This study investigates pine heights by age for actively managed stands in the southeastern U.S. using ICESat-2 ATL08-derived height data and maps derived from the Landsat time series. We intersected ICESat-2 ground tracks with locations of pine plantations and the Landscape Change Monitoring System (LCMS) Fast Loss product to identify previously clear-cut pine plantations. We subtracted the LCMS Fast Loss year from the date of the ICESat-2 acquisition to determine plantation age at the time of the height measurement. We stratified the data for management intensity, where stands that experience both thinning and harvesting were considered actively managed. The goal was to develop age-height relationships across the region to characterize better the impact of management on productivity and site index. This research involved the analysis of over 137,998 ICESat-2 ATL08 segments in actively managed pine stands in the U.S. Southeast. We compared a subset of ICESat-2 heights with heights derived from airborne laser scanning acquisitions (ALS) available through the USGS 3D Elevation Program. The resulting R2 was 0.82, giving us confidence in the ICESat-2 ATL08-derived forest heights. Then, through data processing and analysis, we successfully stratified the spatial patterns of ICESat-2 ATL08 heights in the southeastern region. These patterns provided insights into the distribution and variability of forest heights across the region, contributing to informed decisions in forest management. We identified some challenges in predicting pine stand age through Landsat-derived disturbance products. We found that LCMS Fast Loss labels some heavy thins as a ‘Fast Loss,’ in addition to stand-clearing disturbances like clear-cuts, adding noise to our estimation of stand age. To overcome this issue, we employed a robust model of the logarithm of heights with a reciprocal of age using a random sample consensus (RANSAC) model to calculate site indices at base age 25 (years). Our results showed the site index for the region at a base age of 25 years is 20.1 m with a model R2 of 0.91. We compared the ICESat-2-derived site index with the FIA-derived site index to see the robustness of our results. Then, the modeled site index values were used to produce a map at a base age of 25 years for the U.S. Southeast, offering insights into spatial differences in regional forest productivity. The results of this study have important implications for ecological research, forest management, and well-informed decision-making. Insights into the distribution and trends of actively managed forest heights in the Southeast are gained from studying the vast dataset, allowing for more efficient land management and conservation initiatives. In actively manage stands, our site index equation improves the ability to anticipate site productivity and estimate future timber outputs. The difficulties with age estimation that have been observed highlight the need for better methods for mapping disturbances using remote sensing in forests that use thinning as a silvicultural prescription. / US Forest Service, joint venture agreement 20-JV-11330145-037, and the USDA Mclntire-Stennis Formula Grant program, accession number 7003904, “Precision forestry for southern pine carbon monitoring.” / Master of Science

Recent progress in reducing the extrinsic losses of fluorozirconate optical fibers has increased the material research efforts for these new waveguides. Fluorozirconate fibers, which are inherently more transparent than silica fibers, are predicted to have intrinsic losses as low as 0.001 dB/km at 3.45 μm [11]. Unfortunately, high intrinsic losses still plague these new optical fibers and these losses must be understood before ultra-low loss fibers become a reality. An automated fiber optic characterizer can help determine the loss mechanisms and the optical properties of fluorozirconate fibers so extrinsic loss mechanisms can be understood and eventually controlled. The automated fiber optic characterizer can also speed up the measurement process by using a microcomputer to align the fiber, calculate the results, and plot the graph. This thesis presents the technical issues involved in the design and construction of an automated infrared fiber optic characterizer. The thesis also outlines the test results of a constructed automated fiber optic characterizer. The characterizer measures spectral attenuation between 0.8 μm and 4 μm, differential modal attenuation between 1.6 μm and 4 μm, and numerical aperture at 1.55 μm and 2.55 μm. / Master of Science

Digital Landsat cover-type classifications have often proved less accurate than hoped for, or have been less detailed than needed. Recent research efforts have used additional data to supplement the four bands of Landsat MSS data in an attempt to increase the accuracies of computer classifications. The goal of this study was to evaluate the use of vegetation-related ancillary variables for improving the performance of a Landsat classification of the Great Dismal Swamp. Ancillary data considered to be related to the distribution of vegetation types in the swamp were registered with Landsat multispectral scanner data to a 50 meter UTM grid. The ancillary variables were peat depths and elevations from field surveys, and spectral texture values from the Landsat data. Discriminant analyses of a sample of pixels were performed to investigate the ability of spectral and ancillary data, separately and in combination, to discriminate between vegetation cover types. A layered classification procedure was developed that used discriminant analysis of ancillary data after a previous unsupervised spectral classification. This was compared to a spectral stratification classification and a straightforward unsupervised classification of spectral data alone. The layered procedure resulted in an accuracy of 21.46% for level III classes and 41.71% for level II classes. The accuracies for level III and level II classifications using the unsupervised procedure were 41.58% and 63.77%, respectively. Some possible explanations of the seemingly contradictory results were posed, and alternative procedures suggested. / Master of Science

