Search

In the absence of complete lidar coverage, digital surface models (DSMs) and point clouds produced from the United States Department of Agriculture National Agriculture Imagery Program (NAIP) are increasingly being analyzed for quality and application feasibility. This study compared canopy heights derived from NAIP DSMs (10 m) and point clouds to those derived from lidar data collected over Mountain Lake Biological Station and the Great Smoky Mountains Twin Creeks Site by the National Ecological Observatory Network (NEON) Airborne Observation Platform for 62 mixed deciduous tree plots. Mean dominant height (MDH) was estimated using lidar and the NAIP products using the 90th percentile of heights in a given plot as the independent variable for both the lidar-and NAIP-derived point clouds. The dependent variable was field-measured MDH, calculated using the four tallest trees for each 0.04-hectare plot based on the NEON woody vegetation structure dataset. All data (field and remotely sensed) were collected in 2018. Using maximum likelihood spatial error model for all analyses, the NAIP DSM (10 m resolution) resulted in a strong relationship with MDH (coefficient of determination (R-2) = 0.90, standard error (SE) = 1.71 m). However, the 90th percentiles of heights derived from the point clouds were better at estimating MDH than was the comparatively coarse resolution DSM (NAIP point clouds: R-2 = 0.94, SE = 1.40 m; lidar: R-2 = 0.95, SE= 1.29 m, respectively) and are strongly correlated to each other (R-2 = 0.99, SE = 0.68 m). The main limitation of the NAIP datasets was found to be where shadowing occurred due to steep terrain in the Great Smoky Mountain site. These areas resulted in erroneously high vegetation heights. Mean dominant heights estimated using NAIP DSMs and point clouds are thus comparable to those estimated using lidar data in these closed-canopy temperate deciduous forests where shadowing from steep terrain is not present. The utility of both the NAIP-derived 10 m DSM and the point clouds for estimating tree heights paves the way for statewide mapping of heights over the deciduous forests in Tennessee, Virginia, and possibly beyond. Virginia Tech Interdisciplinary Graduate Education Program in Remote Sensing and the National Science Graduate Research Fellowship Program [1840995] Published version This work was supported by the Virginia Tech Interdisciplinary Graduate Education Program in Remote Sensing and the National Sci-ence Graduate Research Fellowship Program under Grant No.1840995. The authors would like to acknowledge Margaret Cumberland (NEON Field Ecologist II) for her guidance on the NEON woody structure data. Special thanks to the university departments for ensuring access to re-sources during the COVID-19 pandemic.

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.

Gross Primary Productivity (GPP) is the amount of carbon fixed during photosynthesis by all producers in the ecosystem. GPP is dependent on light use efficiency (LUE), photosynthetically active radiation (PAR), and fraction of absorbed PAR (fPAR). To estimate light use efficiency (LUE), which is dependent on the exposure of leaves to photosynthetically active radiation (PAR), the photochemical reflectance index (PRI) is calculated using 531 nm and 570 nm wavelengths. Our team has examined the sensitivity of forest canopy PRI to canopy shadows using airborne hyperspectral data acquired in eastern North Carolina. A bounding box for this study was placed adjacent to a flux tower in a loblolly pine stand to evaluate the variability of LUE derived from the reflectance data acquired in the morning, midday and afternoon, and compare LUE estimates to the flux tower observations. We compute PRI values for the sunlit and shadowed parts of the canopy determined by thresholding a 2 m panchromatic image produced by averaging wavelength bands from 525 nm to 600 nm. We show that PRI for the sunlit canopy is substantially lower than for the shadowed components at all times of day, leading to an overestimate of LUE when using whole-canopy reflectance. Implications for estimating GPP using PRI reflectance as a surrogate for LUE is examined by comparing to the flux tower derivation of GPP. This work is being done to refine measurement requirements for a diurnal constellation concept, the Structure and Function of Ecosystems (SAFE). Virginia Tech. Office of Geographical Information Systems and Remote Sensing

