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You searched for +publisher:"Virginia Tech" +contributor:("Wynne, Randolph H."). Showing records 1 – 30 of 68 total matches.
1. Oliphant, Adam J. Mapping Elaeagnus Umbellata on Coal Surface Mines using Multitemporal Landsat Imagery.
▼ Invasive plant species threaten native plant communities and inhibit efforts to restore disturbed landscapes. Surface coal mines in the Appalachian Mountains are some of the most disturbed landscapes in North America. Moreover, there is not a comprehensive understanding of the land cover characteristics of post- mined lands in Appalachia. Better information on mined lands' vegetative cover and ecosystem recovery status is necessary for implementation of effective environmental management practices. The invasive autumn olive (Elaeagnus umbellata) is abundant on former coal surface mines, often outcompeting native trees due to its faster growth rate. The frequent revisit time and spatial and spectral resolution of Landsat satellites make Landsat imagery well suited for mapping and characterizing land cover and forest recovery on former coal surface mines. I performed a multitemporal classification using a random forest analysis to map autumn olive on former and current surface coal mines in southwest Virginia. Imagery from the Operational Land Imager on Landsat 8 were used as input data for the study. Calibration and validation data for use in model development were obtained using high-resolution aerial imagery. Results indicate that autumn olive cover is sufficiently dense to enable detection using Landsat imagery on approximately 12.6% of the current and former surface coal mines located in the study area that have been mined since the early 1980s. The classified map produced here had a user's and producer's accuracy of 85.3% and 78.6% respectively for the autumn olive coverage class. Overall accuracy in reference to an independent validation dataset was 96.8%. These results indicate that autumn olive growing on reclaimed coal mines in Virginia and elsewhere in the Appalachian coalfields can be mapped using Landsat imagery. Additionally, autumn olive occurrence is a significant landscape feature on former surface coal mines in the Virginia coalfields. Advisors/Committee Members: Wynne, Randolph H. (committeechair), Zipper, Carl E. (committeechair), Ford, William Mark (committee member).
Oliphant, Adam J. “Mapping Elaeagnus Umbellata on Coal Surface Mines using Multitemporal Landsat Imagery.” 2015. Masters Thesis, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/75119.
Oliphant, Adam J. “Mapping Elaeagnus Umbellata on Coal Surface Mines using Multitemporal Landsat Imagery.” 2015. Web. 19 Apr 2019.
Oliphant AJ. Mapping Elaeagnus Umbellata on Coal Surface Mines using Multitemporal Landsat Imagery. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/75119.
2. Friedrich, Meryl J. Population Dynamics of Threatened Piping Plovers on the Niobrara River, Nebraska.
▼ Prairie rivers of the Great Plains, USA, provide important habitat for the federally threatened piping plover (“plover”, Charadrius melodus). Plovers nest on open to sparsely vegetated river sandbars, and their demographic rates are closely linked to habitat availability and quality, as well as river flow. The Niobrara River in northern Nebraska has supported 22–41% of the state’s plovers since species listing in 1986, but the population and habitat are relatively understudied, and both have declined since 2010. The objectives of this study were to understand plover demography, habitat, and the role of the Niobrara in the regional plover population. Periods of high river flow promote creation and maintenance of suitable sandbar nesting habitat, but increased river flow during the plover breeding season can decrease nest and chick survival. We estimated the effect of daily peak river flow on survival rates of 115 nests and 66 chicks on the Niobrara River, 2010–2016, using logistic exposure and Cormack-Jolly-Seber models, respectively. We monitored 1,874 banded hatch-year and adult birds across the regional population (Niobrara River, Lewis and Clark Lake, Gavins Point Reach segment of the Missouri River), and used multi-state mark-recapture models to estimate survival and inter-annual dispersal probabilities among sites relative to habitat availability. We developed land cover datasets from high-resolution aerial imagery to quantify suitable habitat and compare the relative effects of habitat characteristics on nest-site selection and nest success for a subset of years (2010, 2012, 2014, and 2016) using logistic regression models. We included data from a sympatric nester with similar nesting habitat needs, the interior least tern (“tern”, Sternula antillarum athalassos), to improve precision of our models. We compared 63 plover and 92 tern nests to 292 random unused points, and 73 successful (hatched ≥1 egg) to 79 failed nests. Low nest and chick survival and high emigration from the Niobrara appear to be important factors contributing to population decline. Daily nest and chick survival were negatively related to river flow. Nest-site selection was based primarily on distance to the river bank (i.e., the nearest potential source of predators), yet flooding (eggs submerged or washed out of the nest bowl during increased river flow) caused at least as many nest failures as predation. Nests predominantly were surrounded by dry sand habitat, indicating some degree of flood avoidance, but were no farther from water than random, and drier nest sites were no less likely to fail. Dispersal occurred throughout the regional population, but plovers were more likely to leave the Niobrara than to enter it. Expansive flood-created sandbars on the Missouri River, concurrent with a trend towards more vegetated and saturated habitat on the Niobrara, may have drawn birds from the Niobrara population, especially those that dispersed to the Niobrara during sustained Missouri River flooding 2010–2011. The outsized negative effect… Advisors/Committee Members: Catlin, Daniel Herbert (committeechair), Fraser, James D. (committeechair), Wynne, Randolph H. (committee member).
Friedrich, Meryl J. “Population Dynamics of Threatened Piping Plovers on the Niobrara River, Nebraska.” 2018. Masters Thesis, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/83930.
Friedrich, Meryl J. “Population Dynamics of Threatened Piping Plovers on the Niobrara River, Nebraska.” 2018. Web. 19 Apr 2019.
Friedrich MJ. Population Dynamics of Threatened Piping Plovers on the Niobrara River, Nebraska. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/83930.
3. House, Matthew Neal. Identifying Forest Conversion Hotspots in the Commonwealth of Virginia using Multitemporal Landsat Data and Known Change Indicators.
▼ This study examines the effectiveness of using the Normalized Difference Vegetation Index (NDVI) derived from 1326 different Landsat Thematic Mapper and Enhanced Thematic Mapper images in finding isolated housing starts within the Commonwealth of Virginia's forests. Individual NDVI images were stacked by year for the years 1995-2011 and the yearly maximum for each pixel was extracted, resulting in a 17-year image stack of all yearly maxima (a 98.7% data reduction). Using location data from housing starts and well permits, known previously forested housing starts were isolated from all other forest disturbance types. Samples from housing starts and other forest disturbances, as well as from undisturbed forest, were used to derive vegetation index thresholds enabling separation of disturbed from undisturbed forest. Disturbances, once identified, were separated accurately (overall accuracy = 85.4 percent, F-statistic = 0.86) into housing starts and other forest disturbances using a classification tree and only two variables from the Disturbance Detection and Diagnostics (D3) algorithm: the maximum NDVI in the available recovery period and the slope between the NDVI value at the time of the disturbance and the maximum NDVI in the available recovery period. Landsat time series stacks thus show promise for identifying even the small changes associated with exurban development. Advisors/Committee Members: Wynne, Randolph H. (committeechair), Radtke, Philip J. (committee member), Thomas, Valerie Anne (committee member).
House, Matthew Neal. “Identifying Forest Conversion Hotspots in the Commonwealth of Virginia using Multitemporal Landsat Data and Known Change Indicators.” 2017. Masters Thesis, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/86139.
House, Matthew Neal. “Identifying Forest Conversion Hotspots in the Commonwealth of Virginia using Multitemporal Landsat Data and Known Change Indicators.” 2017. Web. 19 Apr 2019.
House MN. Identifying Forest Conversion Hotspots in the Commonwealth of Virginia using Multitemporal Landsat Data and Known Change Indicators. [Internet] [Masters thesis]. Virginia Tech; 2017. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/86139.
