MSc thesis subject: Characterizing turning points and their drivers in rangeland ecosystems in the last 30 years using time series remote sensing data

Being one of the biggest ecosystem of the Earth, drylands are home to many flora and fauna in addition to supporting livelihoods of millions of people for their food security and wellbeing. At the same time, these areas are highly susceptible to influences caused by climate change and human activities. As a result, significant parts of drylands are reported to be affected by land degradation.
Understanding the importance of desertification and land degradation, the United Nations and other global/regional efforts target to halt and reverse land degradation. They also aim to protect, restore and promote sustainable use of terrestrial ecosystems which can be contributed by reaching the target of the SDG-15.4: End desertification and restore degraded land.

Monitoring the long term changes in the vegetation in dryland areas and understanding its causes can bring light in to decision making and mitigation efforts to end desertification and land restoration. Satellite based earth observation is an important source for such assessments as it provides information on Earth’s surface for several decades. With long term historical remote sensing data, abrupt changes that occurred in dryland vegetation and their trends can be identified.

Furthermore, the drivers of abrupt changes (climate change vs human influence) can further be investigated towards mitigation and adaptation measures.

The aim of this research topic is to analyse and identify abrupt changes (turning points) and their trends in a dryland region of Central Asia and to have a detailed look at the spatial variations in the change drives. The research will focus on time series analyses of long term satellite data (for 30 years) at regional to national scales in combination with spatial data analyses of human activity and climate data.


  • To analyse and identify turning points in dryland ecosystem using 30 years of time series remote sensing data
  • To characterize the drivers of turning points at a regional scale



  • Advanced Earth Observation course
  • Good knowledge in scripting is an asset (e.g. R)
  • Analytical skills

Theme(s): Sensing & measuring, Integrated Land Monitoring, Human – space interaction