Pattern Recognition in High-Dimensional Data

Vast amounts of data are produced all the time. Yet this data does not easily equate to useful information: extracting information from large amounts of high dimensional data is nontrivial. People are simply drowning in data. A recent and growing source of high-dimensional data is hyperspectral imaging. Hyperspectral images allow for massive amounts of spectral information to be contained in a single image. In this thesis, a robust supervised machine learning algorithm is developed to efficiently perform binary object classification on hyperspectral image data by making use of the geometry of Grassmann manifolds. This algorithm can consistently distinguish between a large range of even very similar materials, returning very accurate classification results with very little training data. When distinguishing between dissimilar locations like crop fields and forests, this algorithm consistently classifies more than 95 percent of points correctly. On more similar materials, more than 80 percent of points are classified correctly. This algorithm will allow for very accurate information to be extracted from these large and complicated hyperspectral images

Reconstructed storm tracks reveal three centuries of changing moisture delivery to North America

Moisture delivery to western North America is closely linked to variability in the westerly storm tracks of midlatitude cyclones, which are, in turn, modified by larger-scale features such as the El Niño–Southern Oscillation system. Instrumental and modeling data suggest that extratropical storm tracks may be intensifying and shifting poleward due to anthropogenic climate change, but it is difficult to separate recent trends from natural variability because of the large amount of decadal and longer variation in storm tracks and their limited instrumental record. We reconstruct cool-season, midlatitude Pacific storm-track position and intensity from 1693 to 1995 CE using existing tree-ring chronologies along with a network of newly developed chronologies from the U.S. Pacific Northwest, where small variations in storm-track position can have a major influence on hydroclimate patterns. Our results show high interannual-to-multidecadal variability in storm-track position and intensity over the past 303 years, with spectral signatures characteristic of tropical and northern Pacific influences. Comparison with reconstructions of precipitation and tropical sea surface temperature confirms the relationship between shifting drought patterns in the Pacific Northwest and storm-track variability through time and demonstrates the long-term influence of El Niño. These results allow us to place recent storm-track changes in the context of decadal and multidecadal fluctuations across the long-term record, showing that recent changes in storm-track intensity likely represent a warming-related increase amplified by natural decadal variability

Environmental Limitations to Forest Growth and Productivity in North America

Terrestrial primary production—the carbohydrates produced by plants via photosynthesis—is the entry point of energy and carbon into ecosystems, forming the base of the food chain and a sink for anthropogenic CO2. Primary production can be limited by unfavorable environmental conditions, including non-optimal temperatures, water deficits, or inadequate nutrient supply. At present, our ability to model how environmental factors reduce primary production remains limited. This leads to uncertainty both in the remotely sensed models used to monitor primary production and in climate models that depend on accurate representation of the land surface and biosphere. Given the importance of vegetation to humanity and the Earth system, in this dissertation I use tree rings and remote sensing to examine the environmental drivers of forest growth and productivity in North America. In particular, this research examines how forests are influenced by climate, atmospheric circulation, and land surface characteristics like topography and soil quality. I first examine how the seasonality of temperature and precipitation affect growth of ponderosa pine in the U.S. Pacific Northwest. I then develop a new tree-ring “environmental stress” index, which I use to model the climatic, topographic, and edaphic drivers of forest growth across the conterminous U.S. Finally, I examine how variability of the Pacific storm track acts as a synoptic-scale driver of hydroclimate and vegetation activity in western North America. In this research, I show that forest primary productivity is significantly influenced by moisture supply across multiple seasons, particularly in western North America. Westerly Pacific storm tracks are largely responsible for delivery of moisture to this region, and I show that northerly shifts of these storm tracks reduce both water supply and primary production in the northwestern U.S. Using a set of machine learning model experiments, I also demonstrate that models of forest growth that incorporate topographic and soil characteristics outperform those based solely on climate. Taken together, these findings provide a framework for improving the models used to reconstruct past climate from tree-ring data and to monitor primary production with remote sensing, while also providing insight into potential influences of a warming climate on the biosphere.Doctor of Philosoph

Empirical Evidence for the Association of the El Niño-Southern Oscillation with Terrestrial Vegetation Dynamics in the Western United States

Timing of plant life cycle events (phenology) and annual plant productivity represent key interactions between the atmosphere and the biosphere, with implications and feedbacks for climate and ecosystem functions. The El Niño-Southern Oscillation (ENSO) system is the dominant source of interannual climate variability in the western United States, with important effects on temperature, precipitation, and drought. In this study, the connection between ENSO and terrestrial vegetation dynamics is examined using remotely sensed vegetation indices, eddy covariance flux tower observations, ENSO indices, and spatially-resolved climate data. El Niño events are associated with an increase in primary production throughout the western U.S., and with an earlier growing season in much of the Pacific Northwest and parts of the Southwest. The correlation between total annual production and the Southern Oscillation Index is highest in mid- to late-winter prior to the growing season, suggesting some predictive power in advance of the growing season.Master of Art

