Arboriculture & Urban Forestry 48(2): March 2022 volumetric data by using a smartphone or dashboard camera application developed by a graduate student in partnership with the computer science department; monitoring nutrient or toxicity concentrations of run- off with potential to harm downstream vegetation using a third-party tasked RPA carrying a hyperspec- tral sensor; monitoring vegetation health in near time after pollution events, such as low-level ozone forma- tion, using a piloted aircraft carrying several sensors; assessment of turf compaction in public parks after large events using an RPA carrying a spectral sensor and a ground-based autonomous vehicle carrying a payload designed to sample soil; and monitoring fuel load and energy release potential of vegetation in the Wildland-Urban Interface by using an array of envi- ronmental monitors aloft, on the ground, and fixed in place. How many more new opportunities to better manage urban environments are possible for current urban forest professionals, future digital urban forest professionals, or those seeking to enter the fields through nontraditional avenues? CONCLUSION This paper reviewed the current state of technologies in remote sensing and computing relevant for urban forestry monitoring and management across scales and introduced the CACI model—Collection, Analy- sis, Communication, Implementation—to place the technologies and their application in relevant context for urban forestry. Then this paper provided near-future scenarios where these concepts could be applied to meet urban forestry challenges, offered avenues for future career paths related to digital urban forestry, and outlined opportunities for needed hardware and software development to improve urban forestry mon- itoring, management, and communication. Examples of areas of empirical study for emerging RS technology and ML in urban forests include fur- ther refinement on what are relevant spectral data for common urban plant species. We do not know how quickly urban tree species adapt to climate change, or their potential new ranges, or how to best measure relevant parameters with emerging RS technology. Is there a need to develop new sensors that measure thermal data to study species adaptation, stress, or effectiveness of plants for building conditioning in warming urban microclimates? What are user prefer- ences for understanding the meaning of RS outputs such as 3D models or digital surface models versus 157 LiDAR point clouds? Similarly, what are appropriate visualizations to depict the new species and changes to the urban forest in 50 years, how the neighborhood will look with new climate-resilient species, and how using RS outputs can assist user understanding? Lastly, explaining to the public what all these new data and analyses represent and mean for the urban forest across scales will be of increasing importance and will be a required skill for some urban foresters. Clear explanations must lead to calls for action, and action requires planning across diverse groups. How will digital foresters of the future explain what is hap- pening or tell the stories of what is needed for the urban forest, and what tools can assist them in a Big Data world? Urban forestry professionals always have been prepared with appropriate data for the time and have always had a passion for trees that helps tell a compelling story. New data and new stories are nec- essary. The urban forest professions still have time and passion to get ready to lead the way forward, pre- pared with new digital tools for the trees. LITERATURE CITED Agisoft. 2021. Features. St. Petersburg (Russia): Agisoft LLC. [Accessed 2021 June 4]. https://www.agisoft.com/features/ professional-edition Agrawal A, Choudhary A. 2016. Perspective: Materials infor- matics and big data: Realization of the “fourth paradigm” of science in materials science. APL Materials. 4(5):053208. https://doi.org/10.1063/1.4946894 Akbari H, Cartalis C, Kolokotsa D, Muscio A, Pisello AL, Rossi F, Santamouris M, Synnef A, Wong NH, Zinzi M. 2016. Local climate change and urban heat island mitigation techniques— The state of the art. Journal of Civil Engineering and Manage- ment. 22(1):1-16. https://doi.org/10.3846/13923730.2016 .1111934 Allen M, Voogt J, Christen A. 2018. Time-continuous hemi- spherical urban surface temperatures. Remote Sensing. 10(1):3. https://doi.org/10.3390/rs10010003 Alonzo M, McFadden JP, Nowak DJ, Roberts DA. 2016. Map- ping urban forest structure and function using hyperspectral imagery and lidar data. Urban Forestry & Urban Greening. 17:135-147. https://doi.org/10.1016/j.ufug.2016.04.003 Alonzo M, Roth K, Roberts D. 2013. Identifying Santa Barba- ra’s urban tree species from AVIRIS imagery using canonical discriminant analysis. Remote Sensing Letters. 4(5):513-521. https://doi.org/10.1080/2150704X.2013.764027 Altermatt F. 2010. Tell me what you eat and I’ll tell you when you fly: Diet can predict phenological changes in response to climate change. Ecology Letters. 13(12):1475-1484. https:// doi.org/10.1111/j.1461-0248.2010.01534.x Anchang JY, Prihodko L, Ji W, Kumar SS, Ross CW, Yu Q, Lind B, Sarr MA, Diouf AA, Hanan NP. 2020. Toward oper- ational mapping of woody canopy cover in tropical savannas ©2022 International Society of Arboriculture
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