When storms cause trees to break or become uprooted they can be responsible for loss of lives, destruction of buildings/infrastructure and disruption to transport, energy and communications networks. It is possible to reduce the risks of tree failure by pruning or removing dangerous trees. However, given the large numbers of trees it is very difficult to identify those which should be managed.
This project will focus on developing a system for gathering a large and continually expanding database on tree failures from the public (using the ‘citizen science’ approach), tree professionals and relevant organisations. The database will be used to train and validate a model of tree failure risk, which will ultimately be applied to improve the effectiveness of tree management schemes.
The project will incorporate four phases: 1) scoping the requirements for data for improving tree failure modelling, understanding the data that can reasonably be expected from crowdsourcing and determining the motivation or barriers to people contributing data on tree failures; 2) developing the underlying database and an effective user interface (phone app) to enable users to upload and visualise their captured data; 3) developing tools to assess the quality of crowd-sourced data and extract meaningful information from a diverse range of data types (images, text, video); 4) apply the database to train and validate the model of tree failure risk and demonstrate the value of the model in tree risk management.
You will receive training in high levels skills related to data analysis and programming, tree biomechanics and failure risk, research design and scientific writing. Working with CASE partner ADAS, a renowned environmental consultancy, and stakeholders such as utilities companies, you will gain valuable experience of applying science and technology research in the real world, which will provide a strong basis for developing your career beyond the PhD.
// Eligibility: Applicants should hold a minimum of a UK honours degree at 2:1 level or equivalent in a relevant Science, Technology, Engineering and Mathematics subject. You should be able to demonstrate the relevance of your knowledge and skills to the PhD project. In particular an aptitude for programming and working flexibly across different languages and platforms is important, along with demonstrable problem-solving abilities.
For further details please contact Dr. Alan Blackburn at alan.blackburn@lancaster.ac.uk