Detecting and interpreting dynamic changes in urban green spaces by applying big data analytics to Earth observations

More than half of the world's population lives in cities and this is expected to increase to 60% by 2030. Urbanization poses a risk to urban nature and a challenge for urban planners to provide city inhabitants with viable green spaces. Green spaces deliver £2.2 billion of health benefits to England alone and access to green spaces has also been linked to reduced crime, increased mental and physical health, and social cohesion. Another vital service provided by urban green spaces is carbon sequestration (uptake and long-term storage of the greenhouse gas CO2).

In many cities there is no infrastructure in place to routinely and effectively monitor green spaces. Where there is monitoring, it is often limited to an inventory of vegetation types and spatial extent that is collected infrequently. Instruments onboard satellites provide daily measurements (Earth observations) of the spatial coverage of leaves, and the greenness and productivity of vegetation that can be used determine the health and viability of green spaces and responses to environmental and human stressors.

In this work you will apply machine learning and big data analytics techniques (data reduction, data mining, clustering, and trend and regression analysis) to very high spatial resolution (< 1 km) observations of vegetation dynamics from NASA and European Space Agency (ESA) satellites to estimate trends, seasonality, and year-to-year changes in green spaces in five global cities. These cities are selected to provide a dynamic range of development stages and distinct climate zones and topographies to develop flexible data processing algorithms that can be applied to any urban centre in the world. The target cities for the machine to learn the dataset include London, UK (temperate developed city), Birmingham, UK (temperate city undergoing urban renewal), Johannesburg, South Africa (semi-developed subtropical city), Delhi, India (rapidly developing subtropical megacity), Manaus, Brazil (rapidly developing tropical city). Where available (e.g. London, Manaus), satellite-derived results will be compared to ground-based observations and literature reports, and Earth observations of potential drivers of change (e.g. air temperature, rainfall, land use change, wildfires) will be used to determine the underlying factors responsible for detectable changes in green spaces

Entry requirements
Applicants are required to hold/or expect to obtain a UK Bachelor Degree 2:1 or better in a relevant subject. A Master’s Degree in a related field is desirable. The University of Leicester English language requirements apply where applicable.

How to apply
The online application and supporting documents are due by Monday 21st January 2019.
Any applications submitted after the deadline will not be accepted for the studentship scheme.
References should arrive no later than Monday 28th January 2019.
Applicants are advised to apply well in advance of the deadline, so that we can let you know if anything is missing from your application.

Required Materials
1. Online application form
2. Two academic references
3. Transcripts
4. Degree certificate/s (if awarded)
5. Curriculum Vitae
6. EPSRC Studentship Form
7. English language qualification

Applications which are not complete by the deadline will not be considered for the studentship scheme. It is the responsibility of the applicant to ensure the application form and documents are received by the relevant deadlines.

All applications must be submitted online, along with the supporting documents as per the instructions on the website. Please ensure that all email addresses, for yourself and your referees, are correct on the application form.

Project / Funding Enquiries
Application enquiries to [Email Address Removed]
Closing date for applications – 21st January 2019