Msc thesis subject: Analysing the impact of displaced people on the natural environment around refugee camps

The drivers and impacts of displacement are intimately linked to FAO’s global goals of fighting hunger and achieving food security, reducing rural poverty and promoting the sustainable use of natural resources, including forests and other woodlands. Safe Access to Fuel and Energy (SAFE) initiative is a step towards strengthening resilience partnerships to support displaced people and to improve the sustainable management of natural resources in these contexts. This research supports the SAFE initiative by aiming to improve forest loss detection in dry woody environments.

In the last decade, time-series analysis of satellite data has been evaluated as a tool for detecting and monitoring forest change in all forest environments across the world. The availability of frequent, free and open access data from MODIS since 2000, and the opening of the Landsat archive in 2008, pushed the development of multiple specialized time-series analysis algorithms using satellite data for detecting forest change [1][2].

One of the state-of-the-art methods to detect abrupt and gradual changes in time-series is the break detection for additive season and trend (BFAST [3] ) approach and it’s derivate BFAST Monitor [4]. This family of methods was developed for detecting vegetation change in 16-day MODIS composite time series, was later modified to detect drought-related vegetation disturbance in near real-time using MODIS time series, and, in the last years, has been applied to detect forest change in Landsat time series [5],[6],[7]. The algorithm was tested across multiple forest environments with satisfying results, nevertheless dry woody environments still represent a challenge for detecting and quantifying forest loss.


  • Test the performance of BFAST Monitor on Landsat time-series in dry woody environments across SAFE sites in Africa
  • Research optimisation options for improving the forest loss detection in dry woody environments using BFAST Monitor on Landsat time-series.


  • [1] A. Banskota, N. Kayastha, M. J. Falkowski, M. A. Wulder, R. E. Froese, and J. C. White, “Forest Monitoring Using Landsat Time Series Data: A Review,” Can. J. Remote Sens., vol. 40, no. 5, pp. 362–384, 2014.
  • [2] Z. Zhu, “Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications,” ISPRS J. Photogramm. Remote Sens., vol. 130, no. July, pp. 370–384, 2017.
  • [3] J. Verbesselt, R. Hyndman, G. Newnham, and D. Culvenor, “Detecting trend and seasonal changes in satellite image time series,” Remote Sens. Environ., vol. 114, no. 1, pp. 106–115, 2010.
  • [4] J. Verbesselt, A. Zeileis, and M. Herold, “Near real-time disturbance detection using satellite image time series,” Remote Sens. Environ., vol. 123, pp. 98–108, 2012.
  • [5] B. DeVries, J. Verbesselt, L. Kooistra, and M. Herold, “Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series,” Remote Sens. Environ., vol. 161, pp. 107–121, 2015.
  • [6] M. Schultz, J. Verbesselt, V. Avitabile, C. Souza, and M. Herold, “Error Sources in Deforestation Detection Using BFAST Monitor on Landsat Time Series Across Three Tropical Sites,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., no. January 2016, 2015.
  • [7] E. Hamunyela, J. Reiche, J. Verbesselt, and M. Herold, “Using space-time features to improve detection of forest disturbances from Landsat time series,” Remote Sens., vol. 9, no. 6, 2017.


  • R scripting knowledge
  • Good communication skills as the project requires direct contact with FAO

Theme(s): Sensing & measuring, Integrated Land Monitoring