A Workflow to Quantify the Carbon Storage in Urban Trees Using Multispectral ALS Data

City wide, vegetation contributes the largest proportion of carbon storage, which reduces climate warming and urban heat island effects by sequestering CO₂ and storing carbon in biomass. The carbon content stored in individual trees can be estimated by dendrometric parameters, such as the diameter at breast height (DBH), using allometry-based models. With the development of airborne laser scanning (ALS) technology, ALS data and very-high-resolution multispectral imagery have proven to be promising tools for deriving dendrometric parameters in forestry. This chapter describes a workflow that can quantify the carbon storage in urban trees using multispectral ALS data. The workflow consists of four steps: multispectral ALS data processing, vegetation isolation, dendrometric parameters estimation, and carbon storage modeling. Results suggest that ALS-based dendrometric parameter estimation and allometric models can yield consistent performance and accurate estimation. Citywide carbon storage estimation is derived in this chapter for the Town of Whitchurch-Stouffville, Canada, by extrapolating the values within the study area to the entire city based on the specific proportion of each land cover type. The proposed workflow reveals the potential of multispectral ALS data in land cover mapping and carbon storage estimation at the individual tree level.