Integrating measures of grassland function using Remote Sensing

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Grasslands provide vital global ecosystem services, are sensitive to disturbance and invasion, and prone to rapid functional collapse. Critically, grassland ecosystems are carbon sinks or sources depending upon management practices largely surrounding cropping and grazing. Grasslands worldwide have been degraded in recent decades due to intense human activity and climate change and this degradation not only threatens ecosystems but negatively affects ecosystem services. The pressures from degradation emphasize the need to understand, evaluate, and monitor grassland ecosystems. For decades, studies have used remote sensing tools to determine solitary measures of grassland growth and productivity. These include leaf area index, biomass, canopy height and cover, structural heterogeneity, and burn and grazing effects. While this approach has advanced our understanding of how to monitor specific elements of the grassland community in isolation, a barrier to continued progress in assessing community effects of herbivory and human use for ecosystem services is a comprehensive assessment of vascular, non-vascular, biotic and abiotic features of grasslands. A comprehensive grassland assessment is hindered by uncertainties of optical grassland remote sensing resulting from existing non-photosynthetic vegetation (NPV) and biological soil crust (BSC), and constrained by lack of an effective approach to quantify NPV and BSC. In addition, NPV is critical for determining herbivore carrying capacity (HCC) in winter; however, it is an understudied field for remote sensing. Thus, to make the next important advance in large scale grassland ecosystem monitoring and conservation using remote sensing tools, I propose to focus on BSC and NPV and integrate this with established spectral relationships. This will open the door to large advances in regional scale, multi-season measurement, monitoring and conservation through carrying capacity estimation. The proposed program will address three critical research gaps by (1) evaluating the total effect of NPV, BSC, and bare soil on estimating grassland productivity; (2) developing an approach for quantitatively estimating NPV biomass and BSC cover using a remote sensing in mixed grasslands; and (3) integrating NPV into spatiotemporally explicit HCC estimation using remote sensing. The results of this research will also contribute to an understanding of carbon sequestration (affecting climate change), grassland ecology, and grassland hydrology.