Browsing by Author "Pingel, Thomas"
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- Community-engaged heat resilience planning: Lessons from a youth smart city STEM programLim, Theodore C.; Wilson, Bev; Grohs, Jacob R.; Pingel, Thomas (Elsevier, 2022-10-01)While recognition of the dangers of extreme heat in cities continues to grow, heat resilience remains a relatively new area of urban planning. One barrier to the creation and successful implementation of neighborhood-scale heat resilience plans has been a lack of reliable strategies for resident engagement. In this research, the authors designed a two-week summer STEM module for youth ages 12 to 14 in Roanoke, Virginia in the Southeastern United States. Participants collected and analyzed temperature and thermal comfort data of varying types, including from infrared thermal cameras and point sensors, handheld weather sensors, drones, and satellites, vehicle traverses, and student peer interviews. Based on primary data gathered during the program, we offer insights that may assist planners seeking to engage residents in neighborhood-scale heat resilience planning efforts. These lessons include recognizing: (1) the problem of heat in neighborhoods and the social justice aspects of heat distribution may not be immediately apparent to residents; (2) a need to shift perceived responsibility of heat exposure from the personal and home-based to include the social and landscape-based; (3) the inextricability of solutions for thermal comfort from general issues of safety and comfort in neighborhoods; and (4) that smart city technologies and high resolution data are helpful “hooks” to engagement, but may be insufficient for shifting perception of heat as something that can be mitigated through decisions about the built environment.
- Comparing UAS and Pole Photogrammetry for Monitoring Beach ErosionGonzales, Jack; Pingel, Thomas (Virginia Tech, 2021-04-30)Sandy beaches are vulnerable to extreme erosion, especially during hurricanes and other extreme storms, as well as gradual seasonal erosion cycles. Left unchecked, coastal erosion can put people, homes, and other infrastructure at risk. To effectively manage beach resources, coastal managers must have a reliable means of surveying the beach to monitor erosion and accretion. Traditionally, these surveys have used standard ground-based survey methods, but advancements in remote sensing technology have given surveyors new tools to monitor erosion. Structure from Motion (SfM) photogrammetry presents an inexpensive, fast, and reliable method for routine beach surveying. Typically, SfM utilizes photos taken by unmanned aerial systems (UAS), but weather conditions and government regulations can make flying difficult or impossible, especially around crowded areas popular with beachgoers. Photos taken from a tall pole on a mobile platform can also be used for SfM, eliminated the challenges posed by weather and UAS regulations. This poster compares UAS SfM and “photogrammetry on a stick” (POAS) for monitoring beach erosion. Three surveys were conducted on a barrier Island in South Carolina, at monthly intervals, using both UAS SfM and POAS. Both techniques show promise, but POAS is more difficult to generate quality reconstructions from, while UAS provides a faster, smoother workflow.
- Comparing UAV and Pole Photogrammetry for Monitoring Beach ErosionGonzales, Jack Joseph (Virginia Tech, 2021-09-14)Sandy beaches are vulnerable to extreme erosion during large storms, as well as gradual erosion processes over months and years. Without monitoring and adaptation strategies, erosion can put people, homes, and other infrastructure at risk. To effectively manage beach resources and respond to erosion hazards, coastal managers must have a reliable means of surveying the beach to monitor erosion and accretion. These elevation surveys typically incorporate traditional ground-based surveying methods or lidar surveys flown from large, fixed-wing aircraft. While both strategies are effective, advancements in photogrammetric technology offers a new solution for topographic surveying: Structure from Motion (SfM). Using a set of overlapping aerial photographs, the SfM workflow can generate accurate topographic surveys, and promises to provide a fast, inexpensive, and reliable method for routine beach surveying. Unmanned aerial vehicles (UAVs) are often successfully employed for SfM surveys but can be limited by poor weather ad government regulations, which can make flying difficult or impossible. To circumvent these limitations, a digital camera can be attached to a tall pole on a mobile platform to obtain aerial imagery, avoiding the restrictions of UAV flight. This thesis compares these two techniques of image acquisition for routine beach monitoring. Three surveys were conducted at monthly intervals on a beach on the central South Carolina coast, using both UAV and pole photogrammetry. While both methods use the same software and photogrammetric workflow, the UAV produced better results with far fewer processing artifacts compared to pole photogrammetry.