As humanity continues its expansion into space, the understanding of the near-Earth space environment has never been more critical. As the ionosphere and thermosphere form the boundary between Earth's atmosphere and outer space, characterization of these regions is critical to understanding geospace. The Ionospheric Connection Explorer (ICON), launched in 2019, sought to establish the effects of forcing on the ionosphere and thermosphere from below and above, in part by using observations of ultraviolet airglow, which have long been used as a tool for making remote sensing observations of the upper atmosphere. The Extreme Ultraviolet Spectrometer (EUV) instrument was included on ICON to measure atmospheric airglow between 54 and 88 nm in order to estimate the density and structure of the ionosphere. In this work, we analyze the EUV observations throughout the ICON mission, characterizing the signal observed at various wavelengths during normal operations and during nadir and lunar calibrations. We use the ICON EUV data to develop the first algorithm for retrieval of neutral densities from EUV airglow. / Doctor of Philosophy / As humanity continues its expansion into space, the understanding of the near-Earth space environment has never been more critical. The neutral (thermosphere) and charged (ionosphere) particles in the upper atmosphere, around the altitude where satellite orbit, play a key role as the boundary between Earth and space. The Ionospheric Connection Explorer (ICON), launched in 2019, sought to establish how the ionosphere and thermosphere change over time. It measured the density of particles using light emitted from the atmosphere by chemical reactions (airglow). Extreme Ultraviolet (EUV) light is highly energetic, almost as much as X-rays, and the EUV airglow emitted by the atmosphere at certain can be used to detect O^+. In this work, we examine the measurements from the ICON EUV detector at various wavelengths to determine what other particles can be seen. Notably, we find that the measurements contain information about neutral atomic oxygen and molecular nitrogen. We develop a technique for using the EUV airglow brightness to measure the amount of O and N_2, the first of its kind.

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Chapter 1 examines alternative specifications of futures-based forecasting models to improve upon existing approaches constrained by restrictive assumptions and limited information sets. We replace historical averages with rolling regressions and incorporate current market information through the deviation of the current basis from its historical average. To address potential non-stationarity and structural changes in the cash-futures price relationship, we employ a five-year rolling estimation window. Our findings indicate that the rolling regression approach yields significant improvements in both accuracy and information content of cotton season-average price forecasts, primarily at short forecast horizons. Chapter 2 addresses challenges in vulnerability assessment for semi-arid regions dependent on rainfed agriculture, where extreme weather events pose significant risks to household livelihoods. Despite advancements in remotely sensed technology, accurately estimating weather variability's impact on household livelihoods remains challenging. This study evaluates the effects of weather anomaly measures, spatial resolutions (i.e., geographic level at which the weather anomaly measures are evaluated), and household characteristics on household likelihood of falling into poverty (i.e., vulnerability) estimates. Combining household consumption data for Niger with remotely sensed agro-environmental measures, we find significant variations in vulnerability estimates based on the use of various weather condition measures (3 percentage points, equivalent to 600,000 households), spatial resolutions (8 percentage points, totalling 1.6 million households), and household characteristics (10 percentage points, equivalent to approximately 2 million households). Chapter 3 evaluates student learning outcomes from student involvement in hands-on learning settings, specifically focusing on student-managed investment funds. To assess the changes in the obtained technical and practical skills, we combine knowledge tests with grading rubrics. As part of practical skills, we consider commodity market analysis, critical thinking, informed decision-making, and insightful interpretation of market analysis results. We evaluate our students' understanding of commodity markets and their practical trading skills before and after joining the student-managed investment fund program. We find significant improvements in student learning outcomes, with students showing an average increase of 28% in disciplinary or technical knowledge and 38% in practical skills. Our findings highlight the importance of hands-on learning experiences to bridge the gap between theoretical knowledge and real-world application and in developing the well-rounded skill set demanded by the job market. / Doctor of Philosophy / Chapter 1 explores several alternative specifications of futures-based forecasting models to improve existing approaches constrained by restrictive assumptions and limited information sets. Accurate prediction of cotton prices is vital for the agricultural sector, significantly impacting decisions made by farmers, traders, and policymakers. Reliable forecasts enable farmers to optimize their planting and harvesting strategies, allow traders to manage risk more effectively, and guide policymakers in developing informed agricultural policies. However, the inherent volatility of commodity markets, particularly cotton, presents substantial challenges to price forecasting. Traditional forecasting methods often struggle to capture rapid market changes, resulting in less reliable predictions. Our proposed more responsive forecasting approaches lead to a significant gain in accuracy and information content of cotton price projection and provide valuable insights that can enhance decision-making processes throughout the cotton industry. Chapter 2 explores how extreme weather events, like droughts, affect households in semi-arid regions where people's livelihood largely depends on rain-fed farming. While satellite technology helps monitor environmental changes, it is still challenging to accurately measure how weather changes impact people's lives. Our study focuses on Niger and uses household survey data to assess how various factors influence our understanding of the risk of falling into poverty (i.e., household vulnerability) due to adverse weather events. We found that the methods we use to measure weather conditions, the geographic scale at which we measure them, and the household information we include can all significantly alter our estimates of how many households are at risk of becoming poor. For example, different methods for measuring weather impacts can change estimates of household vulnerability by about 3 percentage points, affecting around 600,000 households. The geographic level (administrative unit level or within a 20 km buffer around an enumeration area) at which we assess weather conditions can shift our estimates by 8 percentage points, which is equivalent to 1.6 million households. Additionally, considering different household characteristics can change our estimates by 10 percentage points, impacting around 2 million households. Our findings are crucial for policymakers who aim to better understand and address the effects of weather on vulnerable communities. Chapter 3 evaluates student learning outcomes from participation in the Commodity Investing by Students program, a student-managed investment fund within the Department of Agricultural and Applied Economics at Virginia Tech. Our study focuses on students from the 2022/23 and 2023/24 academic years, assessing both their technical knowledge and practical skills gained during a year-long involvement in the program. To measure changes in technical skills, we administered knowledge-testing quizzes before and after the training class. Practical skills, such as commodity market analysis, critical thinking, informed decision-making, and insightful interpretation of market analysis results, we evaluated through trading projects submitted during and at the end of the training class. We grade these student submissions using a specific practical skill evaluation rubric. We find significant improvements in student learning outcomes. On average, students demonstrated a 28% increase in disciplinary knowledge and a 38% improvement in practical skills. Our findings highlight the effectiveness of hands-on learning in improving both technical knowledge and practical skills that are highly valued in today's job market.