4. Stein, Beth Rachel. The Relationship Between Wildlife Biodiversity and Landscape Characteristics in Virginia.
▼ Wildlife biodiversity provides a variety of ecosystem services and is an important indicator of overall ecosystem health. This research investigates the relationship between wildlife biodiversity and landscape characteristics in Virginia. The goal is to produce predictive models of biodiversity within the Commonwealth using environmental characteristics, including fragmentation metrics at the class- and landscape-levels, as well as other environmental variables. The 1248 12-digit watersheds in Virginia are the sampling units for the analyses, with the state stratified into the seven US Environmental Protection Agencyâ s Level III classification. Data on wildlife alpha diversity is based on two sets of species data maintained by the Virginia Dept. of Game & Inland Fisheries (VDGIF). The first chapter provides an introduction to the issue of biodiversity conservation and the background information for this work. The second chapter describes the study using the 2001 National Land Cover Data to calculate class- and landscape-level fragmentation metrics. Best subset regression is used to determine the best predictors for wildlife biodiversity using these metrics. Final selected models range in predictive power from R2 = 0.41 to 0.73 for each of the 7 ecoregions. The third chapter analyzes the relationship between wildlife biodiversity and various environmental variables in order to determine the strength of these factors as drivers for alpha diversity. These variables are then incorporated with the fragmentation metrics in an attempt to improve the biodiversity models. The environmental variable models had R2 = 0.22 to 0.65 across the ecoregions, while R2 = 0.28 to 0.72 when the environmental and fragmentation variables are combined. The last chapter focuses on the conclusions of the studies, the limitations of the data, and the benefits of this work. Overall, our results underline the importance of using fragmentation metrics in Virginiaâ s wildlife models. Advisors/Committee Members: Thomas, Valerie Anne (committeechair), Wynne, Randolph H. (committee member), Stauffer, Dean F. (committee member).
Stein, Beth Rachel. “The Relationship Between Wildlife Biodiversity and Landscape Characteristics in Virginia.” 2012. Masters Thesis, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/42690.
Stein, Beth Rachel. “The Relationship Between Wildlife Biodiversity and Landscape Characteristics in Virginia.” 2012. Web. 19 Apr 2019.
Stein BR. The Relationship Between Wildlife Biodiversity and Landscape Characteristics in Virginia. [Internet] [Masters thesis]. Virginia Tech; 2012. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/42690.
5. Ahlswede, Benjamin James. What to plant and where to plant it; Modeling the biophysical effects of North America temperate forests on climate using the Community Earth System Model.
▼ Forests affect climate by absorbing CO₂ but also by altering albedo, latent heat flux, and sensible heat flux. In this study we used the Community Earth System Model to assess the biophysical effect of North American temperate forests on climate and how this effect changes with location, tree type, and forest management. We calculated the change in annual temperature and energy balance associated with afforestation with either needle leaf evergreen trees (NET) or broadleaf deciduous trees (BDT) and between forests with high and low leaf-area indices (LAI). Afforestation from crops to forests resulted in lower albedo and higher sensible heat flux but no consistent difference in latent heat flux. Forests were consistently warmer than crops at high latitudes and colder at lower latitudes. In North America, the temperature response from afforestation shifted from warming to cooling between 34° N and 40° N for ground temperature and between 21° N and 25° N for near surface air temperature. NET tended to have lower albedo, higher sensible heat flux and warmer temperatures than BDT. The effect of tree PFT was larger than the effect of afforestation in the south and in the mid-Atlantic. Increasing LAI, a proxy for increased management intensity, caused a cooling effect in both tree types, but NET responded more strongly and albedo decreased while albedo increased for BDT. Our results show that forests' location, tree type, and management intensity can have nearly equal biophysical effects on temperature. A forest will have maximum biophysical cooling effect if it is in the south, composed of broadleaf PFT, and is managed to maximize leaf area index. Advisors/Committee Members: Thomas, Robert Quinn (committeechair), Wynne, Randolph H. (committee member), Strahm, Brian Dale (committee member).
Ahlswede, Benjamin James. “What to plant and where to plant it; Modeling the biophysical effects of North America temperate forests on climate using the Community Earth System Model.” 2015. Masters Thesis, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/74269.
Ahlswede, Benjamin James. “What to plant and where to plant it; Modeling the biophysical effects of North America temperate forests on climate using the Community Earth System Model.” 2015. Web. 19 Apr 2019.
Ahlswede BJ. What to plant and where to plant it; Modeling the biophysical effects of North America temperate forests on climate using the Community Earth System Model. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/74269.
6. Cohen, Jed Jacob. The Effect of Mountain Pine Beetle Induced Tree Mortality on Home Values in the Colorado Front Range.
▼ Throughout the past decade American pine forests have experienced an epidemic of Mountain Pine Beetle (MPB) induced tree mortality. This thesis estimates the losses to home values caused by deteriorating forest quality in the Front Range Counties of Larimer and Boulder Colorado. We employ a repeat sales model that allows for region specific price indices, and non-linear age-related depreciation in home values. We use the time-invariant existence of pine forest near a home to overcome shortcomings in the measurement of MPB damage. We infer from temporal changes in the marginal "effect of pine trees near a home the approximate MPB "effect . We label this strategy the translating commodity approach. Using this strategy we are able to show that diminished forest quality causes forests to become a dis-amenity that negatively affects nearby home values. The total loss in 2011 home values due to their proximity to dying forest is estimated to be $137 million for all the homes in our sample. Such substantial losses may justify a forest management policy shift in order to better mitigate the risk of future MPB outbreaks. Advisors/Committee Members: Moeltner, Klaus (committeechair), Boyle, Kevin (committee member), Wynne, Randolph H. (committee member).
Cohen, Jed Jacob. “The Effect of Mountain Pine Beetle Induced Tree Mortality on Home Values in the Colorado Front Range.” 2013. Masters Thesis, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/23170.
Cohen, Jed Jacob. “The Effect of Mountain Pine Beetle Induced Tree Mortality on Home Values in the Colorado Front Range.” 2013. Web. 19 Apr 2019.
Cohen JJ. The Effect of Mountain Pine Beetle Induced Tree Mortality on Home Values in the Colorado Front Range. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/23170.
7. Zheng, Baojuan. Broad-scale Assessment of Crop Residue Management Using Multi-temporal Remote Sensing Imagery.
▼ Tillage practices have changed dramatically during the past several decades as agricultural specialists have recognized the unfavorable environmental effects of mechanized tillage. Alternatively, conservation tillage management can mitigate adverse environmental impacts of tillage, such as soil and water degradation. Adoption of conservation tillage has continued to increase since its first introduction, which raises questions of when and where it is practiced. Spatial and temporal specifics of tillage practices form important dimensions for development of effective crop management practices and policies. Because Landsat has been and will continue to image the Earth globally, it provides opportunities for systematic mapping of crop residue cover (CRC) /tillage practices. Thus, the overall objective of this study is to develop methodologies to improve our ability to monitor crop management across different landscapes in a time-efficient and cost-effective manner using Landsat TM and ETM+ imagery, which is addressed in three separate studies. The first study found that previous efforts to estimate CRC along a continuum using Landsat-based tillage indices were unsuccessful because they neglected the key temporal changes in agricultural surfaces caused by tilling, planting, and crop emergence at the start of the growing season. The first study addressed this difficulty by extracting minimum values of multi-temporal NDTI (Normalized Difference Tillage Index) spectral profiles, designated here as the minNDTI method. The minNDTI improves crop residue estimation along a continuum (R2 = 0.87) as well as tillage classification accuracy (overall accuracy > 90%). A second study evaluated effectiveness of the minNDTI approach for assessing CRC at multiple locations over several years, and compared minNDTI to hyperspectral tillage index (CAI), and the ASTER tillage index (SINDRI). The minNDTI is effective across four different locations (R2 of 0.56 ~ 0.93). The third study, built upon the second study, addressed the Landsat ETM+ missing data issue, and devised methodologies for producing field-level tillage data at broad scales (multiple counties). In summary, this research demonstrates that the minNDTI technique is currently the best alternative for monitoring CRC and tillage practices from space, and provides a foundation for monitoring crop residue cover at broad spatial and temporal scales. Advisors/Committee Members: Campbell, James B. Jr. (committeechair), Wynne, Randolph H. (committee member), Galbraith, John M. (committee member), de Beurs, Kirsten (committee member).
Zheng, Baojuan. “Broad-scale Assessment of Crop Residue Management Using Multi-temporal Remote Sensing Imagery.” 2012. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/19201.
Zheng, Baojuan. “Broad-scale Assessment of Crop Residue Management Using Multi-temporal Remote Sensing Imagery.” 2012. Web. 19 Apr 2019.
Zheng B. Broad-scale Assessment of Crop Residue Management Using Multi-temporal Remote Sensing Imagery. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/19201.