- Connections Between Present-Day Water Access and Historical RedliningSterling III, Charles W. (Virginia Tech, 2023-12-20)Although challenges in water and sanitation access are often assumed to be issues of low- and middle-income nations, over 400,000 homes in the United States still lack access to complete indoor plumbing. Previous research has demonstrated that the remaining plumbing challenges are more prevalent in communities with high Black and brown populations. This study hypothesizes that the 1930s practice of redlining by the Home Owners' Loan Corporation (HOLC), which systematically denied loans to minority populations, is linked to present-day inadequate plumbing access (i.e. defined as incomplete plumbing above the national average). Digitized HOLC maps for 202 urban areas across the country and US Census data from the 2016-2020 American Community Survey were combined to interpolate the modern-day plumbing access for historic neighborhoods (n=8871 communities). Analysis via binomial logistic regression demonstrated that nationally, redlined communities (HOLC Grade "D") are significantly more likely to have a rate of incomplete plumbing above the national average as compared to greenlined communities (HOLC Grade "A") (0.1352; CI= +0.036). This finding was also observed for three of the nation's four census sub-regions (Northeast, Midwest, West). Slight differences by region in relationships between the proportion of specific racial/ethnic populations on rates of incomplete plumbing demonstrate the need for targeted place-based interdisciplinary examinations of exclusionary practices. The demonstration of the present-day impacts of redlining after nearly 90 years emphasizes the need to intentionally mitigate past injustices to ensure modern-day equity.
- Detection of Tornado Damage via Convolutional Neural Networks and Unmanned Aerial System PhotogrammetryCarani, Samuel James (Virginia Tech, 2021-10-21)Disaster damage assessments are a critical component to response and recovery operations. In recent years, the field of remote sensing has seen innovations in automated damage assessments and UAS collection capabilities. However, little work has been done to explore the intersection of automated methods and UAS photogrammetry to detect tornado damage. UAS imagery, combined with Structure from Motion (SfM) output, can directly be used to train models to detect tornado damage. In this research, we develop a CNN that can classify tornado damage in forests using SfM-derived orthophotos and digital surface models. The findings indicate that a CNN approach provides a higher accuracy than random forest classification, and that DSM-based derivatives add predictive value over the use of the orthophoto mosaic alone. This method has the potential to fill a gap in tornado damage assessment, as tornadoes that occur in wooded areas are typically difficult to survey on the ground and in the field; an improved record of tornado damage in these areas will improve our understanding of tornado climatology.
- Estimating Floodplain Vegetative Roughness using Drone-Based Laser Scanning and Structure from Motion PhotogrammetryAquilina, Charles A. (Virginia Tech, 2020-08-20)We compared high-resolution drone laser scanning (DLS) and structure from motion (SfM) photogrammetry-derived vegetation heights at the Virginia Tech StREAM Lab to determine Manning's roughness coefficient. We utilized two calibrated approaches and a calculated approach to estimate roughness from the two data sets (DLS and SfM), then utilized them in a two-dimensional (2D) hydrodynamic model (HEC-RAS). The calculated approach used plant characteristics to determine vegetative roughness, while the calibrated approaches involved adjusting roughness values until model outputs approached values of field data (e.g., velocity probe and visual observations). We compared the model simulations to seven actual high-flow events during the fall of 2018 and 2019 using measured field data (velocity sensors, groundwater well height, marked flood extents). We used a t-test to find that all models were not significantly different to water surface elevations from our 18 wells in the floodplain (p > 0.05). There was a decrease in RMSE (-0.02 m) using the calculated compared to the calibrated models. Another decrease in RMSE was found for DLS compared to SfM (-0.01 m). This increase might not justify the increased cost of a DLS setup over SfM (~$150,000 versus ~$2,000), though future studies are needed. Our results inform hydrodynamic modeling efforts, which are becoming increasingly important for management and planning as we experience increasing high-flow events in the eastern United States due to climate change.
