Urban dynamics at regional scales has been increasingly important for economics, policies, and land use planning, and monitoring regional scale urban dynamics has become an urgent need in recent years. This study illustrated the use of time series nighttime light (NTL) data from the United States Air Force Defense Meteorological Satellites Program/Operational Linescan System (DMSP/OLS) to delineate urban boundaries and tracked three key urban changes: land cover change, population growth and GDP growth within Virginia. NTL data from different years were inter-calibrated to be comparable by using linear regression model and Pseudo Invariant Features (PIFs) method. Urban patches were delineated by applying thresholding techniques based on digital number (DN) values extracted from DMSP/OLS imagery. Compounded Night Light Index (CNLI) values were calculated to help estimate GDP, and these processes were applied in a time series from 2000 to 2010. Spatial patterns of DN change and the variation of CNLI indicate that human activities were increasing during the 10 years in Virginia. Accuracy of the results was confirmed using ancillary data sources from the U.S. Census and NLCD imagery. / Master of Science / Urban areas concentrate built environment, population, and economic activities, therefore, generating urban sprawl is a simultaneous result of land-use change, economic growth, population growth and so on. Remote sensing has been used to map urban sprawl within individual cities for a long time, while there has been less research focused on regional scale urban dynamics. However, the regional scale urban dynamics for economics, formulating policies, and land use planning has been increasingly important, and monitoring regional scale urban dynamics has become an urgent need in recent years. Here, we illustrated the use of multi-temporal United States Air Force Satellites data to help monitor urban sprawls by delineating urban patches and we measured a variety of urban changes, such as urban population growth and land cover change within Virginia based on the delineation. For doing so, digital number values, which measures the brightness of satellite imagery, were extracted and other relative index values were calculated based on digital number values, and these processes were applied in a time series from 2000 to 2010. Spatial patterns of digital number values change and the variation of another light index values indicate that human activities were increasing during the 10 years in Virginia.

Invasive species introductions, namely the chestnut blight fungus (Cryphonectria parasitica) and hemlock woolly adelgid (Adelges tsugae), have permanently altered the overstory canopy of Appalachian forests by causing the dramatic die-offs of two ecologically significant tree species, American chestnut (Castanea dentata) and eastern hemlock (Tsuga canadensis). These canopy dominants once had significant roles in regulating understory communities. The loss of these trees, along with fire suppression, has driven two common evergreen shrubs, rosebay rhododendron (Rhododendron maximum) and mountain laurel (Kalmia latifolia), to expand and proliferate in areas where they were once restricted. These two common shrubs are recognized agents of change in Appalachian forests because of their abilities to modify soil seed banks, regulate light, and alter the local soil chemistry. This study documented evergreen shrub expansion across the Coweeta Creek basin over the past 36 years analyzing changes in winter greenness using harmonized multi-decadal archives of Landsat imagery. We found the greatest change in winter greenness in relatively dry areas: higher elevations (1275–1300 m), steeper slopes (33°–35°), southward aspects, and far from streams (600–800 m). Historical field data collected in three unmanaged watersheds at Coweeta showed a simultaneous decrease in T. canadensis and increase in R. maximum. We also documented the decline of a xerophytic canopy tree species, pitch pine (Pinus rigida), and an associate understory shrub, K. latifolia. Our analysis of the influence of terrain variables on evergreen shrub expansion allowed us to determine which of the two species was expanding in various locations with reasonable certainty. This study provides spatially explicit data on the expansion of two evergreen shrub species at the Coweeta Hydrologic Laboratory that could be used to pinpoint areas for future management interventions. / 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.

