152 environment with multiple communication platforms used by diverse publics. Communicating effectively to many different audiences will soon be an important skill in an information-rich world. Implementation in this paper is defined as action resulting from results of analysis. This action results in outcomes such as publishing and enforcing a man- agement plan, requesting for a budget to address find- ings, submitting a bid encompassing work for a Request for Proposal, or issuing work orders. Machine learning will be a key component of the future CACI process and is a sub-discipline of artifi- cial intelligence. ML uses data and algorithms to make predictions, models, and decisions, without being explicitly programmed to make predictions and deci- sions, then learns and improves with experience (Malone et al. 2020). ML is a rapidly growing field, is quickly becoming ubiquitous across many fields of human endeavor, and includes plant species identification applications on smartphones and plant disease identi- fication algorithms for handheld sensors (Mohanty et al. 2016; Buja et al. 2021). It is likely at the current pace of ML development that within 10 years, ML algorithms will be available for many urban forest needs, using data from numer- ous places to perform both user-based and machine- based queries and actions, ranging from canopy health assessments across scales, to amount and timing of irrigation or nutrient delivery in constructed landscapes. DISCUSSION Considerations for Management In the future, organizations, departments, and compa- nies will need to decide between: (1) hiring digital forestry staff to perform most or all of the CACI pro- cess; (2) hiring third parties to do most or all of the CACI process; or (3) some combination of the two. These options will require a new set of knowledge and skills to ensure a positive outcome from the proj- ect. Whatever option is chosen, each choice requires that digital forestry staff have knowledge of RS and data analysis. Option (1) is a cost the organization must bear; option (2) is a cost the market will bear; for option (3) both bear the costs. Hiring a specialized team consisting of staff com- petent in both plant knowledge and computer science may be reserved for only a few of the largest organi- zations with the budget to fund skilled specialty teams and necessary office equipment. Conversely, relying ©2022 International Society of Arboriculture Staley: Modern Urban Forestry for Modern Cities on outside contractors to do most of the work will require that an organization has project management staff who can ensure third parties perform work per contracts and that the output makes sense. Balancing these two considerations will be an important task for urban forest managers and require discipline to keep everyone on track. Key questions for operations will be how much capacity, resources, and competency the organization will keep in house versus how much will be acquired via third-party contractors. Other questions include: Who curates the data? Who handles cybersecurity protocols and responsibilities? Who purchases physi- cal infrastructure such as office space, computers, serv- ers, data purchase, data security, and software licenses? Who writes the technical reports and management plans arising from data analysis? Who creates, pres- ents, and shares the visual representations of data and results to the client or the public? These questions will be relevant in many organizations in the future. Career Paths and Other Opportunities to Address Modern Technology Challenges As the demand for Big Data grows, one can envision the existing industry of environment-specific compa- nies such as those providing field technology services for wetland delineation, surveying, construction man- agement, and engineering expanding their offerings to provide urban forestry services (Schewe 2020). Similarly, existing analytics and technology-focused office firms that are lacking field experience or botan- ical education may need individuals from the urban forestry professions to assist in product development, testing, or deployment. Lastly, a market may evolve for firms that specifically offer RS and/or Big Data services tailored for the urban forestry professions or other professions in built or wild landscapes. Organi- zations may forego investment in maintaining physi- cal resources and staff and instead decide to contract services to provide and curate data, provide analysis, and manage software licensing, data security, and physical infrastructure for smaller organizations or companies. Similarly, a niche may arise for a career consultant who bills time by specializing in interpret- ing data or analyses and writes highly technical data reports for diverse environmental clients. Individuals already in the traditional urban forestry professions looking for a change due to age, injury, or life event may want to have an option like those
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