- Evaluating the quality of ground surfaces generated from Terrestrial Laser Scanning (TLS) dataSun, Yanshen (Virginia Tech, 2019-06-24)Researchers and GIS analysts have used Aerial Laser Scanning (ALS) data to generate Digital Terrain Models (DTM) since the 1990s, and various algorithms developed for ground point extraction have been proposed based on the characteristics of ALS data. However, Terrestrial Laser Scanning (TLS) data, which might be a better indicator of ground morphological features under dense tree canopies and more accessible for small areas, have been long ignored. In this research, the aim was to evaluate if TLS data were as qualified as ALS to serve as a source of a DTM. To achieve this goal, there were three steps: acquiring and aligning ALS and TLS of the same region, applying ground filters on both of the data sets, and comparing the results. Our research area was a 100m by 140m region of grass, weeds and small trees along Strouble's Creek on the Virginia Tech campus. Four popular ground filter tools (ArcGIS, LASTools, PDAL, MCC) were applied to both ALS and TLS data. The output ground point clouds were then compared with a DTM generated from ALS data of the same region. Among the four ground filter tools employed in this research, the distances from TLS ground points to the ALS ground surface were no more than 0.06m with standard deviations less than 0.3m. The results indicated that the differences between the ground extracted from TLS and that extracted from ALS were subtle. The conclusion is that Digital Terrain Models (DTM) generated from TLS data are valid.
- Evaluating the Skillfulness of the Hurricane Analysis and Forecast System (HAFS) Forecasts for Tropical Cyclone Precipitation using an Object-Based MethodologyStackhouse, Shakira Deshay (Virginia Tech, 2022-05-24)Tropical cyclones (TCs) are destructive, natural occurring phenomena that can cause the loss of lives, extensive structural damage, and negative economic impacts. A major hazard associated with these tropical systems is rainfall, which can result in flood conditions, contributing to the death and destruction. The role rainfall plays in the severity of the TC aftermath emphasizes the importance for models to produce reliable precipitation forecasts. Hurricane model precipitation forecasts can be improved through precipitation verification as the model weaknesses are identified. In this study, the Hurricane Analysis and Forecast System (HAFS), an experimental NOAA hurricane model, is evaluated for its skillfulness in forecasting TC precipitation. An object-based verification method is used as it is demonstrated to more accurately represent the model skill compared to traditional point-based verification methods. A 600 km search radius is implemented to capture the TC rainfall and the objects are defined by 2, 5, and 10 mm/hr rain rate thresholds. The 2 mm/hr threshold is chosen to predominantly represent stratiform precipitation, and the 5 and 10 mm/hr thresholds are used as approximate thresholds between stratiform and convective precipitation. Shape metrics such as area, closure, dispersion, and fragmentation, are calculated for the forecast and observed objects and compared using a Mann Whitney U test. The evaluation showed that model precipitation characteristics were consistent with storms that are too intense due to forecast precipitation being too central and enclosed around the TC center at the 2 mm/hr threshold, and too cohesive at the 10 mm/hr threshold. Changes in the model skill with lead time were also investigated. The model spin-up negatively impacted the model skill up to six hours at the 2 mm/hr threshold and up to three hours at the 5 mm/hr threshold, and the skill was not affected by the spin-up at the 10 mm/hr threshold. This indicates that the model took longer to realistically depict stratiform precipitation compared to convective precipitation. The model skill also worsened after 48 hours at the 2 and 10 mm/hr thresholds when the precipitation tended to be too cohesive. Future work will apply the object-based verification method to evaluate the TC precipitation forecasts of the Basin-Scale Hurricane Weather Research and Forecasting (HWRF-B) model.
- An Evaluation of DEM Generation Methods Using a Pixel-Based Landslide Detection AlgorithmYoung III, James Russell (Virginia Tech, 2021-08-27)The creation of landslide inventories is an important step in landslide susceptibility mapping, and automated algorithms for landslide detection will increasingly be relied upon as part of the mapping process. This study compares the effects of three different DTM generation methods on a pixel-based landslide detection algorithm developed by Shi et al. (2018) using a set of landslide-prone study areas in Pierce County, Washington. Non-parametric statistical analysis demonstrated that false-positive and false-negative rates were significantly different between DTM generation methods, showing that inpainting presents a more balanced error profile compared to TIN and morphological-based approaches. However, overall accuracy (kappa) rates were still very low overall, suggesting that geomorphometric curvature as an input needs to be processed in a different manner to make these types of pixel-based landslide detection algorithms more useful for landslide inventory database management.
