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Many National Forest Inventory (NFI) stakeholders would benefit from accurate estimates at finer geographic scales than most currently implemented in operational estimates using NFI sample data. In the past decade small area estimation techniques have been shown to increase precision in forest inventory estimates by combining field observations and remote-sensing.We sought to demonstrate the potential for improving the precision of forest inventory growing stock volume estimates for counties in United States of North Carolina, Tennessee, and Virginia, by pairing canopy height models from digital aerial photogrammetry (DAP) and field plot data from the United States NFI. Area-level Fay-Herriot estimators were used to avoid the need for precise (GPS) coordinates of field plots. Reductions in standard errors averaging 30% for North Carolina county estimates were observed, with 19% average reductions in standard errors in both Tennessee and Virginia. Accounting for spatial autocorrelation among adjacent counties provided further gains in precision when the three states were treated as a single forest land population; however, analyses conducted one state at a time showed that good results could be achieved without accounting for spatial autocorrelation. Apparent gains in sample sizes ranged from about 65% in Virginia to 128% in North Carolina, compared to the current number of inventory plots. Results should allow for determining whether acquisition of statewide DAP would be costeffective as a means for increasing the accuracy of county-level forest volume estimates in the United States NFI. This work was supported by USDA, Forest Service Southern Research Station, 20-JV-11330145-074. Published version

Supplemental material, sj-docx-1-tpx-10.1177_01926233221103202 for Scientific and Regulatory Policy Committee Points to Consider for Medical Device Implant Site Evaluation in Nonclinical Studies by Maureen T. O’Brien, JoAnn C. L. Schuh, Lyn M. Wancket, Sarah D. Cramer, Kathleen A. Funk, Nicolette D. Jackson, Kamala Kannan, Kevin Keane, Abraham Nyska, Serge D. Rousselle, Adrienne Schucker, Valerie S. Thomas and Stefan Tunev in Toxicologic Pathology

Supplemental material, sj-docx-1-tpx-10.1177_01926233221103202 for Scientific and Regulatory Policy Committee Points to Consider for Medical Device Implant Site Evaluation in Nonclinical Studies by Maureen T. O’Brien, JoAnn C. L. Schuh, Lyn M. Wancket, Sarah D. Cramer, Kathleen A. Funk, Nicolette D. Jackson, Kamala Kannan, Kevin Keane, Abraham Nyska, Serge D. Rousselle, Adrienne Schucker, Valerie S. Thomas and Stefan Tunev in Toxicologic Pathology

Airborne laser scanning (ALS) is increasingly used to estimate various forest characteristics. Technological improvements in unmanned aerial vehicles (UAVs) and drone laser scanning (DLS) sensors have permitted the acquisition of high pulse density datasets. There is an assumption that higher pulse densities yield greater accuracies in estimating forest characteristics. In this study, we investigated the effect of pulse density (.25, .5, 1, 5, 10, 50, 100 and 300 pulses m���2) on the ability to delineate individual tree crowns (ITCs) and estimate ITC height and crown horizontal diameter, in addition to plot-level leaf area index (LAI). The current study took place in an experimentally varied Pinus taeda L. forest, which included three stem densities: (i) 618; (ii) 1236; and (iii) 1853 trees per hectare (TPH). ITCs were classified directly from the DLS point cloud for each of the pulse densities. The correct delineation of ITCs relative to field tree-coordinates was relatively consistent (��5%) for pulse densities of 5 to 300 pulses m���2. ITC delineation accuracy decreased with lower pulse densities. Planting stem density did impact ITC delineation accuracy. Higher pulse densities, plots with 618 TPH correctly classified ~88% of ITCs, and plots with the 1853 TPH correctly classified ~50% of ITCs. Estimates of tree height were largely unaffected by changes in tree density. Root mean square error (RMSE) for tree height varied from .5 to 2.5 m at pulse densities of 300 to .25 pulses m���2, respectively. Estimates of crown horizontal diameter varied with regard to both pulse and stem density from 1.2 (300 ppm���2 and 1853 TPH) to 4.2 m (.25 ppm���2 and 618 TPH). RMSE varied among stem densities from .6 to 1.2 m as pulse density decreased. There was significant difference in ITC delineation accuracy, particularly when considering stem density, and the estimates of tree height and crown horizontal diameter among the DLS pulse densities used. The accuracy of predicted LAI was largely unaffected by changes in pulse density, when pulse density was above .5 pulses m���2. There was little or no difference in estimates of LAI at these pulse densities. Our results suggest that low-density DLS data may be capable of estimating plot-level forest metrics reliably in some situations, however once the analysis scale is reduced to the individual-tree-level, the influence of pulse density is more substantial. The results here provide guidance to forest managers who must balance metric estimation accuracy and price when planning new ALS or DLS acquisitions.

Estimating forest canopy height

Conclusions Plantation forestry rapidly expanding in Asia Small spatial extent and rapid harvest-regeneration cycle present interesting — but resolvable — remote sensing challenges Understanding the drivers and ramifications of these new trees outside forests vital Accepted version No (Peer reviewed?)

Get into the Christmas spirit(s)! Tracy Hilton, a nursery teacher in Sale, near Manchester, has found a book that is perfect to conjure up the magic of the festive season with your class.

Biological invasions have been observed throughout the world for centuries, often with major consequences to biodiversity and food security. Tying invasion to species identity and associated traits has led to numerous hypotheses on why, and where, some species are invasive. In recent decades, attention to intraspecific variation among invaders has produced questions about their adaptation to climate, land use, and environmental change. I examined the intraspecific variation of invasive Johnsongrass's (Sorghum halepense (L.) Pers.) seedling stress response, propagule cold tolerance, and large-scale niche dynamics for correlation with populations' climatic and ecotypic (i.e., agricultural vs. non-agricultural) origin. Overall, I found a greater number of home climate effects than ecotypic effects on various traits. Non-agricultural seed from cold climates and agricultural seed from warm climates germinated more and faster, while non-agricultural seedlings showed uniform chlorophyll production regardless of home soil carbon origin, unlike their agricultural counterparts. Neither seedling stress response nor propagule cold tolerance interacted with ecotype identity; however, drought stress varied with population origins' aridity and soil fertility, and seed from warm/humid and cold/dry climates was most germinable. Comparison of seed and rhizome cold tolerance also suggested that the latter is a conserved trait that may be limiting S. halepense poleward range expansion. This physiological limit, an unchanged cold temperature niche boundary between continents and ecotypes, and a narrowed niche following transition to non-agricultural lands all imply low likelihood of spread based on climatic niche shift. Instead, evidence points to range expansion driven primarily by climate change and highlights agriculture's role in facilitating invasibility. This tandem approach to climate and land use as drivers of intraspecific variation is transferable to other taxa and can help refine our conception of and response to invasion in the Anthropocene. Doctor of Philosophy Exotic invasive species are a global problem, threatening biodiversity and biosecurity now and in the future. In the last several decades, ecologists have studied many individual invaders and their traits to understand what drives their spread. More recently, abundant differences in traits between populations within an invasive species have raised questions about humans' role in facilitating invasion through climate change, land use, and other disturbances. I studied the invasive Johnsongrass's (Sorghum halepense (L.) Pers.) response to drought, nutrient limitation, and freezing to detect differences between populations based on their climate and ecotype (agricultural vs. non-agricultural) origin. I also tracked differences in the climates the species occupied across the globe and North America and projected its future distribution under climate change. Overall, I found a greater number of home climate effects than ecotypic effects on various traits. Non-agricultural seed from cold climates and agricultural seed from warm climates germinated the most, while non-agricultural seedlings performed consistently regardless of soil carbon origin, unlike their agricultural counterparts. In addition, drought stress varied with population origins' rainfall and soil fertility, and seed germination favored warm/humid and cold/dry origin. Rhizome (underground stem) cold tolerance appears to be a trait that limits S. halepense poleward range expansion. Along with no change in the coldest climates occupied worldwide and no spread to new climates with transition to non-agricultural lands, this implies that Johnsongrass is unlikely to expand its range without external forces. Instead future range expansion will likely be driven by climate change. This coupled approach to climate and land use affecting invasion is transferable to other species and can help refine both our concepts and response strategies.

Whitetop Mountain is Virginia's second highest peak and hosts two globally rare, insular ecosystems: a southern Appalachian grass bald and a red spruce-dominated forest. These areas provide important ecosystem services and habitat for rare and endangered species. They are highly prized for their cultural value and recreational areas that support nearby rural economies. This thesis investigated spatial patterns in both ecosystems on Whitetop. We documented a 24.73% decrease of in the extent of the southern Appalachian grass bald across 68 years through analysis of historical aerial photography. In the red spruce-dominated forest, we used a consumer grade unmanned aerial vehicle (UAV) to survey the health of all trees within a 46 ha sample plot. We assessed (dead, dying, healthy) over 9,000 individual trees based on visual patterns in the imagery and produced spatial products that will inform land managers about where resources are most needed. About 7.4% of the red spruce trees in our study area were classified as dead or dying. A model relating spruce mortality to biophysical landscape factors identified no single predictive factor related to mortality. The addition of optical information from the UAV imagery into the model proved utility for remotely-sensed data in identification of dead spruce within the forest canopy at Whitetop and possibly in other similarly structured forests. This research contributed to the limited body of knowledge surrounding the decline of both southern Appalachian grass balds and red spruce forests and provided technical insights for future mortality monitoring. Master of Science This thesis investigates land cover changes in two rare ecosystems on Whitetop Mountain, Virginia. The mountain has important biological significance and is a cultural landmark. The high-elevation summit hosts plant and animal species characteristic of northern climates, including a red spruce-dominated forest and a southern Appalachian grass bald. This work documented a 24.73% decrease in the size of the rare southern Appalachian grass bald ecosystem at Whitetop Mountain over 68 years and discussed potential drivers and proposed management for conservation. We also successfully used a camera-equipped unmanned aerial vehicle (drone) to produce high quality imagery for spruce mortality detection within the red spruce forest. Of over 9,000 standing spruce trees, 7.4% were categorized as either dead or dying. We built a predictive model to investigate the relationship between mortality and biophysical environmental factors, but did not identify a single causal factor. A second model that included the color band information from the drone camera revealed that different types of aerial imagery could play a valuable role in detection of tree mortality in forests of similar structure. Overall this research contributes to the body of knowledge surrounding the decline of both southern Appalachian grass bald and red spruce ecosystems and provides insights for management.

