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The biomass represented by urban trees is important for urban decision-makers, green space planners, and managers seeking to optimize urban ecosystem services. Carbon storage by urban trees is one of these services. Suitable methods for assessing carbon storage by urban trees are being explored. The latest technologies in remote sensing and data analyses can reduce data collection costs while improving accuracy. This paper introduces an assessment approach that combines ground measurements with unmanned aerial vehicle-based light detection and ranging (LiDAR) data to estimate carbon storage by urban trees. Methods underpinning the approach were tested for the case of the Vancouver campus of the University of British Columbia (UBC), Canada. The study objectives were (1) to test five automated individual tree detection (AITD) algorithms and select one on the basis of the highest segmentation accuracy, (2) to develop a model to estimate the diameter at breast height (DBH), and (3) to estimate and map carbon storage over the UBC campus using LiDAR heights, estimated DBHs, and an existing tree-level above-ground carbon estimation model. Of the segmentation algorithms tested, the Dalponte AITD had the highest F score of 0.83. Of the five CW thresholds (th) tested in the DBH estimation model, we chose one resulting in the lowest Akaike’s information criterion, the highest log-likelihood, and the lowest root-mean-squared error (19.55 cm). Above-ground carbon was estimated for each tree in the study area and subsequently summarized, resulting in an estimated 5.27 kg C·m−2 over the main campus of UBC, Vancouver. The approach could be used in other urban jurisdictions to obtain essential information on urban carbon storage in support of urban landscape governance, planning, and management.

Assessment of Above-Ground Carbon Storage by Urban Trees Using LiDAR Data: The Case of a University Campus