Earth observing instruments, such as those embarked on the Earth Radiation Budget Experiment (ERBE) and Clouds and the Earth's Radiant Energy System (CERES), have been used to monitor the arriving solar and the upwelling solar reflected and longwave emitted radiation from low Earth orbit for the past three decades. These instruments have played a crucial role in studying the Earth's radiation budget and developing a decadal climate data record. Prior to launch, these instruments go through several robust design phases followed by rigorous ground calibration campaigns to establish their baseline characterization spectrally, spatially, temporally, and radiometrically. The knowledge gained from building and calibrating these instruments has aided in technology advancements as the need for developing more accurate instruments has increased. In order to understand the prelaunch performance of these instruments, NASA's Langley Research Center (LaRC) has partnered with the Thermal Radiation Group at Virginia Tech to develop first-principle, dynamic electrothermal, numerical models of scanning radiometers that can be used to enhance the understanding of such instruments. The body of research presented here documents the construction of these models by highlighting their development and results and possible applications to the next generation of Earth radiation budget instrument. Much of the effort reported here is based on the author's contribution to NASA's now-deselected Radiation Budget Instrument (RBI) project. / Doctor of Philosophy / Earth Radiation Budget (ERB) sensors, such as the Earth Radiation Budget Experiment (ERBE) and the Clouds and the Earth's Radiant Energy System (CERES) have been a crucial part of studying the Earth's radiation budget for the past three decades. The Earth's radiation budget is the natural balance that exists between the energy received from the Sun and the energy radiated back into space. These instruments, which measure the radiative energy arriving and leaving at the top of the Earth's atmosphere, enhance understanding of the roles played by clouds and aerosols in reflecting and absorbing energy, thereby cooling or heating the planet. In order to enable the design for the next-generation Earth radiation budget sensors, NASA Langley has partnered with the Thermal Radiation Group at Virginia Tech to develop a capability for high-fidelity computer modeling that permits the complete characterization of an Earth radiation budget instrument. The resulting simulation consists of computer models for optical components, calibration targets, detecting elements and a source that includes information on anisotropy of a given Earth scene-type (clear vs. cloudy scene, ocean, desert, etc.). The modeling tool permits simulation of the entire science data stream as photons entering the instrument are converted to digital counts leaving the instrument, and provides the flexibility to observe various scene-types whether they be calibration targets or Earth scenes. This dissertation highlights the construction of this modeling tool and its capabilities as it is applied to NASA's now-deselected Radiation Budget Instrument.

In order to extract a drainage network from a LANDSAT scene of mountainous terrain, it is necessary to use an illumination model to separate the reflectance information from the topographic information in the LANDSAT data. From the reflectance information, visible stream segments can be detected. From the topographic information, ridges and valleys can be located and assigned relative elevations by an elevation growing model. Based on these, a complete elevation model can be estimated by interpolating between ridges and valleys and this estimate can be improved by making it consistent with the topographic information. Also the LANDSAT imagery can be reconstructed to evaluate the illumination model. In order to label flow directions of visible stream segments, constraints at junctions based on orientations and lengths are defined and a consistent labeling technique is used. / Ph. D.

During their growth, crops may experience a variety of health issues, which often lead to a reduction in crop yield. In order to avoid financial loss and sustain crop survival, it is imperative for farmers to detect and treat crop health issues. Interest in the use of unmanned aerial vehicles (UAVs) for precision agriculture has continued to grow as the cost of these platforms and sensing payloads has decreased. The increase in availability of this technology may enable farmers to scout their fields and react to issues more quickly and inexpensively than current satellite and other airborne methods. In the work of this thesis, methods have been developed for applications of UAV remote sensing using visible spectrum and multispectral imagery. An algorithm has been developed to work on a server for the remote processing of images acquired of a crop field with a UAV. This algorithm first enhances the images to adjust the contrast and then classifies areas of the image based upon the vigor and greenness of the crop. The classification is performed using a support vector machine with a Gaussian kernel, which achieved a classification accuracy of 86.4%. Additionally, an analysis of multispectral imagery was performed to determine indices which correlate with the health of corn crops. Through this process, a method for correcting hyperspectral images for lighting issues was developed. The Normalized Difference Vegetation Index values did not show a significant correlation with the health, but several indices were created from the hyperspectral data. Optimal correlation was achieved by using the reflectance values for 740 nm and 760 nm wavelengths, which produced a correlation coefficient of 0.84 with the yield of corn. In addition to this, two algorithms were created to detect stink bugs on crops with aerial visible spectrum images. The first method used a superpixel segmentation approach and achieved a recognition rate of 93.9%, although the processing time was high. The second method used an approach based upon texture and color and achieved a recognition rate of 95.2% while improving upon the processing speed of the first method. While both methods achieved similar accuracy, the superpixel approach allows for detection from higher altitudes, but this comes at the cost of extra processing time. / Master of Science / Crops can experience a variety of issues as they grow, which can reduce the amount of resulting crop. In order to avoid losing their crops and money, it is critical for farmers to detect and treat these issues. The current methods for detecting the issues can be expensive and have slow turnaround time to find the results. Unmanned aerial vehicles (UAVs) have emerged as a potential to improve upon the current methods and reduce the cost and turnaround time for determining issues. The UAVs can use a wide array of sensors to quickly and easily acquire information about the crop field. Using a variety of cameras, data can be gathered from the wavelengths which can be seen by humans as well as many other wavelengths outside of our visible spectrum. The work in this thesis uses images acquired from visible spectrum cameras as well as multispectral data, which uses a different range of wavelengths. A method was created to process the visible spectrum images to classify areas of the field based upon the health of the crop. This method was implemented on a server to allow a farmer to upload their images through the internet and have the data processed remotely. In addition to this, multispectral images were used to analyze the health of corn crops. The multispectral data can be used to create index values based upon various wavelengths of data. Many index values were analyzed and created to find relationships between these values and the health of the crops and strong relationships were found between some of the indices and the crop health. The final portion of this work uses standard visible spectrum images to detect the presence of stink bugs on crops. Two separate methods were created for this detection and both of these methods were able to accurately find stink bugs with a high success rate. The first method was able to detect the stink bugs from farther away than the second method, however the second method was able to perform the detection much faster.

