UCL Department of Geography
GEOGG124 Terrestrial Carbon: Modelling and Monitoring
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GEOGG124 Terrestrial Carbon: Modelling and Monitoring

(15 credits)

Term 2 (2014)


Prof. Philip Lewis


The Terrestrial Carbon: modelling and monitoring module aims:

  • To outline the role of vegetation in the carbon cycle and the wider climate system
  • To outline how the vegetation carbon cycle can be modelled and use the models in prediction
  • To provide the linkages between the models and remote sensing observations (radiative transfer)
  • To enable the students to use remote sensing (and other) data to constrain, test and criticise the models
  • To expose the students to modern statistical methods in combining data and models


The module will cover:

  • The role of vegetation in the climate system
  • Terrestrial vegetation dynamics modelling
  • Remote sensing of vegetation
  • Radiation interactions with vegetation
  • Model inversion in remote sensing
  • Concepts and maths of data assimilation
  • Using remote sensing data to constrain and test vegetation dynamics models


2 hour unseen exam, 100% of the assessment.


The module will be delivered through:

  • Lectures (3 hour sessions providing concepts, contexts, and critiques)
  • Computer laboratory work (extended practical sessions progressing technical aspects of model implementation and options hands-on experience of relevant software). Practicals will initially be based around specific vegetation models and EO radiative transfer schemes, but also include advanced concepts such as data assimilation.
  • Moodle resources (hosting reading lists, lecture handouts, datasets, guides and practical support materials)

Learning outcomes:

At the end of the module, students should:

  • Appreciate the role of vegetation in the carbon cycle and the climate system
  • Appreciate the role, strengths and weaknesses of models of global vegetation processes
  • Understand the factors affecting remote sensing measurements of vegetation (radiative transfer theory)
  • Understand how to use models and observations in combination to improve estimates of carbon fluxes and pools
  • Have an understanding of data assimilation