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The urban forest is becoming increasingly important in the contexts of urban green space, carbon sequestration and offsets, and socio-economic impacts. This has led to a recent increase in attention being paid to urban environmental management. Tree biomass, specifically, is a vital indicator of carbon storage and has a direct impact on urban forest health and carbon sequestration. As an alternative to expensive and time-consuming field surveys, remote sensing has been used extensively in measuring dynamics of vegetation and estimating biomass. Light detection and ranging (LiDAR) has proven especially useful to characterize the three dimensional (3D) structure of forests. In urban contexts however, information is frequently required at the individual tree level, necessitating the proper delineation of tree crowns. Yet, crown delineation is challenging for urban trees where a wide range of stress factors and cultural influences affect growth. In this paper high resolution LiDAR data were used to infer biomass based on individual tree attributes. A multi-tiered delineation algorithm was designed to extract individual tree-crowns. At first, dominant tree segments were obtained by applying watershed segmentation on the crown height model (CHM). Next, prominent tree top positions within each segment were identified via a regional maximum transformation and the crown boundary was estimated for each of the tree tops. Finally, undetected trees were identified using a best-fitting circle approach. After tree delineation, individual tree attributes were used to estimate tree biomass and the results were validated with associated field mensuration data. Results indicate that the overall tree detection accuracy is nearly 80%, and the estimated biomass model has an adjusted-R2 of 0.5.
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