June 25, 2024

How to Create a Forest Density Map from Lidar

Written by: Amanda Lind


Forest density can be used as a measurement of forest health and growth. Remotely sensed data, such as lidar, can be utilized for forest density analysis while saving time and human effort. While Global Mapper Pro supports both photogrammetric and traditional lidar point clouds, this workflow leverages traditional lidar and its ability to penetrate the forest canopy. This allows for modeling of the canopy as well as the ground below with forest density maps.

A forest density map created from a raster layer and tree points
A tree density raster layer created in Global Mapper Pro.

Density maps highlight areas of high and low point densities. The most common forest density measurements assess individual trees and their proximity to one another. In Global Mapper Pro additional density maps can be created to also identify clusters of trees with any shared attribute such as canopy width, tree height, and more. Alternatively, you can visualize the density of vegetation lidar points using the Draw Lidar by Point Density setting. This doesn’t look at individual trees, but may provide some insight for overall vegetation density and ground cover. 

3 Easy Steps:

  • Classify the point cloud 
  • Create a point feature for each tree using the Automatic Extraction tool 
  • Use the Density Grid tool to assess density measurements 

For a real-world example, see this presentation from our 2024 user conference given by the winner of the 2023 Global Mapper Master competition, Dr. Dwayne Tannant:
GeoTalks: Mapping Tree Density Using Lidar Data – A Tool For Wildfire Risk Assessment

1. Classify the Point Cloud 

Point cloud classifications apply context to the point features for visualization and analysis. We humans can clearly see the difference between tree canopy points and ground points. Computers, however, must identify objects in point clouds based on their attributes and structure. Global Mapper’s automatic vegetation classification tools are built to identify tree-type structures in the data.

Classified vegetation and ground lidar points of a forest
This sparse USGS point cloud of an area in central Maine was classified for ground, and vegetation height in Global Mapper Pro.

Height classes can also be automatically identified to focus on which trees you would like to measure. These classifications can later be used to filter the point cloud to compare the densities of different tree height classes. 

For a full walkthrough on appropriate automatic tree classification settings in Global Mapper Pro, see: Calculating Tree Inventory from Aerial Lidar.  

2. Extract Trees as Vector Features 

The Feature Extraction tool creates vector features from classified points. This is where the tree inventory can be created for easy viewing and export. Among other output options, a vector tree point is created at the center of each segment. These tree-shaped point features contain the tree’s measured attributes, such as height, canopy spread, and classification.

Identified tree features from lidar and their measured attributes
Each detected tree in the point cloud is assigned a vector point feature that contains the tree’s measured attributes.

*Tip: The Tree Count can be read as the number of features in this point layer. 

3. Create Density Grid (Heat Map)

Open the Create Density Grid (Heat Map) tool from the Analysis Toolbar or Vector Analysis drop-down menu, and choose the Extracted Trees layer. This will create a grid layer where the color of each cell value represents the relative density of trees.

Tool settings for creating a forest density map
Adjust the tool settings to best fit your forest type and expectations.
  • Population Field – Choose which attribute to measure the density of. Point Count will measure the density of trees in the forest, but you can also choose other attributes to find clusters of similar characteristics, such as height. 
  • Search Radius – Used to specify how far from a tree an adjacent tree can be and still be considered close. Adjust this to match the desired density of the forest type. If trees are 10m apart is that considered an ideal distance?
  • Cells per Radius – When combined with the Search Radius, this controls how large the pixels are in the resulting density grid. For example, if you have a search radius of 90 meters and a ‘Cells Per Radius’ of 3, each pixel should end up 30 meters across.

This type of raster analysis is sometimes referred to as a Heat Map because of the colors typically used. Red cells have a higher value, in this case they represent the more dense areas, while blue cells are the least dense.

A forest density map showing areas of high and low density

A cluster of trees highlighted by the forest density map
Zooming in on this data, we can see a cluster of dense forest that may require a field visit to investigate possible management needs.

Density grids are a quick and easy way to highlight sections of a stand that are in need of thinning or other management. Through Lidar data and Global Mapper Pro, this analysis can be applied to an entire forest in a fraction of the time it would have taken to execute and process a timber cruise of the same area.

Try Global Mapper Pro with a free 14-day trial today! If you have any questions, please contact us!

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