8. Malambo, Lonesome. Multitemporal mapping of burned areas in mixed landscapes in eastern Zambia.
▼ Fires occur extensively across Zambia every year, a problem recognized as a major threat to biodiversity. Yet, basic tools for mapping at a spatial and temporal scale that provide useful information for understanding and managing this problem are not available. The objectives of this research were: to develop a method to map the spatio-temporal seasonal fire occurrence using satellite imagery, to develop a technique for estimating missing data in the satellite imagery considering the possibility of change in land cover over time, and to demonstrate applicability of these new tools by analyzing the fine-scale seasonal patterns of landscape fires in eastern Zambia. A new approach for mapping burned areas uses multitemporal image analysis with a fuzzy clustering algorithm to automatically select spectral-temporal signatures that are then used to classify the images to produce the desired spatio-temporal burned area information. Testing with Landsat data (30m resolution) in eastern Zambia showed accuracies in predicting burned areas above 92%. The approach is simple to implement, data driven, and can be automated, which can facilitate quicker production of burned area information. A profile-based approach for filling missing data uses multitemporal imagery and exploits the similarity in land cover temporal profiles and spatial relationships to reliably estimate missing data even in areas with significant changes. Testing with simulated missing data from an 8-image spectral index sequence showed highly correlated (R2 of 0.78-0.92) and precise estimates (deviations 4-7%) compared to actual values. The profile-based approach overcomes the common requirement of gap-filling methods that there is gradual or no change in land cover, and provides accurate gap-filling under conditions of both gradual and abrupt changes. The spatio-temporal progression of landscape burning was evaluated for the 2009 and 2012 fire seasons (June-November) using Landsat data. Results show widespread burning (~ 60%) with most fires occurring late (August-October) in the season. Fire occurrence and burn patch sizes decreased with increasing settlement density and landscape fragmentation reflecting human influences and fuel availability. Small fires (< 5ha) are predominant and were significantly under-detected (>50%) by a global dataset (MODIS Burned Area Product (500m resolution)), underscoring the critical need of higher geometric resolution imagery such as Landsat imagery for mapping such fine-scale fire activity. Advisors/Committee Members: Heatwole, Conrad D. (committeechair), Campbell, James B. (committee member), Thomas, Valerie Anne (committee member), Wynne, Randolph H. (committee member).
Malambo, Lonesome. “Multitemporal mapping of burned areas in mixed landscapes in eastern Zambia.” 2014. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/71301.
Malambo, Lonesome. “Multitemporal mapping of burned areas in mixed landscapes in eastern Zambia.” 2014. Web. 19 Apr 2019.
Malambo L. Multitemporal mapping of burned areas in mixed landscapes in eastern Zambia. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/71301.
9. Quirino, Valquiria Ferraz. Evaluating the Potential for Estimating Age of Even-aged Loblolly Pine Stands Using Active and Passive Remote Sensing Data.
▼ Data from an airborne laser scanner, a dual-band interferometric synthetic aperture radar (DBInSAR), and Landsat were evaluated for estimating ages of even-aged loblolly pine stands in Appomattox-Buckingham State Forest, Virginia, U.S.A. The DBInSAR data were acquired using the GeoSAR sensor in summer, 2008 in both the P- and X-bands. The LiDAR data were acquired in the same summer using a small-footprint laser scanner. Loblolly pine stand ages were assigned using the establishment year of loblolly pine stands provided by the Virginia Department of Forestry. Random circular plots were established in stands which varied in age from 5 to 71 years and in site index from 21 to 29 meters (base age 25 years). LiDAR- and GeoSAR-derived independent variables were calculated. The final selected LiDAR model used common logarithm of age as the dependent variable and the 99.5th percentile of height above ground as the independent variable (R2adj = 90.2%, RMSE = 4.4 years, n=45). The final selected GeoSAR models used the reciprocal of age as the dependent variable and had three independent variables: the sum of the X-band magnitude, the 25th percentile of X/P-band magnitudes, and the 90th percentile of the X-band height above ground (R2adj = 84.1%, RMSE = 7.9 years, n=46). The Vegetation Change Tracker (VCT) algorithm was run using a digital elevation layer, a land cover map, and a series of Landsat (5 and 7) images. A comparison was made between the loblolly pine stand ages obtained using the three methods and the reference data. The results show that: (1) although most of the time VCT and reference data ages were different, the differences were normally small, (2) all three remote sensing methods produced reliable age estimates, and (3) the Landsat-VCT algorithm produced the best estimates for younger stands (5 to 22 years old, RMSEVCT=2.2 years, RMSEGeoSAR=2.6 years, RMSELiDAR=2.6 years, n=35) and the model that used LiDAR-derived variables was better for older stands. Remote sensing can be used to estimate loblolly pine stand age, though prior knowledge of site index is required for active sensors that rely primarily on the relationship between age and height. Advisors/Committee Members: Wynne, Randolph H. (committeechair), Thomas, Valerie Anne (committee member), Huemmrich, Karl Fred (committee member), Seiler, John R. (committee member).
Quirino, Valquiria Ferraz. “Evaluating the Potential for Estimating Age of Even-aged Loblolly Pine Stands Using Active and Passive Remote Sensing Data.” 2014. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/71309.
Quirino, Valquiria Ferraz. “Evaluating the Potential for Estimating Age of Even-aged Loblolly Pine Stands Using Active and Passive Remote Sensing Data.” 2014. Web. 19 Apr 2019.
Quirino VF. Evaluating the Potential for Estimating Age of Even-aged Loblolly Pine Stands Using Active and Passive Remote Sensing Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/71309.
10. Hwang, Won Hoi. Investigating the Impact of Urban Tree Planting Strategies for Shade and Residential Energy Conservation.
▼ Expanding urbanization, characterized by increased impervious surfaces and decreased tree canopy, is contributing to rising urban temperatures. This trend has implications for energy consumption, which strategically placed trees can modify by casting shade upon building and ground surfaces. However, urban densification, a paradigm of modern residential land development, often constrains space for planting shade trees. Thus, the overall objective of this dissertation was to investigate shade tree planting strategies and their effects on residential cooling and heating energy conservation for dense urban neighborhoods in U.S. cities on a latitudinal gradient. The first study used a computer program called Shadow Pattern Simulator to examine the effects of tree form, tree placement, and sunlight exposure on shade provision for a residential structure model. Simulation results affirmed the conventional strategy in northern latitudes that recommends planting shade trees on the east or west aspect for maximizing beneficial shade while avoiding tree plantings on the south aspect to minimize any heating penalty of undesirable shade. However, in southern latitudes, planting trees on southerly aspect should not be discounted because the shorter heating season lessens the detrimental heating penalty while providing beneficial season shade. The second study, using an energy simulation program called EnergyPlus, evaluated the effect of a single shade tree upon the energy consumption of the structure model. This study affirmed that energy conservation benefits are influenced by the quantity as well as the quality of tree shade upon building surfaces. In addition, interactions between sun angle, tree form, and tree placement were observed to influence tree shade effects on annual energy consumption. In the third study, based on the first two studies, an alternative tree placement strategy, which reconfigured tree placement around the residential structure, was developed to maximize cooling and heating energy savings while attenuating space conflicts. The alternative strategy was found to be as effective as the conventional strategy while being more responsive to parcel or building orientations in dense urban neighborhoods. Overall, understanding the fundamental interactions between tree form, tree placement, and geographic settings is critical for improving energy conservation benefits of shade trees in dense urban settings. Advisors/Committee Members: Thomas, Valerie Anne (committeechair), Wiseman, Phillip E. (committeechair), Campbell, James B. (committee member), Wynne, Randolph H. (committee member).
Hwang, Won Hoi. “Investigating the Impact of Urban Tree Planting Strategies for Shade and Residential Energy Conservation.” 2015. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/75168.
Hwang, Won Hoi. “Investigating the Impact of Urban Tree Planting Strategies for Shade and Residential Energy Conservation.” 2015. Web. 19 Apr 2019.
Hwang WH. Investigating the Impact of Urban Tree Planting Strategies for Shade and Residential Energy Conservation. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/75168.
