Thesis subject
MSc thesis topic: Exploring land cover degradation and recovery using fractions
Time series of Earth Observation data allows monitoring changes in land cover over time, however, detecting gradual changes in land cover has so far been challenging due to the subtle signal of such land cover change. Even though they are often missed, gradual changes are of high importance, as they represent shifts in ecosystems and land use patterns. Some of the most researched gradual changes are forest degradation and recovery, which have major implications on carbon storage of forest ecosystems.
Other gradual land cover changes include vegetation succession events after land abandonment, wetland expansion and degradation, desertification and urban densification. More insight is needed into the prevalence and patterns of such gradual changes over time to better understand the full scale of land cover change globally.
Background
Traditional land cover maps based on discrete classification by definition are unable to capture gradual changes. In contrast, land cover fraction mapping, which tracks the proportion of each land cover class in each pixel, provides an opportunity to analyse gradual change over time. Combining land cover fraction data and change detection algorithms, such as BFAST Lite, allows us to track trends in each land cover class, and visualise and analyse the results at a global scale using cloud computing platforms. This approach can be compared with existing, simpler algorithms for gradual change detection, such as CODED, which uses predefined endmembers to create a vegetation index for change detection, using the CCDC change detection algorithm. Lastly, better understanding of each gradual land cover change process is important for defining the capabilities and limitations of remote sensing to play a role in monitoring degradation and recovery of various land cover classes.
Objectives and Research questions
- What is the prevalence and intensity of gradual land cover change at the global scale?
- What ecological or social processes does each type of gradual land cover change represent? How well can remote sensing approaches quantify such processes?
- How does the performance of gradual change detection based on land cover fraction mapping compare to existing algorithms such as CODED?
Requirements
- Required: Geoscripting, Remote Sensing
- Optional: Advanced Earth Observation
Literature and information
- Gao, Y., Skutsch, M., Paneque-Gálvez, J., & Ghilardi, A. (2020). Remote sensing of forest degradation: a review. Environmental Research Letters, 15(10), 103001.
- Vásquez-Grandón, A., Donoso, P. J., & Gerding, V. (2018). Forest degradation: when is a forest degraded?. Forests, 9(11), 726.
- Bourgoin, C., Ceccherini, G., Girardello, M., Vancutsem, C., Avitabile, V., Beck, P. S. A., ... & Achard, F. (2024). Human degradation of tropical moist forests is greater than previously estimated. Nature, 631(8021), 570-576.
Expected reading list before starting the thesis research
- Masiliūnas, D., Tsendbazar, N.-E., Herold, M., Marcos, D., & Verbesselt, J. (2023). Postprocessing high frequency global land cover maps for change assessment.
- Bullock, E. L., Woodcock, C. E., & Olofsson, P. (2020). Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis. Remote sensing of Environment, 238, 110968. (See also: https://openmrv.org/web/guest/w/modules/mrv/modules_2/continuous-degradation-detection-coded )
- Zang, J., Qiu, F., Zhang, Y. et al. A dataset of forest regrowth in globally key deforestation regions. Sci Data 12, 154 (2025).
- Zang, J., Qiu, F. & Zhang, Y. A global dataset of forest regrowth following wildfires. Sci Data 11, 1052 (2024).
Theme(s): Modelling & visualisation; Integrated Land Monitoring