Loblolly pine is an important managed tree species within the southeastern United States, and better understanding spatial patterns in its productivity has potential to contribute to both modeling and management of the species. Using recently-created pine management maps specific to Virginia and empirical relationships predicting pine LAI from the Landsat satellite, we conducted a statewide analysis of temporal patterns in stand-level southern pine leaf area index (LAI) following clear-cut and planting. Here, using 28 years of Landsat time-series data for 13,140 stands that were clear-cut between 2014-2017, we examined 1) when LAI peaked over the rotation, and 2) how LAI in each stand compared to a recommended fertilization threshold of 3.5 LAI. We found that, on average, winter LAI reached a maximum of 2.02., which can be approximately doubled to give a summer LAI of 4.04, and within stand peak occurred between years 13 and 15. We also found that around 45.8% of stands achieved an LAI value higher than 3.5: a fertilization threshold recommended for managed stands in Virginia. The dataset produced by our analysis will bolster information required for modeling loblolly pines as a plant functional type in regional land simulations, and the finding that most stands are below the recommended LAI fertilization threshold will fuel further management-motivated research. Master of Science Management of pines in the southeastern U.S. contributes to the region's economy and carbon sequestration potential. In this study, we used Virginia forest harvest maps to identify individual patches (stands) of pine forest which had each gone through a full harvest life cycle (rotation). With unique managed pine stands identified, we used satellite imagery to estimate growth of canopy leaf area over time within each stand, using a metric called leaf area index (LAI). We identified 13,140 separate stands, each with up to 28 years of available data. We took the first full-state census of areas of managed pines in Virginia, and their leaf area development. We acquired one LAI measurement from February of each year, for each stand in Virginia. Using February LAI for each of our stands, we found that an average stand in VA has a maximum winter LAI of 2.02 (meaning an approximate maximum summer LAI of 4.04), and that stands generally reached their peak LAI after around 14 years of growth. It is recommended, in VA, that a landowner fertilize their stand in the middle of a harvest rotation if summer peak LAI is under 3.5, at stand closure. We found that at ten years of stand age, 45.8% of stands were estimated to reach above this threshold. Since this study's dataset is the most comprehensive LAI dataset for managed pines in VA, it may be used to improve management outcomes as well as understand pine productivity for land surface modeling purposes.

Informed forest management requires accurate, up-to-date information. Ground-based forest inventory is commonly conducted to generate estimates of forest characteristics with a predetermined level of statistical confidence. As the importance of monitoring forest resources has increased, budgetary and logistical constraints often limit the resources needed for precise estimates. In this research, the incorporation of ancillary information in planted loblolly pine (Pinus taeda L.) forest inventory was investigated. Additionally, a simulation study using synthetic populations provided the basis for investigating the effects of plot and stand-level inventory aggregations on predictions and projections of future forest conditions. Forest regeneration surveys are important for assessing conditions immediately after plantation establishment. An unmanned aircraft system was evaluated for its ability to capture imagery that could be used to automate seedling counting using two computer vision approaches. The imagery was found to be unreliable for consistent detection in the conditions evaluated. Following establishment, conditions are assessed throughout the lifespan of forest plantations. Using small area estimation (SAE) methods, the incorporation of light detection and ranging (lidar) and thinning status improved the precision of inventory estimates compared with ground data alone. Further investigation found that reduced density lidar point clouds and lower resolution elevation models could be used to generate estimates with similar increases in precision. Individual tree detection estimates of stand density were found to provide minimal improvements in estimation precision when incorporated into the SAE models. Plot and stand level inventory aggregations were found to provide similar estimates of future conditions in simulated stands without high levels of spatial heterogeneity. Significant differences were noted when spatial heterogeneity was high. Model form was found to have a more significant effect on the observed differences than plot size or thinning status. The results of this research are of interest to forest managers who regularly conduct forest inventories and generate estimates of future stand conditions. The incorporation of auxiliary data in mid-rotation stands using SAE techniques improved estimate precision in most cases. Further, guidance on strategies for using this information for predicting future conditions is provided. Doctor of Philosophy Informed forest management requires accurate, up-to-date information. Groundbased sampling (inventory) is commonly used to generate estimates of forest characteristics such as total wood volume, stem density per unit area, heights, and regeneration survival. As the importance of assessing forest resources has increased, resources are often not available to conduct proper assessments. In this research, the incorporation of ancillary information in planted loblolly pine (Pinus taeda L.) forest inventory was investigated. Additionally, a simulation study investigated the effects of two forest inventory data aggregation methods on predictions and projections of future forest conditions. Forest regeneration surveys are important for assessing conditions immediately after tree planting. An unmanned aircraft system was evaluated for its ability to capture imagery that could be used to automate seedling counting. The imagery was found to be unreliable for use in accurately detecting seedlings in the conditions evaluated. Following establishment, forest conditions are assessed at additional points in forest development. Using a class of statistical estimators known as small-area estimation, a combination of ground and light detection and ranging data generated more confident estimates of forest conditions. Further investigation found that more coarse ancillary information can be used with similar confidence in the conditions evaluated. Forest inventory data are used to generate estimates of future conditions needed for management decisions. The final component of this research found that there are significant differences between two inventory data aggregation strategies when forest conditions are highly spatially variable. The results of this research are of interest to forest managers who regularly assess forest resources with inventories and models. The incorporation of ancillary information has potential to enhance forest resource assessments. Further, managers have guidance on strategies for using this information for estimating future conditions.

Forests have the ability to sequester carbon from our atmosphere. Soil respiration (Rs) plays a role in a forest's ability to do so as it is a significant source of carbon dioxide back to the atmosphere. Therefore, understanding the process of Rs under varying conditions is gaining more attention. As of now we have a relatively good understanding of Rs under managed forest ecosystems such as pine plantations. This particular study examined Rs under different overstories and understories in a high elevation Southern Appalachian forest in order to get a better understanding of Rs under a natural hardwood system. The four vegetation types under consideration were an eastern hemlock (Tsuga canadensis L. Carriere) dominated overstory, a hardwood overstory with little to no understory, a mountain laurel (Kalmia latifolia L.) dominated understory, and a cinnamon fern (Osmundastrum cinnamomeum (L.) C.Presl) dominated understory. Differing temporal variations of Rs were observed under the vegetation types. We found monthly differences in rates among vegetation type however, an overall annual difference in Rs rates between vegetation types was not observed. This simply indicates the importance of observing Rs under different time scales to get a better understanding of its variation. We also calculated vegetation indices from remotely-sensed data to explore any relationships to Rs as well as if the indices themselves could improve out model. A vegetation index is a number that is calculated for every pixel in a remotely sensed image and represents plant vigor or abundance. Few significant relationships were found between the indices and Rs. Future work may want to better understand vegetation indices' spatial extent and accuracy in order to find whether they may be beneficial in Rs estimation. Understanding the influence of varying vegetation type and soil temperature and moisture on Rs will ultimately improve our ability to predict what drives changes in carbon fluxes. Master of Science Forests have the ability to sequester carbon from our atmosphere. Soil respiration (Rs) plays a role in a forest’s ability to do so as it is a significant source of carbon dioxide back to the atmosphere. Therefore, understanding the process of Rs under varying conditions is gaining more attention. As of now we have a relatively good understanding of Rs under managed forest ecosystems such as pine plantations. This particular study examined Rs under different overstories and understories in a high elevation Southern Appalachian forest in order to get a better understanding of Rs under a natural hardwood system. The four vegetation types under consideration were an eastern hemlock (Tsuga canadensis L. Carriere) dominated overstory, a hardwood overstory with little to no understory, a mountain laurel (Kalmia latifolia L.) dominated understory, and a cinnamon fern (Osmundastrum cinnamomeum (L.) C.Presl) dominated understory. Differing temporal variations of Rs were observed under the vegetation types. We found monthly differences in rates among vegetation type however, an overall annual difference in Rs rates between vegetation types was not observed. This simply indicates the importance of observing Rs under different time scales to get a better understanding of its variation. We also calculated vegetation indices from remotely-sensed data to explore any relationships to Rs as well as if the indices themselves could improve out model. A vegetation index is a number that is calculated for every pixel in a remotely sensed image and represents plant vigor or abundance. Few significant relationships were found between the indices and Rs. Future work may want to better understand vegetation indices’ spatial extent and accuracy in order to find whether they may be beneficial in Rs estimation. Understanding the influence of varying vegetation type and soil temperature and moisture on Rs will ultimately improve our ability to predict what drives changes in carbon fluxes.

During the late 19th and early 20th centuries, extensive logging reduced the forests of red spruce (Picea rubens) by nearly 99% through portions of West Virginia. In the wake of this disturbance, red spruce has begun regenerating on the ridge and mountaintop areas of Canaan Valley, West Virginia, where heath and grassland communities have both persisted in natural barrens and expanded into formerly forested areas. To understand abiotic and biotic conditions guiding the advance of the red spruce stand, I conducted a broad-scale assessment of thirty-one demographics plots spread across two sites (north Cabin Mountain and Bear Rocks/Dolly Sods), and a more focused assessment of red spruce species associations within thirty-two paired plots at Cabin Mountain. At the 15m x 15m demographics plots, I conducted a count of all P. rubens present, measured specimen height, DBH or diameter at ground level (DGL) for specimens < 1.37m tall, and assessed the relative percent cover of rock, shrub, herbaceous, and tree cover. These data, along with additional abiotic components derived from a DEM, formed the basis of my assessment using a generalized linear mixed model (GLMM) to identify the most significant biophysical variables related to P. rubens count. In the paired plots, I used the relative interactions index (RII) to compare the total cover of each present non-graminoid vascular species and the grouped cover types Rock, Graminoid, Lichen, Litter, and Moss in one 45cm-radius plot with a < 1.37m P. rubens specimen, and one paired 45cm-radius plot in open heath. The significance of differences in total cover were assessed with the Wilcoxon test and Tukey HSD. The GLMM identified percent rock cover and distance from the nearest P. rubens stand to be important correlates of P. rubens count at the demographic plots. Graminoid cover was found to be higher in P. rubens 45cm-radius plots than in paired heath plots, and Vaccinium angustifolium cover was found to be concentrated in 45cm radius plots beyond the first 15cm from the P. rubens stem. These findings reinforce a complex interplay between both the biotic and abiotic characteristics of a microsite and the successful germination and regeneration of a red spruce seedling in the heathland. Master of Science During the late 19th and early 20th centuries, extensive logging reduced the forests of red spruce (Picea rubens) by nearly 99% through portions of West Virginia. In the wake of this disturbance, red spruce has begun regenerating on the ridge and mountaintop areas of Canaan Valley, West Virginia, where heath and grassland communities have both persisted in natural barrens and expanded into formerly forested areas. To understand the necessary abiotic and biotic conditions guiding the advance of the red spruce stand, I conducted a broad-scale assessment of thirty-one demographics plots spread across two sites (north Cabin Mountain and Bear Rocks/Dolly Sods), and a more focused assessment of red spruce species associations within thirty-two paired plots at Cabin Mountain. At the 15m x 15m demographics plots, I measured and counted all red spruce present. I collected landscape and community data on each plot using field surveys and a digital elevation model (DEM) to the variables most associated with a higher count of red spruce in each plot. In the 45cm-radius paired plots, I used simple mathematical comparisons to identify positive and negative interactions between red spruce shorter than breast height (1.37m) and different types of cover, including heathland species such as Vaccinium angustifolium (lowbush blueberry), rock, graminoid, and moss. Modeling indicated that rock cover, and distance between the plot and the nearest stand of red spruce, were found to be the most significant variable affecting the count of red spruce at each plot, with increased rock cover and shorter stand distance both associated with higher numbers of spruce. In the paired plots, there was slightly higher graminoid cover associated with the tree than with the open heath plot, and V. angustifolium was more concentrated beyond 15cm from each red spruce specimen. These findings suggest that rock cover is important for tree regeneration both the biotic and abiotic characteristics of a microsite and the successful germination and regeneration of a tree seedling in heathlands.