11. Stein, Beth R. Applications of Imaging Spectroscopy in Forest Ecosystems at Multiple Scales.
▼ Forests provide a number of ecosystem services which sustain and enrich the wildlife, human societies, and the environment. However, many disturbances threaten forest ecosystems, making it necessary to monitor their health for optimal management and conservation. Although there are many indicators of forest health, changes in biogeochemical cycling, loss of species diversity, and invasive plants are particularly useful due to their vulnerability to the effects of climate change and intensive agricultural land use. Thus, this work evaluates the use of imaging spectroscopy to monitor forest nutrient status, species diversity, and plant invasions in the Mid-Atlantic region. The research is divided into four separate studies, each of which evaluated a unique application for imaging spectroscopy data at a different scale within the forest. The first two studies examined loblolly pine nutrient status at the leaf and canopy scales, respectively. The first study determined that loblolly pine foliar macronutrient concentrations can be successfully modeled across the Southeastern US (R2=0.39-0.74). Following on these results, the second study focused on the relationship between physical characteristics, reflectance, and nutrients. Reflectance values and W scattering coefficients produced successful nitrogen models across loblolly pine plots at the canopy scale. Regression models showed similar explanatory power for nitrogen, although W scattering coefficients were significantly correlated with nitrogen at multiple wavelengths and reflectance variables were not. However, the direction of some of the correlations with W and the unusually high directional area scattering factor values indicate a need for further experimentation. The third study found that several imaging spectroscopy algorithms were moderately successful in identifying wavyleaf basketgrass invasions in mixed deciduous forests (overall accuracy=0.35-0.78; kappa=0.41-0.53). Lastly, the fourth study used a novel imaging spectroscopy/lidar fusion to identify canopy gaps and measure species diversity of understory vegetation. The lidar algorithm identified 29 of 34 canopy gaps, and regression models explained 49 percent of the variance in gap species diversity. In conclusion, imaging spectroscopy can be used to evaluate ecosystem health through forest nutrient status, nitrogen models, species diversity estimates, and identification of invasive plant species. Advisors/Committee Members: Thomas, Valerie Anne (committeechair), Martin, Mary E. (committee member), Wynne, Randolph H. (committee member), Radtke, Philip J. (committee member).
Stein, Beth R. “Applications of Imaging Spectroscopy in Forest Ecosystems at Multiple Scales.” 2015. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/77390.
Stein, Beth R. “Applications of Imaging Spectroscopy in Forest Ecosystems at Multiple Scales.” 2015. Web. 19 Apr 2019.
Stein BR. Applications of Imaging Spectroscopy in Forest Ecosystems at Multiple Scales. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/77390.
12. Kokkinidis, Ioannis. Ecosystem services provided by agricultural land as modeled by broad scale geospatial analysis.
▼ Agricultural ecosystems provide multiple services including food and fiber provision, nutrient cycling, soil retention and water regulation. Objectives of the study were to identify and quantify a selection of ecosystem services provided by agricultural land, using existing geospatial tools and preferably free and open source data, such as the Virginia Land Use Evaluation System (VALUES), the North Carolina Realistic Yield Expectations (RYE) database, and the land cover datasets NLCD and CDL. Furthermore I sought to model tradeoffs between provisioning and other services. First I assessed the accuracy of agricultural land in NLCD and CDL over a four county area in eastern Virginia using cadastral parcels. I uncovered issues concerning the definition of agricultural land. The area and location of agriculture saw little change in the 19 years studied. Furthermore all datasets have significant errors of omission (11.3 to 95.1%) and commission (0 to 71.3%). Location of agriculture was used with spatial crop yield databases I created and combined with models I adapted to calculate baseline values for plant biomass, nutrient composition and requirements, land suitability for and potential production of biofuels and the economic impact of agriculture for the four counties. The study area was then broadened to cover 97 counties in eastern Virginia and North Carolina, investigating the potential for increased regional grain production through intensification and extensification of agriculture. Predicted yield from geospatial crop models was compared with produced yield from the NASS Survey of Agriculture. Area of most crops in CDL was similar to that in the Survey of Agriculture, but a yield gap is present for most years, partially due to weather, thus indicating potential for yield increase through intensification. Using simple criteria I quantified the potential to extend agriculture in high yield land in other uses and modeled the changes in erosion and runoff should conversion take place. While the quantity of wheat produced though extensification is equal to 4.2 times 2012 production, conversion will lead to large increases in runoff (4.1 to 39.4%) and erosion (6 times). This study advances the state of geospatial tools for quantification of ecosystem services. Advisors/Committee Members: Hodges, Steven C. (committeechair), Campbell, James B. (committeechair), Wynne, Randolph H. (committee member), Kim, Mintai (committee member).
Kokkinidis, Ioannis. “Ecosystem services provided by agricultural land as modeled by broad scale geospatial analysis.” 2014. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/47726.
Kokkinidis, Ioannis. “Ecosystem services provided by agricultural land as modeled by broad scale geospatial analysis.” 2014. Web. 19 Apr 2019.
Kokkinidis I. Ecosystem services provided by agricultural land as modeled by broad scale geospatial analysis. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/47726.
13. Subedi, Santosh. Determination of fertility rating (FR) in the 3-PG model for loblolly pine (Pinus taeda L.) plantations in the southeastern United States.
▼ Soil fertility is an important component of forest ecosystem, yet evaluating soil fertility remains one of the least understood aspects of forest science. Phytocentric and geocenctric approaches were used to assess soil fertility in loblolly pine plantations throughout their geographic range in the United States. The model to assess soil fertility using a phytocentric approach was constructed using the relationship between site index and aboveground productivity. Geocentric models used physical and chemical properties of the A-horizon. Soil geocentric models were constructed using two modeling approaches. In the first approach, ordinary least squares methods of multiple regression were used to derive soil fertility estimated from site index using soil physical and chemical properties from the A-horizon. Ordinary least squares methods were found unsuitable due to multicollinearity among the soil variables. In the second approach, a multivariate modeling approach, partial least squares regression, was used to mitigate multicollinearity effects. The best model to quantify soil fertility using soil physical and chemical properties included N, Ca, Mg, C, and sand percentage as the significant predictors. The 3-PG process-based model was evaluated for simulating the response of loblolly pine to changes in soil fertility. Fertility rating (FR) is a parameter in 3-PG that scales soil fertility in the range of 0 to 1. FR values estimated from phytocentric and geocentric approaches were tested against observed production. The 3-PG model prediction of aboveground productivity described 89% percent of the variation in observed aboveground productivity using FR derived from site index and 84% percent of the vari- ation in observed aboveground productivity using FR derived from physical and chemical properties of the A-horizon. A response function to model dynamics of FR (∆FR) due to one time midrotatoin fertilization of N and P was developed using the Weibull function. The magnitude of ∆FR varied with intensity of N and time since application of fertilizer. The hypothesis that repeated fertilization with N and P eliminate major nutrient deficiency in the southeastern US was tested and a relationship between baseline fertility rating and fertilizer response was developed. An inverse relationship was observed between fertilizer response and baseline FR. Advisors/Committee Members: Fox, Thomas R. (committeechair), Wynne, Randolph H. (committee member), Burkhart, Harold E. (committee member), Strahm, Brian Dale (committee member).
Subedi, Santosh. “Determination of fertility rating (FR) in the 3-PG model for loblolly pine (Pinus taeda L.) plantations in the southeastern United States.” 2015. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/52588.
Subedi, Santosh. “Determination of fertility rating (FR) in the 3-PG model for loblolly pine (Pinus taeda L.) plantations in the southeastern United States.” 2015. Web. 19 Apr 2019.
Subedi S. Determination of fertility rating (FR) in the 3-PG model for loblolly pine (Pinus taeda L.) plantations in the southeastern United States. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/52588.