Remote sensing offers many advantages to previous forest measurements, such as limiting costs and time in the field. Light detection and ranging (lidar) has been shown to enable accurate estimates of forest height. Lidar does produce precise measurements for ground elevation and forest height, where and when it is available. However, it is expensive to collect and does not have wall-to-wall coverage in the United States. In this study, we estimated height using the National Agricultural Imagery Program (NAIP) photogrammetric point clouds to create a predicted height map for managed loblolly pine stands in the southeastern United States. Recent studies have investigated the ability of digital aerial photogrammetry (DAP), and more specifically NAIP, as an alternative to lidar as a means of estimating forest height due to its lower costs, frequency of acquisition, and wall-to-wall coverage across the United States. Field-collected canopy height for 534 plots in Virginia and North Carolina were regressed against distributional metrics derived from NAIP and lidar point clouds. The best regression model for predicted pine height used the 90th percentile of height (P90), predicted pine height = 1.09(P90) – 0.43. The adjusted R^2 is 0.93 and the RMSE is 1.44 m. This model is being used to produce a 5 x 5 m canopy height model for all pine stands across Virginia, North Carolina, and Tennessee. NAIP-derived point clouds are thus a viable means of predicting canopy height in southern pines. Virginia Tech. Office of Geographical Information Systems and Remote Sensing

Pine plantations in the Southeastern United states are presently under-quantified using disturbance based metrics of forest change. Methods such as the Global Forest Change data-set have limited accuracy in identifying pine plantations. Direct estimation of pine plantations poses its’ own challenges but the structure of plantations creates an interesting opportunity. The uniform structure and pattern of pine plantations permits the implementation of object identifying neural network techniques using high spatial resolution imagery such as the National Agriculture Imagery Program. This presentation will explore the preliminary results of such a process. Virginia Tech. Office of Geographical Information Systems and Remote Sensing

Forest Productivity Cooperative Webinar

Estimating forest canopy height

Outside forests, Andhra Pradesh is greening, and we are learning by how much— and why. Our objectives are to (1) map smallholder forest plantations in Andhra Pradesh using multitemporal HLS S10 and/or very-high spatial resolution commercial satellite data, and (2) determine the drivers of plantation forest establishment. We have (1) developed a land use model that integrates land quality and spatial aspects of the farm with market variables and farmer production decisions, (2) implemented a household-level socio-economic survey, and (3) completed a Sentinel 2-era classification that separates natural from planted forest with 94% accuracy. Plantation forestry is rapidly expanding in Asia, and understanding the extent, drivers, and ramifications of these new trees outside forests is vital.

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?)

No (Peer reviewed?) true (Invited?)

Take homes Tobler’s Law still in effect with time series – spatial autocorrelation in temporal coherence can help in both preprocessing and estimation Continual process improvement in extant algorithms needed Need additional means by which variations within (parameterization) and across algorithms addressed (the Reverend…) Time series improving higher order products (example with NLCD TCC) enabling near continuous monitoring No (Peer reviewed?) https://landsat.usgs.gov/landsat-science-team-meeting-jul-2017

Forest ecological forecasting results and decision support tools are now available to foresters in the southern United States. McIntire-Stennis Program of NIFA, USDA (Project Number 1007054, “Detecting and Forecasting the Consequences of Subtle and Gross Disturbance on Forest Carbon Cycling”) Pine Integrated Network: Education, Mitigation, and Adaptation Project (PINEMAP), a Coordinated Agricultural Project funded by the US Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA), Award No. 2011-68002-30185

List of all proceedings from the Virginia Tech GIS Symposium, held on April 4, 2014. Virginia Tech. Office of Geographical Information Systems and Remote Sensing

Accurate and unbiased wall-to-wall canopy height maps for large regions are useful to forest scientists and managers for several reasons such as carbon accounting and wildfire fuel-load monitoring. Airborne lidar is establishing itself as the most promising technology for this. However, mapping large areas often involves using lidar data from different projects executed by different agencies, involving varying acquisition dates, sensors, pulse densities, etc. In this work, we address the important question of how accurately one can predict and model canopy heights over large areas of the Southeastern US using a heterogeneous lidar datasets (with more than 90 separate lidar projects). A unique aspect of this effort is the use of extensive and robust field data from the Forest Inventory and Analysis (FIA) program of the US Forest Service. We construct a simple linear model to predict canopy height at plots from distributional lidar metrics. Preliminary results are quite promising: over all lidar projects, we observe a correlation of 81.8% between the 95th percentile of lidar heights and field-measured height, with an RMSE of 3.66 meters (n=3078). We further estimated that ~1.21 m (33%) of this RMSE could be attributed to co-registration inaccuracies. The RMSE of 3.66 m compares quite well to previous efforts that used spaceborne lidar sensors to estimate canopy heights over large regions. We also identify and quantify the importance of several factors (like point density, the predominance of hardwoods or softwood) that also influence the efficacy of our prediction model. Virginia Tech. Office of Geographical Information Systems and Remote Sensing

