Virginia Tech GIS and Remote Sensing Research Symposium
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- 2024 GIS and Remote Sensing Research Symposium(Virginia Tech, 2024-04-05)Schedule for the Virginia Tech GIS and Remote Sensing Symposium, held on April 5, 2024.
- 2023 GIS and Remote Sensing Research Symposium(2023-04-21)Schedule for the Virginia Tech GIS and Remote Sensing Symposium, held on April 21, 2023.
- 2021 GIS and Remote Sensing Research Symposium(2021-04-30)Schedule for the Virginia Tech GIS and Remote Sensing Symposium, held on April 30, 2021.
- Impacts of Nationwide Lockdown due to COVID 19 Outbreak on Air Quality in BangladeshIslam, Md Sariful (Virginia Tech, 2021-04-30)In Bangladesh, a nationwide lockdown was imposed on 26th March 2020. Due to restricted emissions, it was hypothesized that the air quality has been improved during lockdown throughout the country. The study is intended to assess the impact of nationwide lockdown measures on air quality in Bangladesh. Satellite data from different sources were analyzed for four different air pollutants (NO2, SO2, CO, and O3 to assess the changes in the atmospheric concentrations of pollutants across the country. In this study, the concentrations of NO2, SO2, CO, and O3 from 1st February to 30th May of the year 2019 and 2020 were analyzed. The average SO2 and NO2 concentrations were decreased by 43% and 40% respectively, while tropospheric O3 were found to be increased with a maximum of 7%. This analysis reveals that NO2 concentrations are highly correlated with the regional COVID-19 cases (r=0.74) in the country.
- Estimating County level Timber Volume in Virginia Using Small Area EstimationDettmann, 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).
- Understanding multitemporal landscape dynamics through remote sensing and paleoecological modeling in the Virginia Tech Environmental Tracking LabHarris, Ryley Capps; Kennedy, Lisa M.; Swift, Troy; Donahoe, Daniel; Burton, Devon (Virginia Tech, 2021-04-30)The Environmental Tracking Lab at Virginia Tech currently comprises five graduate students under the direction of Dr. Lisa Kennedy. Our team members have prior training in biogeography, physical geography, biology and ecology, geology, fish and wildlife conservation, ecosystem management and restoration, and geospatial and information science. The diverse training and experiences represented in our lab group provide a broad and integrative approach to understanding ecosystem and landscape change at varied temporal and spatial scales. Our members seek to model the ever-changing landscape, identify drivers of change, and predict future changes. Investigation of shorter-term changes using remote sensing, Lidar, and unmanned aerial systems (UAS) technologies, in conjunction with longer-term proxy data modeling, can provide a broad and deep window into environmental and landscape changes. Showcased in this poster are glimpses of a few of our research projects in various stages of execution. This presentation demonstrates some of our capabilities as a laboratory with the goal of increasing recognition and collaboration within our college, the university, and beyond.
- Assessment of the diurnal relationship of photochemical reflectance index to forest light use efficiency by accounting for sunlit and shaded foliageWilliams, 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).
- Pine Plantation Identification using NAIP Imagery and a Convolutional Neural NetworkMiller, Benjamin; Thomas, Valerie A.; Wynne, Randolph H. (Virginia Tech, 2021-04-30)Pine plantations in the Southeastern United states are presently under-quantified using disturbance based metrics of forest change. Methods such as the Global Forest Change data-set have limited accuracy in identifying pine plantations. Direct estimation of pine plantations poses its’ own challenges but the structure of plantations creates an interesting opportunity. The uniform structure and pattern of pine plantations permits the implementation of object identifying neural network techniques using high spatial resolution imagery such as the National Agriculture Imagery Program. This presentation will explore the preliminary results of such a process.
