Virginia Tech GIS and Remote Sensing Research Symposium

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2010 VT-GIS Symposium April 9, 2010: Poster Presentations
(Virignia Tech. Office for GIS and Remote Sensing Research, 2010-04-09)
Abstracts from the poster presentations at the Virginia Tech GIS Symposium, held on April 9, 2010.
2019 Virginia Tech Office for GIS and Remote Sensing Research Symposium
(Office for GIS and Remote Sensing Research, 2019-04-26)
Student poster and web map app showcase abstracts from the symposium held on April 26, 2019, in Newman Library.
Accuracy Assessment of the National Land Cover Database Impervious Surface dataset for Roanoke, Virginia
Parece, Tammy E.; Campbell, James B. Jr. (2014)
The Multi-Resolution Land Characteristics Consortium (MRLC) developed National Land Cover Database Impervious Surface (NLCD IS) data to identify percent developed imperviousness for the coterminous USA. We present the results of an accuracy assessment on this data for the City of Roanoke, Virginia. First, we performed a classic accuracy assessment using a set of random points generated by GIS, and high resolution aerial photographs (1/2 foot resolution), varying the NLCD IS’ percent imperviousness from 10% to 75% per cell, resulting in an overall accuracy of around 70% for most thresholds. Then a polygon impervious surface dataset was delineated in GIS using the same high resolution aerial photos, and subsequently subdivided into 30 meter by 30 meter pixels matching each cell boundary of the NLCD IS data. A second accuracy assessment was performed on a cell by cell basis, comparing the NLCD IS to this newly created impervious surface dataset. Finally, terrain relief, specifically percent slope created from a 30 meter digital elevation model, was added to the analysis to determine if it impacted the accuracy of the NLCD IS data in the cell by cell assessment.
Accuracy Assessment of the NLCD 2006 Impervious Surface for Roanoke and Blacksburg
Zhao, Suwen; Feng, Leyang; Shao, Yang; Dymond, Randel L. (2014)
Impervious surface map products are important for the study of urbanization, urban heat island effects, watershed hydrology, water pollution, and ecosystem services in general. At the conterminous US scale, impervious surfaces are mapped for 2001 and 2006. The accuracy of the 2006 NLCD impervious surface, however, has not been thoroughly examined, especially for small and intermediate size cities (e.g., regional city). In this study, we selected two transects in two cities and visually interpreted aerial photo to develop impervious surface reference maps. We then compared percent impervious surface of the NLCD and aerial photo-interpreted reference maps. The comparison was conducted at 90m resolution to minimize the errors in image registration. Overall, we found that the 2006 NLCD impervious surface matched well with our reference data, although slight skewness at two extremes is present. The R² and RMSE statistics improved when the two datasets are compared at coarse aggregation levels (e.g. 180m).
Analysis of Crop Phenology Using Time-Series MODIS Data and Climate Data
Ren, Jie; Campbell, James B. Jr.; Shao, Yang; Thomas, R. Quinn (2014)
Understanding crop phenology is fundamental to agricultural production, management, planning and decision-making. In the continental United States, key phenological stages are strongly influenced by meteorological and climatological conditions. This study used remote sensing satellite data and climate data to determine key phenological states of corn and soybean and evaluated estimates of these phenological parameters. A time series of Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composites from 2001 to 2013 was analyzed with the TIMESAT program to automatically retrieve key phenological stages such as the start of season (emergence), peak (heading) and end of season (maturity). These stages were simulated with 6 hourly temperature data from 1980 to 2013 on the basis of crop model under the Community Land Model (CLM) (version 4.5). With these two methods, planting date, heading date, harvesting date, and length of growing season from 2001 to 2013 were determined and compared. There should be a good correlation between estimates derived from satellites and estimates produced with the climate data based on the crop model.