Primary productivity is a fundamental ecological process and an important measure of ecosystem response to environmental change. Currently, there is a considerable lapse in our understanding of primary productivity in hot and cold deserts, due to the difficulty of measuring production in cryptogam vegetation. However, remote sensing can provide long-term, spatially-extensive estimates of primary production and are particularly well suited to remote environments, such as in the McMurdo Dry Valleys (MDV) of Antarctica, where cyanobacterial communities are the main drivers of primary production. These microbial communities form multi-layered sheets (i.e., microbial mats) on top of desert pavement. The cryptic nature of these communities, their often patchy spatial distribution, and their ability to survive desiccation make assessments of productivity challenging. I used field-based surveys of microbial mat biomass and pigment chemistry in conjunction with analyses of multispectral satellite data to examine the distribution and activity of microbial mats. This is the first satellite-derived estimate of microbial mat biomass for Antarctic microbial mat communities. I show strong correlations between multispectral satellite data (i.e., NDVI) and ground based measurements of microbial mats, including ground cover, biomass, and pigment chemistry. Elemental (C, N) and isotopic composition (15N, 13C) of microbial mats show that they have significant effects on biogeochemical cycling in the soil and sediment of this region where they occur. Using these relationships, I developed a statistical model that estimates biomass (kg of C) in selected wetlands in the Lake Fryxell Basin, Antarctica. Overall, this research demonstrates the importance of terrestrial microbial mats on C and N cycling in the McMurdo Dry Valleys, Antarctica. Master of Science Primary productivity is an essential ecological process and a useful measure of how ecosystems respond to climate change. Primary production is more difficult to measure in polar desert ecosystems where there is little to no vascular vegetation. Polar regions are also ecosystems where we expect to see significant responses to a changing climate. Remote sensing and image analysis can provide estimates of primary production and are particularly useful in remote environments. For example, in the McMurdo Dry Valleys (MDV) of Antarctica, cyanobacterial communities are the main primary producers. These microbial communities form multi-layered sheets (i.e., microbial mats) on top of rocks and soil. These communities are cryptic, do not cover large areas of ground continuously, and are able to survive desiccation and freezing. All of these characteristics make assessments of productivity especially challenging. For my master’s research, I collected microbial mat samples in conjunction with the acquisition of a satellite image of my study area in the MDV, and I determined biological parameters (e.g., percent ground cover, organic matter, and chlorophyll-a content) through laboratory analyses using these samples. I used this satellite image to extract spectral data and perform a vegetation analysis using the normalized difference vegetation index (i.e., NDVI), which determines areas in the image that contain vegetation (i.e., microbial mats). By linking the spectral data to the biological parameters, I developed a statistical model that estimates biomass (i.e., carbon content) of my study areas. These are the first microbial mat biomass estimates using satellite imagery for this region of Antarctica. Additionally, I researched the importance of microbial mats on nitrogen cycling in Taylor Valley. Using elemental and isotopic analyses, I determined microbial mats have significant effects on the underlying soil and nutrient cycling. Overall, this research demonstrates the importance of terrestrial microbial mats on C and N fixation in Antarctic soil environments.

Vernal pools are small seasonally-ponded wetlands that provide crucial habitat for amphibian reproduction and support trophic levels beyond their boundaries. The Ridge and Valley physiographic province in Pennsylvania is known to have vernal pools, but a regional inventory and geomorphology assessment is needed. My research is split into two independent parts focusing on the higher elevation areas of this region to determine vernal pool distribution and characteristics. Vernal pools were mapped using a LiDAR based suitability model and leaf-off aerial imagery interpretation. Four terrain rasters derived from a 1-meter DEM (modified wind modified wind exposure, terrain surface convexity, topographic position index, and a multiresolution index of valley bottom flatness) were used in the suitability model. An analysis of variance (ANOVA) and Tukey's HSD test found a significant difference using the model between terrestrial (non-wetland) habitat and vernal pools. Photo interpretation and field surveying lead to an inventory of 1011 vernal pools. Geomorphology was assessed from 13 variables to determine the best for vernal pool prediction. Three variables were significant for the occurrence and frequency of vernal pools; saddles with higher surface area, 0.6 to 1.5 kilometers between the summits of parallel ridgelines, and the presence of periglacial related solifluction. Vernal pool distribution is greater than previously known and they occur in predictable settings. Further research should focus on how and where vernal pools form, their impact on water quality, role in forest ecology, and ways to legally protect them at the state level. Master of Science Vernal pools are seasonally-ponded wetlands that are very important for amphibian reproduction. The Appalachian Mountains of Pennsylvania are known to have vernal pools, but comprehensive inventory is lacking. My research consists of two parts that focus on the higher elevation areas and assess the distribution and qualities of the vernal pools. Vernal pools were mapped using a LiDAR based suitability model and leaf-off aerial imagery interpretation. Statistical analysis was completed to prove that there was a significant difference in terrain morphology between non-wetland habitat and vernal pools. This research resulted in a total inventory of 1011 vernal pools. Results found that vernal pools were likely occur in landscape positions with higher surface area, 0.6 to 1.5 kilometers between the summits of parallel ridgelines, and the presence of topographic features indicative of glacial processes. Vernal pools are much more abundant than previously known and they occur in predictable settings. Further research could focus on the formation of vernal pools, impact on water quality, role in forest ecology, and ways to legally protect them at the state level.

Valley fever, or coccidioidomycosis, is a disease caused by the Coccidioides immitis and Coccidioides posadasii fungal species that dwell in the soil but can become airborne and infect a human or mammalian host through their respiratory tract. Disease rates in the western U.S. have significantly increased over the past two decades, creating an emerging public health burden. Studies have been conducted that attempt to elucidate the association between environmental conditions and the growth and dispersal of the pathogen, yet the specific ecology of and environmental precursors to the disease remain uncertain. This research project investigates the relationship between environmental variables and valley fever by modeling the spatial and temporal dynamics of the disease using varying techniques. Chapter 1 discusses relevant literature before discussing the challenges associated with studying valley fever. Chapter 2 analyzes the temporal relationships between valley fever and climatic variables, focusing on Kern County, California, an understudied region in the U.S. where valley fever is highly endemic. Chapter 3 focuses on a regional spatial analysis using ecological niche modeling to better understand the environmental factors that influence the overall spatial distribution of valley fever in the U.S. Finally, combining both spatial and temporal components, Chapter 4 uses a hierarchical Bayesian spatio-temporal model to investigate the patterns and drivers of this disease, focusing on state of California, which saw an approximate 200% increase in cases from 2014 to 2018. Cumulatively, this work offers new insights on relationships between climate, landcover, and valley fever disease risk. Significant findings include climate variables explaining up to 76% of valley fever variability in Kern County, California, the significance of both climatic and landcover variables in characterizing the geographic distribution of the disease, and identification of patterns increasing risk in geographic regions of California not currently considered highly endemic. These findings advance scholarly understandings of valley fever's environmental disease drivers. The results of this research can be applied by public health officials in the allocation of surveillance and public education resources, focusing upon regions that are most likely to encounter the illness. Doctor of Philosophy Valley fever is a fungal disease that causes illness in over ten thousand people in the western U.S. every year. Disease rates have been increasing for the past two decades for unknown reasons, although previous research suggests that climatic variations are likely contributing factors. This research evaluated environmental factors with hypothesized relationships to valley fever disease rates. First, this dissertation explored time-series relationships between climatic factors and valley fever incidence in an understudied county in California. Research findings identified that climatic factors including precipitation from previous seasons and temperature were significantly associated with valley fever incidence in this county. Second, this dissertation assessed where valley fever is found in the western U.S. The likely spatial distribution for the disease was mapped and environmental variables influential to this distribution were identified; they included both climate and landcover variables. Finally, a model was developed to analyze patterns of disease risk in California that considered both space and time, and environmental risk factors potentially contributing to the observed patterns were assessed. Counties with increased risk were identified and significant environmental relationships with valley fever risk were confirmed. The results of this research can be applied by public health officials in allocating surveillance and public education resources, focusing upon regions that are most likely to encounter the illness.