As nitrogen deposition increases globally, ecosystem changes will occur. It is important to understand the growth response of different ecosystems and where nitrogen retention will occur. Stable isotopes of foliar nitrogen can provide insight into how this process is occurring in the soil. Previous studies have found links between foliar nitrogen and optical properties.This study focuses on the Southern Piedmont Forests. A study at the Duke Forest's Blackwood Division in Chapel Hill, North Carolina, allowed for foliar sampling across various soil types, elevations, and species. Concurrent hyperspectral imagery was taken, allowing for the relationship between environmental drivers, optical properties, and nitrogen content to be identified. These relationships will be used to map nitrogen content at the canopy level. Foliar sampling was performed in species identified as major contributors to the canopy. Major canopy contributors were oak, hickory, poplar, sweetgum, and pine. Foliar samples were analyzed for chlorophyll, macronutrients, carbon, nitrogen, and stable isotope N15. The relationship of these characteristics, as well as elevation, soil type, species, and optical properties, were input to predict the spectral signature associated with the N15 content.Ancillary data on elevation, soil type, and species, coupled with hyperspectral imagery, will use the relationships to predict canopy level nitrogen at the image scale. Virginia Tech. Office of Geographical Information Systems and Remote Sensing

A chronologically ordered sequence of satellite images can be used to learn how natural features of the landscape change over time. For example, we can learn how forests react to human interventions or climate change. Before these satellite images can be used for this purpose, they need to be examined for clouds and cloud shadow that may hide important features of the landscape and would lead to misinterpretation of forest conditions. Once clouds and their shadow have been identified, researchers can then look for other images that include the feature of interest, taken a bit earlier or later in time, to fill in the "missing information" for the original image. Therefore, the task of identifying clouds and their shadow is extremely important for the correct and efficient use of each image. Computer algorithms are only imperfectly suited for this task. The aim of this project is to outsource the cloud interpretation task to a global internet community of "turkers" -workers recruited via amazon.com's online job market known as "Mechanical Turk." Virginia Tech. Office of Geographical Information Systems and Remote Sensing

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.

There is widespread implicit agreement within the field of psychotherapy about the therapeutic potential of mental imagery. A review of the literature indicates, however, a paucity of general theory. The literature on mental imagery is mainly concerned with its application i.e. procedures and techniques. Theorising, where it occurs, is usually informed by the specific psychological model espoused by the particular psychotherapeutic modality. In order to advance the utilisation of mental imagery as a therapeutic intervention, more attention needs to be paid to developing broader transtheoretical frameworks. \ud In order to address this theory gap, research is required to identify common factors operating across all therapeutic schools with regard to the application of mental imagery. To this end a study was undertaken to inquire into potential category bases for developing generic typologies in mental imagery. A scoping exercise was undertaken of case studies of mental imagery in clinical practice published in academic journals in order to map out the field. A sample of suitable case vignettes drawn from a wide range of therapeutic approaches was selected. The imagery-related clinical material was abstracted and used as the data for a grounded theory style analysis. This analytic process disclosed one overarching category i.e. the therapeutic function of mental imagery and two core categories: 1. conveying information from the subconscious/wider mind-body system to the conscious mind, and 2. delivering directions from the conscious mind to the subconscious/wider mind-body system. These two categories were further differentiated into the following six specific functions: diagnostic; monitoring; processing; reparative; process management; and framing. It is proposed that this emerging functional typology of mental imagery has the potential to be the base of a coherent unifying transtheoretical model. \ud These findings are discussed and critiqued in the light of the original unabridged data, and the researcher’s own clinical practice with mental imagery. The relevance and implications of these findings are considered with regard to the wider field of psychotherapeutic practice. Recommendations are made regarding the further testing out and refinement of this potential model of the therapeutic functionality of mental imagery.\ud \ud This study is accompanied (and its subject matter further illuminated) by an account of the researcher’s own heuristic inquiry into the subjective and tacit dimensions of her research journey disclosed through symbolising this process as a mental image and monitoring its changes over time.

