Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices

Area of Science:

• Forestry
• Remote Sensing
• Geospatial Analysis

Background:

• Accurate tree individualization is vital for estimating forest parameters like biomass.
• High-density vegetation can reduce the effectiveness of traditional tree detection methods using UAV imagery.
• Color information has the potential to improve canopy detection in challenging environments.

Purpose of the Study:

• To evaluate the efficacy of Color Vegetation Indices (CVIs) for individualizing tree canopies in dense vegetation areas.
• To assess the performance of CVIs in enhancing tree detection for *Pinus radiata* using UAV data.
• To explore CVIs as an alternative method for improving tree crown identification.

Main Methods:

• Acquisition of UAV RGB imagery and generation of a 3D dense point cloud and orthomosaic.
• Application of CVIs to the 3D point cloud to differentiate vegetation and non-vegetation, creating a Digital Elevation Model (DEM) and Canopy Height Model (CHM).
• Automatic crown identification using the CHM, with results compared against manual identification and traditional point cloud methods.

Main Results:

• CVIs applied to 3D point clouds effectively differentiate vegetation, aiding in DEM and CHM generation.
• The use of color information in 3D point clouds proved beneficial for individualizing trees, especially in high-density vegetation.
• Results indicate CVIs offer a viable alternative for enhancing tree individualization compared to traditional methods.

Conclusions:

• Color information derived from 3D point clouds using CVIs is a valuable tool for individualizing trees in dense forest areas.
• This approach offers improved accuracy for tree detection and subsequent biomass estimation.
• CVIs present a promising advancement for high-density forest inventory using UAV technology.