14. Kayastha, Nilam. Application on Lidar and Time Series Landsat Data for Mapping and Monitoring Wetlands.
▼ To successfully protect and manage wetlands, efficient and accurate tools are needed to identify where wetlands are located, the wetland type, what condition they are in, what are the stressors present, and the trend in their condition. Wetland mapping and monitoring are useful to accomplish these tasks. Wetland mapping and monitoring with optical remote sensing data has mainly focused on using a single image or using image acquired over two seasons within the same year. Now that Landsat data are available freely, a multi-temporal approach utilizing images that span multiple seasons and multiple years can potentially be used to characterize wetland dynamics in more detail. In addition, newer remote sensing techniques such as Light Detection and Ranging (lidar) can provide highly detailed and accurate topographic information, which can improve our ability to discriminate wetlands. Thus, the overall objective of this study was to investigate the utility of lidar and multi-temporal Landsat data for mapping and monitoring of wetlands. My research is presented as three independent studies related to wetland mapping and monitoring. In the first study, inter-annual time series of Landsat data from 1985 to 2009 was used to map changes in wetland ecosystems in northern Virginia. Z-scores calculated on tasseled cap images were used to develop temporal profile for wetlands delineated by the National Wetland Inventory. A change threshold was derived based on the Chi-square distribution of the Z-scores. The accuracy of a change/no change map produced was 89% with a kappa value of 0.79. Assessment of the change map showed that the method used was able to detect complete wetland loss together with other subtle changes resulting from development, harvesting, thinning and farming practices. The objective of the second study was to characterize differences in spectro-temporal profile of forested upland and wetland using intra and inter annual time series of Landsat data (1999-2012). The results show that the spector-temporal metrics derived from Landsat can accurately discriminate between forested upland and wetland (accuracy of 88.5%). The objective of the third study was to investigate the ability of topographic variables derived from lidar to map wetlands. Different topographic variables were derived from a high resolution lidar digital elevation model. Random forest model was used to assess the ability of these variables in mapping wetlands and uplands area. The result shows that lidar data can discriminate between wetlands and uplands with an accuracy of 72%. In summary, because of its spatial, spectral, temporal resolution, availability and cost Landsat data will be a primary data source for mapping and monitoring wetlands. The multi-temporal approach presented in this study has great potential for significantly improving our ability to detect and monitor wetlands. In addition, synergistic use of multi-temporal analysis of Landsat data combined with lidar data may be superior to using either data alone for future wetland… Advisors/Committee Members: Galbraith, John M. (committeechair), Thomas, Valerie Anne (committeechair), Wynne, Randolph H. (committee member), Lang, Megan Weiner (committee member).
Kayastha, Nilam. “Application on Lidar and Time Series Landsat Data for Mapping and Monitoring Wetlands.” 2014. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/54011.
Kayastha, Nilam. “Application on Lidar and Time Series Landsat Data for Mapping and Monitoring Wetlands.” 2014. Web. 19 Apr 2019.
Kayastha N. Application on Lidar and Time Series Landsat Data for Mapping and Monitoring Wetlands. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/54011.
15. Shrestha, Rupesh. Inventorying trees in an urban landscape using small-footprint discrete return imaging lidar.
▼ Automation of urban tree inventory using remote sensing is needed not only to reduce inventory costs but also to support carbon accounting for urban planners and policy-makers. However, urban areas are heterogeneous and complex, and a more sophisticated approach is needed for using remote-sensing technology like lidar for tree inventory in urban areas than is required for forested environments. Based on remote sensing and field data from a suburban residential area in the central United States, this dissertation presents a methodology for utilizing airborne small-footprint lidar data to inventory urban trees. This dissertation proposes approaches that have the potential to automate three main activities of urban tree inventory â identifying the locations of trees, classifying the trees into taxonomic categories, and estimating biophysical parameters of individual trees â using airborne lidar data. Mathematical morphological operations followed by a marker-controlled watershed segmentation were found to perform well (r = 0.82 to 0.92) to delineate individual tree crowns in urban areas, especially when the trees occur in relatively isolated conditions. Using various distribution metrics of lidar returns, random forests were used to classify individual trees into different taxonomic classes (broadleaves/conifers, genera, and species). A classification accuracy of 80.5% was obtained when separating trees only into broadleaf and conifer classes, 50.0% for genera, and 51.3% for species. Using spectral metrics from high-resolution satellite imagery in addition to lidar-derived predictors improved the classification accuracies by 10.4% (to 90.9%) for broadleaf and conifer, 8.4% (to 58.4%) for genera and 8.8% (to 60.1%) for species compared to using lidar metrics alone. Prediction models to estimate several biophysical parameters such as height, crown area, diameter at breast height, and biomass were developed using lidar point cloud distributional metrics from individual trees. A high level of accuracy was attained for estimating tree height (R2=0.89, RMSE=1.3m), diameter at breast height (R2=0.82, RMSE=9.1cm), crown diameter (R2=0.90, RMSE=0.7m) and biomass (R2=0.67, RMSE=1.2t). Our results indicate that, while using lidar data alone can achieve the automation of major urban forest inventory tasks to an acceptable level of accuracy, a synergistic use of lidar data with other spectral data such as hyperspectral or orthoimagery, which are usually available at least in the United States for most urban areas, can considerably improve the performance of the lidar-based method. Advisors/Committee Members: Wynne, Randolph H. (committeechair), Campbell, James B. Jr. (committee member), Abbott, A. Lynn (committee member), Nelson, Ross F. (committee member).
Shrestha, Rupesh. “Inventorying trees in an urban landscape using small-footprint discrete return imaging lidar.” 2011. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/37538.
Shrestha, Rupesh. “Inventorying trees in an urban landscape using small-footprint discrete return imaging lidar.” 2011. Web. 19 Apr 2019.
Shrestha R. Inventorying trees in an urban landscape using small-footprint discrete return imaging lidar. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/37538.
16. Peduzzi, Alicia. Estimating forest attributes using laser scanning data and dual-band, single-pass interferometric aperture radar to improve forest management.
▼ The overall objectives of this dissertation were to (1) determine whether leaf area index (LAI) (Chapter 2), as well as stem density and height to live crown (Chapter 3) can be estimated accurately in intensively managed pine plantations using small-footprint, multiple-return airborne laser scanner (lidar) data, and (2) ascertain whether leaf area index in temperate mixed forests is best estimated using multiple-return airborne laser scanning (lidar) data or dual-band, single-pass interferometric synthetic aperture radar data (from GeoSAR) alone or both in combination (Chapter 4). In situ measurements of LAI, mean height, height to live crown, and stem density were made on 109 (LAI) or 110 plots (all other variables) under a variety of stand conditions. Lidar distributional metrics were calculated for each plot as a whole as well as for crown density slices (newly introduced in this dissertation). These metrics were used as independent variables in best subsets regressions with LAI, number of trees, mean height to live crown, and mean height (measured in situ) as the dependent variables. The best resulting model for LAI in pine plantations had an R2 of 0.83 and a cross-validation (CV) RMSE of 0.5. The CV-RMSE for estimating number of trees on all 110 plots was 11.8 with an R2 of 0.92. Mean height to live crown was also well-predicted (R2 = 0.96, CV-RMSE = 0.8 m) with a one-variable model. In situ measurements of temperate mixed forest LAI were made on 61 plots (21 hardwood, 36 pine, 4 mixed pine hardwood). GeoSAR metrics were calculated from the X-band backscatter coefficients (four looks) as well as both X- and P-band interferometric heights and magnitudes. Both lidar and GeoSAR metrics were used as independent variables in best subsets regressions with LAI (measured in situ) as the dependent variable. Lidar metrics alone explained 69% of the variability in temperate mixed forest LAI, while GeoSAR metrics alone explained 52%. However, combining the LAI and GeoSAR metrics increased the R2 to 0.77 with a CV-RMSE of 0.42. Analysis of data from active sensors shows strong potential for eventual operational estimation of biophysical parameters essential to silviculture. Advisors/Committee Members: Wynne, Randolph H. (committeechair), Nelson, Ross F. (committee member), Thomas, Valerie A. (committee member), Fox, Thomas R. (committee member).
Peduzzi, Alicia. “Estimating forest attributes using laser scanning data and dual-band, single-pass interferometric aperture radar to improve forest management.” 2011. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/39456.
Peduzzi, Alicia. “Estimating forest attributes using laser scanning data and dual-band, single-pass interferometric aperture radar to improve forest management.” 2011. Web. 19 Apr 2019.
Peduzzi A. Estimating forest attributes using laser scanning data and dual-band, single-pass interferometric aperture radar to improve forest management. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/39456.
