(Source: Planet.com)


MSc thesis subject: Exploring drivers of deforestation using very high resolution satellite data (Study site: Peru or Sumatra)

Timely information on deforestation is crucial to effectively manage and protect forest resources in the tropics. Poorly managed and illegal activities (e.g. mining) cause a wide range of negative environmental effects and significant financial losses. Remote sensing is the primary tool for providing near real-time information on the location of newly deforested areas.
Providing the location of newly deforested areas only, however, does not provide insight on the cause/driver of deforestation. Land use following deforestation (as a proxy for the direct driver) is crucial information for understanding the deforestation dynamic and for potential intervention. Providing information on the driver of newly deforested areas as soon as possible after the event was detected is among the major needs of stakeholder in tropical regions.
Now, for the first time, denser high resolution satellite data (Planet.com) are available which have the potential to support the rapid analysis of drivers of deforestation.

With the launch of  >100 micro-satellites, Planet.com provides the first time series of high resolution (4-5 m) satellite images in near real-time.

The research will be conducted in tropical forest environment: Peru or Sumatra. For both sites deforestation alerts exist based on satellite time series analysis.

The aim of this research topic is to develop a method to effectively use the high-resolution satellite data to link key driver of change to the detected deforestation events (e.g. mining vs. agricultural expansion).


  • Develop a method to effectively use the high-resolution satellite data in space and time to link the key driver of change to the detected deforestation events


  • Reiche, J., de Bruin, S., Hoekman, D. H., Verbesselt, J. & Herold, M. (2015): A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection. Remote Sensing, 7, 4973-4996. DOI:10.3390/rs70504973.
  • Hansen, M. C., Krylov, A., Tyukavina, A., Potapov, P. V, Turubanova, S., Zutta, B., … Moore, R. (2016). Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, 11(3), 34008. http://doi.org/10.1088/1748-9326/11/3/034008


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