Department of Geography

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Adapting Pink Time to promote self-regulated learning across course and student types
Baird, Timothy D.; Kniola, David J.; Hartter, Joel; Carlson, Kimberly; Rogers, Sarah; Russell, Don; Tise, Joseph (2020)
To explore new opportunities to promote self-regulated learning (SRL) across a variety of contexts, this study applies a novel assignment called Pink Time in seven different courses at two universities. The assignment asks students to “skip class, do anything you want, and give yourself a grade.” In each case, instructors adapted Pink Time to fit the needs of their course. Altogether, 165 students completed 270 self-directed projects and self-assessments targeting five component behaviors of SRL. Findings show that: (1) students were more likely to perceive success in certain behaviors of SRL than in others; (2) students’ perceptions across courses were similar for some behaviors but not others; and (3) subsequent iterations of the assignment supported higher perceived measures of some SRL behaviors but not others. Together these findings illustrate the value and flexibility of this progressive assignment as well as persistent challenges in supporting students’ SRL.
Assessing Deep Convolutional Neural Networks and Assisted Machine Perception for Urban Mapping
Shao, Yang; Cooner, Austin J.; Walsh, Stephen J. (MDPI, 2021-04-15)
High-spatial-resolution satellite imagery has been widely applied for detailed urban mapping. Recently, deep convolutional neural networks (DCNNs) have shown promise in certain remote sensing applications, but they are still relatively new techniques for general urban mapping. This study examines the use of two DCNNs (U-Net and VGG16) to provide an automatic schema to support high-resolution mapping of buildings, road/open built-up, and vegetation cover. Using WorldView-2 imagery as input, we first applied an established OBIA method to characterize major urban land cover classes. An OBIA-derived urban map was then divided into a training and testing region to evaluate the DCNNs’ performance. For U-Net mapping, we were particularly interested in how sample size or the number of image tiles affect mapping accuracy. U-Net generated cross-validation accuracies ranging from 40.5 to 95.2% for training sample sizes from 32 to 4096 image tiles (each tile was 256 by 256 pixels). A per-pixel accuracy assessment led to 87.8 percent overall accuracy for the testing region, suggesting U-Net’s good generalization capabilities. For the VGG16 mapping, we proposed an object-based framing paradigm that retains spatial information and assists machine perception through Gaussian blurring. Gaussian blurring was used as a pre-processing step to enhance the contrast between objects of interest and background (contextual) information. Combined with the pre-trained VGG16 and transfer learning, this analytical approach generated a 77.3 percent overall accuracy for per-object assessment. The mapping accuracy could be further improved given more robust segmentation algorithms and better quantity/quality of training samples. Our study shows significant promise for DCNN implementation for urban mapping and our approach can transfer to a number of other remote sensing applications.
Assessing Strontium and Vulnerability to Strontium in Private Drinking Water Systems in Virginia
Scott, Veronica; Juran, Luke; Ling, Erin; Benham, Brian L.; Spiller, Asa (MDPI, 2020-04-08)
A total of 1.7 million Virginians rely on private drinking water (PDW) systems and 1.3 million of those people do not know their water quality. Because most Virginians who use PDW do not know the quality of that water and since strontium poses a public health risk, this study investigates sources of strontium in PDW in Virginia and identifies the areas and populations most vulnerable. Physical factors such as rock type, rock age, and fertilizer use have been linked to elevated strontium concentrations in drinking water. Social factors such as poverty, poor diet, and adolescence also increase social vulnerability to health impacts of strontium. Using water quality data from the Virginia Household Water Quality Program (VAHWQP) and statistical and spatial analyses, physical vulnerability was found to be highest in the Ridge and Valley province of Virginia where agricultural land use and geologic formations with high strontium concentrations (e.g., limestone, dolomite, sandstone, shale) are the dominant aquifer rocks. In terms of social vulnerability, households with high levels of strontium are more likely than the average VAHWQP participant to live in a food desert. This study provides information to help 1.7 million residents of Virginia, as well as populations in neighboring states, understand their risk of exposure to strontium in PDW.