The southeastern United States is one of the most productive forestry regions in the world, encompassing approximately 100 million ha of forest land, about 87% of which is privately owned. Any alteration in this region's duration or rate of forest recovery has consequential economic and ecological ramifications. Despite the need for forest recovery monitoring in this region, a spatially comprehensive evaluation of forest spectral recovery through time has not yet been conducted. Remote sensing analysis via cloud-computing platforms allows for evaluating southeastern forest recovery at spatiotemporal scales not attainable with traditional methods. Forest productivity is assessed in this study using spectral metrics of southern yellow pine recovery following stand-replacing disturbance. An annual cloudfree (1984-2021) Landsat time series intersecting ten southeastern states was constructed using the Google Earth Engine API. Southern yellow pine stands were detected using the National Land Cover Database (NLCD) evergreen class, and pixels with a rapidly changing spectrotemporal profile, suggesting stand-replacing disturbance, were found using the Landscape Change Monitoring System (LCMS) Fast Loss product. Spectral recovery metrics for 3,654 randomly selected stands in 14 Level 3 EPA Ecoregions were derived from their 38-year time series of Normalized Burn Ratio (NBR) values using the Detecting Breakpoints and Estimating Segments in Trend (DBEST) change detection algorithm. Recovery metrics characterizing the rate (NBRregrowth), duration (Y2R), and magnitude (K-shift) of recovery from stand-replacing disturbances occurring between 1989 and 2011 were evaluated to identify long-term and wide-scale changes in forest recovery using linear regression and spatial statistics respectively. Sampled stands typically recover 35% higher in NBR than pre-disturbance and, on average, spectrally recover within seven years of disturbance. Recovery rate is shown to be increasing over time; temporal slope estimates for NBRregrowth suggest a 33% increase in early recovery rate between 1984 and 2011. Similarly, recovery duration measured with Y2R decreased by 43% during the study period with significant spatial variation. Results suggest that the magnitude of change in stand condition between rotations has decreased by 21% during the study period, has substantial regional divisions in high and low magnitude recovery between coastal and inland stands, and low NBR value sites have the most potential to increase their NBR value. Observed spatiotemporal patterns of spectral recovery suggest that changes in management interventions, atmospheric CO2, and climate over time have changed regional productivity. Results from this study will aid the understanding of changing productivity in southern yellow pine and will inform the management, monitoring, and modeling of this ecologically and economically important forest ecosystem. Master of Science The Southeast United States contains approximately 100 million hectares of forest land and is one of the world's most productive regions for commercial forestry. Forest managers and those who model the effects of different types of forest land on the changing climate need up-to-date information about how productive these forests are at removing carbon and producing wood and how that productivity differs across space and time. In this study, we evaluate the productivity of southern yellow pine stands by measuring stand recovery attributes from a disturbance that removes the majority or all of the trees in the stand. This is accomplished by locating 3,654 of randomly selected disturbed pine stands through ten southeastern states using freely available national data products derived from Landsat satellite imagery, namely a combination of the National Land Cover Database (NLCD) and the Landscape Change Monitoring System (LCMS), which provide information about the type of forest, and the year and severity of disturbance respectively. Annual Landsat satellite imagery from 1984 to 2021 is used to create a series of values over time for each stand representing the stand condition each year using an index called the Normalized Burn Ratio (NBR). A change detection algorithm called DBEST is applied to each stands NBR values to find the timing of disturbance and recovery, which is used to create three metrics characterizing the rate (NBRregrowth), duration (Y2R), and magnitude (K-shift) of recovery. We evaluated how these metrics change through time using linear regression and how they differ across space using regression residuals and spatial statistics. Across the region, stands typically increase in recovery rate, decrease in recovery duration, and decrease in recovery magnitude. On average, stands recover within seven years of disturbance and to a higher NBR value than pre-disturbance. However, there is significant spatial variation in this metric throughout the Southeast. The results indicate that stands with a lower vegetation condition, measured with NBR, before the disturbance had the most significant gain in stand condition after recovery, and stands with initially higher vegetation condition did not increase as much after recovery.

