An alarming rate of forest cover loss combined with more frequent extreme climate events strongly increases the pressure on tropical forest ecosystems. Satellite remote sensing emerged as the primary tool to monitor forest dynamics, and annual forest cover loss is monitored operationally (Hansen et al., 2013).
The constant deforestation in global forests has led to increasing carbon emissions rates. Because forests help trap vast amounts of carbon, a principal greenhouse gas spurring on climate change, rapid loss of forest in places like the Amazon has hiked up.
We know that much of historical deforestation occurred in the low lands (flat areas) due to the easy access. Due to increasing population growth and related hunger for resources, we hypothesize that over the past 20 years deforestation activities may shifted to increasing slopes and higher altitudes.
For this research global annual forest cover loss information are available (Hansen et al., 2013) will provide the basis for your research. Curtis et al (2018) has classified this forest cover loss (from 2000-2015) according to five specific disturbance types: commodity-driven deforestation, forestry, wildfire, urbanization, or shifting agriculture.
Additionally, large-scale products on e.g. population density, GDP and forest type information are available and should be included in the analysis to investigate underlying drivers.
- Analyze topography dependency of global forest cover loss and compare for different regions, and different disturbance types.
- Assess changes of patterns over the past 20 years
- Study if changes of patterns can be explained by underlying drivers (e.g. population density)
- Hansen, M., Potapov, P., & Moore, R. (2013). High-resolution global maps of 21st-century forest cover change. Science, 134, 2011–2014.
- Curtis et al., (2018). Classifying drivers of global forest loss. Science, Vol. 361, Issue 6407, pp. 1108-1111. DOI: 10.1126/science.aau3445
- 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