Arboriculture & Urban Forestry 48(2): March 2022 and Templeton 2015). Today, these data points require collection in the field, then entry of the collected data into a centralized database. A street tree inventory for a city of 100,000 people may take 4 to 5 weeks using a typical crew of 6 people working 40 hours a week, depending upon how many parameters are measured (TJ Wood, personal communication, 2021 May 4). Today, a tremendous amount of urban forest data is already being incidentally collected by third-party RS platforms (Liu 2015; Mohney 2020). For exam- ple, traditional large satellites such as the European Space Agency’s Sentinel 2 and 3 platforms (Phiri et al. 2020), Worldview 3 platform (Ozkan et al. 2020), or constellations of small satellites such as “Doves” manufactured by Planet Labs (Werner 2019) collect RS data at sufficient resolution to be used for some urban forestry applications (Alonzo et al. 2013; Schlemmer et al. 2013; Segarra et al. 2020; PlanIT Geo 2021). Some platforms image a specific area several times a week or better, weather permitting (Bradshaw 2020). Aircraft can be tasked to fly over cities to collect data that can be utilized for urban for- est assessments, for example, the Denver Regional Council of Governments in Colorado, USA (DRCOG 2018) regularly collects leaf-on and leaf-off data for built environment purposes that also contain data usable for urban forestry analyses. Google Earth has information across much of the developed world at sufficient resolution to estimate DBH class, tree genus, and location (Berland and Lange 2017). Remotely Piloted Aircraft (RPA) are also beginning to collect very high-resolution urban forest data (see examples in Figures 1 and 2) that researchers are using to begin to decipher plant reflectance and identify health and key pests (Staley et al. 2019). Much more RPA data will be available soon as aviation governing bodies in coun- tries across the planet approve operations for flying RPA over cities and beyond line of sight (Jones 2017). A description of the types of data currently rele- vant at urban forest vegetation scales and how they are collected follows: • Visible and spectral imagery: These data are collected from satellites, aircraft, RPA, and ground-mounted sensors such as Google Street View vehicles and handheld smartphones. These data can be used to determine tree canopy health, disease, canopy extent, pest presence, and— depending on the sensor—tree species (Thenk- abail et al. 2018). 149 • Thermal data: These data are collected from air- craft, RPA, and some satellites. These data can be used to detect heat islands, plant water stress, and indicators of moisture such as irrigation leaks (Stankevich et al. 2019). • LiDAR data: LiDAR (light detection and rang- ing) is similar to radar, but utilizes light instead of radio waves. These data are collected from aircraft, RPA, ground-mounted sensors, hand- held sensors, and even smartphones (de With 2020). These data can display high-resolution 3D surface and elevation models of canopy, individ- ual trees, or structures. LiDAR can also assist in calculations for wood volume or carbon seques- tration (Tigges and Lakes 2017). • Digital surface models (DSMs): Constructed using data and photogrammetry from satellites, aircraft, and RPA (Escobar Villanueva et al. 2019). These data can depict urban forest and built environment structure across scales. DSMs can be derived from sources such as visual imagery, LiDAR, spectral imagery, or Synthetic Aperture Radar mounted on satellites or aerial platforms. • 3D models: Derived from photogrammetry or other processes using sensors mounted on aerial or ground platforms and constructed from visi- ble, spectral, or LiDAR data using specialized software. • Traditional environmental monitoring data: Here defined as instruments that collect parameters, including soil moisture, temperature, and pan evaporation. If current trends continue, soon a vast amount of data for urban forest trees, both public and private, will routinely be collected for use in urban forestry and curated somewhere for later discovery and analysis. Data Analysis Today, most urban forest inventory data are analyzed on purpose-built inventory software created by third- party entities, stored in the software system, and accessed via simple queries within the software inter- face. Where are all the data on urban forests described above stored and analyzed? Discovering, accessing, and analyzing urban forest data curated on third-party platforms is not straight- forward today. Data must be found across a growing number of sites. Once data are located, computer lan- guages such as Python (Bogdanchikov et al. 2013) or ©2022 International Society of Arboriculture
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