- Examining the Modifiable Areal Unit Problem: Associations Between Surface Mining and Birth Outcomes in Central Appalachia at Multiple Spatial ScalesMcKnight, Molly Xi (Virginia Tech, 2020-06-19)Health studies often rely on aggregated instead of individual-level data to protect patient privacy. However, aggregated data are subject to the modifiable areal unit problem (MAUP), meaning results of statistical analyses may differ depending on the data's scale and areal unit. Past studies have suggested MAUP is context-specific and analyzing multiple spatial scales may provide richer understandings of examined phenomena. More research is needed to understand the role of scale and areal unit in health-related analyses. This study examines associations between surface mining and birth outcomes from 1989 to 2015 in Central Appalachia at the individual; postal; county; and county-sized, non-administrative scales. Evidence from previous studies suggests associations exist between health outcomes and county-level measures of mining activity. This is the first study to examine associations between mining and birth outcomes at more spatially refined exposure estimates. We identified surface mines using Landsat imagery and geocoded birth records. Airsheds, used to quantify the influence area of potential airborne pollutants from surface mining activity, were built using HYSPLIT4. The frequency values of each airshed that intersected each geocoded birth record were summed. These cumulative frequency airshed values were then aggregated. Finally, we implemented multiple regression models, each at a different scale, to examine associations between airsheds and birth outcomes. Results suggest MAUP has minimal impacts on the statistical results of examining associations between surface mining and birth outcomes in Central Appalachia. Results also indicate surface mining is significantly associated with preterm birth and reduced birthweight at each scale.
- High Resolution 3D Modeling Using Oblique Pictometry and Lidar DataAtkins, Maya; Pingel, Thomas (Virginia Tech, 2021-04-30)As part of a larger project to develop a high resolution model of the Virginia Tech campus, we processed over 8,000 non-georeferenced aerial oblique images of Blacksburg area collected by Pictometry in 2019. We sequentially: (a) produced an initial camera position estimate from image footprints in Python, (b) calibrated the image set by creating approximately 200 ground control points (3D GCPs using position and elevation) and over 2,500 image marks manually generated with Google Earth, and (c) after adding final fine referencing using RTK GPS, we calculated the 3D original camera positions using Pix4D software. This challenging project used unconventional methods to establish camera location and orientation by using imagery that was not created with 3D modeling in mind (i.e. low image overlap) and calibrating model cameras using Google Earth derived data for GCP construction. Finally, we used RealityCapture software to fuse lidar imagery with our georeferenced image set to produce a 3D model that combines the spatial accuracy of lidar with the high point density of Structure from Motion (SfM) models. We expect to use the final constructed model for several applications, including to support indoor mapping and navigation and interactive, augmented reality 3D printed maps for people with visual impairments.
- Least Cost Path Modeling Between Inka and Amazon CivilizationsLewis, Colleen Paige (Virginia Tech, 2022-06-09)Least Cost Path Analysis (LCPA) is a GIS-based approach for calculating the most efficient route between a start and end point, often in terms of shortest time or least amount of energy. The approach is often applied in archaeology to estimate locations of sites, and routes between them. We applied LCPA to estimate how sites in the Andes in the eastern portion of the Inka empire may have connected to sites in the western Amazon Basin. Our approach further used the known Inka Road network to test performance of two types of LCP models (linear vs. areal calculation) and four types of cost functions. LCPs can be calculated with an areal approach, where each cell of the DEM is given one overall slope value, or linearly, where the direction of travel across a cell affects the slope value. Four different algorithms were tested: Tobler's Hiking Function (1993), Tobler's Hiking Function with a vertical exaggeration of 2.3 based on human perceptions of slope (Pingel 2010), Pingel's empirical estimation approach (2010), and Pandolf et al.'s energy expenditure equation (1977) using both an areal and linear approach for all the algorithms. An initial study was conducted in the Cusco region and results were compared to the Inka Road network using the linear accuracy assessment method of Goodchild and Hunter (1997) and Güimil-Fariña and Parcero-Oubiña (2015). The findings suggest that the empirical estimation and caloric cost methods were the most accurate and performed similarly, both were more accurate than travel-time based costs, and linear methods were better than areal based methods when using higher resolution DEM inputs.
