iv within the context of urban green infrastructure in New York City, New York, USA. We conducted semi-structured interviews with the tool cre- ators and assessed the e-tools themselves. Results: Our findings indicate that most e-tools are designed to provide access to different types of information about urban social-ecological systems and, passively or more actively, stimulate learning. In addition to rich, complex, exploratory digital learning environments, many tools combine virtual experiences with in-person training, workshops, and coaching. Conclusion: The observed hybrid approaches harness the power of digital platforms to enable diverse usership and share large amounts of data while employing more traditional on-the-ground organizing techniques and thus offer a way forward in an age of increasing dominance of digital data. Future research on e-tool usership, hybrid learning approaches, and connections to stewardship outcomes could enrich the understanding of how e-tools operate as well as their social-ecological potential and impact. Keywords. Digital Tools; E-Tools; Knowledge Exchange; Stewardship; Urban Green Infrastructure. Steffen Rust and Bernhard Stoinski Using Artificial Intelligence to Assist Tree Risk Assessment................................................138 Abstract. Although the industry has raised the standards of tree risk assessment considerably in recent years, the quality of judgements is still very variable and influenced by a wide range of factors. Due to the complexity and diversity of trees and sites, collecting and verifying relevant personal experiences takes tree assessors many years. In many countries, new tree assessors learn from a small number of experienced peers. Artificial intelligence (AI) can be used to collect and condense scattered knowledge and deploy it in a support tool for basic tree assessment. In this project, the application of a commercial AI decision-making system software (Dylogos) to tree assessment is tested. The software is based on a new dynamic nonclassical logic, which combines diverse knowledge sources to an emergent system to support visual tree assessments. A set of rules describes existing knowledge about the mostly unsharp parameters affecting the likelihood of failure and damage. The software evaluates the data collected during a basic tree assessment and provides an estimate of the level of risk posed by the tree. The result and the rea- sons for it are presented in plain language. Users can then examine this estimate and feed their own assessment back into the system to train it further, so that this “white” AI system is self-learning based on experience acquired in practical use. The use of AI in tree risk assessment not only supports the user but can also be used to disseminate knowledge and promote the standardization of decision-making in tree assessment. Important directions for further research and knowledge gaps related to the training of AI systems in the absence of industry-wide, agreed-upon criteria for risk identified in this project are: how to collect sufficient quality-assured data sets to define the initial set of rules; and how to assess the level of expertise of users training the system further. Keywords. Artificial Intelligence; Fuzzy Logic; Tree Inventory; Tree Risk Assessment. Daniel C. Staley Modern Urban Forestry for Modern Cities: Technology Challenges and Opportunities for the Remote Sensing of Urban Forests ...............................................................................147 Abstract. Background: As human populations urbanize, urban forests in many areas are decreasing in canopy extent due to disruptions on sev- eral fronts, including novel pests and diseases, climate change, and changing land uses. Methods: A review of the remote sensing, computing, and environmental literature was performed to provide an overview of current technology capabilities and to detail an agenda for a modern approach to urban forestry challenges. How to prepare current and future professionals to collect and analyze “Big Data,” how to implement results, and what communication skills are needed in a modern world to provide resilient urban forests in the connected future were also reviewed. Results: This paper outlines an agenda for how the urban forestry professions can identify, analyze, and manage emergent disruptions to continue to provide urban forest benefits to residents in its shade. Current remote-sensing systems, the paradigm of Big Data, and collection and analysis platforms are discussed, and relevant scenarios are provided to guide insight into managing forests with a rejuvenated perspective using remote-sensing hardware and software. Conclusions: Modern cities will require modern digital urban forestry management, and current and future professionals must be able to access and utilize technology, sensors, and Big Data to effectively perform vegetation management and communication tasks. This paper details the framework for a new era of modern urban forest management in highly connected, resilient cities. Keywords. Computing; Sensors; Smart Cities; Urban Forest Management. ©2022 International Society of Arboriculture
March 2022
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