UBC Research Data

Evaluating LiDAR technology to Augment Fuel Typing Classification by Enhancing accuracy of Vegetation Resource Indices in The Resort Municipality of Whistler Zheng, Si Ming

Description

This study evaluates the integration of airborne LiDAR (Light Detection and Ranging) data with British Columbia’s Vegetation Resource Inventory (VRI) to identify opportunities for enhancement. LiDAR-derived forest structure metrics were compared against VRI estimates in the Resort Municipality of Whistler, revealing discrepancies and variable correlation strengths. Tree height showed a moderately strong correlation (r ≈ 0.66, p < 0.001) between VRI-projected and LiDAR-measured values. LiDAR heights were on average ~0.86 m lower than VRI estimates, through sharing identical patterns and trends, indicating agreement in vertical structure. In contrast, canopy closure (percent cover) has only a weak correlation (r ≈ 0.27). LiDAR-derived canopy closure values were substantially higher, about 55.8% on average than VRI projections, suggesting that our LiDAR method overestimated due to methodological differences. Conifer vs. broadleaf compositions showed that LiDAR morphological metrics can partly predict species composition: percent conifer in VRI had a moderate negative correlation with LiDAR-derived canopy cover (r ≈ –0.41) and moderate positive correlation with tree shape aspect ratio (r ≈ 0.40) though showing some correlation this is too modest for any conclusive predictive power. Our methodology combined terrain-normalized LiDAR point clouds, individual tree segmentation, and extraction of metrics such as height, crown area, aspect ratio, and crown surface curvature. The results demonstrate that LiDAR can augment VRI by providing more objective measurements, higher spatial resolution, and improved estimates of key forest attributes. These enhancements could benefit applications including forest management, wildfire risk assessment, and carbon accounting.

Item Metadata

Title	Evaluating LiDAR technology to Augment Fuel Typing Classification by Enhancing accuracy of Vegetation Resource Indices in The Resort Municipality of Whistler
Creator	Zheng, Si Ming
Contributor	Zheng, Si Ming
Date Issued	2025-04-22
Description	This study evaluates the integration of airborne LiDAR (Light Detection and Ranging) data with British Columbia’s Vegetation Resource Inventory (VRI) to identify opportunities for enhancement. LiDAR-derived forest structure metrics were compared against VRI estimates in the Resort Municipality of Whistler, revealing discrepancies and variable correlation strengths. Tree height showed a moderately strong correlation (r ≈ 0.66, p < 0.001) between VRI-projected and LiDAR-measured values. LiDAR heights were on average ~0.86 m lower than VRI estimates, through sharing identical patterns and trends, indicating agreement in vertical structure. In contrast, canopy closure (percent cover) has only a weak correlation (r ≈ 0.27). LiDAR-derived canopy closure values were substantially higher, about 55.8% on average than VRI projections, suggesting that our LiDAR method overestimated due to methodological differences. Conifer vs. broadleaf compositions showed that LiDAR morphological metrics can partly predict species composition: percent conifer in VRI had a moderate negative correlation with LiDAR-derived canopy cover (r ≈ –0.41) and moderate positive correlation with tree shape aspect ratio (r ≈ 0.40) though showing some correlation this is too modest for any conclusive predictive power. Our methodology combined terrain-normalized LiDAR point clouds, individual tree segmentation, and extraction of metrics such as height, crown area, aspect ratio, and crown surface curvature. The results demonstrate that LiDAR can augment VRI by providing more objective measurements, higher spatial resolution, and improved estimates of key forest attributes. These enhancements could benefit applications including forest management, wildfire risk assessment, and carbon accounting.
Subject	Earth and Environmental Sciences; forest inventory; Vegetation Resource Inventory; remote sensing; tree morphology; forest structure
Geographic Location	Whistler; British Columbia
Type	Dataset
Date Available	2025-04-12
Provider	University of British Columbia Library
License	CC-BY 4.0
DOI	10.14288/1.0448481
URI	https://doi.org/10.5683/SP3/VXKMD0
Publisher DOI	https://doi.org/10.5683/SP3/VXKMD0
Rights URI	http://creativecommons.org/licenses/by/4.0
Country	Canada
Aggregated Source Repository	Dataverse

Item Media

Item Citations and Data

Licence

CC-BY 4.0

Open Collections