The University of Southampton

ENVS6029 Environmental Modelling & Spatial Analysis

Module Overview

Almost all environmental phenomena vary spatially and over time. Some varied examples would be the spread of a pollutant from a point source, the occurrence of an infectious disease, the global threats from earthquakes, and the incidence of elephant poaching. All of these examples include an undesirable element and the assessment of the risk of something happening that we as humans do not like is a common feature of many environmental models. One of the greatest challenges in environmental science is therefore to model where things happen, why and what will change in the future. The purpose of this module is to introduce you to the world of environmental modelling where there is a strong spatial, and often temporal, element leading to some assessment of risk. Work of this nature is at the cutting-edge of environmental research because it has huge implications for the planning of environmental futures e.g. at government level in the setting of policy surrounding climate change adaptation, to biodiversity conservation in predicting the future of threatened species, and in the insurance industry in relation to assessing the risks of hazards. In order to bring you up to the level where you are able to run analyses of real practical value, this module will focus on just two examples that build your skill set week by week so that on completing the module, you will be capable of undertaking new work in new settings unaided. This will provide you with a strong basis for employment in environmental modelling and spatial analysis, or the foundation for starting advanced research.

Aims and Objectives

Module Aims

The overall aim of this specialist module is to introduce students to environmental modelling in a spatial context, working at a sufficient level to tackle real environmental issues rather than simply academic exercises. The module builds on a basic knowledge of GIS that the student must have acquired through a previous module and assumes a basic understanding of data handling and manipulation techniques, for example, using Excel. By the end of studying this module, students will: 1) Appreciate the variety of modelling approaches used in environmental science. 2) Understand why spatial modelling is particularly valuable yet also challenging. 3) Know the sources of spatial environmental data and how to manipulate them. 4) What species distribution modelling is and how it works by coupling advanced statistical procedures with GIS. 5) How to forward project climate based species distribution models in order to explore possible future effects of climate change. 6) How data mining and exploratory tools are used to find associations between global patterns in environmental phenomena. 7) How land use changes may be modelled and their effects forecast.

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • The need for both a multi-disciplinary and an interdisciplinary approach in advancing knowledge and understanding of Earth systems, drawing, as appropriate, from the natural and the social sciences
  • The processes which shape the natural world at different temporal and spatial scales and their influence on and by human activities
  • The terminology, nomenclature and classification systems used in environmental science
  • Methods of acquiring, interpreting and analysing environmental science information with a critical understanding of the appropriate contexts for their use
  • Issues concerning the availability and sustainability of resources, for example, the different value sets relating to the Earth's resources as commodities and/or heritage
  • The contribution of environmental science to debate on environmental issues and how knowledge of these forms the basis for informed concern about the Earth and its people
  • The contribution of environmental science to the development of knowledge of the world we live in
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Recognising and using subject-specific theories, paradigms, concepts and principles
  • Analysing, synthesising and summarising information critically, including prior research
  • Applying knowledge and understanding to complex and multidimensional problems in familiar and unfamiliar contexts
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • Communicating appropriately to a variety of audiences in written, verbal and graphical forms
  • Appreciating issues of sample selection, accuracy, precision and uncertainty during collection, recording and analysis of data in the field and laboratory
  • Preparing, processing, interpreting and presenting data, using appropriate qualitative and quantitative techniques and packages including geographic information systems
  • Solving numerical problems using computer and non-computer-based techniques
  • Using the internet critically as a means of communication and a source of information
  • Developing the skills necessary for self-managed and lifelong learning (e.g. working independently, time management and organisation skills)
  • Identifying and working towards targets for personal, academic and career development
  • Developing an adaptable and flexible approach to study and work
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Planning, conducting, and reporting on environmental investigations, including the use of secondary data
  • Collecting, recording and analysing data using appropriate techniques in the field and laboratory
  • Referencing work in an appropriate manner


