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The University of Southampton

GEOG3065 Terrestrial Ecosystems: Carbon Modelling and Monitoring

Module Overview

The module will provide understanding of current ( and future) biophysical products derived from remote sensing data and how they are being used in regional to global scale monitoring of current vegetation function and condition.The module will expose students to a range of advanced data analysis methods to extract quantitative biophysical information from remote sensing data and how to use them as input to ecosystem models. The module will enable students to link these methods and techniques to investigate some of the major societal challenges (e.g. food security through estimation of crop yield) and implications of changes in climatic condition on terrestrial ecosystem.

Aims and Objectives

Module Aims

To outline the role of vegetation in terrestrial ecosystem processes in particular cycling carbon between soil and atmosphere and how remote sensing data can be used to monitor and model such processes.

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Understand how vegetation biophysical variables can be derived from remote sensing data.
  • Understand the need for pre-processing and will be able to assess the pros and cons of different methods.
  • Develop simple models and calibration and validation processes.
  • Critically assess published papers, particularly their choice of pre-processing methods, including calibration and validation


The module starts with basic understanding of spectral reflectance characteristics to derive biophysical products from satellite data and their validation. It also introduces the concepts of Quality Assessments of remote sensing products, advances image analysis methods and modelling to understand Terrestrial ecological processes. There is also an opportunity to have an ‘industry led’ session to understand the processes of developing operational products/services from remote sensing data. Specific topics include: 1. Operational use of remote sensing data 2. Estimation of vegetation biophysical variables from satellite data 3. Validation of satellite derived vegetation biophysical products 4. Methods to enhance spatial and spectral information from remote sensing data 5. Analysis of time series of remote sensing data 6. Using remote sensing to estimate regional scale agriculture production 7. Modelling terrestrial vegetation primary productivity

Learning and Teaching

Teaching and learning methods

A combination of teaching and learning methods are employed to provide students with the necessary knowledge, structure and opportunities to achieve the learning outcomes. Lectures provide a sound knowledge base and structure for further independent study. In order to demonstrate a high level of achievement of the learning outcomes, students are expected to supplement the knowledge gained from lectures with further independent study (primarily reading of the literature). Practical computing sessions provide students with opportunities to develop practical skills and to link theory with practice. Attendance and full participation in all elements of the unit (lectures, practical and independent study) is essential if students are to achieve the learning outcomes to a high level. Whilst self-paced learning is to be encouraged, students should note that the practical sessions are deliberately timetabled to fit in with the lecture programme; thus students are likely to benefit most by undertaking the practical in the order that they are provided and at the scheduled times. Students should also view the practical sessions as the main opportunity to seek clarification from teaching staff on any matters about which they are not clear.

Independent Study128
Total study time160



MethodPercentage contribution
Poster Presentation 20%
Report  (2500 words) 50%
Report  (1500 words) 30%


MethodPercentage contribution
Coursework assignment(s) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisite: GEOG2007 or GEOG3032

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