Abstract
This study provides urban forest tree inventories for the University Endowment Lands, Pacific Spirit Regional Park, Musqueam First Nation, and Southlands of British Columbia for 2015 and 2021. It describes urban forest structure and composition based on LiDAR-derived urban forest attributes including tree count, maximum height (zmax), crown area, and species type (coniferous/broadleaf). Hotspot analyses revealed spatially biased distributions of hot and cold spots, indicating heterogeneous urban forest structure and composition across the study area. Hot spots characterized by taller coniferous trees and higher mean crown areas were found across coastal, park & forest, or residential zones. Cold spots characterized by shorter trees and smaller mean crown areas were found in zones with more infrastructure. Overall, forest attributes differed between species type and inventory-year. Results suggest that anthropogenic drivers, such as urban development and land-use change, interact variably with tree attributes to influence the structure, composition, and distribution pattern of urban forests. The study highlights the applicability of using LiDAR-derived urban forest inventories to prioritize conservation and management strategies at the local level. By understanding the complex interactions between urban forest attributes and change drivers, communities can optimize green space utilization, sustainability, and equality within urban forest ecosystems.
MGEM Student: Dimitrios Markou
Key words: geospatial
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Markou, Dimitrios, 2024, “Using LiDAR to characterize urban forest structure and composition and locate hotspots based on derived individual tree attributes“, https://doi.org/10.5683/SP3/YMTRV0, Borealis, V1