The module is built around two projects which are carried out in detail. Topic 1: Modelling the present and future distributions of a threatened species: Species distribution modelling (SDM) has grown from a research approach practised by a few specialists just 20 years ago, to a mainstream activity in biodiversity conservation and disease risk forecasting. New software, data layers and high speed computers nowadays make it easy to build models - and equally easy to misunderstand and misuse the outputs. The focus of this topic is therefore on understanding and incorporating the uncertainties in the model-building process. Over a six week period, we will build a spatial model for the distribution of a species, analyse what drives the model and why, and then project it forwards to forecast the effects of global climate change. We will then analyse what the changes mean for the species and global conservation policy. Topics to be covered: 1. Obtaining and processing species data for model building. Sources of data; handling duplicates; spatial errors; spatial resolution and sampling period. 2. Obtaining and processing environmental data layers. Sources of data; spatial errors; resampling to common resolutions and coordinate systems; vector to raster conversions. 3. Niche theory and modelling algorithms. Pros and cons; Maximum Entropy (Maxent) modelling; passing GIS data in and out of Maxent; building basic Maxent models. 4. Advanced Maxent modelling. Response curves with and without interaction; MESS plots; spatial analysis of critical variables; setting thresholds on binary variables. 5. Modelling the effects of climate change. SRES and global climate models; sources of data; incorporating uncertainty. 6. Measuring change and its implications. Comparing distributions; map comparison software; identifying global responsibility shifts. Topic 2: Modelling global land cover change and trends in earth surface phenomena: Satellite monitoring of the earth's surface and atmosphere is yielding unprecedented quantities of data in the form of image time series (i.e. repeated snapshots of the earth over time). Careful inspection and analysis of these data allows us to explore the connections between phenomena (e.g. El Niño and precipitation patterns) and model future trends (e.g. in land cover) in ways that were almost impossible just a few years ago. Such analysis is, of course, complex and involved, but the Idrisi GIS package has automated a unique suite of very advanced, highly sophisticated tools for the purpose in its Earth Trends Modeler and Land Change Modeler applications. Topic 2 will focus on the Land Change Modelers and the module content will include: 1. Obtaining and pre-processing environmental time series. Sources of data; spatial errors; mixing sensors; resampling to common resolutions and coordinate systems. 2. Maximum likelihood methods for land cover classification based on pixels and objects. 3. Using the Markov Chain assessments of change and the Multilayer Perceptron for modelling causes of land cover change. 2. REDD and the location of protected areas for carbon sequestration. 3. Using the outputs. Interpreting the model outputs; using them for environmental policy.

Learning and Teaching

Teaching and learning methods

This module uses lectures followed by double practical slots where the principles learned are applied on the computer as its principal mode of delivery. Independent learning will be supported by recommended text books, original scientific papers and internet resources. Feedback will be obtained weekly through practical exercises, through direct and e-mail contact with the lecturer and demonstrators, and through information placed on Blackboard. Self-directed practical training will be by completion of the exercises set in each practical class, building towards the assessed reports.

Preparation for scheduled sessions6
Follow-up work30
Wider reading or practice18
Completion of assessment task60
Practical classes and workshops24
Total study time150

Resources & Reading list

Teaching space, layout and equipment required Lecture rooms: One hour per week.. Two hours per week (double slot). Desktop PCs with as much RAM as possible Idrisi Selva or TerrSet installed and kept up to date as patches are released One demonstrator needed per 10 students.

Peterson, A.T. et al. (2011). Ecological Niches and Geographic Distributions. 

Franklin, J. (2010). Mapping Species Distributions: Spatial Inference and Prediction (Ecology, Biodiversity and Conservation).. 

In addition, students are advised to consult the online help files and available documentation for Idrisi Selva or TerrSet.. 

Computer rooms:. 



MethodPercentage contribution
Report  (4500 words) 60%
Report  (3500 words) 40%


MethodPercentage contribution
Revised Report  (3500 words) 40%
Revised Report  (4500 words) 60%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisite: ENVS2008 - GIS for Environmental Scientists.

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