Assessing Urban Community Gardens’ Impact on Net Primary Production using NDVI
Parece, Tammy E.; Campbell, James B. Jr. (Access, 2017-05-25)
Community gardens are one form of urban agriculture–growing of food and non-food products for sale or consumption within urban and peri-urban areas. Urban community gardens provide many benefits, including provisioning of fresh and nutritious foods, supporting environmental education, nurturing social interaction and community building, and contributing to sustainability. In many cities worldwide, urban agriculture is now integrated within urban planning programs. Although social, community, and nutritional benefits of community gardens are well documented, few quantitative assessments of their environmental benefits exist. None have applied Normalized Difference Vegetation Index (NDVI) as an environmental metric. NDVI is widely used in forestry and agriculture to track changes in vegetation phenology, assess vegetation stress and health, and, in urban areas, to separate vegetation from impervious surfaces. NDVI has a positive relationship with net primary production. We used NDVI product from U.S. satellites–Landsats 5, 7, and 8–to assess urban community garden sites. We conducted a time series analysis over the 2007 to 2015 growing seasons (May–September) for three eastern U.S. cities–Roanoke, VA; Pittsburgh, PA; and Buffalo, NY. Our results show that establishment of community gardens alter seasonal NDVI trajectories, sometimes with initial declines, but then increasing over time. Furthermore, NDVI profiles reveal the vigorous character of urban agriculture.
Assessing Urban Landscape Variables’ Contributions to Microclimates
Parece, Tammy E.; Li, Jie; Campbell, James B. Jr.; Carroll, David F. (Hindawi, 2015-12-24)
The well-known urban heat island (UHI) effect recognizes prevailing patterns of warmer urban temperatures relative to surrounding rural landscapes. Although UHIs are often visualized as single features, internal variations within urban landscapes create distinctive microclimates. Evaluating intraurban microclimate variability presents an opportunity to assess spatial dimensions of urban environments and identify locations that heat or cool faster than other locales. Our study employs mobile weather units and fixed weather stations to collect air temperatures across Roanoke, Virginia, USA, on selected dates over a two-year interval. Using this temperature data, together with six landscape variables, we interpolated (using Kriging and Random Forest) air temperatures across the city for each collection period. Our results estimated temperatures with small mean square errors (ranging from 0.03 to 0.14); landscape metrics explained between 60 and 91% of temperature variations (higher when the previous day’s average temperatures were included as a variable). For all days, similar spatial patterns appeared for cooler and warmer areas in mornings, with distinctive patterns as landscapes warmed during the day and over successive days. Our results revealed that the most potent landscape variables vary according to season and time of day. Our analysis contributes new dimensions and new levels of spatial and temporal detail to urban microclimate research.
Characterizing major agricultural land change trends in the Western Corn Belt
Shao, Yang; Taff, Gregory N.; Ren, Jie; Campbell, James B. Jr. (Elsevier, 2016-12-01)
In this study we developed annual corn/soybean maps for the Western Corn Belt within the United States using multi-temporal MODIS NDVI products from 2001 to 2015 to support long-term cropland change analysis. Based on the availability of training data (cropland data layer from the USDA-NASS), we designed a cross-validation scheme for 2006–2015 MODIS data to examine the spatial generalization capability of a neural network classifier. Training data points were derived from a three-state subregion consisting of North Dakota, Nebraska, and Iowa. Trained neural networks were applied to the testing sub-region (South Dakota, Kansas, Minnesota, and Missouri) to generate corn/soybean maps. Using a default threshold value (neural network output signalP0.5), the neural networks performed well for South Dakota and Minnesota. Overall accuracy was higher than 80% (kappa > 0.55) for all testing years from 2006 to 2015. However, we observed high variation of classification performance for Kansas (overall accuracy: 0.71–0.82) and Missouri (overall accuracy: 0.65–0.77) for various testing years. We developed a threshold-moving method that decreases/increases threshold values of neural network output signals to match MODIS-derived corn/soybean acreage with the NASS acreage statistics. Over 70% of testing states and years showed improved classification performance compared to the use of a default 0.5 threshold. The largest improvement of kappa value was about 0.08. This threshold-moving method was used to generate MODIS-based annual corn/soybean map products for 2001–2015. A non-parametric Mann-Kendall test was then used to identify areas that showed significant (p < 0.05) upward/downward trends. Areas showing fast increase of corn/soybean intensities were mainly located in North Dakota, South Dakota, and the west portion of Minnesota. The highest annual increase rate for a 5-km moving window was about 6.8%.