Forests perform many important ecological functions and provide numerous environmental services to humanity. Currently forests are under ever increasing pressures from humans through deforestation, changes in land use, and anthropogenic climate change. Managed forests play an important role in supplying forest products to the global population, necessitating the need to predict how forests will respond to climate change and how this will influence future wood product supplies. In this dissertation I used loblolly pine (Pinus taeda), the most extensively cultivated tree species in the United States, as a study system to simulate how climate change and forest management could alter the dynamics of managed forests in the future. Using the land component (the Community Land Model) of the widely used Community Earth System Model (CESM), I developed and validated a set of tools necessary to simulate the loblolly pine plantation system using the vegetation demography model embedded in CESM (FATES). This included developing a representation of loblolly pine using data from the literature, which was better able to capture forest growth and development observed in field studies than FATES's existing conifer tree representation. I added the ability to simulate several aspects of forest management not previously supported in FATES by creating the Vegetation Management Module, which I showed was able to realistically reproduce the common management practice of stand thinning. I used these new tools to perform simulations of how loblolly pine will grow across the Southeastern United States until the end of the 21st century, under the high and low climate change scenarios developed by the scientific community in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our experiments show that loblolly pine productivity may as much as double by the end of the century, with total wood harvest over that period increasing by almost half. I also showed that different management activities had significant effects on loblolly plantation yields, with mid-rotation stand thinning having an effect under both climate scenarios on par with increases due to the extreme climate change scenario SSP5 RCP8.5. I showed that these changes in wood yields could decrease the forest area in the Southeast required to meet the wood product demands over the rest of the century. These changes in plantation productivity could interact with socioeconomic factors to drive changes in land use and carbon storage in the Southeastern U.S. This work increases our understanding of how managed forests in the U.S.\ will be affected by climate change and how our management choices modulate that response. The techniques and tools developed here open up new areas of research into the role of forest management in the climate system. Doctor of Philosophy Forests benefit humans by regulating Earth's climate and by providing natural resources such as wood. In the Southeastern United States forestry is an important industry. Tree farms of southern pine trees produce a large percentage of the region's wood. Predicting how forests will grow in the future is important for planning and making investments. However, the burning of fossil fuels has increased carbon dioxide in the atmosphere and is changing Earth's climate. This is affecting how fast trees grow and how much wood can be harvested from forests. The methods that foresters have traditionally used to predict how trees will grow in the future do not account for climate change, and thus may not be as accurate in the future. An alternative is to use the computer models that scientists have developed to predict both how global climate will change in the future and how forests are influenced by climate. These computer programs can be used to predict how natural forests will grow in the future, but aren't set up to predict managed forests well. I made changes to one of these programs to make it possible to simulate the managed loblolly pine forests of the Southeastern United States. First, I tested these changes to make sure that simulated forests grew like real forests do today. Then I simulated how pine forests in Southeastern United States could grow over the next century with climate change. I found that pine forests will grow faster and allow more wood to be harvested as carbon dioxide in the atmosphere increases. If climate changes are extreme, loblolly forests could produce 70\% more wood than today by the end of the 21st century. I also showed that the manner in which forests were managed in simulations changes the amount of wood they produced, with some management practices increasing wood harvested by 50\% over the rest of the century. Because climate change could increase the amount of wood that can be produced from a fixed area of forest, I investigated how this might change the area of forest plantation in the Southeastern United States. Based on projections of demand for wood for the rest of the century I calculated how much loblolly pine forest would be needed to produce this wood over the next century. I found that increases in forest productivity due to climate change and forest management could decrease the forest area required to grow the wood we need. This could change how we use forests in the Southeastern United States, which in turn could have impacts on the climate.

