148 700 nanometers and in the near-ultraviolet to short- wave infrared portion of the electromagnetic spectrum between 380 and 2,500 nanometers (Jones and Vaughan 2010); a more complete description of types of RS data is found below. Today, satellites, aircraft, unpiloted vehicles, ground-based sensors, and even handheld sensors carried in pockets (de With 2020) annually collect terabytes of earth observation data (Hansen et al. 2013; Hohn et al. 2021). Platforms to compile and analyze earth observation data are prolif- erating (Mauri et al. 2017). Courses on computing languages used to operate these platforms are some of the most requested in universities and online across the world (Dierbach 2014). The urban forestry pro- fessions can take advantage of RS and the Big Data revolution in computing to vastly improve forest assessment, monitoring, and management. Threats to Urban Forests Urban forests provide many benefits to humans who live, work, and play in their shade (Dwyer et al. 2000; McPherson et al. 2016). Nonetheless, urban forest canopy cover is decreasing across much of the devel- oped world due to several current and emerging threats (Kaspar et al. 2017; Doick et al. 2020; Nowak and Greenfield 2020). Examples of current and emerging biotic threats to urban forests include emerald ash borer (Agrilus planipennis)(Poland and McCullough 2006), spotted lanternfly (Lycorma delicatula)(Urban 2020), and ash dieback (Hymenoscyphus fraxineus) (Díaz-Yáñez et al. 2020). Abiotic threats include anthropogenic climate change (Nowak et al. 2014; Ordóñez and Duinker 2014), which is increasing urban heat (Akbari et al. 2016) and changing weather patterns (Melillo et al. 2014; Masson et al. 2020). Densification of the built environment (Chun and Guldmann 2018; Næss et al. 2019) may result in both tree removal (Martino et al. 2021) and reduced area for new large-statured trees (McPherson et al. 2002; Haaland and Konijnendijk van den Bosch 2015). An emergent phenomenon that could create both signifi- cant challenge and opportunity is an evolution in driverless vehicles, discussed below. How can the urban forestry professions take advan- tage of RS, AI, and Big Data to improve urban forest health? This paper will discuss the current capabili- ties and likely future directions of RS and computing, then suggest a technology path forward for the urban forestry professions to ensure future generations will enjoy the many benefits of urban forests. ©2022 International Society of Arboriculture Staley: Modern Urban Forestry for Modern Cities MATERIALS AND METHODS A scoping review was performed of the relevant liter- ature in the unpiloted aerial systems (UAS), artificial intelligence, and remote sensing disciplines to bring together a disparate set of systems soon to be import- ant to the arboricultural disciplines. In addition, a scoping review was performed of current online analytical and data-compilation plat- forms, and the trade literature for the arboricultural and UAS disciplines. Due to the rapidly changing nature of the current technological environment, a thorough analysis also was performed of recent past and cur- rent news articles and press releases for the AI and UAS disciplines to get a sense of likely development directions in these respective fields. A challenging aspect of any review conducted for a set of rapidly changing disciplines is choosing what is relevant information. Guiding these choices is the author’s experience in the field interacting with a dispa- rate set of partners, from growers, pilots, UAS and sen- sor manufacturers, researchers, and software developers. Working backwards from an assumed endpoint— what industries and businesses expect to be the most likely futures toward which they are striving—is the main driver of the methodology of this review. Although it is by no means certain, a future where the arboricultural disciplines use modern and future tech- nology to assist in the analysis and typical work of the profession is very likely, therefore the aim of this paper is to prepare the arboricultural professions for a presumed high-tech future. RESULTS What follows is an outline of the findings important to the arboricultural disciplines: what types of data are or will be relevant in a high-tech future, and how they are or will be collected, analyzed, and applied. Then discussed will be how management can approach the coming disruption in the profession, as well as a discussion of how current or future workers can prepare themselves for the future. Lastly, a set of scenarios illustrate how the results can be applied to real-world problems that exist now or likely will exist soon. Data Collection Collecting data on urban forests is traditionally per- formed by visiting a site and physically measuring parameters such as species, location, diameter at breast height (DBH), and health condition (Gordon
March 2022
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