ITCSegment algorithm

Knowledge graph centered on ITCSegment algorithm with 62 nodes and 135 connections. Top connected: Spruce, Atriplex canescens, Populus, Populus tremuloides, Picea engelmannii.

Method synopsis

Region-growing algorithm that iteratively incorporates LiDAR points into candidate tree canopies starting from seed locations identified as local height maxima with height-dependent neighborhoods.

Synthesized from method descriptions across 2 papers using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — A Bayesian record linkage approach to applications in tree demography using overlapping LiDAR scans (2025), Annals of Applied Statistics. Implementations in other papers (listed below) may differ.

1. LiDAR point cloud processing and tree segmentation
   Point clouds were processed to generate 1/3 m resolution canopy height models. Individual tree crowns and attributes were extracted using the ITCsegment algorithm, a region-growing approach that iteratively incorporates points into candidate tree canopies starting from seed locations (putative tree tops) selected using local maximum filter with height-dependent neighborhoods. Canopy volumes were calculated by summing canopy heights using 1 m resolution canopy surface models generated with the pit-free algorithm.
   Quantities: 1/3 m resolution for height models, 1 m resolution for surface models, seed locations identified as local maximaDuration: Processing conducted after each scan collectionConditions: Post-processing conducted in laboratory conditions
   Equipment: ITCsegment algorithm (Dalponte and Coomes 2016), lidR R package version 3.4, pit-free algorithm

Typical Equipment

• ITCsegment algorithm (Dalponte and Coomes 2016)
• lidR R package version 3.4

Output Measurements

• individual tree locations
• canopy volumes
• tree heights
• crown polygons

Papers Using This Protocol (2)

Species Studied With This Protocol