Plant functional traits capture essential morphological, physiological, or phenological characteristics of plants that influence growth, resource allocation, and survival. These traits can be used to identify plant adaptations to changes in the environment. Functional traits that are indicators of water status in plants can be used to identify adaptative mechanisms to overcome drought. Measuring functional traits using standard approaches is costly, time consuming, and destructive. Vegetation spectroscopy has been shown to estimate a wide variety of plant functional traits, but still can involve extensive field work. Using proximal spectral measurements as a training input for unpiloted aerial vehicle (UAV) collections can potentially bridge spatial gaps between point-based reference and proximal measurements and whole-field UAV measurements. This research proposes a non-destructive approach to transition from proximal spectroscopy to high resolution UAV imagery to predict photosynthetic and water relation traits in maize hybrids grown in two different environments under varying levels of water availability. Both proximal and UAV collected spectral measurements covered the visible, near infrared, and shortwave infrared regions. Partial least squares regression (PLSR) models were developed for both proximal spectra, using reference measurements, and UAV spectra, using proximal predictions as response variable. Many UAV-based PLSR models, including chlorophyll concentration, CO2 assimilation, osmotic potential, and succulence, performed well with goodness of fit statistics over 0.60. These preliminary results highlight the opportunity to advance the capabilities of UAV-based hyperspectral imaging to rapidly and non-destructively predict leaf-level functional traits related to drought, improving breeding approaches and genotype selection.

In closed-canopy forests, the availability of photosynthetically active light has been a focal point of research, emphasizing the role of light as a resource in limiting carbon assimilation and individual tree growth. However, light shapes the functioning of forest ecosystems through multiple mechanisms. Here, using a series of studies from a network of tree diversity experiments, we explore the multifaceted ways in which light---in terms of both quantity and quality---shapes productivity in mixed-species forests. Spectral reflectance from remote sensing of forest canopies is being increasingly used to detect how tree diversity influences productivity. We demonstrate that airborne imaging spectroscopy captures functionally important differences among canopies related to their structure, chemistry, and underlying biological interactions. Ground-based analyses can show in detail how photosynthetically active light is partitioned among species in mixed-species communities. We show that greater interception of light and greater efficiency of light use, generated by inter- and intra-specific differences, combine to enhance productivity in mixed-species forests. Light may shape forest function not only as a resource but also as a stressor and cue. Plants can perceive light at various wavelengths, use this information to assess their neighborhoods, and subsequently adjust their physiology and allocation. We characterize how light quality---from the ultraviolet to shortwave infrared---varies among and within canopies of differing diversity. We explore how these diversity-light quality relationships arise and connect across levels of biological organization from leaf-level trait expression to forest function. Together these studies lend insight into light-mediated mechanisms that drive relationships between biodiversity and productivity in forest ecosystems---insights that are crucial to predict how biodiversity change will affect future forest function.