- Linking GIS, youth environmental literacy, and city government functions to define and catalyze community heat resilience planning in Roanoke, VADillon, Maxwell Stewart (Virginia Tech, 2022-06-10)Statistics show that chronic heat exposure and extreme heat waves are the leading cause of death amongst natural disasters in urban spaces across the United States, outpacing the likes of more notable phenomena such as hurricanes, tornadoes, and earthquakes. Heat in urban spaces is not distributed equally due to the urban heat island effect, a phenomenon which significantly elevates temperatures due to the various absorption characteristics of built environment features. Historical discriminatory mortgage lending schemes and planning practices that targeted communities of color have intensified that issue, endangering the health and well-being of marginalized neighborhoods to this day. Although generating feasible design solutions to mitigate the impact of heat in urban spaces represents a substantial challenge, utilizing readily available data sources to garner the social and political support required for actionable change is likely the more complex issue. Because youth are typically less jaded by external social and political influences and will either enjoy the benefits or suffer the consequences related to the built environment for their entire adult life, they possess a unique potential to serve as a vehicle for generating community momentum for the implementation of heat resilience solutions. This thesis explores the spatial distribution of heat throughout neighborhoods in Roanoke, Virginia by exploring both land surface temperature and air temperature discrepancies by Home Owners' Loan Corporation (HOLC) classification and census tract. I find that HOLC polygons not labeled "A" possess a considerably higher average temperature than the most "desirable" classification, and that there is a statistically significant inverse relationship between mean land surface temperature (aggregation of Landsat raster files) and census tract socio demographic characteristics such as median household income and percentage of residents aged 65 and over. This thesis also examines the potential of youth-focused science education programs to catalyze the political will necessary to enact resilience planning efforts that no single governmental agency is responsible for. I analyzed the various impacts that artifacts produced by a 2021 science education program conducted with Roanoke City middle school students inflicted on a 2022 focus group comprised of influential Roanoke public officials. I show the reasoning which supports that four primary opportunity and challenge categories – Breaking Down Silos, Spreading Awareness, Places and Venues, and Resources and Funding – can serve as foundational discussion components for heat resilience planning panels in the future. This thesis advances the awareness of disproportionate exposure to heat in urban spaces and contributes to theories attempting to trigger heat resilience planning efforts.
- Plant Successional Patterns at Sperry Glacier Foreland, Glacier National Park, MT, USASchulte, Ami Nichole (Virginia Tech, 2023-06-12)Regional and local changes in the climate have been driving rapid glacial retreat in many glaciers since the Little Ice Age. This retreat provides a unique opportunity to study succession across the chronosequences of glacier forelands. Patterns of plant colonization and succession on terrain exposed by retreating glaciers give insight into factors influencing alpine ecosystem change and recovery. Understanding these patterns and processes is important for conserving alpine landscapes and flora as glaciers disappear. This study sought to investigate how various biotic and abiotic factors influence plant successional patterns in the dynamic alpine environment of Sperry Glacier, a Little Ice Age, mid-latitude cirque glacier in Glacier National Park, Montana. Through field data collection, additional Geographic Information System (GIS) derived variables, and subsequent geostatistical analysis, I specifically assessed: (1.) vegetative trends (percent cover, species richness, Shannon's diversity, species evenness, composition, and species turnover) over a 170-year chronosequence, and (2.) vegetative trends over field and GIS-derived site conditions (e.g., surface fragmentation, concavity, flow accumulation, and solar irradiance). Sixty-one plots (each 8 square meters) were placed throughout the glacier foreland using a random sample stratified by terrain date. Percent cover, species richness, Shannon's diversity, and species evenness were calculated for each plot. All sampled vegetation was identified with taxonomic resolution down to species whenever possible. I assessed vegetative trends across terrain age ranges using Kruskal-Wallis and Dunn's tests. I used two models, generalized linear models (GLMs) and Classification and Regression Trees (CARTs), to assess field and GIS-derived biophysical correlates (e.g., surface fragmentation, concavity, terrain variables, and solar irradiance with vegetative trends), followed by Kruskal-Wallis tests, Dunn's tests, and scatterplots. Species richness and vegetation cover were greater on older terrain. Plant composition changed over terrain age, with Penstemon ellipticus favoring older terrain and Boechera lemmonii favoring moderately aged terrain. Moderate drainage and concave plots, which were important in the GLMs, explained increased species richness and Shannon's diversity across different site conditions. The CARTs were able to predict species richness, vegetation cover, Shannon's diversity, and species evenness with surface fragment sized from gravel to cobble, topographic position index, and flow accumulation. These findings show that both temporal and biophysical site conditions influence successional trends across the foreland, though different vegetation measures are most influenced differently.