Coastal Erosion and Human Perceptions of Revetment Protection in the Lower Meghna Estuary of Bangladesh
Crawford, Thomas W.; Islam, Md Sariful; Rahman, Munshi Khaledur; Paul, Bimal Kanti; Curtis, Scott; Miah, Md. Giashuddin; Islam, Mohammad Rafiqul (MDPI, 2020-09-22)
This study investigates coastal erosion, revetment as a shoreline protection strategy, and human perceptions of revetments in the Lower Meghna estuary of the Bangladesh where new revetments were recently constructed. Questions addressed were: (1) How do rates of shoreline change vary over the period 2011–2019? (2) Did new revetments effectively halt erosion and what were the magnitudes of erosion change? (3) How have erosion rates changed for shorelines within 1 km of revetments, and (4) How do households perceive revetments? High-resolution Planet Lab imagery was used to quantify shoreline change rates. Analysis of household survey data assessed human perceptions of the revetment’s desirability and efficacy. Results revealed high rates of erosion for 2011–2019 with declining erosion after 2013. New revetments effectively halted erosion for protected shorelines. Significant spatial trends for erosion rates existed for shorelines adjacent to revetments. Survey respondents overwhelmingly had positive attitudes about a desire for revetment protection; however, upstream respondents expressed a strong majority perception that revetment acts to make erosion worse. Highlights of the research include integration of remote sensing with social science methods, the timing of the social survey shortly after revetment construction, and results showing significant erosion change upstream and downstream of new revetments.
Community Structure, Biodiversity, and Ecosystem Services in Treeline Whitebark Pine Communities: Potential Impacts from a Non-Native Pathogen
Tomback, Diana F.; Resler, Lynn M.; Keane, Robert E.; Pansing, Elizabeth R.; Andrade, Andrew J.; Wagner, Aaron C. (MDPI, 2016-01-19)
Whitebark pine (Pinus albicaulis) has the largest and most northerly distribution of any white pine (Subgenus Strobus) in North America, encompassing 18° latitude and 21° longitude in western mountains. Within this broad range, however, whitebark pine occurs within a narrow elevational zone, including upper subalpine and treeline forests, and functions generally as an important keystone and foundation species. In the Rocky Mountains, whitebark pine facilitates the development of krummholz conifer communities in the alpine-treeline ecotone (ATE), and thus potentially provides capacity for critical ecosystem services such as snow retention and soil stabilization. The invasive, exotic pathogen Cronartium ribicola, which causes white pine blister rust, now occurs nearly rangewide in whitebark pine communities, to their northern limits. Here, we synthesize data from 10 studies to document geographic variation in structure, conifer species, and understory plants in whitebark pine treeline communities, and examine the potential role of these communities in snow retention and regulating downstream flows. Whitebark pine mortality is predicted to alter treeline community composition, structure, and function. Whitebark pine losses in the ATE may also alter response to climate warming. Efforts to restore whitebark pine have thus far been limited to subalpine communities, particularly through planting seedlings with potential blister rust resistance. We discuss whether restoration strategies might be appropriate for treeline communities.