17. Brooks, Evan Beren. Fourier Series Applications in Multitemporal Remote Sensing Analysis using Landsat Data.
▼ Researchers now have unprecedented access to free Landsat data, enabling detailed monitoring of the Earth\'s land surface and vegetation. There are gaps in the data, due in part to cloud cover. The gaps are aperiodic and localized, forcing any detailed multitemporal analysis based on Landsat data to compensate. Harmonic regression approximates Landsat data for any point in time with minimal training images and reduced storage requirements. In two study areas in North Carolina, USA, harmonic regression approaches were least as good at simulating missing data as STAR-FM for images from 2001. Harmonic regression had an R2"0.9 over three quarters of all pixels. It gave the highest RPredicted2 values on two thirds of the pixels. Applying harmonic regression with the same number of harmonics to consecutive years yielded an improved fit, R2"0.99 for most pixels. We next demonstrate a change detection method based on exponentially weighted moving average (EWMA) charts of harmonic residuals. In the process, a data-driven cloud filter is created, enabling use of partially clouded data. The approach is shown capable of detecting thins and subtle forest degradations in Alabama, USA, considerably finer than the Landsat spatial resolution in an on-the-fly fashion, with new images easily incorporated into the algorithm. EWMA detection accurately showed the location, timing, and magnitude of 85% of known harvests in the study area, verified by aerial imagery. We use harmonic regression to improve the precision of dynamic forest parameter estimates, generating a robust time series of vegetation index values. These values are classified into strata maps in Alabama, USA, depicting regions of similar growth potential. These maps are applied to Forest Service Forest Inventory and Analysis (FIA) plots, generating post-stratified estimates of static and dynamic forest parameters. Improvements to efficiency for all parameters were such that a comparable random sample would require at least 20% more sampling units, with the improvement for the growth parameter requiring a 50% increase. These applications demonstrate the utility of harmonic regression for Landsat data. They suggest further applications in environmental monitoring and improved estimation of landscape parameters, critical to improving large-scale models of ecosystems and climate effects. Advisors/Committee Members: Wynne, Randolph H. (committeechair), Thomas, Valerie Anne (committeechair), Radtke, Philip J. (committee member), Woodcock, Curtis E. (committee member), Coulston, John W. (committee member).
Brooks, Evan Beren. “Fourier Series Applications in Multitemporal Remote Sensing Analysis using Landsat Data.” 2013. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/23276.
Brooks, Evan Beren. “Fourier Series Applications in Multitemporal Remote Sensing Analysis using Landsat Data.” 2013. Web. 19 Apr 2019.
Brooks EB. Fourier Series Applications in Multitemporal Remote Sensing Analysis using Landsat Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2013. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/23276.
18. Altrichter, Adam Edward. Landscape history and contemporary environmental drivers of microbial community structure and function.
▼ Recent work in microbial ecology has focused on elucidating controls over biogeographic patterns and connecting microbial community composition to ecosystem function. My objective was to investigate the relative influences of landscape legacies and contemporary environmental factors on the distribution of soil microbial communities and their contribution to ecosystem processes across a glacial till sequence in Taylor Valley, Antarctica. Within each till unit, I sampled from dry areas and areas with visible evidence of recent surface water movement generated by seasonal melting of ephemeral snow packs and hillslope ground ice. Using T-RFLP 16S rRNA gene profiles of microbial communities, I analyzed the contribution of till and environmental factors to community similarity, and assessed the functional potential of the microbial community using extracellular enzyme activity assays. Microbial communities were influenced by geochemical differences among both tills and local environments, but especially organized by variables associated with water availability as the first axis of an NMDS ordination was strongly related to shifts in soil moisture content. CCA revealed that tills explained only 3.4% of the variability in community similarity among sites, while geochemical variables explained 18.5%. Extracellular enzyme activity was correlated with relevant geochemical variables reflecting the influence of nutrient limitation on microbial activity. In addition, enzyme activity was related to changes in community similarity, particularly in wet environments with a partial Mantel correlation of 0.32. These results demonstrate how landscape history and environmental conditions can shape the functional potential of a microbial community mediated through shifts in microbial community composition. Advisors/Committee Members: Barrett, John E. (committeechair), Webster, Jackson R. (committee member), Wynne, Randolph H. (committee member), Gooseff, Michael N. (committee member).
Altrichter, Adam Edward. “Landscape history and contemporary environmental drivers of microbial community structure and function.” 2010. Masters Thesis, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/31883.
Altrichter, Adam Edward. “Landscape history and contemporary environmental drivers of microbial community structure and function.” 2010. Web. 19 Apr 2019.
Altrichter AE. Landscape history and contemporary environmental drivers of microbial community structure and function. [Internet] [Masters thesis]. Virginia Tech; 2010. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/31883.
19. Ren, Jie. Multi-temporal Remote Sensing of Changing Agricultural Land Uses within the Midwestern Corn Belt, 2001-2015.
▼ The Midwest US has experienced significant changes in agricultural land use and management practices in recent decades. Cropland expansion, crop rotation change, and crop phenology changes could lead to divergent environmental impacts on linked ecosystems. The overall objective is to examine agricultural land use and management changes and their impacts on water quality in the Midwest US, which is addressed in three separate studies. The first study examined spatial and temporal dimensions of agricultural land use dynamics in east-central Iowa, 2001-2012. Results of this study indicated that increases in corn production in response to US biofuel policies had been achieved mainly by altering crop rotation. This study also examined spatial relationships between cultivated fields and crop rotation practices with respect to underlying soils and terrain. The most intensively cultivated land had shallower slopes and fewer pedologic limitations than others, and the corn was planted on the most suitable soils. The second study characterized key crop phenological parameters (SOS and EOS) for corn and soybean and analyzed their spatial patterns to evaluate their change trends in the Midwest US, 2001-2015. Results showed that MODIS-derived SOS and EOS values are sensitive to input time-series data and threshold values chosen for crop phenology detection. The non-winter MODIS NDVI time-series input data, and a lower threshold value (i.e., 40%) both generated better results for SOS and EOS estimates. Spatial analyses of SOS and EOS values displayed clear south-north gradient for corn and trend analyses of SOS revealed only a small percentage of counties showed statistically significant earlier trends within a user-defined temporal window (2001-2012). The third study integrated remote sensing-derived products from the first two studies with the SWAT model to assess impacts of agricultural management changes on sediment and nutrient yields for three selected watersheds in the Midwest US. With satisfied calibration and validation results for stream flows, sediment and nutrient yields, considered under differing management scenarios, were compared at different spatial scales. Results showed that intensive crop rotation, advancing the planting date with the same length of growing season, and longer growing seasons, dramatically increased, maintained, and slightly reduced sediment, total nitrogen, and total phosphorous yields, respectively. Overall, these studies together illuminate relationships between broad-scale agricultural policies, management decisions, and environmental impacts, and the value of multi-temporal, broad-scale, geospatial analysis of agricultural landscapes. Advisors/Committee Members: Campbell, James B. Jr. (committeechair), Hodges, Steven C. (committee member), Wynne, Randolph H. (committee member), Sugumaran, Ramanathan (committee member), Shao, Yang (committee member).
Ren, Jie. “Multi-temporal Remote Sensing of Changing Agricultural Land Uses within the Midwestern Corn Belt, 2001-2015.” 2016. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/81559.
Ren, Jie. “Multi-temporal Remote Sensing of Changing Agricultural Land Uses within the Midwestern Corn Belt, 2001-2015.” 2016. Web. 19 Apr 2019.
Ren J. Multi-temporal Remote Sensing of Changing Agricultural Land Uses within the Midwestern Corn Belt, 2001-2015. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/81559.
