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Related Experiment Video

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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

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Individual Tree Crown Delineation Using Multispectral LiDAR Data.

Faizaan Naveed¹, Baoxin Hu¹, Jianguo Wang¹

¹Department of Earth and Space Science and Engineering, York University, Keele Street, Toronto 4700, ON M3J 1P3, Canada.

Sensors (Basel, Switzerland)

|December 15, 2019

Summary

This study improves individual tree crown (ITC) delineation using multispectral Light Detection and Ranging (LiDAR) data. Integrating spectral and height information achieved 80% accuracy, outperforming previous methods.

Keywords:

individual tree crown delineation multi-scale analysis multispectral LiDAR neutrosophic logic seeded region growing

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Area of Science:

Forestry
Remote Sensing
Geospatial Analysis

Background:

Accurate individual tree crown (ITC) delineation is crucial for forest inventory and analysis.
Existing methods often struggle to effectively integrate diverse data sources for precise segmentation.

Purpose of the Study:

To develop and validate an improved framework for ITC delineation using multispectral LiDAR data.
To enhance treetop detection and region-based segmentation for more accurate crown mapping.

Main Methods:

Utilized multispectral LiDAR data (1550 nm, 1064 nm, 532 nm) from Teledyne Optech's Titan sensor.
Improved a multi-scale algorithm for treetop detection and proposed an advanced region-based segmentation method.
Integrated spectral, contextual, and height information for segmentation, using detected treetops as seeds.

Main Results:

Achieved 80% accuracy in ITC delineation, surpassing the marker-controlled watershed (MCW) technique by 12%.
Demonstrated that integrating spectral and height information significantly improved delineation compared to using either data type alone.
Validated the proposed framework in a study area in Toronto, Canada.

Conclusions:

The proposed framework effectively integrates multispectral LiDAR data for enhanced ITC delineation.
Combining spectral and height information is key to improving the accuracy of individual tree crown segmentation.
This approach offers a significant advancement for individual tree analysis in urban forestry and ecological studies.