The conversion of grasslands to woodlands is an ecosystem transformation that threatens grassland biodiversity, the provision of important ecosystem services, and the sustainability of rural livelihoods. A global phenomenon, woody plant encroachment (WPE) has been particularly problematic in the Southern Great Plains of the United States where the actions of private landowners are integral to sustaining grasslands. Increased diversity in landowners’ motivations for owning land have shifted the social landscape of rural areas necessitating a better understanding of landowners’ perspectives about WPE and their subsequent management actions. Towards this purpose, I employed a mail survey to private landowners in the Edwards Plateau of Texas, Central Great Plains of Oklahoma, and Flint Hills of Kansas to investigate landowner perceptions and management responses to WPE. First, I assessed landowners’ acceptance of WPE as a function of how they relate to their land (i.e., sense of place), their beliefs about the positive and negative consequences of woody plants, and their perceived threat of grassland conversion. Then, I examined the drivers of landowners’ goal intentions to manage woody plants and their current use of five adaptive management practices that prevent WPE. My results demonstrate that landowners vary in their sensitivity to WPE based on how they feel connected to their land. This was true even though most landowners had low acceptance thresholds for WPE, believed it led to numerous negative outcomes, and perceived it as increasingly threatening at greater levels of encroachment. Most landowners wanted to control or remove woody plants and were actively engaged in management practices to do so. These findings address uncertainties about landowners’ acceptance of WPE and grassland conservation actions and provide broad implications for how people perceive and respond to ecosystem transformation. Master of Science Around the world, grasslands are converting to tree and shrub woodlands at an unprecedented rate. This transformation profoundly reduces habitat available for grassland plants and animals and diminishes many ecosystem services that people and rural communities rely on. This loss of grasslands has been especially far-reaching throughout the Southern Great Plains of the United States. Because most of this region is privately owned, the management actions of landowners play a crucial role in preventing or allowing this conversion to continue. Recent shifts in land ownership motivations expanding beyond traditional agricultural production have created increased uncertainty about how private landowners view and react to this change. To investigate how landowners perceive and respond to this woody plant encroachment (WPE) phenomenon, I conducted a mail survey of landowners in the Edwards Plateau of Texas, the Central Great Plains of Oklahoma, and the Flint Hills of Kansas. Using sense of place, landowners’ beliefs about the potential positive and negative consequences of woody plants, and their perceptions of how threatening grassland conversion is, I assessed the thresholds at which landowners’ do or do not accept WPE. Then, I examined how acceptance of WPE relates to landowners’ management goals and current use of management practices to control or reduce woody plants. I found that most landowners believed that woody plants had many negative consequences and perceived increasing levels of threat at greater levels of encroachment. This related to low levels of acceptance for woody plants in grasslands. However, landowners’ threat perceptions and acceptance of WPE varied based on their sense of place. Finally, most landowners wanted to control or remove woody plants and were actively engaged in management practices to do so. My results provide critical information regarding how current landowners’ view and respond to grassland conversion and offer broad implications for how people perceive and respond to large-scale environmental change.

The Forest Vegetation Simulator (FVS) is a growth and yield modeling system widely-used for predicting stand and tree-level attributes for management and planning applications in North American forests. The accuracy of FVS predictions for a range of tree and stand level attributes depends a great deal on the performance of the diameter increment model and its predictions of change in diameter at breast height (DBH) over time. To address the challenge of predicting growth in highly variable and geographically expansive forest systems, FVS was designed to include an internal calibration algorithm that makes use of growth observations, when available, from permanent inventory plots. The basic idea is that observed growth rates on a collection of remeasured trees are used to adjust or "calibrate" FVS diameter growth predictions. Therefore, DBH modeling was the focus of this investigation. Five methods were proposed for local calibration of individual tree DBH growth predictions and compared to two sets of results generated without calibration. Data from the US Forest Service's Forest Inventory and Analysis (FIA) program were used to test the methods for eleven widely-distributed forest tree species in Virginia. Two calibration approaches were based on median prediction errors from locally-observed DBH increments spanning a five year average time interval. Two were based on simple linear regression models fitted to the locally-observed prediction errors, and one method employed a mixed effects regression model with a random intercept term estimated from locally-observed DBH increments. Data witholding, specifically a leave-one-out cross-validation was used to compare results of the methods tested. Results showed that any of the calibration approaches tested in general led to improved accuracy of DBH growth predictions, with either of the median-based methods or regression based methods performing better than the random-effects-based approach. Equivalence testing showed that median or regression-based local calibration methods met error tolerances within ± 12% of observed DBH increments for all species with the random effects approach meeting a larger tolerance of ± 17%. These results showed improvement over uncalibrated models, which failed to meet tolerances as high as ± 30% for some species in a newly-fitted DBH growth model for Virginia, and as high as ± 170% for an existing model fitted to data from a much larger region of the Southeastern United States. Local calibration of regional DBH increment models provides an effective means of substantially reducing prediction errors when a relatively small set of observations are available from local sources such as permanent forest inventory plots, or the FIA database. MS The Forest Vegetation Simulator (FVS) is a growth and yield model widely-used for predicting stand dynamics, management and decision support in North American forests. Diameter increment is a major component in modeling tree growth. The system of integrated analytical tools in FVS is primarily based on the performance of the diameter increment model and the subsequent use of predicted in diameter at breast height (DBH) over time in forecasting tree attributes. To address the challenge of predicting growth in highly variable and geographically expansive forest systems, FVS was designed to include an internal calibration algorithm that makes use of growth observations, when available, from permanent inventory plots. The basic idea was that observed growth rates on a small set of remeasured trees are used to adjust or “calibrate” FVS growth predictions. The FVS internal calibration was the subject being investigated here. Five alternative methods were proposed attributed to a specific site or stand of interest and compared to two sets of results, which were based on median prediction errors, generated without calibration. Results illustrated that median-based methods or regression based methods performed better than the random-effects-based approach using independently observed growth data from Forest Service FIA re-measurements in Virginia. Local calibration of regional DBH increment models provides an effective means of substantially reducing prediction errors. The results of this study should also provide information to evaluate the efficiency of FVS calibration alternatives and a possible method for future implementation.

In an era of global-scale climate change, agricultural production faces a unique challenge due to its reliance on stochastic natural endowments, including temperature, precipitation, and water availability for irrigation. This dissertation presents a series of essays to examine how agricultural producers react and adapt to challenges presented by climate change and scarce irrigation water allocated through the prior appropriation doctrine. The dissertation approaches the problem from three distinct perspectives: institutional differences, climate and water availability, as well as producers' expectation on future endowments. Chapter 2 presents an institutional perspective, in which I investigate how different water allocation mechanisms within the prior appropriation doctrine result in differences in producers' crop allocation decisions. I find that water users in irrigation districts are able to plant more water-intensive crops than farmers outside irrigation districts. Chapter 3 presents the interaction between nature and human systems, in which I examine how the physiological complementarity of temperature and water availability diffuses from crop yield (at the intensive margin) to crop allocation strategies (at the extensive margin). Using a theoretical model I show that the observed complementarity reflects a combination of two mechanisms: yield impact through physiological complementarity, and adaptation response through shifting crop allocation patterns. Using an empirical model, I find that farmers adapt to changing climate conditions by growing more profitable crop mixes when presented with more growing degree-days (GDD), precipitation and groundwater access. Chapter 4 presents a behavioral perspective, in which I test how producers' expectation formation processes lead to short term over-adjustments to weather and water availability fluctuations. Using a fixed-effect regression on lagged weather and water realizations, I find that agricultural producers engage in a combination of cognitive biases, including the availability heuristic and the reinforcement strategy. Adopting these alternative learning mechanisms causes farmers to significantly over-react to more recent fluctuations in weather and water availability when making ex ante acreage and crop allocation decisions. Ph. D. In an era of global-scale climate change, agricultural production faces a unique challenge due to its reliance on variable natural factors, including temperature, precipitation, and water availability for irrigation. This dissertation presents a series of essays to examine how agricultural producers react and adapt to challenges presented by climate change and scarce irrigation water allocated through the prior appropriation doctrine. Chapter 2 presents an institutional perspective, in which I investigate how different water allocation regimes result in differences in producers’ cropping decisions. I find that irrigation districts benefit its users by allowing them to plant more water-intensive crops than farmers outside irrigation districts. Chapter 3 presents a natural science perspective, in which I examine how temperature and water availability jointly affect agricultural production and adaptation. I find that farmers significantly adapt to changing climate conditions by growing more profitable crop mixes when presented with higher temperature, precipitation, and groundwater access. Chapter 4 presents a behavioral perspective, in which I test how agricultural decision making are affected by how producers form expectations over future climate. I find that agricultural producers engage in a combination of cognitive biases when forming expectations, and as a result over-react to more recent fluctuations in weather and water availability when making acreage and crop allocation decisions.

Lyme disease is the most significant vector-borne disease in the United States. Its southward advance over the last several decades has been quantified, and previous research has examined the potential role of climate change on the disease's expansion, but no research has considered the role of future land cover patterns upon its distribution. This research examines Lyme disease risk in the southeastern United States based on estimated land cover projections under four different Intergovernmental Panel on Climate Change Special Report Emissions Scenarios (IPCC-SRES) A1B, A2, B1, and B2. Results are aggregated to census tracts which are the basic unit of analysis for this study. This study applied previously established relationships between Lyme disease and land cover in Virginia to the projected land cover layers under each scenario. The study area, the southeastern United States, was defined from Level III Ecoregions that are present in Virginia and extend throughout the Southeast. Projected land cover data for each scenario were obtained from the USGS. The projected land cover datasets are compatible with the National Land Cover Dataset (NLCD) categories and had seventeen land cover categories. The raster datasets were reclassified to four broad land cover types: Water, Developed, Forest, and Herbaceous areas and the relationship between certain landscape configurations were analyzed using FRAGSTATS 4.2. Significant variables established in previous research were used to develop a spatial Poisson regression model to project Lyme disease incidence for each decade to the year 2100. Results indicated that potential land cover suitability for Lyme disease transmission will increase under two scenarios (A1B and A2) while potential land cover suitability for Lyme disease transmission was predicted to decrease under the other two scenarios (B1 and B2). Total area under the highest category of potential land cover suitability Lyme disease transmission was calculated for each year under each scenario. The A2 scenario experiences the most rapid acceleration of potential land cover suitability for Lyme disease transmission, with an average increase of 16,163.95 km² per decade, while the A1B scenario was projected to show an average increase of 3,458.47 km² per decade. Conversely, the B1 scenario showed an average decrease of 595.7 km² per decade and the B2 scenario showed the largest decrease of potential land cover suitability for Lyme disease transmission with an average decrease of 2,006.83 km² per decade. This study examined the potential spatial distribution of potential land cover suitability for Lyme disease transmission in the southeastern United States under four different future land cover scenarios. The results indicate geographic regions of the study area that are at greatest risk of potential land cover suitability for Lyme disease transmission under four different predictive scenarios developed by the IPCC. The A1B and A2 land cover projections are predicted to have an overall increase in areas where the Lyme disease transmission cycle will be enhanced by 2100 and the scenarios have a primary focus on economic development. Economic concerns outweigh environmental concerns for the A1B scenario, in addition to a high standard of living. The A2 scenario describes rapid population growth which results in high rates of land cover conversion to developed land; in addition, this scenario describes a reduction of environmental protection. The B1 and B2 land cover projections are predicted to have an overall decrease in areas of high Lyme disease transmission by 2100 and these scenarios have a central focus on environmental sustainability. The B1 scenario is characterized by a high environmental awareness which results in lower demand for forest products. A common theme for the B1 scenario is restoration and forest protection. Finally, the B2 scenario is described as improving local and regional environmental value which results in a high demand for biofuels and repossession of degraded lands, and an overall increase of forest cover. This study was the first to predict potential land cover suitability for Lyme disease risk and geographic distribution using projected land cover in the southeastern United States, and the results of this research can aid in the reduction of Lyme disease as it continues to expand in the south. Master of Science

