Individual Tree Segmentation – Suitable Forest Types

Expectations when applying Tree Segmentation to different forest environments.

Forested Areas Most Suitable for LP360 Individual Tree Segmentation (ITS)

1. Managed Forests (Ideal Conditions)

The ITS tool performs best in managed or structured forest environments where tree spacing and canopy shapes are relatively consistent. Examples include:

• Orchards
• Plantations
• Commercial timber plots with regular planting patterns

In these environments, predictable crown sizes and inter‑tree spacing allow the algorithm to segment trees cleanly and accurately.

2. Forests with Distinct, Well‑Separated Canopies

ITS works well in areas where:

• Individual tree crowns are easily distinguishable
• Trees achieve sufficient height above low vegetation
• Canopies do not heavily overlap

These conditions support the tool’s ability to detect crown boundaries and derive accurate tree counts, heights, and crown extents.

3. Sites with Good Ground Classification

Accurate ground classification is essential because ITS uses height‑above‑ground to identify trees. Forested areas where ground returns are achievable—without extreme occlusion—produce the most reliable results.

Forested Areas Less Suitable for LP360 Tree Segmentation

1. Dense, Natural “Wild” Forests

In unmanaged forests with highly irregular canopy structure, overlapping crowns, or multilayered vegetation, ITS may:

• Merge adjacent trees
• Miss individual stems
• Produce higher false‑positive or false‑negative counts

These environments challenge the geometric assumptions the tool relies upon.

2. Areas Without Strong Canopy Returns

If the LiDAR collection does not capture canopy information (e.g., trunk‑focused mobile scans or extremely dense overstory that blocks returns), segmentation accuracy will be limited.

Summary

Best results occur in structured or semi‑structured forest environments where tree geometry is consistent and clear canopy definition is available.
While ITS can operate on most forested LiDAR datasets, performance will vary depending on canopy density, tree spacing, understory complexity, and the quality of ground classification.

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