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:
We exploit Earth observation, focusing on remote sensing at a range of spatial and temporal scales using a multitude of data capture technology. UAS have been used to study lianas in tropical forest trees, while satellite sensors have been used to study land cover and its dynamics over large areas to address major science questions. Application areas are diverse, focusing especially on ecology, but range from studies of human slavery through to the effects of pollutants on the environment. We also contribute to defining best practices to using captured data.
- Arellano, P., Tansey, K., Balzter, H., Boyd, D.S., 2015. Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images. Environmental Pollution 205, 225-239.
- Costa, H., Foody, G.M., Boyd, D.S., 2017. Using mixed objects in the training of object-based image classifications. Remote Sensing of Environment 190, 188-197.
- Espírito-Santo, F.D.B., Gloor, M., Keller, M., Malhi, Y., Saatchi, S., Nelson, B., Junior, R.C.O., Pereira, C., Lloyd, J., Frolking, S., Palace, M., Shimabukuro, Y.E., Duarte, V., Mendoza, A.M., López-González, G., Baker, T.R., Feldpausch, T.R., Brienen, R.J.W., Asner, G.P., Boyd, D.S., Phillips, O.L., 2014. Size and frequency of natural forest disturbances and the Amazon forest carbon balance. 5, 3434.
- Olofsson, P., Foody, G.M., Herold, M., Stehman, S.V., Woodcock, C.E., Wulder, M.A., 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment 148, 42-57.
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) which we have applied to support biodiversity characterisation and hydrology and water resource modelling at catchment and global scales, as well as to support participatory decision-making in flood risk management practice.
- Foody, G.M., 2014. Rating crowdsourced annotations: evaluating contributions of variable quality and completeness. International Journal of Digital Earth 7, 650-670.
- Mount, N.J., Maier, H.R., Toth, E., Elshorbagy, A., Solomatine, D., Chang, F.J., Abrahart, R.J., 2016. Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan. Hydrological Sciences Journal 61, 1192-1208.
- Rocchini, D., Garzon-Lopez, C.X., Marcantonio, M., Amici, V., Bacaro, G., Bastin, L., Brummitt, N., Chiarucci, A., Foody, G.M., Hauffe, H.C., He, K.S., Ricotta, C., Rizzoli, A., Rosà, R., 2017. Anticipating species distributions: Handling sampling effort bias under a Bayesian framework. Science of The Total Environment 584, 282-290.
Geospatial visualisation is undertaken in a range of contexts including computer desktop, visually immersive lab environments, and via in-field mobile devices. 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.
- Adams, A., Fitzgerald, E., Priestnall, G., 2013. Of Catwalk Technologies and Boundary Creatures. ACM Trans. Comput.-Hum. Interact. 20, 1-34.
- Meek, S., Priestnall, G., Sharples, M., Goulding, J., 2013. Mobile capture of remote points of interest using line of sight modelling. Computers and Geosciences 52, 334-344.
- Priestnal, G., Gardiner, J., Durrant, J. and Goulding, J. 2012 Projection Augmented Relief Models (PARM): Tangible Displays for Geographic Information.