This is an exciting opportunity to explore machine learning and big data methods, in combination with earth observation data, to extract information on land cover and habitat condition. It will use a range of remote sensing data sets, including LIDAR, aerial photography and high resolution satellite data. Land cover is a key environmental variable and is typically classified from satellite data, which has a relatively coarse spatial scale (> 20m). However, land cover change and key habitats often occur at spatial scales below this. Large data sets of aerial photography, LiDAR and high resolution satellite data are increasingly being collected, but efficiently extracting useful information from them requires development of new methods. Over recent years the deep learning and machine learning communities have made rapid progress in developing methods for classifying aerial photography, however, this work has typically focussed on the requirements of the military and disaster response.
This project will use machine learning methods to classify remote sensing data for environmental purposes, as well as for quantifying habitat condition. It will explore novel applications including the combined use of LiDAR and aerial photography, and different types, combinations and scales of earth observation data. The PhD will be structured around a series of case studies exploring different applications and spatial scales and will be underpinned by field data sets held by CEH. The aim of the project is to demonstrate the potential of machine learning methods and to gain an understanding of when (and how) they are most usefully applied. Successful development of these methods will transform our ability to monitor the natural environment remotely by revolutionising the type and speed of products we produce. The student will be supervised by Dr Clare Rowland and Professor Alan Blackburn and the PhD will be awarded by the University of Lancaster.
This PhD is interdisciplinary in nature and as such would such would suit applicants from a wide range of numerate, scientific backgrounds, including (but not limited to) candidates with degrees in Environmental Science, Geography, Ecology, Biological Sciences, Physics or Engineering. MSc’s in a relevant subject such as Remote Sensing, Environmental Modelling or Data Science would be an advantage, but an MSc, whilst desirable, is not essential.
For further details please contact Dr Clare Rowland firstname.lastname@example.org