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, non-mechanized forest clearing for agriculture.
Accurate information on the extend of forest cover is crucial. Existing global and regional forest cover data sets (e.g. Hansen et al., 2013; Martone et al., 2017) show limitations in peatland forest where confusion between forested and non-forested areas (e.g. wetlands) is common.
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 and radar satellite data.
New Sentinel-1 radar and Sentinel-2 optical satellite data now provide dense information on the status of tropical forests. During this thesis, Sentinel radar and optical data will be used in combination with auxiliary datasets (e.g. regional land cover datasets, elevation data) to improve existing forest cover information of tropical peatland forest. Also, the use of L-band SAR Alos Palsar data might be considered. A model (e.g. random forest) will be developed for the Congo Basin and potentially tested in other geographies (e.g. Indonesia, Peru).
All dataset are available in Google Earth Engine.
Very high resolution Planet imagery is available as reference data.
Software: Google Earth Engine
- Develop a model for improved tropical peatland forest cover mapping using Sentinel data and auxiliary datasets
- Generate a training and validation dataset
- Validate the results and assess transferability at other geographies.
- Hansen, M., Potapov, P., & Moore, R. (2013). High-resolution global maps of 21st-century forest cover change. Science, 134, 2011–2014. Martone et al., (2017):
- The global forest/non-forest map from TanDEM-X interferometric SAR data. Remote Sensing of Environment, 205, 352-373. https://doi.org/10.1016/j.rse.2017.12.002
- 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