Resilience and pastoralism: satellite-based decision support system for pastures

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 (EO) 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 vegetation and water indices (e.g., NDVI, NDWI), as well as
monitoring plant photosynthetic activity using the 3 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 EO 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 in combination with seasonal
climate forecast