LiDAR proved to be a suitable technology for forestry applications as it can penetrate the canopy and accurately detect tree structure and ground points.
Additionally, the LiDAR sensors can be mounted on planes to systematically cover large areas which is a great advantage compared with traditional methods that focus on small sample plots.
In this post we are going to show how LiDAR in combination with Artificial Intelligence (AI) can add value by providing accurate forest inventory parameters on large scale areas.
"Flai gave meaning to our airborne LiDAR point clouds and created added value for forest inventory and carbon trading applications." Günther Bronner, Managing director, Umweltdata GmbH
Number of points
4.4 billion points
95 pts / m2
Automatically detected trees
4’600 ha / 11400 acres
Use case with Umweltdata
Umweltdata is a remote sensing company focused on forest inventories and biotope mapping. The company conducted more than 100 forest inventory projects with over 70 000 sample plots, and more than 200 forest management planning projects with areas bigger than 1000 ha.
The client provided us with a raw point cloud of the forest area of 4600 hectares near Nuremberg, Germany. The average point cloud density is 95 pts/m2 with the total of 4.4 billion points. Flai’s task was to produce point cloud semantic segmentation with relevant labels and vectorize all trees in the data.
How and what do we process?
Flai specializes in semantic segmentation of LiDAR point clouds using AI models. We have created an AI model specifically suited for forestry applications using machine learning on LiDAR point clouds. Our Forestry AI model can predict the following categories:
Tree trunks and
From segmented point clouds Flai has developed algorithms to extract vector information (layers):
Tree trunk centers
Fitted 3D lines of tree trunks and
Polygons of tree canopies.
The vector files include the following attributes that are suitable for forest inventory purposes:
Position at a height of 1.3m (breast height)
Tree diameter at 1.3m,
Trunk and canopy ID
Area of the canopy
Unclassified (intensity) and classified point cloud:
How Flai extracts a tree from point cloud
As we said, algorithms extract vector data from a point cloud. A rough explanation of methodology:
The points classified as tree trunk are projected on a 2D plain
Where point density inside a small area is sufficient, a 3D line is fitted onto these points (representation of tree trunk with 3D vector)
From Ground points, we create DEM and calculate the location of a tree at breast height (1.3m).
How automated parameter extraction is revolutionizing forest inventory
In conclusion, the use of LiDAR point clouds and Flai AI solution to automatically extract parameters of millions of trees is a significant advancement in the field of forest inventory and management. By providing high-resolution 3D information about the tree trunks and forest canopies, LiDAR point clouds enable accurate and efficient extraction of tree parameters such as the tree position, tree height, trunk diameter, and crown area. Additionally, the use of AI algorithms in this process can greatly reduce the time and labor required for manual data analysis, which is especially beneficial when dealing with large datasets.
One of the major benefits of this method is that it allows for the generation of a comprehensive inventory of the forest, down to the level of individual trees. This is a significant improvement over traditional inventory methods, which rely on sample plots and may not provide a complete picture of the forest's overall condition. Furthermore, by allowing the collection of data at a large scale and high frequency, this method can provide valuable insights into the dynamics of the forest ecosystem, such as changes in tree density, health, and forest regeneration over time.
Overall, the use of LiDAR point clouds and AI-assisted data analysis is a powerful tool for enhancing our understanding and management of forest ecosystems. It can play an essential role in sustainable forest management, conservation, and monitoring forest health.