How Do You Perform Manual Tree Extraction from ALS and ULS Point Clouds?

LiDAR technology has revolutionized forestry and environmental studies, offering highly accurate 3D data of landscapes. Airborne Laser Scanning (ALS) and Unmanned Aerial Systems Laser Scanning (ULS) are two popular methods for collecting point cloud data. These datasets often contain valuable information about tree structures, including their location, height, and canopy spread. While automated processes exist for extracting tree data, manual tree extraction still holds significance in scenarios requiring precision or dealing with noisy datasets. Here’s a guide to performing manual road-side or forestry tree extraction from ALS and ULS point clouds.

Understanding ALS and ULS Point Clouds

ALS collects data from sensors mounted on aircraft, covering large areas efficiently. The point clouds generated are ideal for regional or landscape-level studies. ULS, on the other hand, uses drones for data acquisition, offering finer resolution suitable for smaller-scale projects or detailed analyses. Both methods produce a dense 3D dataset that includes vegetation, terrain, and man-made structures.

Tools and Software

Specialized software is essential to manually extract trees from point clouds. These tools offer functionalities for 3D visualization, segmentation, and annotation. Basic requirements include:

• A computer with sufficient processing power.
• LiDAR data processing software with manual editing capabilities.
• Point cloud files in LAS or LAZ format.

Steps for Manual Road or Forest Tree Extraction

1. Load and Visualize the Point Cloud Import the ALS or ULS point cloud into the software. Use filters to remove noise or irrelevant data such as ground points and buildings. Apply color-coding based on height to distinguish vegetation layers.
2. Segment the Point Cloud Focus on the areas of interest by cropping or isolating sections containing trees. Tools like polygon selection or bounding boxes help narrow down specific regions.
3. Identify Individual Trees Manually identify tree crowns by examining the height and spread of the point clusters. Switch between 2D and 3D views to accurately distinguish individual trees, especially in dense forests.
4. Annotate and Extract Tree Metrics Label individual trees and extract metrics such as height, crown diameter, and location coordinates. Most software allows you to assign attributes to the selected tree points for further analysis.
5. Export Data Save the extracted tree data in a suitable format (CSV, shapefile, etc.) for use in GIS platforms or statistical analysis tools. Ensure metadata is included to maintain data integrity.

Benefits and Applications

Manual tree extraction ensures accuracy when automated methods struggle, such as in mixed or overlapping canopies. The detailed insights gained can aid in forestry management, biodiversity studies, and urban planning.

Polosoft Technologies offers comprehensive LiDAR-based tree classification and forest cover classification analysis services, enabling clients to unlock actionable insights from complex point cloud datasets. By leveraging advanced algorithms and expert manual techniques, Polosoft classifies tree species, estimates biomass, and maps forest cover with unparalleled accuracy.

These services are invaluable for sustainable forest management, ecological conservation, and carbon sequestration studies, ensuring that businesses and environmental organizations can make data-driven decisions with confidence.

While time-intensive, manual tree extraction from ALS and ULS point clouds provides unmatched precision in forestry analysis. Combining this technique with automated methods can lead to a robust understanding of forest ecosystems, ensuring better decision-making for environmental conservation and resource management.