Quantifying the influences of grazing, climate and their interactions using Landsat TM images
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Appropriate grazing management ensures sustainable productivities of grassland ecosystems while maintaining grassland services. Thus, it is important to understand the influences of grazing management on grassland ecosystems, which can be monitored by measuring grassland response (e.g. Leaf area index [LAI]) to grazing management. However, the measured grassland response includes the impact not only of grazing management, but also of other factors and their interactions, such as climate variability and fire. Therefore, to better study the effects of grazing management, grassland response to grazing needs to be quantified separately from that of other factors which influence grasslands and their interactions. The aim of our research was to quantify these interactions using Landsat TM images with long-term datasets at a regional scale. We studied vegetation using a manipulative grazing experiment that applied a range of low to high cattle stocking rates from 2008 to 2011 in a northern native mixed-prairie in Saskatchewan. Results show that precipitation, temperature, interaction between temperature and precipitation, cattle density, interaction between temperature and cattle density, and the interaction among cattle density and climate parameters explained 65.5, 14.5, 9.8, 1.7, 1.4 and 0.5% of the variation in grassland LAI, respectively; thus, precipitation has the dominant effect in mixed-grass prairies, while temperature and the interaction between temperature and precipitation have only moderate effects, and grazing intensity and the interaction between grazing intensity and climate variations have relatively low effects. The results also suggest that grassland response to grazing can be quantitatively separated from that of climate variability with the prior knowledge of grazing intensity, even though the influences of precipitation on LAI overrode the effects of short term grazing.
Climate fluctuation; grazing; leaf area index; perpendicular vegetation index; remote sensing
25. Xu, D., Koper, N., and Guo, X. 2017. Quantifying the influences of grazing, climate and their interactions using Landsat TM images. Grassland Science. 64: 118-127