MSc thesis subject: Assessing the relation between peatland vegetation patterns and climate using remote sensing

Boreal peatlands are wet ecosystems containing large amounts of carbon, stored in the form of dead plant remains. When the climate changes, this carbon may be released into the atmosphere, further accelerating climate change. Remote sensing plays a key role in monitoring and predicting how exactly peatland ecosystems will respond to climate change.


The vegetation of boreal peatlands determines the rate of carbon accumulation and hence is an important characteristic to monitor. Moreover, when viewed from air, vegetation of peatlands show a remarkably systematic patterned structure. This patterned structure is believed to result from underlying processes and to contain information about the sensitivity of peatlands to changes in climate and land use. Will the transition occur smoothly or does it exhibit ‘tipping point’ behaviour, which shows large consequences from small changes in climate, occur unpredictably and are hard to reverse. So far, the relation between climate and pattern characteristics remains yet to be explored.


  • Determine how vegetation patterns change along a climatic gradient in Sweden/Finland using classified high-res digital aerial images (RGB + IR) and terrain models.
  • Derive vegetation pattern properties (such as connectivity, patch size, aggregation)
  • Use current and future climate scenarios to find out how vegetation patterns may shift due to climate change


  • Experience with programming (e.g. R) and working with big spatial datasets is valuable but not necessary.

Theme(s): Sensing & measuring; Integrated Land Monitoring