Questions remain regarding the impacts of late-Holocene human activities and environmental change on landscapes of the Caribbean islands. This dissertation examined the long-term environmental history of two sites in the northern Caribbean primarily through the analysis of proxy data sources contained in sediment cores. At Laguna Alejandro, a coastal lagoon in the southwestern Dominican Republic, we interpreted, from sediment lithology and stable oxygen isotope data, at least ten storm events over the past 1,000 years, producing the first long record of storm activity from the island. During the Little Ice Age (1400−1800 CE), we interpreted an increased frequency of hurricane landfalls at the study site with longer ecosystem recovery times and decreased fire activity versus during earlier, more moist periods of the late-Holocene. At Freshwater Pond, an inland pond on Barbuda, we interpreted vegetation disturbance from presence of disturbance pollen taxa and biomass burning near the pond from abundance of macroscopic (>125 µm) charcoal from sediments representing ~150 BCE–1250 CE, with consistency of burning and human history on the island informed by the archaeological record suggesting fire activity was primarily due to Pre-Columbian inhabitants. Microscopic charcoal analysis indicated that extra-local burning, primarily island-wide, continued until ~1610 CE then declined, possibly reflecting a change in land-use practices by Europeans who entered the region in 1492 CE and established a permanent settlement on the island in the 1660s. My study on modern pollen from surface soils and sediments, the first from lowland seasonally-dry vegetation of the Greater Antilles, informed our ideas on vegetation-pollen representation in different plant communities, including tropical dry forest, thorn forest, mangrove, mudflat, and lagoon. My modern pollen results also aided in the interpretation of stratigraphic pollen in the study of nearby L. Alejandro’s sediments and revealed changes in floristic composition at the study site through time. Pollen of maize (Zea mays) and Prosopis juliflora in sediments representing ~1760 CE document human subsistence agriculture and disturbance to tropical dry forest in the watershed. PHD

The growing Landsat data archive represents more than four decades of continuous Earth observation. Landsat's role in scientific analysis has increased dramatically in recent years as a result of the open-access policy of the U.S. Geological Survey (USGS). However, this rich data record suffers from relatively low temporal resolution due to the 16-day revisit period of each Landsat satellite. To estimate Landsat images at other points in time, researchers have proposed data-fusion approaches that combine existing Landsat data with images from other sensors, such as MODIS (Moderate Resolution Imaging Spectroradiometer) from the Terra and Aqua satellites. MODIS provides daily revisits, however, with a spatial resolution that is significantly lower than that of Landsat. Fusion of Landsat and MODIS is challenging because of differences in their spatial resolution, band designations, swath width, viewing angle and the noise level. Fusion is even more challenging for heterogeneous landscapes. In the first part of our work, the multiresolution analysis offered by the wavelet transform was explored as a suitable environment for Landsat and MODIS fusion. Our proposed Wavelet-based Spatiotemporal Adaptive Reflectance Fusion Model (WSTARFM) is the first model to merge Landsat and MODIS successfully. It handles the heterogeneity of the landscapes more effectively than the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) does. The system has been tested on simulated data and on actual data of two study areas in North Carolina. For a challenging heterogeneous study area near Greensboro, North Carolina, WSTARFM produced results with median R-squared values of 0.98 and 0.95 for the near-infrared band over deciduous forests and developed areas, respectively. Those results were obtained by withholding an actual Landsat image, and comparing it with a predicted version of the same image. These values represent an improvement over results obtained using the well-known STARFM technique. Similar improvements were obtained for the red band. For the second (homogeneous) study area, WSTARFM produced comparable prediction results to STARFM. In the second part of our work, Landsat-MODIS fusion has been explored from the temporal perspective. The fusion is performed on the Landsat and MODIS per-pixel time series. A new Multisensor Adaptive Time Series Fitting Model (MATSFM) is proposed. MATSFM is the first model to use mapped MODIS values to guide the fitting applied to the sparse Landsat time series. MATSFM produced results with median R-squared of 0.98 over the NDVI images of the first heterogeneous study area compared to 0.97 produced by STARFM. For the second study area, MATSFM also produced better prediction accuracy than STARFM. Ph. D.

Understanding irrigator responses to previous changes in water availability is critical to building effective institutions that allow for efficient and resilient management of water resources in the face of potentially increasing scarcity due to climate change. Using remote sensing data, I examined irrigator responses to seasonal changes in water availability in Idaho's Snake River Plain over the past 33 years. Google Earth Engine's high performance cloud computing and big data processing capabilities were used to compare the performance of three spectral indices, three compositing algorithms and two sensors for 2002 and 2007 for distinguishing between irrigated and non-irrigated parcels. We demonstrate that, on average, the seasonal-maximum algorithm yields a 60% reduction in county scale root mean square error (RMSE) over the accepted single-date approach. We use the best performing classification method, a binary threshold of the seasonal maximum of the Normalized Difference Moisture Index (NDMI), to identify irrigated and non-irrigated lands in Idaho's Snake River Basin for 1984-2016 using Landsat 5-8 data. NDMI of irrigated lands was found to generally increase over time, likely as a result of changes in agricultural practices increasing crop productivity. Furthermore, we find that irrigators with rights to small areas, and those with only surface water rights are more likely to have a major reduction (>25%) in irrigated area and conversely those with a large, groundwater rights are more likely to have major increases (>25%) in the extent of their irrigation. Master of Science

This study examines the effectiveness of using the Normalized Difference Vegetation Index (NDVI) derived from 1326 different Landsat Thematic Mapper and Enhanced Thematic Mapper images in finding isolated housing starts within the Commonwealth of Virginia's forests. Individual NDVI images were stacked by year for the years 1995-2011 and the yearly maximum for each pixel was extracted, resulting in a 17-year image stack of all yearly maxima (a 98.7% data reduction). Using location data from housing starts and well permits, known previously forested housing starts were isolated from all other forest disturbance types. Samples from housing starts and other forest disturbances, as well as from undisturbed forest, were used to derive vegetation index thresholds enabling separation of disturbed from undisturbed forest. Disturbances, once identified, were separated accurately (overall accuracy = 85.4 percent, F-statistic = 0.86) into housing starts and other forest disturbances using a classification tree and only two variables from the Disturbance Detection and Diagnostics (D3) algorithm: the maximum NDVI in the available recovery period and the slope between the NDVI value at the time of the disturbance and the maximum NDVI in the available recovery period. Landsat time series stacks thus show promise for identifying even the small changes associated with exurban development. Master of Science

In the past 15 years, a large amount of public-domain lidar data has been collected over the Southeastern United States. Most of these acquisitions were undertaken by government agencies, primarily for non-forestry purposes. That is, they were collected mostly to aid in the creation of digital terrain models and to support hydrological and engineering assessments. Such data is not ideal for forestry purposes mainly due to the low pulse density per square meter, the high scan angles and low swath overlaps associated with these acquisitions. Nevertheless, the large area of coverage involved motivated this work. In this dissertation, I first look at how such lidar data (from non-forestry acquisitions) can be combined with National Forest Inventory tree height data to generate a large-area canopy height model. A simple linear regression model was developed using two lidar-based metrics as predictors: the 85th percentile of heights of canopy first returns and the coefficient of variation of the heights of canopy first returns. This model had good predictive ability over 76 disparate lidar projects, covering an area of approximately 297,000 square kilometers between them. Factors leading to the residual lack-of-fit of the model were also analyzed and quantified. For example, predictive ability was found to be better for softwood forests, forests with more homogeneous vegetation structure and for terrains with gentler slopes. Given that as much as 30% of the US is covered by public domain non-forestry lidar acquisitions, this is a first step for constructing a national wall-to-wall vertical vegetation structure map, which can then be used to ask important questions regarding forest inventories, carbon sequestration, wildlife habitat suitability and fire risk mitigation. Then, I examined whether such lidar data could be further used to predict understory shrub presence over disparate forest types. The predictability of classification model was low (accuracy = 62%, kappa = 0.23). Canopy occlusion factors and the heterogeneity of the understory layer were implicated as the main reasons for this poor performance. An analysis of the metrics chosen by the modeling framework highlighted the importance of non-understory metrics (metrics related to canopy openness and topographic aspect) in influencing shrub presence. As the proposed set of metrics were developed over a wide range of temperate forest types and topographic conditions of Southeastern US, it is expected that it will be useful for more localized future studies. Lastly, I explored the possibility of combining lidar-derived canopy height maps with Landsat-derived stand-age maps to predict plantation pine site index over large areas (site index is a measure of forest productivity). The model performance was assessed using a Monte Carlo technique (RMSE = 3.8 meters, relative RMSE = 19%). A sample site index map for large areas of Virginia and South Carolina was generated (map coverage area: 832 sq. km) and implications were discussed. Analysis of the resulting map revealed the following: (1) there is an increase in site index in most areas, compared to the 1970s, and (2) approximately 83% of the area surveyed had low levels of productivity (defined as site index < 22.0 meters for base age of 25 years). This work highlights the efficacy of combining lidar-based canopy height maps with other similar remote sensing based datasets to understand aspects of forest productivity over large areas, and to help make policy-relevant recommendations. Ph. D.

The interrelationship between trees and humans is primordial. As pressures on natural resources grow and become more complex this innate connection drives an increased need for improved data and analytical techniques for assessing the status and trends of forests, trees, their products, and their services. Techniques for using readily available data such as the Forest Inventory and Analysis (FIA) database and output from forest disturbance detection algorithms derived from Landsat data, such as Vegetation Change Tracker (VCT), for estimating forest attributes across time from the state and inventory unit level down to the stand and pixel level are presented. Progressively more comprehensive harvest and parcel boundary records are incorporated appropriately. Quantification of attributes, including non-timber forest products and fine-scale age estimates, across the landscape both historically and into the future is emphasized. Spatial information on the distribution of forest resources by age-class provides knowledge of timber volume through time and across the landscape to support forest management for sustained production. In addition to monitoring forest resources in regards to their value as products for human consumption, their measurement facilitates analysis of the relationship of their spatial and temporal abundance to other resources such as water and wildlife. Ph. D.