- Spatial Patterns and Variations of Tornado Damage as Related to Southeastern Appalachian Forests and Terrain from the Franklin County, Virginia EF-3 TornadoForister, Peter Harding (Virginia Tech, 2021-06-24)Strong tornadoes have impacted the central Appalachian Mountains multiple times in recent years. The topography of this region leads to unique spatial patterns of tornado damage as the tornado vortices pass over ridges in forested areas, and this damage can be detected with vegetation indices derived from remotely sensed imagery. The objectives of this study were to 1) Classify forest damage from the April 19, 2019 EF-3 tornado in Franklin County, VA using remotely-sensed images, 2) Quantify the spatial patterns of forest damage intensity across the path using derived vegetation indices and terrain variables (primarily slope, aspect, elevation, and exposure), and 3) Use regression models to determine if relationships exist among terrain variables along the and forest damage patterns. I generated EVI and NDII vegetation indices from Sentinel-2 imagery and compared the derived damage to the underlying terrain variables. Results revealed that the two vegetation indices were effective for classifying tornado damage, and discrete damage classes aligned well with NWS EF-scale tornado intensity estimations. ANOVA testing suggested that EF-3 equivalent damage was more likely to occur on downslope topography, leeward of the tornado's direction of travel. OLS and geographically weighted regression (GWR) modeling performed poorly, suggesting that an alternative method may be more suitable for modeling, the scale of assessment was inadequate, or that important predictor variables were not captured. Overall, the intensity of the tornado was clearly modified by terrain interactions, and the remote sensing methodology used was effective for reliably identifying and rating damage in forested areas.
- Spatial Patterns on Virginia's Second Highest Peak: Land Cover Dynamics and Tree Mortality in Two Rare EcosystemsHarris, Ryley Capps (Virginia Tech, 2020-06-12)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.
- Spatial Studies to Support the Management of Long Distance TrailsMeadema, Peter Fletcher (Virginia Tech, 2023-02-24)Trails are essential transportation infrastructure supporting access to protected natural areas and providing recreation to hikers, runners, cyclists, equestrians, motorists, and many more worldwide. This research presents spatial studies intended to improve understanding of the environmental, managerial, and use-related factors that influence management of and physical and experiential conditions on long-distance hiking trails. The first study investigates a dataset from the Appalachian Trail (AT) to examine methods for using high resolution digital elevation models to measure terrain steepness near trails and along trails or potential trail routes. This analysis supports trail planning and assessment efforts because these terrain metrics strongly influence physical trail sustainability and are useful to evaluate the difficulty of travel along trails. The second study analyzes long-distance use patterns on the Pacific Crest Trail (PCT) as depicted by a computer model developed from a survey administered to long-distance hikers, trail counters, observations, and registers. In addition to describing use patterns, the process is intended to inform the selection of methods for visitor use monitoring in response to the complexity and level of controversy of management needs. The third study examines the spatial relationships between the PCT, a national scenic trail, and other congressionally designated land areas including wilderness, wild and scenic rivers, and national monuments and how this complexity is manifested in camping management strategies and impacts along the trail corridor. This analysis supports managing for the multiple congressional mandates across the PCT landscape and improves understanding and management of interagency transboundary travel on the trail.
