Arboriculture & Urban Forestry 48(5): September 2022 UTC using empirical data in semi-arid, midsized cit- ies, highlighting the need for such research. Given the fact that urban forestry and environmen- tal managers are relying on results from models, such as i-Tree, to inform decision-making strategies for planting and maintaining UTC in cities, residential neighborhoods, and around single-family homes, we aimed to evaluate whether or not trees in landscapes around private residences had an impact on house- hold cooling energy consumption. Since there is evi- dence that impervious surfaces may increase urban heating and potentially lead to increased energy con- sumption in homes in the summertime, we also eval- uated the role of these surfaces around single-family homes. Our focus in this study was on landscape maintenance and design around residential buildings, since a majority of any city’s constructed land area is composed of this particular land use, especially in sprawling cities across the United States. Studies have also shown residential landscapes, in particular, can affect how many ecosystem services are provided to residents (Larson et al. 2016; Mao et al. 2020). Fur- ther, cities are starting to consider how different poli- cies can influence residents to change their landscapes for increased biodiversity (Aronson et al. 2017; Jimenez et al. 2022) and reduced household water consump- tion (Rasmussen et al. 2021). Specifically, our goals were to (1) evaluate whether UTC and impervious surfaces in landscapes around single-family residences can have an impact on household energy consumption, and (2) provide empirical evidence for where UTC and impervious surfaces in landscapes around homes may have the most impact. Similar to previous studies, we expected that single-family homes in the study area would experience the greatest summer cooling electricity savings with increased tree canopy on the west side of homes, but that the magnitude of this relationship would vary based on the distance from homes. Addi- tionally, we expected that with greater impervious cover around the home, summertime cooling electric- ity consumption would increase, regardless of azimuth and distance from homes, due to the role impervious surfaces have in urban heating. By using a large sam- ple of empirical data, this study provides a significant contribution to the current understanding of the role of land cover, specifically tree canopy and impervi- ous surfaces, on energy consumption. Our results will help inform future modeling efforts regarding land 263 cover and energy use and can impact city planning and development by revealing where tree canopy and impervious surfaces are having the most impact on summertime cooling electricity consumption in single- family homes. MATERIALS AND METHODS Study Location Our study area was a midsize, growing city of approx- imately 170,000 people (United States Census Bureau 2020) located in northern Colorado along the Front Range of the Rocky Mountains at approximately 5,000 ft (1,524 m) above sea level. The city is in a semi-arid region with average rainfall of 15 in (38.1 cm) per year, average snowfall of 50 in (127 cm) per year, and approximately 300 days of sunshine annually (National Weather Service 2018). The temperature average in the summer months is about 72 °F (22.2 °C) but can reach a maximum average of 97 °F (36.1 °C) during the day (National Weather Service 2018). While there are a limited number of naturally occurring trees, the city has prioritized the maintenance and devel- opment of an extensive UTC through various programs (Community Canopy Program 2017). The UTC in the study area includes a diversity of deciduous species that are successful in the climate, such as the littleleaf linden (Tilia cordata) and Kentucky coffeetree (Gym- nocladus dioica)(Approved Street Trees 2021). Land-Cover Data High-resolution land-cover data (1 m²) were derived from WorldView-2 satellite imagery and LiDAR using object-based feature extraction techniques (Zhou and Troy 2008; O’Neil-Dunne et al. 2013; Beck et al. 2016; Rasmussen et al. 2021). A hybrid-stratified ran- dom accuracy assessment with 2,400 points calcu- lated the overall accuracy of the land-cover data set to be 95% (Congalton and Green 2019). This classifica- tion method has been conducted in other cities includ- ing New York, Baltimore, and Raleigh (Troy et al. 2007; MacFaden et al. 2012; Bigsby et al. 2014). The land-cover data set consisted of 7 classes: tree can- opy, other vegetation (e.g., grasses, shrubs, etc.), bare soil, water, buildings, roads/railroads, and other impervious surfaces (e.g., driveways, sidewalks, etc.) (Figure 1, Table 1). All processing of land-cover data was completed using tools in ArcGIS Pro (Version 2.7; Esri, Redlands, CA, USA). For the purposes of this study, we reclassified land cover into 3 classes: ©2022 International Society of Arboriculture
September 2022
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