Predictions of how forest productivity and carbon sequestration will respond to climate change are essential for making forest management decisions and adapting to future climate. However, current predictions can include considerable uncertainty that is not well quantified. To address the need for better quantification of uncertainty, we calculated and compared ecosystem model parameter, ecosystem model process, climate model, and climate scenario uncertainty for predictions of Southeastern U.S. pine forest productivity. We applied a data assimilation using Metropolis-Hastings Markov Chain Monte Carlo to fuse diverse datasets with the Physiological Principles Predicting Growth model. The spatially and temporally diverse data sets allowed for novel constraints on ecosystem model parameters and allowed for the quantification of uncertainty associated with parameterization and model structure (process). Overall, we found that the uncertainty is higher for parameter and process model uncertainty than the climate model uncertainty. We determined that climate change will result in a likely increase in terrestrial carbon storage and that higher emission scenarios increase the uncertainty in our predictions. In addition, we determined regional variations in biomass accumulation due to a response to the change in frost days, temperature, and vapor pressure deficit. Since the uncertainty associated with ecosystem model parameter and process uncertainty was larger than the uncertainty associated with climate predictions, our results indicate that better constraining parameters in ecosystem models and improving the mathematical structure of ecosystem models can improve future predictions of forest productivity and carbon sequestration. Master of Science

This study assessed five fertilizer treatments (control – no fertilizer, urea, urea treated with N-(n-butyl) thiophosphoric triamide (NBPT), coated urea + NBPT (CUF), polymer coated urea (PCU) ) during two application seasons (spring, summer) to: 1) compare fertilizer nitrogen (N) losses (see Chapter 2); 2) evaluate temporal N uptake patterns of loblolly pine (see Chapter 3); and 3) evaluate fertilizer N cycling and partitioning in a loblolly pine ecosystem (see Chapter 4). Chapter 2 results showed enhanced efficiency fertilizers (EEFs) significantly reduced ammonia (NH3) volatilization losses compared to urea. Mean NH3 volatilization after spring fertilization ranged from 4% to 26% for EEFs versus 26% to 40% for urea, and 8% to 23% for EEFs versus 29% to 49% for urea in summer. Chapter 3 results showed an increase in timing and development of foliage in fertilized compared to unfertilized plots. In addition, the cumulative N uptake by loblolly pines increased over the entire growing season from N originating from fertilizer and natural sources. Chapter 4 results showed greater fertilizer N recovery for EEFs in both spring and summer (80%, 70-80% respectively) compared to urea (60%, 50% respectively) with most fertilizer N recovered from mineral soil (20% to 50%) and loblolly pines (10% to 50%). Three primary conclusions come from this research: 1) EEFs reduce NH3 volatilization after N fertilization compared to urea regardless of application timing and weather conditions (see Chapter 2); 2) N uptake by loblolly pines increases over the entire growing season after N fertilization (see Chapter 3); more fertilizer N remains in the ecosystem with EEFs compared to urea with most fertilizer N remaining in the soil (see Chapter 4). From these findings, we hypothesize that the EEFs in this study: 1) reduce ammonia volatilization which 2) translates to an increase in fertilizer nitrogen remaining in the loblolly pine plantation system that 3) increases the amount of plant available nitrogen for an extended period into the stand rotation and 4) increases fertilizer nitrogen use efficiency (FNUE) for all enhanced efficiency fertilizers investigated in this study compared to the conventional form of fertilizer N used in forestry, urea. Ph. D.

Forests provide a number of ecosystem services which sustain and enrich the wildlife, human societies, and the environment. However, many disturbances threaten forest ecosystems, making it necessary to monitor their health for optimal management and conservation. Although there are many indicators of forest health, changes in biogeochemical cycling, loss of species diversity, and invasive plants are particularly useful due to their vulnerability to the effects of climate change and intensive agricultural land use. Thus, this work evaluates the use of imaging spectroscopy to monitor forest nutrient status, species diversity, and plant invasions in the Mid-Atlantic region. The research is divided into four separate studies, each of which evaluated a unique application for imaging spectroscopy data at a different scale within the forest. The first two studies examined loblolly pine nutrient status at the leaf and canopy scales, respectively. The first study determined that loblolly pine foliar macronutrient concentrations can be successfully modeled across the Southeastern US (R2=0.39-0.74). Following on these results, the second study focused on the relationship between physical characteristics, reflectance, and nutrients. Reflectance values and W scattering coefficients produced successful nitrogen models across loblolly pine plots at the canopy scale. Regression models showed similar explanatory power for nitrogen, although W scattering coefficients were significantly correlated with nitrogen at multiple wavelengths and reflectance variables were not. However, the direction of some of the correlations with W and the unusually high directional area scattering factor values indicate a need for further experimentation. The third study found that several imaging spectroscopy algorithms were moderately successful in identifying wavyleaf basketgrass invasions in mixed deciduous forests (overall accuracy=0.35-0.78; kappa=0.41-0.53). Lastly, the fourth study used a novel imaging spectroscopy/lidar fusion to identify canopy gaps and measure species diversity of understory vegetation. The lidar algorithm identified 29 of 34 canopy gaps, and regression models explained 49 percent of the variance in gap species diversity. In conclusion, imaging spectroscopy can be used to evaluate ecosystem health through forest nutrient status, nitrogen models, species diversity estimates, and identification of invasive plant species. Ph. D.

Expanding urbanization, characterized by increased impervious surfaces and decreased tree canopy, is contributing to rising urban temperatures. This trend has implications for energy consumption, which strategically placed trees can modify by casting shade upon building and ground surfaces. However, urban densification, a paradigm of modern residential land development, often constrains space for planting shade trees. Thus, the overall objective of this dissertation was to investigate shade tree planting strategies and their effects on residential cooling and heating energy conservation for dense urban neighborhoods in U.S. cities on a latitudinal gradient. The first study used a computer program called Shadow Pattern Simulator to examine the effects of tree form, tree placement, and sunlight exposure on shade provision for a residential structure model. Simulation results affirmed the conventional strategy in northern latitudes that recommends planting shade trees on the east or west aspect for maximizing beneficial shade while avoiding tree plantings on the south aspect to minimize any heating penalty of undesirable shade. However, in southern latitudes, planting trees on southerly aspect should not be discounted because the shorter heating season lessens the detrimental heating penalty while providing beneficial season shade. The second study, using an energy simulation program called EnergyPlus, evaluated the effect of a single shade tree upon the energy consumption of the structure model. This study affirmed that energy conservation benefits are influenced by the quantity as well as the quality of tree shade upon building surfaces. In addition, interactions between sun angle, tree form, and tree placement were observed to influence tree shade effects on annual energy consumption. In the third study, based on the first two studies, an alternative tree placement strategy, which reconfigured tree placement around the residential structure, was developed to maximize cooling and heating energy savings while attenuating space conflicts. The alternative strategy was found to be as effective as the conventional strategy while being more responsive to parcel or building orientations in dense urban neighborhoods. Overall, understanding the fundamental interactions between tree form, tree placement, and geographic settings is critical for improving energy conservation benefits of shade trees in dense urban settings. Ph. D.

Data from an airborne laser scanner, a dual-band interferometric synthetic aperture radar (DBInSAR), and Landsat were evaluated for estimating ages of even-aged loblolly pine stands in Appomattox-Buckingham State Forest, Virginia, U.S.A. The DBInSAR data were acquired using the GeoSAR sensor in summer, 2008 in both the P- and X-bands. The LiDAR data were acquired in the same summer using a small-footprint laser scanner. Loblolly pine stand ages were assigned using the establishment year of loblolly pine stands provided by the Virginia Department of Forestry. Random circular plots were established in stands which varied in age from 5 to 71 years and in site index from 21 to 29 meters (base age 25 years). LiDAR- and GeoSAR-derived independent variables were calculated. The final selected LiDAR model used common logarithm of age as the dependent variable and the 99.5th percentile of height above ground as the independent variable (R2adj = 90.2%, RMSE = 4.4 years, n=45). The final selected GeoSAR models used the reciprocal of age as the dependent variable and had three independent variables: the sum of the X-band magnitude, the 25th percentile of X/P-band magnitudes, and the 90th percentile of the X-band height above ground (R2adj = 84.1%, RMSE = 7.9 years, n=46). The Vegetation Change Tracker (VCT) algorithm was run using a digital elevation layer, a land cover map, and a series of Landsat (5 and 7) images. A comparison was made between the loblolly pine stand ages obtained using the three methods and the reference data. The results show that: (1) although most of the time VCT and reference data ages were different, the differences were normally small, (2) all three remote sensing methods produced reliable age estimates, and (3) the Landsat-VCT algorithm produced the best estimates for younger stands (5 to 22 years old, RMSEVCT=2.2 years, RMSEGeoSAR=2.6 years, RMSELiDAR=2.6 years, n=35) and the model that used LiDAR-derived variables was better for older stands. Remote sensing can be used to estimate loblolly pine stand age, though prior knowledge of site index is required for active sensors that rely primarily on the relationship between age and height. Ph. D.

Fires occur extensively across Zambia every year, a problem recognized as a major threat to biodiversity. Yet, basic tools for mapping at a spatial and temporal scale that provide useful information for understanding and managing this problem are not available. The objectives of this research were: to develop a method to map the spatio-temporal seasonal fire occurrence using satellite imagery, to develop a technique for estimating missing data in the satellite imagery considering the possibility of change in land cover over time, and to demonstrate applicability of these new tools by analyzing the fine-scale seasonal patterns of landscape fires in eastern Zambia. A new approach for mapping burned areas uses multitemporal image analysis with a fuzzy clustering algorithm to automatically select spectral-temporal signatures that are then used to classify the images to produce the desired spatio-temporal burned area information. Testing with Landsat data (30m resolution) in eastern Zambia showed accuracies in predicting burned areas above 92%. The approach is simple to implement, data driven, and can be automated, which can facilitate quicker production of burned area information. A profile-based approach for filling missing data uses multitemporal imagery and exploits the similarity in land cover temporal profiles and spatial relationships to reliably estimate missing data even in areas with significant changes. Testing with simulated missing data from an 8-image spectral index sequence showed highly correlated (R2 of 0.78-0.92) and precise estimates (deviations 4-7%) compared to actual values. The profile-based approach overcomes the common requirement of gap-filling methods that there is gradual or no change in land cover, and provides accurate gap-filling under conditions of both gradual and abrupt changes. The spatio-temporal progression of landscape burning was evaluated for the 2009 and 2012 fire seasons (June-November) using Landsat data. Results show widespread burning (~ 60%) with most fires occurring late (August-October) in the season. Fire occurrence and burn patch sizes decreased with increasing settlement density and landscape fragmentation reflecting human influences and fuel availability. Small fires (< 5ha) are predominant and were significantly under-detected (>50%) by a global dataset (MODIS Burned Area Product (500m resolution)), underscoring the critical need of higher geometric resolution imagery such as Landsat imagery for mapping such fine-scale fire activity. Ph. D.