Assessing Seasonal Changes of Spatial Complexity in Riverscapes using Drone-Based Laser Scanning
Aquilina, Charles A.; Hession, W. Cully; Lehman, Laura; Resop, Jonathan P. (Virginia Tech, 2019-04-26)
Light detection and ranging (lidar) is a form of remote sensing using laser pulses to measure distances. Recent advancement in lidar technology has made units small enough to mount on drones, which makes high-quality data more accessible. Recent studies have utilized drone-based photogrammetry to measure characteristics of streams and rivers, as well as their associated riparian areas. These areas have been referred to as riverscapes. The physical characteristics of riverscapes are traditionally difficult to measure due to ever-changing characteristics across space and time. Drone-based laser scanning (DLS), is uniquely positioned to measure changing physical characteristics as it allows for increased temporal (daily, monthly, seasonal flights) and spatial (more than 400 pts/m2 at 30-m flight elevation) resolutions. It has more upfront costs compared to photogrammetry, as a DLS system (large drone and lidar) is vastly more expensive than a small drone with a digital camera payload. However, lidar can penetrate through vegetation, allowing for high-quality ground data, as well as vegetation points, which is a limitation of photogrammetry. One use of this ground and vegetation data is to analyze small changes of the topography to estimate complexity (an important habitat variable), as well as obstructions to flow such as vegetation. These obstructions to flow result in increased roughness, which is an important metric in biological studies and hydraulic modeling. In previous studies, estimating roughness was limited to visual observations or back-calculating from flow measurements, which can be time consuming and does not produce continuous spatial data. Using DLS-derived ground and vegetation, we will monitor small changes in vegetation and topography over the course of the stream both longitudinally, laterally, and through time. We will test various methods of computing roughness from detailed lidar point clouds to determine roughness. Some possibilities estimating roughness and complexity include the standard deviation of the elevation change, the variation between maximum and minimum elevations in a pixel, slope variability, surface roughness factors, and others. These values can be compared to a calibrated 2D hydraulic flood modeling (HEC-RAS), DLS has the potential to change the way we map and understand spatial complexity and habitat characteristics of riverscapes.
Assessing the utility of NAIP digital aerial photogrammetric point clouds for estimating canopy height of managed loblolly pine plantations in the southeastern United States
Ritz, Alison; Thomas, Valerie A.; Wynne, Randolph H. (Virginia Tech, 2021-04-30)
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.
Assessment of the diurnal relationship of photochemical reflectance index to forest light use efficiency by accounting for sunlit and shaded foliage
Williams, Paige Tatum; Harding, David J.; Thomas, Valerie A.; Wynne, Randolph H.; Ranson, Kenneth J.; Huemmrich, Karl F.; Middleton, Elizabeth; Campbell, Petya K. (Virginia Tech, 2021-04-30)
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).
Attribute Standardization of Car Crashes and Its Potential Uses
Mitchell, Allison; Hamilton, Lonnie III; Newman, Joseph (Virginia Tech, 2019-04-26)
The primary focus of our research endeavor centers around standardizing the spatial attributes of police-reported crash records in the Commonwealth of Virginia. The Center for Geospatial Information Technology at Virginia Tech (CGIT) is working in support of the Virginia Department of Motor Vehicles Highway Safety Office’s mission to improve public safety. This data will be used by highway safety officials to identify particularly dangerous intersections and road segments across the commonwealth. We evaluated the crash factors and characteristics present in the dataset to better understand the potential that geospatial techniques can provide to the highway safety community. We elected to analyze crashes involving the black bear (Ursus Americanus) to see what observation could be made. To start, it was necessary to define the criteria to identify crashes involving bears. This was initially done manually by using an SQL request to obtain all records from 2018 crash data where the word ‘bear’ is referenced in the officer’s narrative. From there, we conducted a manual sort of the remaining data to help craft future, more efficient SQL requests for other years Once all records involving bears have been found, the data will be rendered in ArcGIS. Some exploratory analyses we plan on conducting involve identifying routes with high incidences of bear-related crashes, overlaying the crashes with known Wildlife Urban Interfaces (zones where housing density >6.17 housing units/km2 and vegetation cover >50%), and overlaying the crashes with the known habitats of the black bear in the commonwealth to observe if and how they may differ.
Beaver-driven dynamics of a peatland ecotone: Identification of landscape features with Lidar and geomorphon analysis
Swift, Troy P.; Kennedy, Lisa M. (Virginia Tech, 2021-04-30)
Beaver are renowned for their role as ecosystem engineers. Their ponds and vegetation consumption can greatly alter local hydrology and ratios of meadow to woodland. Beavers also actively buffer their environments against drought and wildfire susceptibility, and influence important climate parameters like carbon retention and methanogenesis (Rozhkova-Timina et al. 2018). This investigation focuses on beaver impacts on the boreal peatland ecotones enmeshing Cranberry Glades Botanical Area (~300 ha, ~1000 masl), a National Natural Landmark in mountainous West Virginia. Beaver activity has been suggested (Stine et al. 2011) to have an important role in the formation and maintenance of peatland conditions at Cranberry Glades. Using Lidar, geomorphon analysis, and aerial imagery, we were able to identify and reconstruct shifting hydrological patterns associated with beaver dams and ponds. The three-year interval worked well, allowing time for widespread changes in beaver infrastructure while conserving utility of reference imagery. Future work will include analysis of the most recent beaver activity, refinement of classification workflows, generation of more accurate physical models using drone-acquired Lidar and better ground filtering, and more complete incorporation of historical imagery.