20. Gopalakrishnan, Ranjith. Large-area forest assessment and monitoring using disparate lidar datasets.
▼ In the past 15 years, a large amount of public-domain lidar data has been collected over the Southeastern United States. Most of these acquisitions were undertaken by government agencies, primarily for non-forestry purposes. That is, they were collected mostly to aid in the creation of digital terrain models and to support hydrological and engineering assessments. Such data is not ideal for forestry purposes mainly due to the low pulse density per square meter, the high scan angles and low swath overlaps associated with these acquisitions. Nevertheless, the large area of coverage involved motivated this work. In this dissertation, I first look at how such lidar data (from non-forestry acquisitions) can be combined with National Forest Inventory tree height data to generate a large-area canopy height model. A simple linear regression model was developed using two lidar-based metrics as predictors: the 85th percentile of heights of canopy first returns and the coefficient of variation of the heights of canopy first returns. This model had good predictive ability over 76 disparate lidar projects, covering an area of approximately 297,000 square kilometers between them. Factors leading to the residual lack-of-fit of the model were also analyzed and quantified. For example, predictive ability was found to be better for softwood forests, forests with more homogeneous vegetation structure and for terrains with gentler slopes. Given that as much as 30% of the US is covered by public domain non-forestry lidar acquisitions, this is a first step for constructing a national wall-to-wall vertical vegetation structure map, which can then be used to ask important questions regarding forest inventories, carbon sequestration, wildlife habitat suitability and fire risk mitigation. Then, I examined whether such lidar data could be further used to predict understory shrub presence over disparate forest types. The predictability of classification model was low (accuracy = 62%, kappa = 0.23). Canopy occlusion factors and the heterogeneity of the understory layer were implicated as the main reasons for this poor performance. An analysis of the metrics chosen by the modeling framework highlighted the importance of non-understory metrics (metrics related to canopy openness and topographic aspect) in influencing shrub presence. As the proposed set of metrics were developed over a wide range of temperate forest types and topographic conditions of Southeastern US, it is expected that it will be useful for more localized future studies. Lastly, I explored the possibility of combining lidar-derived canopy height maps with Landsat-derived stand-age maps to predict plantation pine site index over large areas (site index is a measure of forest productivity). The model performance was assessed using a Monte Carlo technique (RMSE = 3.8 meters, relative RMSE = 19%). A sample site index map for large areas of Virginia and South Carolina was generated (map coverage area: 832 sq. km) and implications were discussed. Analysis of the resulting map… Advisors/Committee Members: Thomas, Valerie Anne (committeechair), Wynne, Randolph H. (committeechair), Coulston, John W. (committee member), Fox, Thomas R. (committee member).
Gopalakrishnan, Ranjith. “Large-area forest assessment and monitoring using disparate lidar datasets.” 2017. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/84856.
Gopalakrishnan, Ranjith. “Large-area forest assessment and monitoring using disparate lidar datasets.” 2017. Web. 19 Apr 2019.
Gopalakrishnan R. Large-area forest assessment and monitoring using disparate lidar datasets. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/84856.
21. Lorentz, Laura J. Hyperspectral Reflectance and Stable Isotopic Nitrogen: Tools to Assess Forest Ecosystem Nitrogen Cycling.
▼ The use of nitrogenous fertilizers in agricultural and forestry practices coupled with increased fossil fuel combustion and resulting nitrogen (N) deposition across the landscape have contributed to a near doubling of N inputs to terrestrial ecosystems. With such dramatic changes have come adverse environmental consequences including the acidification of soil and water resources and an increased rate of biodiversity loss in both flora and fauna. A method of rapidly predicting ecosystem susceptibility to N loss across large spatial scales would facilitate the identification of those systems most likely to contribute to potentially adverse environmental impacts. To begin the development of such a framework, this research utilizes study sites located throughout the geographic ranges of Douglas-fir (Pseudotsuga menziesii) and loblolly pine (Pinus taeda) to explore relationships between hyperspectral remote sensing, N stable isotope ratios ("15N) and growth response to nitrogenous fertilizer. In both species multiple linear regression models relating leaf-level reflectance to "15N showed strong predictive capabilities, with some models explaining more than 65% of the variance in "15N. Significant correlations between "15N metrics and growth response to N fertilization were also observed in both species. Additional exploratory analysis of the inclusion of "15N metrics with other environmental and edaphic variables to predict fertilizer growth response showed an increase in model performance with the addition of the enrichment factor (EF ="15NFol - "15NSoil). This research demonstrates the ability of hyperspectral reflectance to predict "15N and reveals the potential of "15N to be included in future models to predict fertilizer growth response. Advisors/Committee Members: Strahm, Brian Dale (committeechair), Thomas, Valerie Anne (committeechair), Seiler, John R. (committee member), Wynne, Randolph H. (committee member).
Lorentz, Laura J. “Hyperspectral Reflectance and Stable Isotopic Nitrogen: Tools to Assess Forest Ecosystem Nitrogen Cycling.” 2013. Masters Thesis, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/51214.
Lorentz, Laura J. “Hyperspectral Reflectance and Stable Isotopic Nitrogen: Tools to Assess Forest Ecosystem Nitrogen Cycling.” 2013. Web. 19 Apr 2019.
Lorentz LJ. Hyperspectral Reflectance and Stable Isotopic Nitrogen: Tools to Assess Forest Ecosystem Nitrogen Cycling. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/51214.
22. Joshi, Pratik Prasad. Ion-neutral coupling in the geomagnetically disturbed mid-latitude ionosphere as observed by SuperDARN HF radars and NATION Fabry-Perot Interferometers.
▼ The earth's ionosphere-thermosphere region is a coupled environment which is governed by interactions between the overlapping neutral constituents and ionospheric plasma. The mid-latitude thermosphere-ionosphere system is very complex owing to its sensitivity to both the polar and equatorial processes. The mid-latitudes is also a relatively unexplored and less understood region primarily due to the paucity of observing instruments that have traditionally been available. However, the past 9 years of mid-latitude expansion of the Super Dual Auroral Radar Network (SuperDARN) has provided new access to continuous large-scale observations of the sub-auroral ionosphere. On the other hand, the past 3 years of mid-latitude expansion of the North American Thermosphere Ionosphere Observation Network (NATION) Fabry-Perot interferometer array, has created a critical resource for measuring the thermospheric neutral winds. The overlap of these two observing networks in the mid-east North American sector has resulted in a strong ground-based large-scale platform for co-located study of mid-latitude thermosphere-ionosphere dynamics for the first time. The coupling between ions and neutrals is a very important process for controlling the thermospheric dynamics. Ion-neutral coupling at high latitudes has been studied in many previous papers, but there have been very few studies focused on the mid-latitude region. Hence, in this work we have studied the ion-neutral coupling mechanisms and timescales at mid-latitudes during disturbed geomagnetic conditions by using the co-located observations from the SuperDARN-NATION array. The study has focused on the main phase as well as the late recovery phase of a geomagnetic storm which occurred on October 2-3, 2013. Ion drag is known to drive the neutral circulation during the main phase of storm at auroral latitudes, while the neutral wind disturbance dynamo mechanism is known to generate ionospheric electric fields and currents during the recovery phase. By using the methods of ion-neutral momentum exchange theory and time lagged correlation analysis, we analyzed the timescales at which the ion-neutral coupling operates. The ions are observed to drive the neutral winds on a timescale of ~ 84 minutes in the storm main phase which is significantly faster than expected from the driving due to local ion-drag alone (~ 124 minutes). This suggests that along with ion-drag, other local and non-local storm-time influences like Joule heating are also playing an important part in driving the neutral winds. On the other hand, in the late recovery phase, the neutral winds are found to be strongly coupled with the ions and maintain the ion convection without any significant time delay which is consistent with effect of the 'disturbance dynamo' or 'neutral-flywheel' persisting well into the late recovery phase. The timescales and underlying physics understood through this work serve as an important contribution to our knowledge of ion-neutral coupling processes at the middle latitudes. Looking forward, the… Advisors/Committee Members: Baker, Joseph Benjamin (committeechair), Ruohoniemi, John Michael (committeechair), Wynne, Randolph H. (committee member), Clauer, C Robert (committee member).
Joshi, Pratik Prasad. “Ion-neutral coupling in the geomagnetically disturbed mid-latitude ionosphere as observed by SuperDARN HF radars and NATION Fabry-Perot Interferometers.” 2015. Masters Thesis, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/56561.
Joshi, Pratik Prasad. “Ion-neutral coupling in the geomagnetically disturbed mid-latitude ionosphere as observed by SuperDARN HF radars and NATION Fabry-Perot Interferometers.” 2015. Web. 19 Apr 2019.
Joshi PP. Ion-neutral coupling in the geomagnetically disturbed mid-latitude ionosphere as observed by SuperDARN HF radars and NATION Fabry-Perot Interferometers. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/56561.
23. Gökkaya, Kemal. Prediction of Foliar Biochemistry in a Boreal Forest Canopy Using Imaging Spectroscopy and LiDAR Data.