The forests of the eastern United States reflect complex and highly dynamic patterns of change. This thesis seeks to explore the highly variable nature of these changes and to develop techniques that will enable researchers to examine their temporal and spatial patterns. The objectives of this research are to: 1) determine whether the forest change dynamics in the eastern US differ across levels of the urban hierarchy; 2) identify and explore key micropolitan areas that deviate from anticipated trends in forest change; and 3) develop and apply techniques for Big Data exploration of Landsat satellite images for forest cover analysis over large regions. Results demonstrate that forest change at the micropolitan level of urbanization differs from rural and metropolitan forest dynamics. The work highlights the dynamic nature of forest change within the Piedmont Atlantic megaregion, largely attributed to the forestry industry. This is by far the most dominant change phenomenon in the region but is not necessarily indicative of permanent forest change. A longer temporal analysis may be required to separate the contribution of the forest industry from permanent forest conversion in the region. Techniques utilized in this work suggest that emerging tools that provide supercomputing/parallel processing capabilities for the analysis of big satellite data open the door for researchers to better address different landscape signals and to investigate large regions at a high temporal and spatial resolution. The opportunity now exists to conduct initial assessments regarding spatio-temporal land cover trends in the southeast in a manner previously not possible. Master of Science

Varietal forestry may increase the productivity of loblolly pine (Pinus taeda L.) in the Southern United Sates. However, the effects of these genetic x environment interactions are still poorly understood. In this study we examined the responses in growth, stand uniformity and leaf level physiology of loblolly pine clonal varieties and families to silvicultural intensity and site effects. We also looked for patterns in observed traits that were consistent between crown ideotypes. Two varieties of each crown ideotype (narrow vs broad crowns) and two families (controlled mass pollinated (CMP) and open pollinated (OP) family) were tested on the Virginia Piedmont (VA) and the North Carolina Coastal Plain (NC) under different silvicultural intensities (operational vs intensive), and planting density (617, 1235 and 1852 trees per hectare). Data were collected during the first four growing season after establishment. At NC, intensive silviculture increased crown-width, height and dbh by 33%, 14%, and 23%, respectively. At VA, intensive silviculture increased crown-width, height and dbh by 41%, 10%, and 23%, respectively. Intensive silviculture also increased slightly but significantly the stand uniformity of stem growth. However, the differences in productivity between silvicultural treatments were not explained by differences in leaf-level physiology. Across all treatments and sites the varieties generally grew faster than the OP family, but the differences were higher at VA. Varieties did not differ in stem growth, but the broadest crown variety had greater stand uniformity, photosynthetic rate (Asat), carbon isotope discrimination (∆¹³) and lower fascicle size than the OP family. None of the traits assessed inthis study was consistent within the ideotypes. Varieties classified in the same crown-ideotypes may respond differently to the environmental effects of site and silviculture, which reinforces the need of matching varietal forestry with precision silviculture to achieve gains in productivity. Ph. D.

This case study used Zhongwei City in northwest China to quantify the urbanization and revegetation processes (1990-2011) through a unified sub-pixel measure of vegetation cover. Research strategies included: (1) Conduct sub-pixel vegetation mapping (1990, 1996, 2004, and 2011) with Random Forest (RF) algorithm by integrating high (OrbView-3) and medium spatial resolution (Landsat TM) data; (2) Examine simple Dark Object Subtraction (DOS) atmospheric correction method to support temporal generalization of sub-pixel mapping algorithm; (3) And characterize patterns of vegetation cover dynamics based on change detection analysis. We found the RF algorithm, combined with simple DOS, showed good generalization capability for sub-pixel vegetation mapping. Predicted sub-pixel vegetation proportions were consistent for "pseudo-invariant" pixels. Vegetation change analysis suggested persistent urban development within the city boundary, accompanied by a continuous expansion of revegetated area at the city fringe. Urban development occurred at both the suburban and urban core areas, and was mainly shaped by transportation networks. A transition in revegetation practices was documented: the large-scale governmental revegetation programs were replaced by the commercial afforestation conducted by industries. This study showed a slight increase in vegetation cover over the time period, balanced by losses to urban expansion, and a likely severe degradation of vegetation cover due to conversion of arable land to desert vegetation. The loss of arable land and the growth of artificial desert vegetation have yielded a dynamic equilibrium in terms of overall vegetation cover during 1990 to 2011, but in the long run vegetation quality is certainly reduced. Master of Science

To successfully protect and manage wetlands, efficient and accurate tools are needed to identify where wetlands are located, the wetland type, what condition they are in, what are the stressors present, and the trend in their condition. Wetland mapping and monitoring are useful to accomplish these tasks. Wetland mapping and monitoring with optical remote sensing data has mainly focused on using a single image or using image acquired over two seasons within the same year. Now that Landsat data are available freely, a multi-temporal approach utilizing images that span multiple seasons and multiple years can potentially be used to characterize wetland dynamics in more detail. In addition, newer remote sensing techniques such as Light Detection and Ranging (lidar) can provide highly detailed and accurate topographic information, which can improve our ability to discriminate wetlands. Thus, the overall objective of this study was to investigate the utility of lidar and multi-temporal Landsat data for mapping and monitoring of wetlands. My research is presented as three independent studies related to wetland mapping and monitoring. In the first study, inter-annual time series of Landsat data from 1985 to 2009 was used to map changes in wetland ecosystems in northern Virginia. Z-scores calculated on tasseled cap images were used to develop temporal profile for wetlands delineated by the National Wetland Inventory. A change threshold was derived based on the Chi-square distribution of the Z-scores. The accuracy of a change/no change map produced was 89% with a kappa value of 0.79. Assessment of the change map showed that the method used was able to detect complete wetland loss together with other subtle changes resulting from development, harvesting, thinning and farming practices. The objective of the second study was to characterize differences in spectro-temporal profile of forested upland and wetland using intra and inter annual time series of Landsat data (1999-2012). The results show that the spector-temporal metrics derived from Landsat can accurately discriminate between forested upland and wetland (accuracy of 88.5%). The objective of the third study was to investigate the ability of topographic variables derived from lidar to map wetlands. Different topographic variables were derived from a high resolution lidar digital elevation model. Random forest model was used to assess the ability of these variables in mapping wetlands and uplands area. The result shows that lidar data can discriminate between wetlands and uplands with an accuracy of 72%. In summary, because of its spatial, spectral, temporal resolution, availability and cost Landsat data will be a primary data source for mapping and monitoring wetlands. The multi-temporal approach presented in this study has great potential for significantly improving our ability to detect and monitor wetlands. In addition, synergistic use of multi-temporal analysis of Landsat data combined with lidar data may be superior to using either data alone for future wetland mapping and monitoring approaches. Ph. D.

The use of nitrogenous fertilizers in agricultural and forestry practices coupled with increased fossil fuel combustion and resulting nitrogen (N) deposition across the landscape have contributed to a near doubling of N inputs to terrestrial ecosystems. With such dramatic changes have come adverse environmental consequences including the acidification of soil and water resources and an increased rate of biodiversity loss in both flora and fauna. A method of rapidly predicting ecosystem susceptibility to N loss across large spatial scales would facilitate the identification of those systems most likely to contribute to potentially adverse environmental impacts. To begin the development of such a framework, this research utilizes study sites located throughout the geographic ranges of Douglas-fir (Pseudotsuga menziesii) and loblolly pine (Pinus taeda) to explore relationships between hyperspectral remote sensing, N stable isotope ratios ("15N) and growth response to nitrogenous fertilizer. In both species multiple linear regression models relating leaf-level reflectance to "15N showed strong predictive capabilities, with some models explaining more than 65% of the variance in "15N. Significant correlations between "15N metrics and growth response to N fertilization were also observed in both species. Additional exploratory analysis of the inclusion of "15N metrics with other environmental and edaphic variables to predict fertilizer growth response showed an increase in model performance with the addition of the enrichment factor (EF ="15NFol - "15NSoil). This research demonstrates the ability of hyperspectral reflectance to predict "15N and reveals the potential of "15N to be included in future models to predict fertilizer growth response. Master of Science

Researchers now have unprecedented access to free Landsat data, enabling detailed monitoring of the Earth's land surface and vegetation. There are gaps in the data, due in part to cloud cover. The gaps are aperiodic and localized, forcing any detailed multitemporal analysis based on Landsat data to compensate. Harmonic regression approximates Landsat data for any point in time with minimal training images and reduced storage requirements. In two study areas in North Carolina, USA, harmonic regression approaches were least as good at simulating missing data as STAR-FM for images from 2001. Harmonic regression had an R^2"0.9 over three quarters of all pixels. It gave the highest R_Predicted^2 values on two thirds of the pixels. Applying harmonic regression with the same number of harmonics to consecutive years yielded an improved fit, R^2"0.99 for most pixels. We next demonstrate a change detection method based on exponentially weighted moving average (EWMA) charts of harmonic residuals. In the process, a data-driven cloud filter is created, enabling use of partially clouded data. The approach is shown capable of detecting thins and subtle forest degradations in Alabama, USA, considerably finer than the Landsat spatial resolution in an on-the-fly fashion, with new images easily incorporated into the algorithm. EWMA detection accurately showed the location, timing, and magnitude of 85% of known harvests in the study area, verified by aerial imagery. We use harmonic regression to improve the precision of dynamic forest parameter estimates, generating a robust time series of vegetation index values. These values are classified into strata maps in Alabama, USA, depicting regions of similar growth potential. These maps are applied to Forest Service Forest Inventory and Analysis (FIA) plots, generating post-stratified estimates of static and dynamic forest parameters. Improvements to efficiency for all parameters were such that a comparable random sample would require at least 20% more sampling units, with the improvement for the growth parameter requiring a 50% increase. These applications demonstrate the utility of harmonic regression for Landsat data. They suggest further applications in environmental monitoring and improved estimation of landscape parameters, critical to improving large-scale models of ecosystems and climate effects. Ph. D.