Indoor spaces, where we spend the majority of our lives, greatly impact our work, social interactions, and well-being. In recognition of the central role that buildings play in our lives, architects and designers have increasingly focused on creating spaces that intentionally promote interaction and collaboration between building occupants. One challenge arising from this trend is evaluating the efficacy of new designs. This study used object tracking data for the Fall 2023 semester from a collection of lidar sensors installed in a portion of a mixed-use academic building on a university campus to algorithmically detect occupancy and serendipitous collisions between people - patterns of simultaneous movement and pause that indicate that two or more individuals have stopped and had a meaningful interaction. The algorithm detected over 14,000 collisions throughout the semester with high spatial and temporal precision. Occupancy and collisions were highly related over several scales of temporal and spatial analysis. Furthermore, several interesting patterns emerged, including (a) collisions peaked early in the semester, then declined before leveling off, (b) occupancy peaked in mid-afternoon, while collisions peaked in the late afternoon and early evening, (c) collisions peaked later in the week than did occupancy, and (d) specific hotspots were apparent at important nodes such as the bottom of stairs and near elevators. The patterns found in this study can provide insight as to how interactions can be measured using remote sensing data, and can aid designers in attempting to increase collaboration in shared indoor environments. / Master of Science / We spend lots of our times in buildings, and they are very important for our well-being. Designers have recently been focusing on promoting collaboration and interaction between people within building spaces. Despite their importance, these interactions within buildings have been challenging to categorize and analyze. This study used object-tracking data for the Fall 2023 semester from a collection of lidar sensors, which were intermittently placed in the ground-floor public spaces of a new hybrid residential-academic university building on Virginia Tech's Blacksburg campus. A computer program was written to parse through this data, and detect unplanned collisions between people; patterns of movement and pause that indicate that two or more people have stopped and had a meaningful interaction (for example, running into a friend while walking down the hallway). This study was able to detect collisions relatively well using a computer algorithm. The patterns and distributions of these collisions were then analyzed in time and space. The number of collisions and the number of people present in the space were highly related on all scales of time and space. In terms of time itself, collisions happened the most at the beginning of the semester, where they then dropped off. Collisions happened more frequently both later in the day (in afternoon, evening, and night hours) and later in the week (on Thursday, Friday, and Saturday). In terms of space, these collisions happened most frequently in the areas around the elevator, at the base of the stairs, and in the building's main lobby area. They happened less in hallways and near some seating areas. The patterns revealed from this study can help us better understand how to detect interactions between people within buildings, and can help designers increase the amount of these interactions.

