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Hand, Kathryn Louise
(2023).
DOI: https://doi.org/10.21954/ou.ro.00094429
Abstract
Trees are key features of our urban environment providing a variety of ecosystem services. Over recent decades, the benefits which urban trees provide to society have become better understood, and there is increasing interest in how to best manage our urban trees to deliver the greatest benefit. The amount of ecosystem services provided by any one tree depends on location, nearby environment, condition, species and size, as well as how long the tree can survive and grow for. Much of our current knowledge on urban trees and their benefits is based on a small number of international studies, which may not be transferrable to the diversity of trees across an urban area and their various environmental and anthropogenic influences. Given this diversity, there is a daunting data gap to be filled. Citizen science approaches could provide the opportunity to fill gaps at scale, while also improving local awareness, interest and engagement with urban trees and their benefits.
In this thesis, I first undertake a sensitivity analysis of the i-Tree Eco model, the most widely used tool to quantify the composition, structure and ecosystem service provision of urban forests. This analysis revealed the greatest sensitivity in ecosystem service estimations arises from missing crown size information, metrics which are often absent from urban tree inventories. It also found that simulated citizen scientist input data generated lower error in estimated ecosystem service overall, compared to missing data. This analysis, along with a review of the i-Tree Eco model itself, highlighted urban tree data gaps which could affect estimation of benefits and limit i-Tree Eco’s future use informing urban tree management. In the following chapters, I select three of these data gaps: tree size, leaf area and growth rate, and for each I propose and evaluate solutions using citizen science approaches.
Firstly, the size of urban trees, including trunk diameter, height and crown diameter, are metrics that are often missing from urban tree inventories. Field collection of this data can be costly and may require specialist equipment, but its absence reduces the accuracy of ecosystem service assessments. To overcome this barrier, I test a newly developed ‘tree-odolite’ tool, which enables tree size to be measured from Google StreetView imagery. I find the tree-odolite allows urban tree size to be collected much faster than field measurements with only a small impact on ecosystem service estimations.
Secondly, I address how best to measure urban tree leaf area, a little studied but integral metric for many ecosystem service estimation models. I tested eight different methods, varying from citizen scientist-friendly smartphone approaches to advanced technological solutions (leaf area index meters), against 13 fully destructively sampled urban trees. I found substantial challenges to using technical tools for urban trees and conclude that the approach most consistent with the ‘gold-standard’ destructive sampling is smartphone photography; this is also the most accessible of all the methods evaluated. The results here highlight issues in the use of conventional methods of estimating leaf area for urban trees, but identifies an opportunity in use of smartphone photography for rapid collection of leaf area information.
Lastly, I explore tree growth, key to improving our ability to predict how urban trees change over time. I ran a two-year citizen science study to monitor growth of 99 urban trees using simple band dendrometers. I found citizen scientists could accurately measure growth and report it through a webform. While there were challenges encountered including impact from vandalism and limited engagement for some trees, these could be mitigated for in future study designs. Combined researcher and citizen scientist measurements were sufficiently detailed to identify variation in growth by species, land use and climate.
Overall, in this thesis I show that tools suitable for citizen science can help address sizeable urban tree data knowledge gaps. These tools can be used to collect high quality data. In well-designed citizen science programmes, they could do so at a scale which would allow the diversity of tree species, climate conditions and urban environment types to be investigated. A more detailed understanding of our urban forests would better inform decisions such as which species to plant, where best to plant and how best to manage urban trees to maximise their benefits to society, while simultaneously helping to connect local people with their nearby nature and improve their understanding of the benefits of urban trees.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)