The Caretta caretta, or Loggerhead sea turtle, is a protected species found across all temperate and subtropical oceans. Previous research has identified that the Caretta caretta has preferences for nesting sites based on beach width, beach slope, and vegetation coverage, which facilitated the development of a nesting site suitability index. In this thesis, these indices were integrated with standard coastal geomorphology metrics in the ESRI Suitability Modeler to pinpoint potential nesting locations for the C. caretta on various beach reaches. The results were then validated against observed nesting site data. The results of this study can inform critical decisions regarding beach use and maintenance as it pertains to sea turtle conservation. Overall, this study demonstrates the utility of geospatial analysis and suitability models in understanding and safeguarding sea turtle nesting habitats.

Yes / The Gandak River originates in Nepal and merges with the Ganga River in India. The Gandak River is experiencing significant geomorphological alterations due to climate change and anthropogenic causes. In this study, an attempt has done to examine river bank erosion & accretion, shifting of the river bank, sinuosity, and braiding index of the Gandak River between Sahibganj and the confluence with the Ganga River, covering a length of 92.4 km from 1989 to 2022 (33 years) using remote sensing and geospatial technologies. The delineation of the river bank line for different periods, along with the quantification of erosion and accretion of the river’s right and left banks, were analysed using GIS, including the sinuosity and braiding patterns. The overall sinuosity value ranged from 1.16 to 1.01 and did not follow any specific pattern in significant reaches. The sinuosity value was almost constant over the most d/s reach of 30.74 km. The braiding index of the River was found to be the maximum between Ismailpur and Baijalpur and the minimum value between Munja and Chakia in 2015 and 1995 respectively. This study revealed that the river is shifting to the right, and bank protection measures were needed. Finally, the proposed investigation revealed the braiding phenomenon, river shifting in the transverse direction, and shifting of the meander bend was primarily responsible for the erosion and accretion of the river banks. This study will benefit local government agencies, concerned authorities, and people residing along the banks of the Gandak River by providing insights into the river’s migration patterns. Further, this knowledge aids in better planning of riverbank protection measures and developing a navigation system.

No / In archaeological prospection, computer processing is essential for all stages of data manipulation. This article investigates the contributions which informatics has made in the past and looks at its potential for the future. It is shown how the workflow of satellite imagery, aerial photography and geophysical prospection can be broken down into measurements, acquisition, processing, visualisation and interpretation. Based on these categories, the advantages of digital data manipulations are explored with individual examples. It is shown that informatics can greatly assist with the final archaeological analysis of the measurements but that human experience and assessments are crucial for a meaningful interpretation.