Year-round Monitoring of Vegetation Conditions in an East African Rangeland: Implications for Livestock Forage Production in Response to Climate Variability and Local System Shocks

Abstract:

Rangelands are globally the primary domain of agropastoral activity. This includes Sub-Saharan Africa where they support the basic livelihoods of millions; by providing livestock forage in the form of woody leaf and herbaceous plant material. However, seasonal biomass production in rangelands is vulnerable at regional scales to the high degree of rainfall variability and risk of droughts, and at local scales to sustained land cover/land use change and local perturbances (e.g. fire, locusts). We present a suite of prototyped earth observations tools, developed using Google Earth Engine, that allow for efficient monitoring of vegetation conditions in an East African (Kenya) rangeland environment. Our applications combine proof of concept algorithms relevant to assessing annual tree/shrub canopy cover using Sentinel 1 (microwave) and Sentinel -2 (optical) data, as well as wet season (leaf production) and dry season (biomass decline) vegetation dynamics. For wet season dynamics, we apply previously developed methods for partitioning satellite derived leaf area index (LAI) into woody and herbaceous components, constrained by the proportion of woody plants provided by independent canopy cover data. For monitoring dry season biomass decline, we employ satellite derived greenness and short wave infra-red spectral band information to distinguish photosynthetic, non-photosynthetic vegetation and bare soil proportions in a machine learning framework. The combined suite of applications allows for a continuous survey of plant biomass relevant to both grazing and browsing livestock. The high cadence (weekly) and low latency (< 2 weeks) of outputs should enhance rangeland resource management, bolster livestock insurance programs, and allow of detection and mitigation of food security risks.