The improved spectral resolution of modern hyperspectral sensors provides a means for discriminating subtly different classes of on ground materials in remotely sensed images. However, in order to obtain statistically reliable classification results, the number of necessary training samples can increase exponentially as the number of spectral bands increases. Obtaining the necessary number of training signals for these high-dimensional datasets may not be feasible. The problem can be overcome by preprocessing the data to reduce the dimensionality and thus reduce the number of required training samples. In this thesis, three dimensionality reduction methods, all based on parametric projection pursuits, are investigated. These methods are the Sequential Parametric Projection Pursuits (SPPP), Parallel Parametric Projection Pursuits (PPPP), and Projection Pursuits Best Band Selection (PPBBS). The methods are applied to very high spectral resolution data to transform the hyperspectral data to a lower-dimension subspace. Feature extractors and classifiers are then applied to the lower-dimensional data to obtain target detection accuracies. The three projection pursuit methods are compared to each other, as well as to the case of using no dimensionality reduction preprocessing. When applied to hyperspectral data in a precision agriculture application, discriminating sicklepod and cocklebur weeds, the results showed that the SPPP method was optimum in terms of accuracy, resulting in a classification accuracy of >95% when using a nearest mean, maximum likelihood, or nearest neighbor classifier. The PPPP method encountered optimization problems when the hyperspectral dimensionality was very high, e.g. in the thousands. The PPBBS method resulted in high classification accuracies, >95%, when the maximum likelihood classifier was utilized; however, this method resulted in lower accuracies when the nearest mean or nearest neighbor classifiers were used. When using no projection pursuit preprocessing, the classification accuracies ranged between ~50% and 95%; however, for this case the accuracies greatly depended on the type of classifier being utilized.

<p dir="ltr">Reflectance spectroscopy has been increasingly used in forestry due to its ability to rapidly, efficiently, and non-destructively detect tree stress, enabling timely and cost-effective forest management decisions. This dissertation synthesizes three studies and five experiments to understand and improve our ability to use spectral data to estimate a variety of foliar physiochemical traits and identify spectral responses in multi-stress environments, thus, advancing our understanding and application of hyperspectral data in forest management.</p><p dir="ltr">The first study seeks to refine the hyperspectral approach to monitoring tree stress by selecting optimal wavelength ranges to enhance the estimation of foliar traits, such as CO₂ assimilation rate, specific leaf area, leaf water content, and concentrations of foliar nitrogen, sugars, and gallic acid. The study revealed that model performance varied significantly across the different wavelength ranges tested and consistently, including longer wavelength regions improved trait estimation for all traits modeled. This research also established a framework for discovering novel or previously unknown absorption features associated with functional traits, thereby laying the groundwork for expanded spectral applications. This advancement enables the estimation of diverse foliar traits and facilitates detailed stress detection in trees.</p><p dir="ltr">The second study focuses on assessing the effectiveness of hyperspectral data in estimating foliar functional trait responses to various biotic and abiotic stressors and to differentiate those stressors in black walnut (<i>Juglans nigra </i>L.) and red oak (<i>Quercus rubra</i> L.) seedlings. We demonstrated that spectral data can reliably estimate a wide range of foliar traits, highlighting its potential as a surrogate for reference data in understanding plant responses to stress. This research revealed that spectral leaf predictions can effectively provide stress-specific insights into tree physiochemical responses to biotic and abiotic stressors.</p><p dir="ltr">The third study explores the application of hyperspectral reflectance to identify drought-induced foliar responses in black walnut seedlings during their initial field establishment. Chemometric models developed from greenhouse experiments were applied to spectral data collected in the field to assess their transferability and accuracy in predicting various leaf traits under drought stress. Using only spectral data, we demonstrated that seedlings show distinct spectral responses to past and ongoing drought stress, with varying degrees depending on seed provenances. This research aims to provide practical insights for utilizing spectral analysis in real-world conditions and understanding the challenges of using spectral tools in the field.</p><p dir="ltr">Collectively, this dissertation demonstrates the robust potential of hyperspectral reflectance technology in advancing the monitoring of tree health. By optimizing spectral range selection, reliably estimating tree foliar traits under stress conditions, differentiating various stressors in controlled environments, and effectively detecting current and past drought stress in field conditions, this research offers valuable insights for improving forest health monitoring and management strategies in response to environmental challenges.</p>