Comparing UAS and Pole Photogrammetry for Monitoring Beach Erosion
Gonzales, 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.
Crowds for Clouds: Using an Internet Workforce to Interpret Satellite Images
Yu, Ling; Ball, Sheryl B.; Blinn, Christine E.; Moeltner, Klaus; Peery, Seth; Thomas, Valerie A.; Wynne, Randolph H. (2014)
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."
Delineating Subwatersheds within the Monongahela National Forest: The Implications of a Watershed Approach on Forest Planning
Rader, Kristin (2014)
The Monongahela National Forest comprises over 900,000 acres of biologically diverse land in West Virginia. It is the largest tract of public land within the state, and it attracts anglers, hunters, and other outdoor enthusiasts to the region. It is important to maintain the health and beauty of the forest as it plays a major role in the socioeconomic well-being of the state. The Monongahela National Forest is creating an ecological classification system to characterize its watersheds to improve management practices. For this classification to be successful and useable for modifying the Forest Management Plan, smaller watersheds are required. This project was designed using ESRI ArcGIS 10.1 to create subwatersheds for water sampling locations within the Monongahela National Forest. Specifically, the Spatial Analyst Toolbox and Model Builder were utilized. Nearly two hundred subwatersheds were created for use in this classification system. The methodology has been converted into a ‘How to…’ guide to allow replication for additional sampling locations within the Forest, in other National Forests, as well as future student projects at Virginia Tech. Additionally, application examples were demonstrated to show the ease of performing statistical analyses using this model, such as analyzing temperature and elevation effects. Furthermore, land use implications can be identified within the subwatersheds. The Forest Management Plan could be adapted or maintained based on these land use implications.
Ecosystem Service Benefits from Public and Private Conservation Lands
Scott, L.; Gillespie, J.; Villamagna, A. (2014)
Research Objective: To quantify and compare ecosystem services (ES) capacity for Publicly Protected Areas (PPAs) and private conservation easements. Method: The study area was state and federal PPAs and private conservation easements in NC and VA. We developed and used existing ES capacity models to quantify ES capacity using GIS. Focal ES were surface water regulation, groundwater protection, water quality regulation, erosion control, recreational fishing, carbon storage, and biodiversity support. National Conservation Easement Database was used to map private conservation easements with environmental systems, recreation and education, open forest, and open farm purposes with a gap status of 1, 2, or unknown. USGS National Inventory of Protected Areas (PAD-US) was used to map State and Federal PPAs with a gap status 1 or 2. All ES capacities were standardized on a scale of 0-1 for comparison. Results: Biodiversity support was significantly greater in federal PPAs, especially throughout VA. Surface water regulation was the only ES where federal and state PPA capacity was significantly greater than private conservation easements. Ecosystem service capacity for private conservation easements was equal or greater to federal and state PPAs for all services except surface water regulation. Private land conservation protects ES and may have positive regional impacts where PPAs are not present. Although smaller, private easements can protect ES in more diffuse areas throughout the region. Existing ES capacity can be used to identify conservation areas with potential to enhance ES protection. Riparian filtration, erosion control, carbon storage, and surface water regulation capacity are the most practical services to incentivize ES protection on private lands.
Effect of establishment fertilization on leaf area development of loblolly pine plantation stands in the southeastern United States
House, Matt (Virginia Tech, 2021-04-30)
Loblolly pine plantations in the southeastern United States are some of the most intensively managed forest plantations in the world. Within intensive management one common practice is fertilizing a stand/site at establishment. The objective of this study was to investigate the effect of establishment fertilization on the leaf area development of loblolly pine planation stands across time. Sub-objectives included comparison of fertilized stands with stands that had no intervention and examination of whether identifying fertilized stands and no intervention stands could be applied across the landscape. To account for the size of the study area and different landscape types (elevation and proximity to a coast) the study area was also stratified by hardiness zone. Additionally, the study was stratified by soil type, specifically CRIFF (Cooperative Research in Forest Fertilization) groupings. Leaf area index (LAI) is a meaningful biophysical parameter and an important functional and structural element of a plantation stand. The Landsat satellite missions provides plantation managers and scientists a way to estimate LAI over time. Google Earth Engine (GEE) provides the ability to leverage the Landsat archive to obtain LAI estimates over large areas and through time. Stand boundaries were buffered inwards 30m to minimize mixed pixels and to match the spatial resolution of Landsat. LAI was computed (using: SR * 0.3329155 - 0.00212) to create trajectories of mean Stand LAI over time for analysis.