- Mapping Land use and Land Cover change on Brazil's land tenure categories using Google Earth EngineShinde, Nilesh (Virginia Tech, 2021-04-30)Brazilian territory expands up to 851.6 million ha, of which 44.2% land is privately owned, and 36.1% land is public owned.The privately owned land are registed under the nationwide tenure registries such as Cadastro Ambiental Rural (CAR), Land Management System (SIGEF), Terra Legal, Quilombola territory. The public land comes under Indigenous Reserves, Conservation Units, Communitary Territory, Military Land and Rural Settlement. In this paper, we employ 4.5 million property-level locations to understand the trajectory of land use and land cover change across Brazil’s Land tenure categories. Using Google Earth Engine (GEE), we employ MapBiomas collection 5 landuse data from 1985-2019. The paper provides the first quantitative and spatially explicit assessment of the coverage, gaps, and uncertainties in the land use categories across variety of land tenure status of the entire Brazilian territory. Data is organized in the most detailed property level, but it allows integration in the various jurisdiction levels where land policy and decision occur, from the municipal to the federal scale.
- Observing a Global Pandemic from Space: Evaluating Participatory Geographic Information Systems (PGIS) during the SARS-CoV-2 PandemicDuChesne, Danielle (Virginia Tech, 2021-04-30)When the novel SARS-CoV-2 virus emerged in December 2019, GIS technologies and web-based GIS dashboards were rapidly employed to share information regarding disease spread and impact on society. As GIS-based tools are capable of providing spatial complexity, interactivity, and interconnectedness, its growth in popularity to help solve multifaceted problems has also grown. These efforts from citizens and scientists alike to engage in Participatory GIS (PGIS) were essential for timely and effective epidemic monitoring and response. However, the original intent of PGIS to involve the public in geographical mapping to uncover context-sensitive place-based information (Brown & Kyttä, 2014) has also created discrepancies such as ignoring the sociopolitical context of data and disregards common geovisualization best practices. The goal of this poster aims to evaluate the challenges of PGIS in analyzing data as it was used during the current global pandemic by exploring COVIDPoops19, a PGIS dashboard tracking wastewater testing as well as describing potential solutions from interdisciplinary frameworks that allow for better decision making, planning, and community action.
- Beaver-driven dynamics of a peatland ecotone: Identification of landscape features with Lidar and geomorphon analysisSwift, 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.
- 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.
- Assessing the utility of NAIP digital aerial photogrammetric point clouds for estimating canopy height of managed loblolly pine plantations in the southeastern United StatesRitz, 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.
- Preserving historical maps of Blacksburg, VA helps to focus archeological studies of SolitudeOwens, Chris (Virginia Tech, 2021-04-30)By comparing historical maps to our current landscapes we can find historical buildings forgotten to time as a means of discovering and learning about our pasts. Unfortunately, we cannot reflect on our past without taking steps towards its preservation. As the Sesquicentennial of Virginia Tech approaches, research focusing on Virginia Tech’s history has become of particular interest. There is now interest in conducting Ground Penetrating Radar (GPR) around the old Solitude building to uncover potential artifacts. To assist this research a site evaluation was done to identify potential scan sites; Historical maps of Virginia Tech were digitized and referenced to help identify landmarks of interest and utilities data from the university was mapped to help avoid obstructions. This poster will demonstrate the importance of mapping, data preservation, and the value of historical resources, as they pertain to the preparation of an archeological study of the historical farm around Solitude.
- Effects of Clearing Linear Features through Forest Patches in WV and VAMoore, 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.
