New approaches to measuring and valuing urban forests

Theme: Pan-disciplinary

Primary Supervisor:

Mathias Disney

Geography, UCL

Mathias Disney's Profile Picture

Secondary Supervisor:

Philip Lewis

Geography, UCL

Philip Lewis's Profile Picture

Project Description:

More than half of the world’s population lives in cities, and urban trees are critical to human health and well-being. Trees provide shade, mitigate floods, absorb carbon dioxide (CO₂), filter air pollution and provide habitats for birds, mammals and other plants. Measuring the size, shape and mass of trees, let alone their wider benefits, particularly their less tangible but arguably more important impact on health and wellbeing. This project will develop better ways to quantify and value urban forests both in the UK and overseas and test the benefits of locally-gathered ‘citizen science’ datasets to achieve this. The student would test hypotheses relating tree size and shape to mass, and how tree number and cover can be valued more effectively. The project would also explore correlations between tree size, shape and cover and levels of pollution available from open source pollution data. This would be done by collecting very detailed measurements of urban trees using terrestrial laser scanning (TLS) to quantify 3D size and structure. The CASE partner will help the student develop a schools project to enable young people to collect local manual measurements of tree location, size, shape and species. The student will test the value of these local reference datasets for calibrating the TLS, airborne and satellite estimates ecosystem service value of local trees. The project would focus on the UK initially, to take advantage of the Environment Agency open lidar data, but would aim to extend globally where data were available.

Policy Impact of Research:

Providing a better way to quantify ecosystem services of urban trees will help inform and improve urban planning, with respect to health, wellbeing and resilience to climate change. Led by the CASE partner, will help quantify the benefits of locally gathered datasets for tree mapping, particularly through schools.


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