Brazil reduced its annual deforested area from 27772 km2 to 4571 km2 from 2004 to 2012. This phenomenal achievement resulted from multiple government initiatives, most notably the Action Plan for the Prevention and Control of Deforestation in the Legal Amazon (PPCDAm). However, these ambitious deforestation control policies yield multiple spillover effects. This dissertation examines the effects of the two initiatives from the PPCDAm program, namely the Forest Code of 2012 and the Green Municipality Program. The first chapter provides causal evidence that land registration abates conflicts in Pará. The chapter discusses policy implications in three discussions, prospective deforestation control, potential agricultural growth, and livelihoods promotion within CAR and its related policies. The results from this chapter provoke a question about the drop in land conflicts that stimulates forest conservation on private landholdings. Thereon, my second chapter deals with the dynamic land clearing decision of private landholders in the Brazilian Amazon. The results suggest that the persistence of compliance, thus forest conservation on privately held land, is driven mainly by past compliance and municipality-level incentives. As these two chapters established that land registration abates conflicts, and private landholders are driven by specific incentives to preserve the forest on their land. My third chapter investigates the impact of the provincial governance promotion program on secondary forest recovery. Municipalities participating in the local government improvement program steadily observe an expansion in secondary forest areas. To sum up, my dissertation explores the spillover effects of the deforestation control policy, starting with achieving fewer land conflicts and investigating the local incentives to promote forest protection on private land. Lastly, I provide evidence that the governance promotion program will result in secondary forest recovery. Doctor of Philosophy Brazil reduced its annual deforested area from 27772 km2 to 4571 km2 from 2004 to 2012. This phenomenal achievement resulted from multiple government initiatives, most notably the Action Plan for the Prevention and Control of Deforestation in the Legal Amazon (PPCDAm). This dissertation presents the unintended consequences of two policies under PPCDAm, namely the Forest Code of 2012 and the Green Municipality Program, on land conflicts, environmental policy compliance, and secondary forest recovery. The first chapter provides robust evidence that perceived land tenure security (via land registration) effectively reduces land conflicts. Further, the chapter invokes if the drop in land conflicts stimulates forest conservation on private landholdings. Subsequently, my second chapter deals with the dynamic land clearing decision of private landholders in the Brazilian Amazon. The results suggest that the persistence of compliance, thus forest conservation on privately held land, is driven mainly by past compliance and municipality-level incentives. As these two chapters established that land registration abates conflicts, and private landholders are driven by specific incentives to preserve the forest on their land. My third chapter investigates the impact of the provincial governance promotion program on secondary forest recovery. Municipalities participating in the local government improvement program steadily observe an expansion in secondary forest areas. In summary, the dissertation begins with a study of the unintended consequences of the deforestation control policy, starting with achieving fewer land conflicts. Then, I present a study of the local incentives to promote forest protection on private land. Lastly, I present that the local governance promotion program will result in secondary forest recovery.

Master of Science Forests in the eastern United States have changed substantially in response to the introduction of highly competitive invasive species. Some overstory tree species have been virtually eliminated from their functional role in regulating understory vegetation in many southern Appalachian ecosystems. Die-offs of these trees have allowed understory evergreen shrubs to expand into areas where they were once restricted. Shrubs that have expanded in response to overstory tree die-offs can alter the ecology of forests for the foreseeable future. Our work leveraged multi-decadal archives of wintertime satellite imagery to document the spread of understory evergreen shrubs in a watershed located in western North Carolina. We investigated the relationship of this spread to local environmental characteristics like elevation, steepness (slope), slope direction (north, south, east, west), and distance-from-stream. The greatest changes in evergreen vegetation were documented on terrain at relatively high elevations, locations farther from streams, on southerly aspects, and on relatively steep terrain. We included historical field data collected during the same time period that showed a simultaneous increase in two understory evergreen shrub species after the die-off of ecologically significant overstory tree species. This information will help forest managers by describing areas where substantial spread has occurred and potentially use this information to inform future management action.

Forest plantations make up a large percentage of managed forest land globally. Assessing plantation productivity is vital from both commodity production and carbon management standpoints. Measuring the productivity of these areas is essential given their rapid growth and turnover. Transparent metrics to compare reported carbon storage with estimated values are required for internationally transferred mitigation outcomes under Article 6.2 of the Paris Agreement. Data from the Global Ecosystems Dynamics Investigation (GEDI) provide an excellent opportunity to measure plantation forests over large areas. We focused our efforts on Eucalyptus in southern Brazil and used data from an industrial partner to investigate plantation metrics (height, diameter, volume, stems per hectare, etc.) and to create a model of plantation diameter using Support Vector Regression (SVR). SVR enabled a robust model of tree diameter even given the heteroskedasticity and spatial auto correlation present in the GEDI data, which deleteriously impacted attempts at linear modeling. We could predict tree diameter in these plantations to within 1 cm using space-borne lidar, with broad implications for using space-borne lidars to monitor carbon accretion in secondary forest plantation. Master of Science Forest management practices have shifted in some cases to very crop-like forest plantings. These areas are functionally different from a 'natural' forest. Understanding the structure of these areas in a rapid and consistent manner is important to quantify the amount of carbon stored within these forests for international climate agreements such as the Paris Agreement. This effort focuses upon Eucalyptus forests in Southern Brazil. Using measurements from a lidar instrument (a lidar system fires a laser beam from space to the ground, recording the 'deflection' of the laser beam and the amount of time it takes to return to the sensor to measure features on the ground) we were able to measure the diameter of the trees to within a centimeter in these forests.