- Remote characterization of Antarctic microbial mat communitiesPower, Sarah N.; Salvatore, Mark R.; Sokol, Eric R.; Stanish, Lee F.; Barrett, John E. (Virginia Tech, 2021-04-30)The McMurdo Dry Valleys, Antarctica are ecosystems where life approaches its environmental limits. Cyanobacteria, however, have adapted to survive in this extreme environment as the most dominant life form and the main drivers of primary productivity (i.e., photosynthesis). Cyanobacterial communities exist on soil surfaces adjacent to glacial meltwater streams layered in mats up to several cm thick. The cryptic nature of these communities and their patchy distribution make assessments of productivity challenging. We used satellite imagery coupled with in situ surveying, imaging, and sampling to systematically estimate microbial mat biomass in selected wetland regions in Taylor Valley, Antarctica. On January 19th, 2018, the WorldView-2 multispectral satellite acquired an image of our study areas, where we surveyed and sampled seven 100 m2 plots of microbial mats for percent ground cover, ash-free dry mass, and pigment content. Multispectral analyses revealed spectral signatures consistent with photosynthetic activity (relatively strong reflection at near-infrared wavelengths and relatively strong absorption at visible wavelengths), with average NDVI values of 0.09 to 0.28. Strong correlations of microbial mat ground cover (R2 = 0.84), biomass (R2 = 0.74), chlorophyll-a content (R2 = 0.65), and scytonemin content (R2 = 0.98) with logit transformed NDVI values demonstrate that satellite imagery can detect both the presence of microbial mats and their key biological properties. Using the NDVI – biomass correlation we developed, we estimate carbon (C) stocks of 21,715 kg (14.7 g C m-2) in the Canada Glacier Antarctic Specially Protected Area. By quantitatively comparing biological surface observations to NDVI, this is the first satellite-derived estimate of microbial mat biomass for this region of Antarctica.
- Tracking Northern Appalachian Political Participation and its Consequences, 2000-2020Shayer, Ryan; Scales, Stewart (Virginia Tech, 2021-04-30)Over the past twenty years, political participation levels have had significant impacts on the results of presidential elections. The Northern Appalachia region, defined by the Appalachian Regional Commission (ARC) to include all ARC counties in New York, Pennsylvania, and Maryland, as well as several in northern Ohio and West Virginia, has had a particularly significant impact. Considering the divisiveness of the last two presidential elections, popular and academic sources have sought to explain a variety of trends found in these counties’ voting habits (Brownstein, 2016; Fahey and Wells, 2016). This project uses GIS and original research to examine the relationship between political participation levels and the percentage of the county-level vote earned by a Democrat candidate during the six presidential elections from 2000 to 2020, with the goal of determining whether or not a statistically significant relationship exists between the two. Additionally, it examines the reasons for ‘flipping’ of counties from one election to the next, as well as the geospatial patterns of both ‘flipping’ and support for a given candidate. The main objective of this project is to provide the general public, research community, and government agencies with a better understanding of the importance, and the place of Northern Appalachia within the lens of national politics. Preliminary results indicate that in most years, a significant relationship exists between voter turnout levels and eventual county-level outcomes. The project utilizes open-source data from the US Census Bureau, MIT Election Lab, and Politico, as well as ArcGIS for data visualization.
- 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.
- An Unsupervised Probabilistic Method for Large Scale Flood Mapping: Exploring Archive of Sentinel-1A/B Satellites over IndiaSherpa, Sonam Futi (Virginia Tech, 2021-04-30)Synthetic aperture radar (SAR) imaging provides an all-weather sensing technique that is suitable for near-real-time mapping of disasters such as floods. In this article, I use SAR data acquired by Sentinel-1A/B satellites to investigate a flood event that affected the Indian state of Kerala in August 2018. I apply a Bayesian approach to generate probabilistic flood maps, which contain for each pixel its probability to be flooded rather than binary flood information. I find that the extent of the flooded area begins to increase throughout Kerala after August 8, with the highest values on August 9 and August 21. I observe no apparent correlation between the spatial distributions of the flooded areas and the rainfall amounts at the district level of the study area. Instead, larger flooded areas are in the districts of Alappuzha and Kottayam, located in the downstream floodplain of the Idduki dam, which released a significant volume of water on August 16. The lack of apparent correlation is likely due to two reasons: first, there is often some delay between the rainfall event and the flooding, especially for rather large catchments where flood waves need some time to reach floodplains from higher elevations. Second, rainfall is more abundant at overhead catchments (hills and mountains), whereas flood occurs further downstream in the floodplains. Further comparison of our SAR-based flood maps with optical data and flood maps produced by moderate resolution imaging spectroradiometer highlights the advantages of our data and approach for rapid response purposes and future flood forecasting.
- Effect of establishment fertilization on leaf area development of loblolly pine plantation stands in the southeastern United StatesHouse, 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.