Net Primary Productivity Response to Climate Change in the Mount Kenya Ecosystem

This chapter evaluates methods to estimate terrestrial ecosystem carbon assimilation and to monitor it over time at both local and national scales. This study presents simulated models that rely on vegetation indices and cover from Landsat 7 ETM sensors and vegetation stress scalars of temperature and moisture generated from the Thornthwaite water balance model. Temperature and moisture are the main climatic variables that affect vegetation productivity in the context of climate change at the landscape level. The temperature stress scalar in this study was derived from the seasonal optimal temperature for plant production from 28 years of climate data, while water stress was estimated from monthly water deficits, based on comparison of observed moisture supply (precipitation) to potential evapotranspiration (PET) demand derived using the Thornthwaite and Mather method (1957). The Carnegie-Ames-Stanford approach (CASA) model was used to estimate monthly patterns of carbon fixation using 30 M resolution satellite remote sensing images of the surface vegetation characteristics and driven by spatially interpolated climate data. IPCC climate general circulation models (GCM) scenarios were downscaled using a statistical downscaling model (SDSM) to generate locally useful data for impact analysis. Different vegetation classes show significant levels of sensitivity to temperature and moisture variability depicted by the varied net primary productivity (NPP) levels. Indigenous vegetation is well adapted and generally exhibited high NPP around Mount Kenya. This method can be useful in monitoring and evaluating ecosystem function of carbon sequestration and in facilitating optimisation of reducing carbon emissions from deforestation and degradation (REDD) through vegetation selection in forestry programmes.