Tree canopy cover is an essential measure of forest health and productivity, which is widely studied due to its relevance to many disciplines. For example, declining tree canopy cover can be an indicator of forest health, insect infestation, or disease. This dissertation consists of three studies, focused on the spatial modeling and evaluation of tree canopy cover, drawing on recent developments and best practices in the fields of remote sensing, data collection, and statistical analysis.newlinenewline The first study evaluates how well harmonic regression variables derived at the pixel-level using a time-series of all available Landsat images predict values of tree canopy cover. Harmonic regression works to approximate the reflectance curve of a given band across time. Therefore the coefficients that result from the harmonic regression model estimate relate to the phenology of the area of each pixel. We use a time-series of all available cloud-free observations in each Landsat pixel for NDVI, SWIR1 and SWIR2 bands to obtain harmonic regression coefficients for each variable and then use those coefficients to estimate tree canopy cover at two discrete points in time. This study compares models estimated using these harmonic regression coefficients to those estimated using Landsat median composite imagery, and combined models. We show that (1) harmonic regression coefficients that use a single harmonic coefficient provided the best quality models, (2) harmonic regression coefficients from Landsat-derived NDVI, SWIR1, and SWIR2 bands improve the quality of tree canopy cover models when added to the full suite of median composite variables, (3) the harmonic regression constant for the NDVI time-series is an important variable across models, and (4) there is little to no additional information in the full suite of predictors compared to the harmonic regression coefficients alone based on the information criterion provided by principal components analysis. The second study presented evaluates the use of crowdsourcing with Amazon's Mechanical Turk platform to obtain photointerpretated tree canopy cover data. We collected multiple interpretations at each plot from both crowd and expert interpreters, and sampled these data using a Monte Carlo framework to estimate a classification model predicting the "reliability" of each crowd interpretation using expert interpretations as a benchmark, and identified the most important variables in estimating this reliability. The results show low agreement between crowd and expert groups, as well as between individual experts. We found that variables related to fatigue had the most bearing on the "reliability" of crowd interpretations followed by whether the interpreter used false color or natural color composite imagery during interpretation. Recommendations for further study and future implementations of crowdsourced photointerpretation are also provided. In the final study, we explored sampling methods for the purpose of model validation. We evaluated a method of stratified random sampling with optimal allocation using measures of prediction uncertainty derived from random forest regression models by comparing the accuracy and precision of estimates from samples drawn using this method to estimates from samples drawn using other common sampling protocols using three large, simulated datasets as case studies. We further tested the effect of reduced sample sizes on one of these datasets and demonstrated a method to report the accuracy of continuous models for domains that are either regionally constrained or numerically defined based on other variables or the modeled quantity itself. We show that stratified random sampling with optimal allocation provides the most precise estimates of the mean of the reference Y and the RMSE of the population. We also demonstrate that all sampling methods provide reasonably accurate estimates on average. Additionally we show that, as sample sizes are increased with each sampling method, the precision generally increases, eventually reaching a level of convergence where gains in estimate precision from adding additional samples would be marginal. Doctor of Philosophy Tree canopy cover is an essential measure of forest health, which is widely studied due to its relevance to many disciplines. For example, declining tree canopy cover can be an indicator of forest health, insect infestation, or disease. This dissertation consists of three studies, focused on the spatial modeling and evaluation of tree canopy cover, drawing on recent developments and best practices in the fields of remote sensing, data collection, and statistical analysis. The first study is an evaluation of the utility of harmonic regression coefficients from time-series satellite imagery, which describe the timing and magnitude of green-up and leaf loss at each location, to estimate tree canopy cover. This study compares models estimated using these harmonic regression coefficients to those estimated using median composite imagery, which obtain the median value of reflectance values across time data at each location, and models which used both types of variables. We show that (1) harmonic regression coefficients that use a simplified formula provided higher quality models compared to more complex alternatives, (2) harmonic regression coefficients improved the quality of tree canopy cover models when added to the full suite of median composite variables, (3) the harmonic regression constant, which is the coefficient that determines the average reflectance over time, based on time-series vegetation index data, is an important variable across models, and (4) there is little to no additional information in the full suite of predictors compared to the harmonic regression coefficients alone.newlinenewline The second study presented, evaluates the use of crowdsourcing, which engages non-experts in paid online tasks, with Amazon's Mechanical Turk platform to obtain tree canopy cover data, as interpreted from aerial images. We collected multiple interpretations at each location from both crowd and expert interpreters, and sampled these data using a repeated sampling framework to estimate a classification model predicting the "reliability" of each crowd interpretation using expert interpretations as a benchmark, and identified the most important variables in estimating this "reliability". The results show low agreement between crowd and expert groups, as well as between individual experts. We found that variables related to fatigue had the most bearing on the reliability of crowd interpretations followed by variables related to the display settings used to view imagery during interpretation. Recommendations for further study and future implementations of crowdsourced photointerpretation are also provided. In the final study, we explored sampling methods for the purpose of model validation. We evaluated a method of stratified random sampling with optimal allocation, a sampling method that is specifically designed to improve the precision of sample estimates, using measures of prediction uncertainty, describing the variability in predictions from different models in an ensemble of regression models. We compared the accuracy and precision of estimates from samples drawn using this method to estimates from samples drawn using other common sampling protocols using three large, mathematically simulated data products as case studies. We further tested the effect of smaller sample sizes on one of these data products and demonstrated a method to report the accuracy of continuous models for different land cover classes and for classes defined using 10% tree canopy cover intervals. We show that stratified random sampling with optimal allocation provides the most precise sample estimates. We also demonstrate that all sampling methods provide reasonably accurate estimates on average and we show that, as sample sizes are increased with each sampling method, the precision generally increases, eventually leveling off where gains in estimate precision from adding additional samples would be marginal.

