Analysing the impact of large mammal herbivores on vegetation structure in Eastern African savannas combining high spatial resolution multispectral remote sensing data and field observations

It is well understood, that grazing and browsing in the African savanna ecosystem modulates tree-grass ratios. However, many of the large mammals are under pressure due to land use and climate change. It is challenging to predict how their altered abundance or range shifts will interact with savanna structure. Herbivore exclusion experiments can help to better understand the impacts of herbivores of different sizes on vegetation structure and composition, including interactions with rainfall. Here, we combine field data of the Ungulate Herbivory Under Rainfall Uncertainty (UHURU) exclusion experiment and high spatial resolution satellite images of the PlanetScope and Sentinel-2 series to investigate the impacts of herbivores on vegetation structure in a Kenyan savanna. Field data as well as NDVI values derived from Sentinel-2 and NDVI contrast values of PlanetScope show that presence of herbivores lowers vegetation cover and modify the woody vegetation structure depending on which herbivores are present. The vegetation grew tallest when mega-sized herbivores were absent but meso-sized and small herbivores were present, which resulted in high NDVI contrast values. The absence of herbivores resulted in fewer bare ground patches and increased green biomass, such as a higher mean canopy width, which led to higher NDVI values. Few studies have explored the potential of passive remote sensing data to assess herbivory impacts beyond the plot scale and over longer time-periods; however, these previous studies solely focused on the NDVI. Here we demonstrate the added value of also using GLCM texture measures to investigate effects on a savanna ecosystem in response to presence or absence of herbivores. Combining these data with plot measurements our study demonstrates the benefits of combining field and space perspectives in ecosystem studies.