Comparing Urban Impervious Surface Identification Using Landsat and High Resolution Aerial Photography
Parece, Tammy E.; Campbell, James B. Jr. (MDPI, 2013-10-10)
This paper evaluates accuracies of selected image classification strategies, as applied to Landsat imagery to assess urban impervious surfaces by comparing them to reference data manually delineated from high-resolution aerial photos. Our goal is to identify the most effective methods for delineating urban impervious surfaces using Landsat imagery, thereby guiding applications for selecting cost-effective delineation techniques. A high-resolution aerial photo was used to delineate impervious surfaces for selected census tracts for the City of Roanoke, Virginia. National Land Cover Database Impervious Surface data provided an overall accuracy benchmark at the city scale which was used to assess the Landsat classifications. Three different classification methods using three different band combinations provided overall accuracies in excess of 70% for the entire city. However, there were substantial variations in accuracy when the results were subdivided by census tract. No single classification method was found most effective across all census tracts; the best method for a specific tract depended on method, band combination, and physical characteristics of the area. These results highlight impacts of inherent local variability upon attempts to characterize physical structures of urban regions using a single metric, and the value of analysis at finer spatial scales.
Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
Iiames, John S.; Cooter, Ellen; Pilant, Andrew N.; Shao, Yang (MDPI, 2020-08-26)
Modeled leaf area index (LAI) in conjunction with satellite-derived LAI data streams may be used to support various regional and local scale air quality models for retrospective and future meteorological assessments. The Environmental Policy Integrated Climate (EPIC) model holds promise for providing LAI within a dynamic range for input into climate and air quality models, improving on current LAI distribution assumptions typical within atmospheric modeling. To assess the potential use of EPIC LAI, we first evaluated the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product collections 5 and 6 (i.e., Mc5, Mc6) with in situ LAI estimates upscaled at four 1.0 km resolution research sites distributed over the Albemarle-Pamlico Basin in North Carolina and Virginia, USA. We then compared the EPIC modeled 12.0 km resolution LAI to aggregated MODIS LAI (Mc5, Mc6) over a 3 × 3 grid (or 36 km × 36 km) centered over the same four research sites. Upscaled in situ LAI comparison with MODIS LAI showed improvement with the newer collection where the Mc5 overestimate of +2.22 LAI was reduced to +0.97 LAI with the Mc6. On three of the four sites, the EPIC/MODIS LAI comparison at 12.0 km resolution grid showed similar weighted mean LAI differences (LAI 1.29–1.34), with both Mc5 and Mc6 exceeding EPIC LAI across most dates. For all four research sites, both MODIS collections showed a positive bias when compared to EPIC LAI, with Mc6 (LAI = 0.40) aligning closer to EPIC than the Mc5 (LAI = 0.61) counterpart. Despite modest differences between both MODIS collections and EPIC LAI, the overestimation trend suggests the potential for EPIC to be used for future meteorological alternative management applications on a regional or national scale.
Compassionately Hidden: The Church Telling Local Homeless to “Come to Our House"
Oliver, Robert D.; Robinson, Matthew; Koebel, C. Theodore (Gamma Theta Upsilon, 2015)
In early 2011, the To Our House (TOH) thermal shelter program opened its doors to homeless men in the New River Valley Area (NRV) of Virginia. The program was a grass roots response to the death of a well-known local homeless man and the goal of the program is to provide winter shelter for single adult men by using rotating host sites at local churches. We highlight that in the NRV local churches have sought to remedy a socially unjust situation by providing shelter for men that was previously unavailable. We illustrate that faith-based outreach in the New River Valley can be viewed as positive compassionate outreach by a caring community. While acknowledging the benefits of this compassionate outreach to more than 25 men in the NRV, we also offer a cautionary note regarding the dilemmas of this outreach suggesting that it has the potential to mask the problems of the local housing market.
Conservation and Unscripted Development: Proximity to park associated with development and financial diversity
Baird, Timothy D. (The Resilience Alliance, 2014)
Decades of research on the social dynamics of biodiversity conservation has shown that parks and protected areas have added hardship to rural communities throughout much of the developing world. Nonetheless, some recent studies have found evidence of poverty alleviation near protected areas. To build on these conflicting accounts, I use a comparative, mixed-methods design to examine opportunistic, unplanned, i.e., unscripted, development in indigenous communities near Tarangire National Park (TNP) in northern Tanzania. I ask the questions: (1) How is proximity to TNP related to community-level infrastructural development? (2) How has the process of development changed over time? and (3) How is proximity to TNP related to infrastructure-related social outcomes at the household-level? Results from semistructured interviews show that, compared with distant communities, communities near TNP have developed more extensive education and water infrastructure in the past decade by procuring financial support from a greater diversity of external organizations, including wildlife-related organizations. Correspondingly, household survey results show that education measures are positively associated with proximity to TNP, controlling for other factors. These findings support the notion that development can accrue near protected areas in ways that are diverse, uncoordinated, and opportunistic, and correspondingly distinct from heralded community-based conservation, community-based natural resource management, and integrated conservation and development project initiatives.
Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake
Cooner, Austin J.; Shao, Yang; Campbell, James B. Jr. (MDPI, 2016-10-20)
Remote sensing continues to be an invaluable tool in earthquake damage assessments and emergency response. This study evaluates the effectiveness of multilayer feedforward neural networks, radial basis neural networks, and Random Forests in detecting earthquake damage caused by the 2010 Port-au-Prince, Haiti 7.0 moment magnitude (Mw) event. Additionally, textural and structural features including entropy, dissimilarity, Laplacian of Gaussian, and rectangular fit are investigated as key variables for high spatial resolution imagery classification. Our findings show that each of the algorithms achieved nearly a 90% kernel density match using the United Nations Operational Satellite Applications Programme (UNITAR/UNOSAT) dataset as validation. The multilayer feedforward network was able to achieve an error rate below 40% in detecting damaged buildings. Spatial features of texture and structure were far more important in algorithmic classification than spectral information, highlighting the potential for future implementation of machine learning algorithms which use panchromatic or pansharpened imagery alone.
Developing a Topographic Model to Predict the Northern Hardwood Forest Type within Carolina Northern Flying Squirrel (Glaucomys sabrinus coloratus) Recovery Areas of the Southern Appalachians
Evans, Andrew M.; Odom, Richard H.; Resler, Lynn M.; Ford, W. Mark; Prisley, Stephen P. (Hindawi, 2014-08-28)
The northern hardwood forest type is an important habitat component for the endangered Carolina northern flying squirrel (CNFS; Glaucomys sabrinus coloratus) for den sites and corridor habitats between boreo-montane conifer patches foraging areas. Our study related terrain data to presence of northern hardwood forest type in the recovery areas of CNFS in the southern Appalachian Mountains of western North Carolina, eastern Tennessee, and southwestern Virginia. We recorded overstory species composition and terrain variables at 338 points, to construct a robust, spatially predictive model. Terrain variables analyzed included elevation, aspect, slope gradient, site curvature, and topographic exposure. We used an information-theoretic approach to assess seven models based on associations noted in existing literature as well as an inclusive global model. Our results indicate that, on a regional scale, elevation, aspect, and topographic exposure index (TEI) are significant predictors of the presence of the northern hardwood forest type in the southern Appalachians. Our elevation + TEI model was the best approximating model (the lowest AICc score) for predicting northern hardwood forest type correctly classifying approximately 78% of our sample points. We then used these data to create region-wide predictive maps of the distribution of the northern hardwood forest type within CNFS recovery areas.
Diffusion 2013-2014 - #1
(Virginia Tech, 2013-08-29)
Diffusion is the long running newsletter of the Department of Geography at Virginia Tech.
Diffusion 2013-2014 - #2
(Virginia Tech, 2013-10-23)
Diffusion is the long running newsletter of the Department of Geography at Virginia Tech.
Diffusion 2013-2014 - #3
(Virginia Tech, 2014-02-07)
Diffusion is the long running newsletter of the Department of Geography at Virginia Tech.
Diffusion, 1979-1980
(Virginia Tech, 1980)
Diffusion is the long running newsletter of the Department of Geography at Virginia Tech.
Diffusion, 1980-1981
(Virginia Tech, 1981)
Diffusion is the long running newsletter of the Department of Geography at Virginia Tech.
Diffusion, 1981-1982
(Virginia Tech, 1982)
Diffusion is the long running newsletter of the Department of Geography at Virginia Tech.

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