vegetation into different classes based on height. During future analysis, this allows you to exclude low vegetation, such as grasses, or focus on trees of a certain height. Vegetation classification height thresholds are set in the Shared Settings section of the Automatic Point Cloud Analysis tool. Change these values to reflect the vegetation structure in your study area/dataset. More information on these settings can be found in the Knowledge Base.

2. Detect Encroaching Vegetation

Now that your point cloud is classified, you can move on to encroachment detection. Optionally, you can create classes for the encroaching vegetation. Open the Filter Lidar tool and right-click on an unlabeled class to rename it with the distance and color to label the selected features with. Any class with the label Reserved for ASPRS Definition is up for grabs.

Next, use the Select Lidar by Distance tool to select the vegetation points that are within a specified distance of the extracted features. Use the “Specify Lidar Classifications to Select From” to specify the vegetation classes, and the “…. to Search Near” section to specify the features being encroached upon. If searching for features at varying distances, such as in the example below, adjust the Maximum Search Distance value for each iteration. Start with the smallest distance, as these reclassified points will automatically be excluded from larger distances.

Once the points are selected, they will need to be distinguished from their neighbors. This can be done through manually assigning classifications or by copying them to a new layer. To assign a new classification, use the Manual Classification tool to apply classifications to the selected points.

Tip: Edits applied to the point cloud within a Global Mapper workspace do not affect the original lidar point cloud. To see original values, reload the original file to compare it against the edits.

3. Distribute this Data for Field Work

The distinguished areas of encroachment can also be viewed in the field with Global Mapper Mobile®. This mobile app is a simplified version of the desktop application for iOS and Android devices, providing easy access for maintenance crews or other “boots on the ground” employees. Filter the point cloud to one of the encroachment classifications, and create a vector bounding box to demarcate the location and size of each encroachment. This can be easily exported with any other desired data. Field crews can update the workspace from a handheld iOS or Android device to mark completed areas, and send the data back to the office to monitor the progress of the project.

For more information on how to use Global Mapper Mobile for your next project, see:

• Data Sharing between Global Mapper Desktop and Global Mapper Mobile
• Using External GNSS Devices with Global Mapper Mobile Pro
• Watch What It Takes To Fly A Drone on YouTube

Learn more about cleaning, processing, and analyzing point cloud data in Global Mapper Pro with a free 14-day trial today! If you have any questions, please contact us!