This sub-theme has activity across the continuum of data capture through to information generation about our environment.

Our Informatics staff and PhD students are conducting research on:


Data capture

We exploit Earth observation, focusing on remote sensing at a range of spatial and temporal scales using a multitude of data capture technology. Recent work using unoccupied aerial systems (UAS), also known as drones, has focused on the study of lianas in tropical forest trees and on mapping bankside vegetation and river habitats. We also use satellite sensors to study land cover and its dynamics over large areas to address major science questions. Application areas are diverse, focusing especially on ecology and hydrology but range from studies of human-environment interactions (The Rights Lab; LUCAS) through to the effects of pollutants on the environment.

We also use thermal infrared remote sensing to understand terrestrial and freshwater temperature patterns and energy exchanges and hyperspectral remote sensing as a proxy for plant and soil diversity. Through the above, we are contributing to defining best practices to using captured data.

Recent publications

  • Dugdale S.J., Hannah D.M., Malcolm I.A. 2020. An evaluation of different forest cover geospatial data for riparian shading and river temperature modelling. River Research and Applications, 36, 709-723
  • Boyd D.S., Jackson B., Wardlaw J., Foody G.M., Marsh S., Bales K. 2018 Slavery from Space: Demonstrating the role for satellite remote sensing to inform evidence-based action related to UN SDG number 8. ISPRS Journal of Photogrammetry and Remote Sensing. 142, 380-388.
  • Waite C., van der Heijden G., Field R. and Boyd D.S. 2019. A view from above: Unmanned Aerial Vehicles (UAVs) provide a new tool for assessing liana infestation in tropical forest canopies. Journal of Applied Ecology. 56, 902-912.
  • Zhang Y., Ling F., Foody G. M., Ge Y., Boyd D. S., Li X., Du Y., Atkinson P. M. 2019. Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007-2016. Remote Sensing of Environment 224, 74-91.

Computational methods

Turning captured data into information requires methodological innovation and a major research strand within the Informatics sub-theme is the application of computational methods to support the modelling of physical systems and the development of quantitative techniques that can span the space between physical and socio-economic systems.

To these ends, we are at the forefront of testing of novel 'intelligent' algorithms and machine learning methods (as well as human learning through citizen science) to extract environmental metrics from earth observation. We are using these techniques applied to support biodiversity characterisation and water resource monitoring at catchment and global scales.

Recent publications

  • Dugdale S.J., Malcolm I.A., Hannah D.M. 2019. Drone-based Structure-from-Motion provides accurate forest canopy data to assess shading effects in river temperature models. Science of the Total Environment 678, 326-340.
  • Foody G. M., Ling F., Boyd D. S., Li X., Wardlaw J. 2019. Earth observation and machine learning to meet sustainable development goal 8.7: mapping sites associated with slavery from space. Remote Sensing 11 (3), 266 (12pp).
  • Ling F., Boyd D.S., Ge Y., Foody G.M. Li X., Zhang Y., Shi L., Shang C., Li X., Du Y. 2019. Measuring River Wetted Width from remotely sensed imagery at the sub-pixel scale with a Deep Convolutional Neural Network. Water Resources Research 55, 5631-5649.
  • Li X., Foody G.M., Boyd D.S., Ge Y., Zhang Y., Du Y., Ling F. 2020. SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion. Remote Sensing of Environment, 237, p.111537.


Geospatial visualisation is undertaken in a range of contexts including computer desktop, visually immersive lab environments, and via in-field mobile devices (eg terrestrial laser scanners). Research within the Informatics sub-theme has explored the relative merits of a range of mobile, augmented and virtual technologies across many application domains including teaching environments, museums and visitor centres and in spatial decision support.

Recent developments have explored the use of 3D printed landscape models augmented using novel projection rendering techniques to create engaging displays for community engagement, for example to communicate the spatial and temporal extent of flood inundation scenarios.

Recent publications

  • Shenkin A., Chandler C., Boyd D. Jackson T., Jami B.J.., Disney M., Majalap N., Reuben N.,Foody G.M., Reynolds G., Wilkes P., Cutler M., van der Heijden G.M.F., Burslem D.F.R.P., Coomes D., Bentley L.P., Malhi Y. 2019. The world’s tallest tropical tree in three dimensions. Frontiers in Forests and Global Change 32.
  • Priestnall G. 2019. Rediscovering the power of physical relief models: Mayson’s Ordnance Model of the Lake District, Cartographica.54:4, pp261-277
  • Priestnall, G., Cheverst, K. 2019. Understanding visitor interaction with a Projection Augmented Relief Model Display: Insights from an in-the-wild study in the English Lake District. Personal and Ubiquitous Computing.



School of Geography
Sir Clive Granger Building
University of Nottingham
University Park
Nottingham, NG7 2RD

+44 (0)115 951 5559