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.

Remote sensing offers many advantages to previous forest measurements, such as limiting costs and time in the field. Light detection and ranging (lidar) has been shown to enable accurate estimates of forest height. Lidar does produce precise measurements for ground elevation and forest height, where and when it is available. However, it is expensive to collect and does not have wall-to-wall coverage in the United States. In this study, we estimated height using the National Agricultural Imagery Program (NAIP) photogrammetric point clouds to create a predicted height map for managed loblolly pine stands in the southeastern United States. Recent studies have investigated the ability of digital aerial photogrammetry (DAP), and more specifically NAIP, as an alternative to lidar as a means of estimating forest height due to its lower costs, frequency of acquisition, and wall-to-wall coverage across the United States. Field-collected canopy height for 534 plots in Virginia and North Carolina were regressed against the 90th percentile derived from NAIP point clouds. The model for predicted pine height using the 90th percentile of height (P90) is predicted pine height = 1.09(P90) – 0.43. The adjusted R^2 is 0.93, and the RMSE is 1.44 m. This model is being used to produce a 5 m x 5 m canopy height model for all pine stands across Virginia, North Carolina, and Tennessee. NAIP-derived point clouds are thus a viable means of predicting canopy height in southern pines. M.S. Collecting accurate measurements of pine plantations is essential to managing them to maximize various ecosystem goods and services. However, it can be difficult and time-consuming to collect these measurements by hand. This study demonstrates that point clouds derived from digital stereo aerial photograms enable calculating forest height to an accuracy sufficient for pine plantation management. We developed a simple linear regression model to predict forest canopy height using the 90th percentile of the photo-derived heights above the ground in a given area. With this model, we created a map of pine plantation canopy heights (consisting of 5 m x 5 m grid cells, each containing a canopy height estimate) for pine forests in Virginia, North Carolina, and Tennessee. Digital aerial photography from the National Agricultural Imagery Program (NAIP) is repeated every three years for a given state, allowing growth to be mapped over time. Photography collected by NAIP and similar programs also has uniform acquisition parameters in a given year applicable over large regions. State- and national photography programs like NAIP are also less expensive than other data sets, like airborne laser scanning data, that enable estimation of tree height.

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.