Student information

MSc thesis subject: Analysing spatial and temporal distribution of forest cover loss in the Congo Basin

An alarming rate of forest cover loss combined with more frequent extreme climate events strongly increases the pressure on tropical forest ecosystems. The Congo Basin is home to the second largest humid tropical forests after the Amazon, performing globally important ecosystem services and providing livelihood to the regional population. An estimated 84% of forest disturbance area in the region is due to small-scale, nonmechanized forest clearing for agriculture.

Satellite remote sensing emerged as the primary tool to monitor forest dynamics, and has relied predominantly on coarse-to-medium scale resolution (30 – 500 m) optical satellite data. Limited data availability due to persistent cloud cover in the tropics and in many other regions restricts the robust application of optical remote sensing to monitoring at annual time-scales, limiting the ability to track forest change events consistently on a near real-time basis. The lack of spatial resolutions limits the capacity to detect small-scale and complex changes. Satellite radar remote sensing uses long-wavelength energy that penetrates through clouds and is sensitive to changes of forests physical structure. These characteristics are major advantages for monitoring forest dynamics and estimating related biomass stocks.

Annual rates of forest cover loss in the Congo Basin and it’s drivers have already been studied at 30m resolution (Landsat) scale (Tyukavina et al., (2018). While the Landsat-based GLAD alerts provide forest cover loss information at sub-annual resolution, persistent cloud cover leads to large data gaps and delayed detection of deforestation events. This does not allow to study the temporal distribution of deforestation events throughout the year in a robust manner.

New Sentinel-1 radar-based alerts (Reiche et al., 2018) now provide temporally consistent (every 6-12 days) forest cover loss information at high temporal resolution and spatial scale (10 m) (Reiche et al., this group). Based on these information, the spatial (size and location) and temporal resolution of deforestation will be studied in the Congo Basin region.


  • Analyse the spatial and temporal distribution of forest cover loss in the Congo Basin using a sample-based approach
  • Assess the potential of 10 m radar-based alerts to study small scale changes (and compare with other near real-time alerts (e.g. 30m GLAD; 250 m FORMA))


  • Tyukavina et al., (2018). Congo Basin forest loss dominated by increasing smallholder clearing. Science Advances, Vol. 4, no. 11, eaat2993. DOI: 10.1126/sciadv.aat2993 Reiche, J., et al. (2018)
  • Characterizing Tropical Forest Cover Loss Using Dense Sentinel-1 Data and Active Fire Alerts. Remote Sensing, 10, 5, 777, doi:10.3390/RS10050777.


  • Advanced Earth Observation course
  • Geo-scripting course (Good knowledge in scripting is an asset; e.g. R, python, java script)

Theme(s): Modelling & visualisation; Integrated Land Monitoring