- Spatial Tools for Management of Protected Natural Areas: Case Studies in Camping Management and Trail Impact AssessmentArredondo, Johanna Rochelle (Virginia Tech, 2023-11-03)This dissertation comprises two distinct journal articles, each contributing significant advancements to recreation ecology by examining the effectiveness of various spatial tools in camping and trail management. The first article leverages strategic spatial planning, considering topography and spacing, to limit camping impacts and enhance visitor experiences. It investigates the long-term effectiveness of a sustainable camping management strategy on the Appalachian Trail, whereby protected area managers select and actively encourage or require visitors to camp on excavated "side-hill" campsites in sloping terrain. One of the most degraded camping locations along the popular Appalachian Trail was selected for this longitudinal study, which, in May 2002, involved the closure of 19 existing campsites in flat terrain, with use shifted to 14 newly constructed side-hill campsites in adjacent sloping terrain. Over the subsequent 17 years, the recovery of the closed campsites and the evolution of the newly established side-hill campsites were monitored and assessed. Results from this study reveal that a multifaceted approach integrating both direct and indirect management actions successfully achieved their management objectives to sustain the site's exceptionally high use while minimizing both resource and social impacts. This study highlights the ability of constructed side-hill campsites to resist expansion over time and provides valuable findings, insights, and "lessons learned" to guide protected area managers in selecting and implementing effective management strategies and actions in other high-use settings. The second study evaluates terrestrial photogrammetry as a spatial tool for trail impact assessment. Protected natural areas like Joshua Tree National Park (JTNP) rely heavily on trails to facilitate visitor access while spatially concentrating environmental impacts to their treads. Assessing the condition of these trails is difficult due to the logistical challenges inherent in conventional field data-gathering techniques. While technological advancements such as Uncrewed Aerial Vehicles (UAV) introduce Structure-from-Motion (SfM) capabilities for trail monitoring, they are not without limitations, including prohibitive costs, legal restrictions, and operational challenges, particularly when monitoring trails enveloped by canopy cover. This study presents a novel approach to trail assessment using terrestrial photogrammetry, wherein a consumer-grade camera captures high-resolution imagery that is processed using SfM techniques. The study compared manual measurements of 46 trail transects in JTNP with measurements from Digital Elevation Models (DEMs) generated from SfM point clouds. The outcomes revealed a high level of agreement between the two methods, with the measurements derived from photogrammetric DEM data exhibiting consistently higher values compared to the field measurements, especially in the central regions of the transects. A statistically significant positive relationship between transect width and mean differences between GIS and field tread incision measurements suggests that the disparity may arise from the sagging of the tape measure across the trail, indicating photogrammetric methods might offer greater accuracy. The paper outlines methods for capturing high-resolution 3D trail data using cost-effective techniques and discusses the practicality and possibilities of using the technique in trail monitoring programs. This has far-reaching implications and positions terrestrial photogrammetry as a compelling alternative to drone-based acquisitions, particularly in areas where UAV operations are restricted, discouraged, or impractical.
- Tracking Human Movement Indoors Using Terrestrial LidarKarki, Shashank (Virginia Tech, 2024-06-03)Recent developments in surveying and mapping technologies have greatly enhanced our ability to model and analyze both outdoor and indoor environments. This research advances the traditional concept of digital twins—static representations of physical spaces—by integrating real-time data on human occupancy and movement to develop a dynamic digital twin. Utilizing the newly constructed mixed-use building at Virginia Tech as a case study, this research leverages 11 terrestrial lidar sensors to develop a dynamic digital model that continuously captures human activities within public spaces of the building. Three distinct object detection methodologies were evaluated: deep learning models, OpenCV-based techniques, and Blickfeld's lidar perception software, Percept. The deep learning and OpenCV techniques analyzed projected 2D raster images, while Percept utilized real-time 3D point clouds to detect and track human movement. The deep learning approach, specifically the YOLOv5 model, demonstrated high accuracy with an F1 score of 0.879. In contrast, OpenCV methods, while less computationally demanding, showed lower accuracy and higher rates of false detections. Percept, operating on real-time 3D lidar streams, performed well but was susceptible to errors due to temporal misalignment. This study underscores the potential and challenges of employing advanced lidar-based technologies to create more comprehensive and dynamic models of indoor spaces. These models significantly enhance our understanding of how buildings serve their users, offering insights that could improve building design and functionality.
- Understanding perception of different urban thermal model visualizationsBarua, Gunjan (Virginia Tech, 2023-03-17)While satellite-based remote sensing techniques are often used for studying and visualizing the urban heat island effect, they are limited in terms of resolution, view bias, and revisit times. In comparison, modern UAVs equipped with infrared sensors allow very fine-scale (cm) data to be collected over smaller areas and can provide the means for a full 3D thermal reconstruction over limited spatial extents. Irrespective of the data collection method, the thermal properties of cities are typically visually represented using color, although the choice of colormap varies widely. Previous cartographic research has demonstrated that colormap and other cartographic choices affect people's understanding. This research study examines the difference in map reading performance between satellite and drone-sourced thermal pseudo-color images for three map reading tasks, the impact of color map selection on map reading, and the potential benefits of adding shading to thermal maps using high-resolution digital surface models for improved interaction. Participants expressed a preference for the newly designed rainbow-style color map "turbo" and the FLIR "ironbow" colormap. However, user preferences were not strongly related to map reading performance, and differences were partly explained by the extra information afforded by multi-hue and shading-enhanced images.