96 Lu et al: Modeling the Shading Effect of Vancouver’s Urban Tree Canopy higher energy demand and GHG emissions (Sailor and Pavlova 2003). Urban canopy, including street, park, and private trees, is well known for its ecologi- cal values (Krayenhoff et al. 2020), its social values (Kweon et al. 1998; Lafortezza et al. 2009; Nesbitt et al. 2017), and its contribution to regulate microcli- mates and even lower cities’ GHG emissions (Palme et al. 2020; Pigliautile et al. 2020; Sabrin et al. 2021) due to its cooling and shading effects (Huang et al. 1987; Tooke et al. 2012; Morakinyo et al. 2017). Key Characteristics of Urban Canopies in the Context of Shading Urban canopy can lower air and surface temperature through evapotranspiration (Metselaar 2012) and casts shade that prevents solar radiation from heating the air and land surface (Bowler et al. 2010; Yu Q et al. 2020). As a natural cooling device for building occu- pants and pedestrians, urban canopy offers up to 9 °C reduction in building surface temperature, potentially lowering up to 30% of the overall cooling energy demand (Akbari et al. 1992; Berry et al. 2013). In general, there are 4 key urban canopy character- istics that can influence shading ability. Firstly, the coverage and height of the canopy (i.e., canopy cover hereafter) directly determine the shaded area cast by a given tree crown. Ziter et al. (2019) suggested that a nonlinear relationship exists between temperature reduction and canopy cover size in a midsized city in the Upper Midwest United States. Middel et al. (2015) found that an increase in canopy cover from 10% to 25% can potentially reduce its surrounding air tem- perature by 2 °C in a residential neighborhood in the City of Phoenix, Arizona. Secondly, canopy cover density can interplay with shading, as trees closer together tend to have a lower solar permeability and therefore offer better shading quality (Aminipouri et al. 2019). Thirdly, the location of canopy can indi- rectly impact the overall shading quality (Palme et al. 2020). For example, in Melbourne, Australia, east-west oriented street trees often provide better shading (Norton et al. 2015), and trees located closer to a building or streets cast more direct shade than trees located farther away (Berry et al. 2013). Lastly, can- opy shading ability can vary across different physio- logical traits of an urban canopy, such as its species, age, health condition, and height. Deciduous trees, compared to coniferous trees, can also have seasonal fluctuations between leaf-on and leaf-off months. ©2022 International Society of Arboriculture Modeling Thermal Dynamics of Built Environment and Urban Canopy The thermal dynamics of the built environment (i.e., building and street) is highly complex, including both micro-level building components and macro-level variables (e.g., building locations, street width/density, climate conditions, etc.). Additionally, urban canopy characteristics (e.g., size, density, location, species, etc.) are interwoven with micro- and macro-level urban built elements, creating additional complexity for researchers and urban planners. As a result, the major- ity of the previous research on urban canopy cooling focused on limited spatial extent (Yu Q et al. 2020). Even fewer empirical studies have been conducted to focus on canopy shading on both building and street level due to expensive field study costs, and they were often limited by small sample sizes and the abil- ity to set up proper control experiments (Berry et al. 2013). The lack of reliable data and models therefore hinders the possibilities for future temporal studies. Tree shade changes spatially across urban environ- ments, yet there is no consistent method to effectively quantify the spatial variations of canopy shade, as it strongly depends on various complex factors such as spatial scale, location, and solar geometry (Yu X et al. 2020). Spectral information (e.g., the normalized dif- ference vegetation index) from remotely sensed images has been commonly used to investigate the spatial (Jin et al. 2020) and temporal variations (Cze- kajlo et al. 2020) of urban vegetation. In general, remote sensing data are more consistent and tempo- rally stable compared to manually collected field data (Shahtahmassebi et al. 2021). Yet multispectral or even hyperspectral data offer limited information on detailed 3D urban canopy structure (e.g., tree height) sensitive to the shading effect (Yu X et al. 2020). Other remote sensing technologies such as aerial laser scanning (ALS, or commonly known as LiDAR) have become increasingly common in modeling urban canopies (Tooke et al. 2012; Plowright et al. 2017) and solving other urban vegetation–related challenges. Compared to multispectral remote sensing images, ALS is an attractive tool due to its capability of mea- suring urban canopy in 3D at a fine spatial resolution (e.g., sub 1 m). ALS also enables researchers to gen- erate accurate digital surface models (DSMs) and individually delineated single canopy crowns in com- plex urban settings (Chance et al. 2016; Plowright et al. 2016; Plowright et al. 2017). Yet despite the
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