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

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Livestock accounts for 37.5% of Kenya's land area, 12% of its GDP and 40% of itsagricultural sector, but is susceptible to frequent droughts and degradation due toovergrazing. In this context, this Sussex University-led work will assess the potential ofnew earth observation (EO) datasets (e.g., Sentinel 1 and 2) to deliver near real-timemonitoring and prediction of useful and accessible biomass for pastoralism.The accessibility of pasture areas will be mapped using a participatory approach withlocal 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), andthen tracking biomass dynamics, plant health, and phenological cycles of these PFTs. PFTsin pastures include annual and perennial grasses and deciduous and evergreen shrubs. Thisclassification will be achieved using spectral mixture analysis or machine learningtechniques, taking advantage of spectral and temporal differences in Sentinel's spectra.Pasture dynamics will be monitored from i) biomass dynamics relating Sentinel-1 radarbackscatter and interferometric height with ground biomass observations; ii) vegetationhealth and stress using vegetation and water indices (e.g., NDVI, NDWI), as well asmonitoring 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 regularlywith the real-time update of EO and climate data. Changes to phenological cycles, biomassand 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 asautoregressive moving average methods, Gaussian Processes in combination with seasonalclimate forecast