Computational Modelling Group


ENVI is a commercial remote sensing and image processing software package. It is developed by ITTVIS, and is currently at version 4.7. It provides an environment for displaying and processing remotely sensed data, and includes a large number of processing tools (such as image filtering, topographic modelling and image classification), all of which are accessible through a graphical user interface.

ENVI is written in IDL, and has an extensive IDL application programming interface, thus allowing ENVI's processing tools to be called from IDL programs. This then allows processing to take place with no user input, making these batch processing tasks suitable for use on High Performance Computing systems such as Iridis.

ENVI can be extended by writing IDL code, and a number of extensions (some of which have been developed by members of the University of Southampton) have been released for free at the ITTVIS Code Library.

At the University of Southampton, ENVI is available on all staff UDE machines (it can be installed from Start->All Programs->Additional Software), and a number of student workstation clusters.

For queries about this topic, contact Robin Wilson.


A novel method for monitoring air pollution from satellites at very high resolution

Joanna Nield, Jason Noble, Edward Milton (Investigators), Robin Wilson

Developing methods to monitor the clarity of the atmosphere from satellites at 100,000 times the resolution of previous methods. This can then be used to monitor air pollution, correct satellite images and provide data for climate studies. Simulation is used to model the effects of atmospheric pollution on light passing through the atmosphere, and to test the method under 'synthetic atmospheres'.

Automated selection of suitable atmospheric calibration sites for satellite imagery

Robin Wilson, Edward Milton (Investigators)

Ground calibration targets (GCTs) play a vital role in atmospheric correction of satellite sensor data in the optical region, but selecting suitable targets is a subjective and time- consuming task. This project is developing methods to automatically select suitable GCTs, using a combination of remotely sensed multispectral and topographic data.

Simulating Sleeping Sickness: a two-host agent-based model

Jason Noble, Peter Atkinson (Investigators), Simon Alderton

Sleeping sickness is a vector-borne, parastic disease which affects millions of people across 36 sub-Saharan African countries. Using agent-based models, we aim to gain a greater understanding of the interactions between the tsetse fly vector and both animal and human hosts.

Building an accurate representation will allow the testing of local interventation scenarios including the closing of watering holes, and the selective spraying of cattle with insecticides.

Spatial variability of the atmosphere in southern England

Joanna Nield, Jason Noble, Edward Milton (Investigators), Robin Wilson

No-one really knows how variable key atmospheric parameters such as Aerosol Optical Thickness and Water Vapour content are over relatively small areas. This study aims to find out!

The application of automated pattern metrics to surface moisture influences on modelled dune field development

Robin Wilson, Joanna Nield (Investigators)

Areas of sand dunes (known as dunefields) develop complex patterns over time. These are influenced by both the past and present environmental conditions, including surface moisture, vegetation distribution and human impact. This project develops a method of automated pattern analysis which allow the patterns produced by a large number of sand dune evolution simulations (performed using the DECAL model) to be quantified over time.

Validation of GPS-derived water vapour estimates

Joanna Nield, Jason Noble, Edward Milton (Investigators), Robin Wilson

Measurements from GPS base stations can be processed to provide estimates of the water vapour content in the atmosphere. These are lots of these base stations across the world and they take measurements very frequently, making them perfect data sources for scientific use. However, we need to understand their accuracy - and this project aims to do this.


Peter Atkinson
Professor, Geography (FSHS)
Edward Milton
Professor, Geography (FSHS)
Reno Choi
Senior Research Fellow, Geography (FSHS)
Jason Noble
Research Fellow, Electronics and Computer Science (FPAS)
Robin Wilson
Research Fellow, Geography (FSHS)
Simon Alderton
Postgraduate Research Student, Geography (FSHS)
Petrina Butler
Administrative Staff, Research and Innovation Services