No / As this volume, the final monograph of the SPLASHCOS network, was being finalised, the European Research Council agreed to fund a major new project relating to the marine palaeolandscapes of the southern North Sea. Emerging from the earlier work of the North Sea Palaeolandscapes Project (NSPP), the Lost Frontiers project seeks to go beyond the maps generated by that ground-breaking research. Led by researchers in the fields of archaeogeophysics, molecular biology and computer simulation, the project seeks to develop a new paradigm for the study of past environments, ecological change and the transition between hunter gathering societies and farming in North West Europe. Following from earlier work, the project will seek to release the full potential of the available seismic reflectance data sets to generate topographical maps of the whole of early Holocene Doggerland that are as accurate and complete as possible. Using these data, the study will then reconstruct and simulate the emerging palaeoenvironments of Doggerland using conventional palaeoenvironmental data, as well as ancient DNA extracted directly from sediment cores along the routes of two submerged river valleys. Using this base data, the project aims to transform our understanding of the colonisation and development of floral, faunal and human life, to explore the Mesolithic landscapes and to identify incipient Neolithic signals indicating early contact and development within the region of Doggerland. / European Research Council’s support for the Lost Frontiers Project through the provision of an Advanced Grant (Grant Agreement 670518 ERC-2014-ADG/ERC-2014-ADG).

Gross primary productivity (GPP) describes the total photosynthesis (i.e., carbon fixation) for an ecosystem and is an important component of the global land carbon budget. Accurate measurements of forest carbon sequestration are crucial, as climate, soils, and management practices influence carbon uptake. Disturbances such as wildfires, diseases, insect infestations, and extreme weather events significantly impact forest health and functionality. Remote sensing is useful for monitoring global ecosystems and estimating forest vitality. High-resolution images and lidar (Light Detection and Ranging) data offer enhanced insights into forest light utilization. Combining lidar with spectral data provides a comprehensive view of forest ecosystems, integrating both structural and physiological information. This work includes three separate studies under a common objective to establish an improved understanding of forest productivity in different forest compositions using a combination of physiological function from optical imagery and morphological structure from lidar. The first study investigates the photochemical reflectance index (PRI) and how illumination affects the diurnal and vertical distributions of two managed pine stands with varying ages and row orientations, using airborne hyperspectral and lidar data. We developed a novel method to classify canopy illumination into sunlit, shaded, and mixed light areas using the hyperspectral data and a simulated panchromatic band. PRI values varied between sunlit and shaded foliage throughout the day, reflecting differences in foliage efficiency depending on light conditions and forest structure. The second study evaluates how well remotely quantified plant functional traits predict GPP across U.S. forests. Using data from NEON's airborne remote sensing and in situ flux tower measurements, the study assessed hyperspectral optical indices as physiological traits and lidar-derived products as morphological and environmental traits. By developing multiple linear regression models with separated and combined trait groups, the best prediction model combined morphological, environmental, and physiological traits with a PRESS R2 of 0.75. This underscores the importance of integrating various functional traits for accurate forest productivity predictions. The last study detects insect-induced tree mortality with separated and combined models of structure from lidar and physiology from satellite imagery. Although all models tend to overestimate tree mortality, integrating lidar data enhances predictions by 6% offering valuable structural context. A central theme of this work is that lidar-derived structural measurements were crucial in every chapter. / Doctor of Philosophy / Plant photosynthesis, converting sunlight and carbon dioxide to chemical energy (carbohydrates) and oxygen, is fundamental to all life on Earth. Measuring how much carbon forests capture is essential to understanding the global carbon cycle. Forests must be healthy to be productive, so forest health monitoring is also vital. Remote sensing is a tool to measure objects without physically touching the object you are measuring. From remote sensing, we can take pictures of forests and record information on how much sunlight is reflected. Remote sensing technologies like lidar use lasers to portray trees and forests in three dimensions (3D). Combining pictures with 3D shapes can provide a more comprehensive understanding of how trees store carbon. This work incorporates three separate studies using a combination of pictures and 3D data. The first study looks at the changes in light utilization of a pine plantation throughout the day and within the canopy. The second study uses a range of forest descriptors with different compositions to predict their productivity. The last study shows how imagery from space and 3D data can locate dead and live trees. The overall thrust of the study shows that incorporating 3D portrayals of the forest enhances our understanding of forest productivity.