Effects of Clearing Linear Features through Forest Patches in WV and VA
Moore, Sierra; Klopfer, Scott D. (Virginia Tech, 2021-04-30)
Recent pipeline construction through predominantly forested mountain areas presents many concerns for environmentalists. Impacts from construction are often measured in total area of forest removal, but this may not capture the extent of change to the landscape. Other effects, such as increased fragmentation and edge go unmeasured. We examined changes to the forest landscape resulting from the Mountain Valley Pipeline; a recently constructed corridor that runs through West Virginia and Virginia. We identified affected forest patches using the 2016 National Land Cover Dataset and analyzed both pre- and post-construction patch characteristics. The total area of forest removed was 1,182.57 ha, (0.03%). The total core forest decreased by 5,781.33 ha (2.7%). The number of forest patches increased from 242 to 667, with an average of 2.9 new patches per original patch. The edge density increased 5.4% between pre and post pipeline (0.0059 m/ha to 0.0062 m/ha). Area/Perimeter ratio increased between pre and post construction (0.049 to 0.2524). Our results demonstrate that area, alone, is insufficient to determine the total impacts of linear construction on forest in the study area, particularly since the loss of core forest and increasing edge have well-documented impacts to ecological processes.
The Effects of Land Cover Change on the Spatial Distribution of Lyme Disease in Northern Virginia Since 2005
Stevenson, Megan N.; Kolivras, Korine N. (Virginia Tech, 2019-04-26)
Lyme disease has been a growing problem in the United States over the last few decades, and is currently the most common vector-borne disease in the country. This research evaluates the land cover within specified counties of northern Virginia to find a correlation between forest fragmentation, suburbanization, and cases of human Lyme disease; as has been demonstrated in other Lyme endemic regions in the United States. Few studies have focused specifically on northern Virginia when considering the impacts of land cover change on Lyme disease. Discovered through the use of GIS and Geospatial Modelling Environment softwares, the cluster of Lyme disease cases in northern Virginia could be attributed to the forest fragmentation within the study region, which creates an ideal habitat for black-legged ticks and allows for an increase in Lyme disease transfer from vector to humans. The goal is for the research findings to be applicable to other regions with similar land cover types. Regions with similar characteristics would then be able to recognize the potential risk of human Lyme disease and implement ways to reduce the Lyme disease risk associated with suburban development. The purpose of this study is to answer the following research questions: 1) How has the spatial distribution of Lyme disease in Northern Virginia changed since 2005 with respect to land cover? 2) Which suburban communities are more at risk for Lyme disease when considering their land cover types and the increasing spatial distribution of Lyme disease?
Estimating County level Timber Volume in Virginia Using Small Area Estimation
Dettmann, Garret; Radtke, Phil (Virginia Tech, 2021-04-30)
Accurate estimates of forest components such as, biomass, composition, or health are important for forest management and policy decisions. The USDA Forest Service Forest Inventory and Analysis (FIA) program serves as a national survey system to assess such forest characteristics within the United States (US). When making estimates at the county level or smaller spatial scale with FIA plot data, the accuracy in estimation of forest components drops. Here we use NAIP data in conjunction with FIA data in order to improve upon county level timber volume estimation precision in Virginia using Small area estimation (SAE).
Floodplain Hydraulics: LiDAR Applications
Jones, C. Nathan; Scott, Durelle T. (2014)
As climate becomes increasingly variable, the costs to infrastructure and our society as a whole are going to rise drastically. Since the year 2000, damages due to hurricanes are estimated to be ~$260 Billion in the United States alone. This has led to changes in the national flood insurance program and could place more emphasis on flood modelling in the coming years. Here, we present a case study where remotely sensed data was used to increase the accuracy of a two-dimensional hydrodynamic flood model. The study site is a 14 km reach of the Tangipahoa River in Southern Louisiana. The reach has extensive floodplains with complex microtopography, making flowpath characterization difficult. Utilizing Light Detection and Ranging (LiDAR) data freely available from the state of Louisiana, a surface model was developed and storage zones within the floodplain were delineated. Further, information about the forest structure derived from the LiDAR was used to estimate each storage zone’s resistance to flow in the form of the Manning’s N coefficient. This produced a roughness estimation that more closely resembles the spatial heterogeneity of roughness experienced in the floodplain, thus increasing the predictive capability of the flood model.
High Resolution 3D Modeling Using Oblique Pictometry and Lidar Data
Atkins, 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.

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