Planting woody biomass for energy production can be used as a mine reclamation procedure to satisfy the Surface Mining Control and Reclamation Act of 1977 (SMCRA) and provide renewable energy for the United States. This study examines the economic viability of bioenergy production on previously mined lands using multiple hardwood species and treatments. Five species were planted at two densities; one-half of the trees were fertilized in year two. Height and diameter of the trees were measured annually for five years; the first three years by cooperating researchers at Virginia Tech, the last two years specifically for this report. Current and predicted mass of the species, effects of planting density and fertilizer application, and the land expectation value (LEV) of each treatment were summarized. A sensitivity analysis was conducted to determine how changes in production costs, stumpage price, rotation length, and interest rate affect the economic feasibility of bioenergy production. Renewable energy and mine reclamation policies were investigated and it was determined that woody bioenergy can be planted as a mine reclamation procedure and may receive financial incentives. Production cost appears to have the largest impact on LEV and is often the difference between positive and negative returns for the landowner. The extra cost of fertilization and high density planting do not increase LEV; the unfertilized, low density treatments have the best LEV in all examined scenarios. In general, bioenergy was found to be economically viable as a mine reclamation procedure only in limited circumstances. In low cost, high price scenarios, bioenergy crops could have the potential to reforest both active and abandoned mine lands throughout southern Appalachia. Master of Science

This dissertation investigated the practicality and expediency of applying remote sensing data fusion products to the analysis of dryland vegetation phenology. The objective of the first study was to verify the quality of the output products of the spatial and temporal adaptive reflectance fusion method (STARFM) over the dryland Arizona study site. Synthetic 30 m resolution images were generated from Landsat-5 Thematic Mapper (TM) data and a range of 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance datasets and assessed via correlation analysis with temporally coincident Landsat-5 imagery. The accuracy of the results (0.61 < R2 < 0.94) justified subsequent use of STARFM data in this environment, particularly when the imagery were generated from Nadir Bi-directional Reflectance Factor (BRDF)-Adjusted Reflectance (NBAR) MODIS datasets. The primary objective of the second study was to assess whether synthetic Landsat data could contribute meaningful information to the phenological analyses of a range of dryland vegetation classes. Start-of-season (SOS) and date of peak greenness phenology metrics were calculated for each STARFM and MODIS pixel on the basis of enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) time series over a single growing season. The variability of each metric was calculated for all STARFM pixels within 500 m MODIS extents. Colorado Plateau Pinyon Juniper displayed high amounts of temporal and spatial variability that justified the use of STARFM data, while the benefit to the remaining classes depended on the specific vegetation index and phenology metric. The third study expanded the STARFM time series to five years (2005-2009) to examine the influence of site characteristics and climatic conditions on dryland ponderosa pine (Pinus ponderosa) forest phenological patterns. The results showed that elevation and slope controlled the variability of peak timing across years, with lower elevations and shallower slopes linked to higher levels of variability. During drought conditions, the number of site variables that controlled the timing and variability of vegetation peak increased. Ph. D.

Wildlife biodiversity provides a variety of ecosystem services and is an important indicator of overall ecosystem health. This research investigates the relationship between wildlife biodiversity and landscape characteristics in Virginia. The goal is to produce predictive models of biodiversity within the Commonwealth using environmental characteristics, including fragmentation metrics at the class- and landscape-levels, as well as other environmental variables. The 1248 12-digit watersheds in Virginia are the sampling units for the analyses, with the state stratified into the seven US Environmental Protection Agency's Level III classification. Data on wildlife alpha diversity is based on two sets of species data maintained by the Virginia Dept. of Game & Inland Fisheries (VDGIF). The first chapter provides an introduction to the issue of biodiversity conservation and the background information for this work. The second chapter describes the study using the 2001 National Land Cover Data to calculate class- and landscape-level fragmentation metrics. Best subset regression is used to determine the best predictors for wildlife biodiversity using these metrics. Final selected models range in predictive power from R2 = 0.41 to 0.73 for each of the 7 ecoregions. The third chapter analyzes the relationship between wildlife biodiversity and various environmental variables in order to determine the strength of these factors as drivers for alpha diversity. These variables are then incorporated with the fragmentation metrics in an attempt to improve the biodiversity models. The environmental variable models had R2 = 0.22 to 0.65 across the ecoregions, while R2 = 0.28 to 0.72 when the environmental and fragmentation variables are combined. The last chapter focuses on the conclusions of the studies, the limitations of the data, and the benefits of this work. Overall, our results underline the importance of using fragmentation metrics in Virginia's wildlife models. Master of Science

The analysis of visibility between two points on the earth's terrain is a common use of GIS software. Most commercial GIS software packages include the ability to generate a viewshed, or a map of terrain surrounding a particular location that would be visible to an observer. Viewsheds are often generated using "bare-earth" Digital Elevation Models (DEMs) derived from the process of photogrammetry. More detailed models, known as Digital Surface Models (DSMs), are often generated using Light Detection and Ranging (LIDAR) which uses an airborne laser to scan the terrain. In addition to having greater accuracy than photogrammetric DEMs, LIDAR DSMs include surface features such as buildings and trees. This project used a visibility algorithm to predict visibility between observer and target locations using both photogrammetric DEMs and LIDAR DSMs of varying resolution. A field survey of the locations was conducted to determine the accuracy of the visibility predictions and to gauge the extent to which the presence of surface features in the DSMs affected the accuracy. The use of different resolution terrain models allowed for the analysis of the relationship between accuracy and optimal grid size. Additionally, a series of visibility predictions were made using Monte Carlo methods to add random error to the terrain elevation to estimate the probability of a target's being visible. Finally, the LIDAR DSMs were used to determine the linear distance of terrain along the lines-of-sight between the observer and targets that were obscured by trees or bushes. A logistic regression was performed between that distance and the visibility of the target to determine the extent to which a greater amount of vegetation along the line-of-sight impacted the target's visibility. Master of Science

The overall objectives of this dissertation were to (1) determine whether leaf area index (LAI) (Chapter 2), as well as stem density and height to live crown (Chapter 3) can be estimated accurately in intensively managed pine plantations using small-footprint, multiple-return airborne laser scanner (lidar) data, and (2) ascertain whether leaf area index in temperate mixed forests is best estimated using multiple-return airborne laser scanning (lidar) data or dual-band, single-pass interferometric synthetic aperture radar data (from GeoSAR) alone or both in combination (Chapter 4). In situ measurements of LAI, mean height, height to live crown, and stem density were made on 109 (LAI) or 110 plots (all other variables) under a variety of stand conditions. Lidar distributional metrics were calculated for each plot as a whole as well as for crown density slices (newly introduced in this dissertation). These metrics were used as independent variables in best subsets regressions with LAI, number of trees, mean height to live crown, and mean height (measured in situ) as the dependent variables. The best resulting model for LAI in pine plantations had an R2 of 0.83 and a cross-validation (CV) RMSE of 0.5. The CV-RMSE for estimating number of trees on all 110 plots was 11.8 with an R2 of 0.92. Mean height to live crown was also well-predicted (R2 = 0.96, CV-RMSE = 0.8 m) with a one-variable model. In situ measurements of temperate mixed forest LAI were made on 61 plots (21 hardwood, 36 pine, 4 mixed pine hardwood). GeoSAR metrics were calculated from the X-band backscatter coefficients (four looks) as well as both X- and P-band interferometric heights and magnitudes. Both lidar and GeoSAR metrics were used as independent variables in best subsets regressions with LAI (measured in situ) as the dependent variable. Lidar metrics alone explained 69% of the variability in temperate mixed forest LAI, while GeoSAR metrics alone explained 52%. However, combining the LAI and GeoSAR metrics increased the R2 to 0.77 with a CV-RMSE of 0.42. Analysis of data from active sensors shows strong potential for eventual operational estimation of biophysical parameters essential to silviculture. Ph. D.

The need for an efficient dimensionality reduction technique has remained a critical challenge for effective analysis of hyperspectral data for vegetation applications. Discrete wavelet transform (DWT), through multiresolution analysis, offers oppurtunities both to reduce dimension and convey information at multiple spectral scales. In this study, we investigated the utility of the Haar DWT for AVIRIS hyperspectral data analysis in three different applications (1) classification of three pine species (Pinus spp.), (2) estimation of leaf area index (LAI) using an empirically-based model, and (3) estimation of LAI using a physically-based model. For pine species classification, different sets of Haar wavelet features were compared to each other and to calibrated radiance. The Haar coefficients selected by stepwise discriminant analysis provided better classification accuracy (74.2%) than the original radiance (66.7%). For empirically-based LAI estimation, the models using the Haar coefficients explained the most variance in observed LAI for both deciduous plots (cross validation R² (CV-R²) = 0.79 for wavelet features vs. CV-R² = 0.69 for spectral bands) and all plots combined (CV R² = 0.71 for wavelet features vs. CV-R² = 0.50 for spectral bands). For physically-based LAI estimation, a look-up-table (LUT) was constructed by a radiative transfer model, DART, using a three-stage approach developed in this study. The approach involved comparison between preliminary LUT reflectances and image spectra to find the optimal set of parameter combinations and input increments. The LUT-based inversion was performed with three different datasets, the original reflectance bands, the full set of the wavelet extracted features, and the two wavelet subsets containing 99.99% and 99.0% of the cumulative energy of the original signal. The energy subset containing 99.99% of the cumulative signal energy provided better estimates of LAI (RMSE = 0.46, R² = 0.77) than the original spectral bands (RMSE = 0.69, R² = 0.42). This study has demonstrated that the application of the discrete wavelet transform can provide more accurate species discrimination within the same genus than the original hyperspectral bands and can improve the accuracy of LAI estimates from both empirically- and physically-based models. Ph. D.

This dissertation describes research investigating the potential for using Landsat data to identify and characterize woody canopy cover on reclaimed coal-mined lands through three separate studies. The objective of the first study was to assess whether surface coal mines in the forested central Appalachian regions of the US can be separated from the other prevalent forest-replacing disturbances through analysis of an interannual chronosequence of Landsat images. Disturbances were classified using descriptors of the disturbance/recovery trajectories: disturbance minimum, recovery slope and recovery maximum. Three vegetation indices (VIs) (normalized difference vegetation index, NDVI; tasseled cap greenness/brightness ratio, TC G/B; and inverse of Landsat band 3, B3I) were used to analyze multitemporal trajectories generated using both pixels and objects. Classification accuracies using objects were better than those obtained using pixels for all VIs. The highest object-based classification accuracy was achieved using TC G/B (89%), followed by NDVI (88%) and B3I (80%). The objective of the second study was to evaluate performance of a woody canopy cover (including both native and invasive species) estimation method based on the 2011 National Land Cover Database (NLCD) protocol for both mined and non-mined areas of the central Appalachians. Potential explanatory variables included raw and derived bands from leaf-on and leaf-off Landsat scenes plus terrain descriptors. Results show that the model developed to estimate canopy cover for mines (R2 = 0.78, Adj. R2 = 0.77, RMSE = 16%) is more robust than the models developed for non-mines, mixed, and all areas combined. The objective of the third study was to determine whether four disturbance/recovery parameters (recovery time, disturbance minimum, recovery slope, and recovery maximum), alone or in combination with variables identified in the second study, enable robust estimation of woody canopy cover on reclaimed surface coal mines. Of the disturbance/recovery parameters, only recovery time made a significant contribution to the model (R2 0.45, Adj. R2 0.44, RMSE 14%). Addition of leaf-on and leaf-off NDVI improved the R2 to 0.54 (Adj. R2 0.53, RMSE 13%). Analysis of Landsat data has strong potential for identifying reclaimed mines and characterizing the extent to which woody canopy has recovered post-reclamation. Ph. D.