Master of Science / Department of Clinical Sciences / Robert L. Larson / Bradley J. White / We performed research that investigated the ability of three dimensional accelerometers to classify cattle behavior and also describe the circadian patterns within that behavior. The first of three studies (validation study) tested a decision tree classification system and its ability to describe behaviors of lying, standing, and walking. Classification accuracies for lying, standing, and walking behaviors were 99.2%, 98.0%, and 67.8% respectively, with walking behavior having significantly lower accuracy (P<0.01). This study also tested the accuracy of classifying the above behaviors using different device reporting intervals, or epochs. Reporting intervals of 3, 5, and 10 seconds (s) were evaluated in their ability to describe cattle behaviors of lying, standing, and walking. Classification accuracies for the 3s, 5s, and 10s reporting interval were 98.1%, 97.7%, and 85.4% respectively, with no difference in classification accuracy of the 3s and 5s epochs (P=0.73) while the 10s epoch exhibited significantly lower overall accuracy (P<0.01). This validated accelerometer monitoring system was then implemented in two studies (Winter 2007 and Spring 2008) where the devices were used to describe behavior patterns of beef calves in a drylot production setting. Lying behavior of the cattle was analyzed and found to be significantly associated (P<0.001) with hour of the day. Calves in these studies spent most (> 55%) of the nighttime hours (2000 to 0400) involved in lying behavior and spent the least percentage of time lying (<30%) during periods of time where feed was presented at the bunk (0700 and 1700). Mean lying time was also associated with trial day (P<0.01) and most trial days (67.5%) calves spending between 45% and 55% of time lying. Variation of lying behavior was found between individuals (range 29% to 66%); however, consistency in lying behavior was found within individual calves across study periods. The accelerometer monitoring system studies presented here provide evidence these devices have utility in recording behaviors (lying, standing, and walking) of individual beef calves raised in typical production settings.

Assessment of leaf and canopy chlorophyll content provides information on plant physiological status; it is related to nitrogen content and hence, photosynthesis process, net primary productivity and carbon budget. In this study, a method is developed for the retrieval of total chlorophyll content (Chlorophyll a+b) per unit leaf and per unit ground area based on improved vegetation structural parameters which are derived using multispectral multi-angle remote sensing data. Structural characteristics such as clumping and gaps within a canopy affect its solar radiation absorption and distribution and impact its reflected radiance acquired by a sensor. One of the main challenges for the remote sensing community is to accurately estimate vegetation structural parameters, which inevitably influence the retrieval of leaf chlorophyll content. Multi-angle optical measurements provide a means to characterize the anisotropy of surface reflectance, which has been shown to contain information on vegetation structural characteristics. Hyperspectral optical measurements, on the other hand, provide a fine spectral resolution at the red-edge, a narrow spectral range between the red and near infra-red spectra, which is particularly useful for retrieving chlorophyll content. This study explores a new refined measurement concept of combining multi-angle and hyperspectral remote sensing that employs hyperspectral signals only in the vertical (nadir) direction and multispectral measurements in two additional (off-nadir) directions within two spectral bands, red and near infra-red (NIR). The refinement has been proposed in order to reduce the redundancy of hyperspectral data at more than one angle and to better retrieve the three-dimensional vegetation structural information by choosing the two most useful angles of measurements. To illustrate that hyperspectral data acquired at multiple angles exhibit redundancy, a radiative transfer model was used to generate off-nadir hyperspectral reflectances. It has been successfully demonstrated that the off-nadir hyperspectral simulations could be closely reconstructed based on the nadir hyperspectral reflectance and off-nadir multi-spectral reflectance in the red and NIR bands. This is shown using the Compact High-resolution Imaging Spectrometer (CHRIS) and Compact Airborne Spectrographic Imager (CASI) data acquired over a forested area in the Sudbury region (Ontario, Canada). Through intensive validation using field data, it is demonstrated that the combination of reflectances at two angles, the hotspot and darkspot, through the Normalized Difference between Hotspot and Darkspot (NDHD) index has the strongest response to changes in vegetation clumping, an important structural component of canopy. Clumping index (Ω) and Leaf Area Index (LAI) maps are generated based on previous algorithms as well as empirical relationships developed in this study. To retrieve chlorophyll content, inversion of the 5-Scale model is performed by developing Look-Up Tables (LUTs) that are based on the improved structural characteristics developed using multi-angle data. The generated clumping index and LAI maps are used in the LUTs to estimate leaf reflectance. Inversion of the leaf reflectance model, PROSPECT, is further employed to estimate chlorophyll content per unit leaf area. The estimated leaf chlorophyll contents are in good agreement with field-measured values. The refined measurement concept of combining hyperspectral with multispectral multi-angle data provides the opportunity for simultaneous retrieval of vegetation structural and biochemical parameters.