Symmetries of Cairo-Prismatic Tilings

We study and catalog isoperimetric, planar tilings by unit-area Cairo and Prismatic pentagons. In particular, in counterpoint to the five wallpaper symmetry groups known to occur in Cairo-Prismatic tilings, we show that the five with order three rotational symmetry cannot occur

Persistence of pressure patterns over North America and the North Pacific since AD 1500

Changes in moisture delivery to western North America are largely controlled by interrelated, synoptic-scale atmospheric pressure patterns. Long-term records of upper-atmosphere pressure and related circulation patterns are needed to assess potential drivers of past severe droughts and evaluate how future climate changes may impact hydroclimatic systems. Here we develop a tree-ring-based climate field reconstruction of cool-season 500 hPa geopotential height on a 2° × 2° grid over North America and the North Pacific to AD 1500 and examine the frequency and persistence of preinstrumental atmospheric pressure patterns using Self-Organizing Maps. Our results show extended time periods dominated by a set of persistent upper-air pressure patterns, providing insight into the atmospheric conditions leading to periods of sustained drought and pluvial periods in the preinstrumental past. A striking shift from meridional to zonal flow occurred at the end of the Little Ice Age and was sustained for several decades

The opposing transcriptional functions of Sin3a and c-Myc are required to maintain tissue homeostasis.

How the proto-oncogene c-Myc balances the processes of stem-cell self-renewal, proliferation and differentiation in adult tissues is largely unknown. We explored c-Myc's transcriptional roles at the epidermal differentiation complex, a locus essential for skin maturation. Binding of c-Myc can simultaneously recruit (Klf4, Ovol-1) and displace (Cebpa, Mxi1 and Sin3a) specific sets of differentiation-specific transcriptional regulators to epidermal differentiation complex genes. We found that Sin3a causes deacetylation of c-Myc protein to directly repress c-Myc activity. In the absence of Sin3a, genomic recruitment of c-Myc to the epidermal differentiation complex is enhanced, and re-activation of c-Myc-target genes drives aberrant epidermal proliferation and differentiation. Simultaneous deletion of c-Myc and Sin3a reverts the skin phenotype to normal. Our results identify how the balance of two transcriptional key regulators can maintain tissue homeostasis through a negative feedback loop

Consistent Classification of Landsat Time Series with an Improved Automatic Adaptive Signature Generalization Algorithm

Classifying land cover is perhaps the most common application of remote sensing, yet classification at frequent temporal intervals remains a challenging task due to radiometric differences among scenes, time and budget constraints, and semantic differences among class definitions from different dates. The automatic adaptive signature generalization (AASG) algorithm overcomes many of these limitations by locating stable sites between two images and using them to adapt class spectral signatures from a high-quality reference classification to a new image, which mitigates the impacts of radiometric and phenological differences between images and ensures that class definitions remain consistent between the two classifications. We refined AASG to adapt stable site identification parameters to each individual land cover class, while also incorporating improved input data and a random forest classifier. In the Research Triangle region of North Carolina, our new version of AASG demonstrated an improved ability to update existing land cover classifications compared to the initial version of AASG, particularly for low intensity developed, mixed forest, and woody wetland classes. Topographic indices were particularly important for distinguishing woody wetlands from other forest types, while multi-seasonal imagery contributed to improved classification of water, developed, forest, and hay/pasture classes. These results demonstrate both the flexibility of the AASG algorithm and the potential for using it to produce high-quality land cover classifications that can utilize the entire temporal range of the Landsat archive in an automated fashion while maintaining consistent class definitions through time

Phenological Characteristics of Global Ecosystems Based on Optical, Fluorescence, and Microwave Remote Sensing

Growing seasons of vegetation generally start earlier and last longer due to anthropogenic warming. To facilitate the detection and monitoring of these phenological changes, we developed a discrete, hierarchical set of global “phenoregions” using self-organizing maps and three satellite-based vegetation indices representing multiple aspects of vegetation structure and function, including the normalized difference vegetation index (NDVI), solar-induced chlorophyll fluorescence (SIF), and vegetation optical depth (VOD). Here, we describe the distribution and phenological characteristics of these phenoregions, including their mean temperature and precipitation, differences among the three satellite indices, the number of annual growth cycles within each phenoregion and index, and recent changes in the land area of each phenoregion. We found that the phenoregions “self-organized” along two primary dimensions: degree of seasonality and peak productivity. The three satellite-based indices each appeared to provide unique information on land surface phenology, with SIF and VOD improving the ability to detect distinct annual and subannual growth cycles in some regions. Over the nine-year study period (limited in length by the short satellite SIF record), there was generally a decrease in the spatial extent of the highest productivity phenoregions, though whether due to climate or land use change remains unclear