Estimating urban above ground biomass with multi-scale LiDAR

Area of Science:

• Urban forestry
• Ecosystem services
• Remote sensing

Background:

• Urban trees provide vital ecosystem services, including mitigating heat island effects and air pollution.
• Assessing aboveground biomass (AGB) in urban forests is challenging due to diverse tree structures and complex land cover.
• Light detection and ranging (LiDAR) offers a promising method for directly measuring urban tree structure and AGB.

Purpose of the Study:

• To develop and apply new allometric models for estimating AGB in urban trees.
• To identify individual trees in urban environments using a novel individual tree detection (ITD) method.
• To generate a borough-wide estimate of urban AGB using multi-scale LiDAR data.

Main Methods:

• Terrestrial LiDAR measurements were used to derive new allometric models specific to urban tree structures.
• An airborne LiDAR dataset was utilized with a new ITD method to identify individual trees across the study area.
• The derived allometry was applied to identified trees to estimate AGB, with uncertainty analysis performed.

Main Results:

• Camden has a median AGB density of 51.6 Mg ha⁻¹, with woodland areas comparable to temperate and tropical forests.
• Multiple linear regression using tree height and crown area effectively predicted tree volume (93% variance explained).
• The new ITD method accurately identified trees, though AGB was underestimated by up to 25% due to crown overlap; wood density was the largest uncertainty source.

Conclusions:

• Urban areas are increasingly important as carbon sinks due to global urbanization trends.
• Multi-scale LiDAR technology provides an effective tool for spatially explicit mapping of urban forest structure and AGB.
• Accurate assessment of urban AGB is crucial for carbon accounting and effective urban forest management.