- Who We Are
- How We Work
- Our Science
- News & Events
- Find a Scientist
- Become a Member
University of Sussex
Sunday, October 15, 2017
Friday, December 1, 2017
Livestock accounts for 37.5% of Kenya’s land area, 12% of its GDP and 40% of its agricultural sector, but is susceptible to frequent droughts and degradation due to overgrazing. In this context, this Sussex University-led work will assess the potential of new earth observation datasets (e.g., Sentinel 1 and 2) to deliver near real-time monitoring and prediction of useful and accessible biomass for pastoralism. The accessibility of pasture areas will be mapped using a participatory approach with local stakeholders to identify issues related to land tenure, conservation requirements, migration patterns, water, and other socio-cultural factors.
Monitoring of useful biomass will rely on mapping major plant functional types (PFTs), and then tracking biomass dynamics, plant health, and phenological cycles of these PFTs. PFTs in pastures include annual and perennial grasses, and deciduous and evergreen shrubs. This classification will be achieved using spectral mixture analysis or machine learning techniques, taking advantage of spectral and temporal differences in Sentinel’s spectra. Pasture dynamics will be monitored from i) biomass dynamics relating Sentinel-1 radar backscatter and interferometric height with ground biomass observations; ii) vegetation health and stress using various vegetation and water indices (e.g., NDVI, NDWI), as well as monitoring plant photosynthetic activity using the three red-edge Sentinel-2 bands; iii) phenology through fitting logistic functions on various indices.
Finally, predictive models of pastures will be developed which will be updated regularly with the real-time update of earth observation and climate data. Changes to phenological cycles, biomass and plant health will be related to meteorological variables and other geospatial datasets (e.g. soil texture, livestock). Forecasting will rely on time-series analyses such as autoregressive moving average methods, Gaussian Processes, or Kalman filter techniques, in combination with seasonal climate forecasts.
The outcome of this research will support pastoralists communities in Kenya, to decide the suitability and location of pastureland for their various livestock through: a) improved understanding of spatio-temporal distribution of pastures; b) improved understanding of ecological changes and resilience of pastures; and c) near-future predictions of pasture suitability. This will enhance their livelihood resilience in the wake of large and extensive droughts, overgrazing, and land cover change.
The project is a direct complement to the ForPAc project, and in addition to working with several institutions involved in ForPAc, this PhD will also interact significantly with the International Livestock Research Institute (ILRI), and researchers from the University of Leicester.
Skills and experience:
This project is suitable for students with a degree in physical geography, ecology or environmental science with some experience in using earth observation data. Students will be mainly working in a unix programming environment with R, python or similar, previous experience is desired but not essential.
Eligibility and funding:
Students must hold an undergraduate degree (equivalent of upper second-class honours) and preferably a Masters qualification in relevant disciplines. See http://www.sussex.ac.uk/study/phd/degrees/global-studies/geography-phd for full details of entry requirements for both home and overseas students, including language.
The studentship offers a stipend of approximately £ £14553 per annum (tax free) and covers fees at the UK/EU or overseas student rate for a period of three years with a possible further half year extension.