Best Practices for AI Forestry

Introduction

This article complements the AI Forestry documentation by helping users determine whether their dataset is a good candidate for AI Forestry and when Individual Tree Segmentation (ITS).

AI Forestry is designed to classify forest point clouds and generate forestry products such as trunk locations, crown polygons, trunk measurements, and canopy height models (CHM). The model uses a generalized deep-learning workflow optimized for tree-level analysis in natural forest environments.

This article provides recommendations for selecting suitable datasets and explains the conditions that typically produce the best AI Forestry results.

🌲 Use AI Forestry for Natural Forests

AI Forestry performs best in environments where trees exhibit distinct crown and trunk structures.

Recommended use cases include:

• Natural forests
• Mixed hardwood forests
• Mature conifer forests
• Forest inventory projects
• Biomass estimation
• Forest health assessments
• Large-scale forestry mapping

These forest types typically contain trees with sufficient height, crown separation, and visible stem structure for the AI model to accurately identify individual trees.

📏 Tree Height Matters

AI Forestry is designed to identify trees rather than shrubs, brush, or small vegetation.

For best results:

✅ Trees should generally be taller than 3 meters

✅ Mature trees are typically detected more reliably than young trees

⚠️ Trees near or below 3 meters may not be detected consistently

Young plantations or recently planted stands often produce lower detection rates because they fall near the minimum height threshold and may not yet exhibit recognizable tree structure.

Example

A mature forest containing 10 to 30 m tall trees will generally produce better results than a plantation containing young trees that are only 2 to 4 m tall.

🌳 Visible Trunks Improve Detection

AI Forestry uses both canopy and trunk information when modeling trees.

For optimal results, the point cloud should contain:

• Canopy returns
• Lower canopy returns
• Visible trunk returns near ground level

If trunk returns are not present, the AI may still identify canopy features but can struggle to generate:

• Trunk center locations
• Trunk measurements
• DBH-related outputs
• Trunk geometry

Good Candidate Dataset

• Mature trees
• Clearly visible stems
• Distinct trunk structure
• Returns captured throughout the canopy profile

Challenging Dataset

• Foliage extending to ground level
• Little or no visible stem structure
• Dense clusters of vegetation
• Trees that visually resemble bushes

Rule of thumb: If a tree trunk is difficult to identify visually in the point cloud, the AI model will likely have difficulty identifying it as well.

🌲 Tree Types That May Produce Reduced Results

Certain forest conditions are more challenging for the AI Forestry model.

Young Plantation Forests

Young plantation trees often:

• Have limited trunk visibility
• Are close to the minimum tree height threshold
• Have compact crowns with little separation

This can reduce both trunk detection and individual tree extraction.

Dense Conical Conifers

Young fir, spruce, and similar conifer species may produce reduced results when:

• Branches extend close to the ground
• Trunks are hidden within dense foliage
• Trees appear as cone-shaped vegetation clusters

In these situations, crown detection may be successful while trunk detection remains limited.

Mixed Height Forests

Datasets containing very tall natural forests adjacent to short plantation trees can be more challenging.

When possible, process areas with similar forest characteristics separately or isolate the target vegetation before processing.

📡 Data Collection Recommendations

Data quality has a significant impact on AI Forestry performance.

Recommended practices:

Capture Lower Canopy Returns

Whenever possible, collect data that captures:

• Trunk structure
• Lower branches
• Ground-level returns around trees

Maintain Adequate Point Density

Sufficient point density is required to define:

• Crown shapes
• Stem locations
• Tree geometry

Higher density alone does not guarantee success if trunk returns are missing.

Validate the Point Cloud Before Processing

Before running AI Forestry:

1. Inspect the point cloud in 3D.
2. Verify that trunks can be visually identified.
3. Confirm that target trees are larger than approximately 3 m.
4. Check that individual crowns can be distinguished.

Test a Sample Area First

Before processing very large projects:

1. Extract a representative sample area.
2. Run AI Forestry.
3. Review trunk detections and crown polygons.
4. Confirm results meet project requirements.

This approach can help avoid processing large datasets that may not be ideal candidates for AI Forestry.

🌲 AI Forestry vs Individual Tree Segmentation (ITS)

AI Forestry and Individual Tree Segmentation (ITS) are designed for different forestry applications.

When ITS May Be a Better Choice

Consider using ITS when:

• Trees are planted in rows
• Most trees are young
• Trunks are not visible in the point cloud
• Tree counting is the primary objective
• Working in orchards, plantations, or managed tree farms

These environments often contain regularly spaced vegetation that aligns well with the ITS workflow.

✅ Summary Recommendations

For best AI Forestry results:

• Use datasets containing trees taller than approximately 3 m.
• Prefer natural forest environments over plantations and orchards.
• Ensure trunk returns are visible within the point cloud.
• Capture lower canopy and stem information during data collection.
• Review trees visually before processing.
• Process representative samples before submitting large projects.
• Compare results with ITS when working in plantations, orchards, or tree farms.

Related Articles

• Advanced AI Classification – Ground+, Forestry, and Utilities
• AI Forestry
• Tree Segmentation Tool in LP360