The intensification of aridity due to anthropogenic climate change is likely to have a large impact on the growth and survival of plant species in the southwestern U.S. where species are already vulnerable to high temperatures and limited precipitation. Global climate change impacts plants through a rising temperature effect, CO2 effect, and land management. In order to forecast the impacts of global climate change, it is necessary to know the current conditions and create a baseline for future comparisons and to understand the factors and players that will affect what happens in the future. The objective of Chapter 1 is to create the very first high resolution, accurate, park-wide map that shows the distribution of dominant plants on the Colorado Plateau and serves as a baseline for future comparisons of species distribution. If we are going to forecast what species have already been impacted by global change or will likely be impacted in the future, we need to know their physiology. Chapter 2 surveys the physiology of the twelve most abundant non-tree species on the Colorado Plateau to help us forecast what climate change might do and to understand what has likely already occurred. Chapter 1. Our objective was to create an accurate species-level classification map using a combination of multispectral data from the World View-3 satellite and hyperspectral data from a handheld radiometer to compare pixel-based and object-based classification. We found that overall, both methods were successful in creating an accurate landscape map. Different functional types could be classified with fairly good accuracy in a pixel-based classification but to get more accurate species-level classification, object-based methods were more effective (0.915, kappa coefficient=0.905) than pixel-based classification (0.79, kappa coefficient=0.766). Although spectral reflectance values were important in classification, the addition of other features such as brightness, texture, number of pixels, size, shape, compactness, and asymmetry improved classification accuracy.Chapter 2. We sought to understand if patterns of gas exchange to changes in temperature and CO2 can explain why C3 shrubs are increasing, and C3 and C4 grasses are decreasing in the southwestern U.S. We conducted seasonal, leaf-level gas exchange surveys, and measured temperature response curves and A-Ci response curves of common shrub, forb, and grass species in perennial grassland ecosystems over the year. We found that the functional trait of being evergreen is increasingly more successful in climate changing conditions with warmer winter months. Grass species in our study did not differentiate by photosynthetic pathway; they were physiologically the same in all of our measurements. Increasing shrub species, Ephedra viridis and Coleogyne ramosissima displayed functional similarities in response to increasing temperature and CO2.

The intensification of aridity due to anthropogenic climate change is likely to have a large impact on the growth and survival of plant species in the southwestern U.S. where species are already vulnerable to high temperatures and limited precipitation. Global climate change impacts plants through a rising temperature effect, CO2 effect, and land management. In order to forecast the impacts of global climate change, it is necessary to know the current conditions and create a baseline for future comparisons and to understand the factors and players that will affect what happens in the future. The objective of Chapter 1 is to create the very first high resolution, accurate, park-wide map that shows the distribution of dominant plants on the Colorado Plateau and serves as a baseline for future comparisons of species distribution. If we are going to forecast what species have already been impacted by global change or will likely be impacted in the future, we need to know their physiology. Chapter 2 surveys the physiology of the twelve most abundant non-tree species on the Colorado Plateau to help us forecast what climate change might do and to understand what has likely already occurred. Chapter 1. Our objective was to create an accurate species-level classification map using a combination of multispectral data from the World View-3 satellite and hyperspectral data from a handheld radiometer to compare pixel-based and object-based classification. We found that overall, both methods were successful in creating an accurate landscape map. Different functional types could be classified with fairly good accuracy in a pixel-based classification but to get more accurate species-level classification, object-based methods were more effective (0.915, kappa coefficient=0.905) than pixel-based classification (0.79, kappa coefficient=0.766). Although spectral reflectance values were important in classification, the addition of other features such as brightness, texture, number of pixels, size, shape, compactness, and asymmetry improved classification accuracy.Chapter 2. We sought to understand if patterns of gas exchange to changes in temperature and CO2 can explain why C3 shrubs are increasing, and C3 and C4 grasses are decreasing in the southwestern U.S. We conducted seasonal, leaf-level gas exchange surveys, and measured temperature response curves and A-Ci response curves of common shrub, forb, and grass species in perennial grassland ecosystems over the year. We found that the functional trait of being evergreen is increasingly more successful in climate changing conditions with warmer winter months. Grass species in our study did not differentiate by photosynthetic pathway; they were physiologically the same in all of our measurements. Increasing shrub species, Ephedra viridis and Coleogyne ramosissima displayed functional similarities in response to increasing temperature and CO2.