▼ The use of satellite and airborne remote sensing data to predict foliar macronutrients and pigments for a boreal mixedwood forest composed of black and white spruce, balsam fir, northern white cedar, white birch, and trembling aspen was investigated. Specifically, imaging spectroscopy (IS) and light detection and ranging (LiDAR) are used to model the foliar N:P ratio, macronutrients (N, P, K, Ca, Mg) and chlorophyll. Measurement of both foliar macronutrients and foliar chlorophyll provide critical information about plant physiological and nutritional status, stress, as well as ecosystem processes such as carbon (C) exchange (photosynthesis and net primary production), decomposition and nutrient cycling. Results show that airborne and spaceborne IS data explained approximately 70% of the variance in the canopy N:P ratio with predictions errors of less than 8% in two consecutive years. LiDAR models explained more than 50% of the variance in the canopy N:P ratio with similar predictions errors. Predictive models using spaceborne Hyperion IS data were developed with adjusted R2 values of 0.73, 0.72, 0.62, 0.25, and 0.67 for N, P, K, Ca and Mg, respectively. The LiDAR model explained 80% of the variance in canopy Ca concentration with an RMSE of less than 10%, suggesting strong correlations between forest height and Ca. Two IS derivative indices emerged as good predictors of chlorophyll across time and space. When the models of these two indices with the same parameters as generated from Hyperion data were applied to other years' data for chlorophyll concentration prediction, they could explain 71, 63 and 6% and 61, 54 and 8 % of the variation in chlorophyll concentration in 2002, 2004 and 2008, respectively with prediction errors ranging from 11.7% to 14.6%. Results demonstrate that the N:P ratio, N, P, K, Mg and chlorophyll can be modeled by spaceborne IS data and Ca can only be predicted by LiDAR data in the canopy of this forest. The ability to model the N:P ratio and macronutrients using spaceborne Hyperion data demonstrates the potential for mapping them at the canopy scale across larger geographic areas and being able to integrate them in future studies of ecosystem processes. Advisors/Committee Members: Thomas, Valerie Anne (committeechair), Wynne, Randolph H. (committee member), Seiler, John R. (committee member), Noland, Thomas L. (committee member).
Gökkaya, Kemal. “Prediction of Foliar Biochemistry in a Boreal Forest Canopy Using Imaging Spectroscopy and LiDAR Data.” 2012. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/77988.
Gökkaya, Kemal. “Prediction of Foliar Biochemistry in a Boreal Forest Canopy Using Imaging Spectroscopy and LiDAR Data.” 2012. Web. 19 Apr 2019.
Gökkaya K. Prediction of Foliar Biochemistry in a Boreal Forest Canopy Using Imaging Spectroscopy and LiDAR Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/77988.
24. Chance, Eric Wilson. Irrigator Responses to Changes in Water Availability in Idaho's Snake River Plain.
▼ Understanding irrigator responses to previous changes in water availability is critical to building effective institutions that allow for efficient and resilient management of water resources in the face of potentially increasing scarcity due to climate change. Using remote sensing data, I examined irrigator responses to seasonal changes in water availability in Idaho's Snake River Plain over the past 33 years. Google Earth Engine's high performance cloud computing and big data processing capabilities were used to compare the performance of three spectral indices, three compositing algorithms and two sensors for 2002 and 2007 for distinguishing between irrigated and non-irrigated parcels. We demonstrate that, on average, the seasonal-maximum algorithm yields a 60% reduction in county scale root mean square error (RMSE) over the accepted single-date approach. We use the best performing classification method, a binary threshold of the seasonal maximum of the Normalized Difference Moisture Index (NDMI), to identify irrigated and non-irrigated lands in Idaho's Snake River Basin for 1984-2016 using Landsat 5-8 data. NDMI of irrigated lands was found to generally increase over time, likely as a result of changes in agricultural practices increasing crop productivity. Furthermore, we find that irrigators with rights to small areas, and those with only surface water rights are more likely to have a major reduction (>25%) in irrigated area and conversely those with a large, groundwater rights are more likely to have major increases (>25%) in the extent of their irrigation. Advisors/Committee Members: Cobourn, Kelly M. (committeechair), Thomas, Valerie A. (committeechair), McGuire, Kevin J. (committee member), Wynne, Randolph H. (committee member).
Chance, Eric Wilson. “Irrigator Responses to Changes in Water Availability in Idaho's Snake River Plain.” 2017. Masters Thesis, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/78361.
Chance, Eric Wilson. “Irrigator Responses to Changes in Water Availability in Idaho's Snake River Plain.” 2017. Web. 19 Apr 2019.
Chance EW. Irrigator Responses to Changes in Water Availability in Idaho's Snake River Plain. [Internet] [Masters thesis]. Virginia Tech; 2017. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/78361.
25. Ghannam, Sherin Ghannam. Multisensor Multitemporal Fusion for Remote Sensing using Landsat and MODIS Data.
▼ The growing Landsat data archive represents more than four decades of continuous Earth observation. Landsat's role in scientific analysis has increased dramatically in recent years as a result of the open-access policy of the U.S. Geological Survey (USGS). However, this rich data record suffers from relatively low temporal resolution due to the 16-day revisit period of each Landsat satellite. To estimate Landsat images at other points in time, researchers have proposed data-fusion approaches that combine existing Landsat data with images from other sensors, such as MODIS (Moderate Resolution Imaging Spectroradiometer) from the Terra and Aqua satellites. MODIS provides daily revisits, however, with a spatial resolution that is significantly lower than that of Landsat. Fusion of Landsat and MODIS is challenging because of differences in their spatial resolution, band designations, swath width, viewing angle and the noise level. Fusion is even more challenging for heterogeneous landscapes. In the first part of our work, the multiresolution analysis offered by the wavelet transform was explored as a suitable environment for Landsat and MODIS fusion. Our proposed Wavelet-based Spatiotemporal Adaptive Reflectance Fusion Model (WSTARFM) is the first model to merge Landsat and MODIS successfully. It handles the heterogeneity of the landscapes more effectively than the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) does. The system has been tested on simulated data and on actual data of two study areas in North Carolina. For a challenging heterogeneous study area near Greensboro, North Carolina, WSTARFM produced results with median R-squared values of 0.98 and 0.95 for the near-infrared band over deciduous forests and developed areas, respectively. Those results were obtained by withholding an actual Landsat image, and comparing it with a predicted version of the same image. These values represent an improvement over results obtained using the well-known STARFM technique. Similar improvements were obtained for the red band. For the second (homogeneous) study area, WSTARFM produced comparable prediction results to STARFM. In the second part of our work, Landsat-MODIS fusion has been explored from the temporal perspective. The fusion is performed on the Landsat and MODIS per-pixel time series. A new Multisensor Adaptive Time Series Fitting Model (MATSFM) is proposed. MATSFM is the first model to use mapped MODIS values to guide the fitting applied to the sparse Landsat time series. MATSFM produced results with median R-squared of 0.98 over the NDVI images of the first heterogeneous study area compared to 0.97 produced by STARFM. For the second study area, MATSFM also produced better prediction accuracy than STARFM. Advisors/Committee Members: Abbott, Amos L. (committeechair), Wynne, Randolph H. (committee member), Baker, Joseph Benjamin (committee member), Batra, Dhruv (committee member), Thomas, Valerie Anne (committee member), Hussein, Mohamed E. (committee member).
Ghannam, Sherin Ghannam. “Multisensor Multitemporal Fusion for Remote Sensing using Landsat and MODIS Data.” 2017. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/81092.
Ghannam, Sherin Ghannam. “Multisensor Multitemporal Fusion for Remote Sensing using Landsat and MODIS Data.” 2017. Web. 19 Apr 2019.
Ghannam SG. Multisensor Multitemporal Fusion for Remote Sensing using Landsat and MODIS Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/81092.
26. Awadallah, Mahmoud Sobhy Tawfeek. Image Analysis Techniques for LiDAR Point Cloud Segmentation and Surface Estimation.
▼ Light Detection And Ranging (LiDAR), as well as many other applications and sensors, involve segmenting sparse sets of points (point clouds) for which point density is the only discriminating feature. The segmentation of these point clouds is challenging for several reasons, including the fact that the points are not associated with a regular grid. Moreover, the presence of noise, particularly impulsive noise with varying density, can make it difficult to obtain a good segmentation using traditional techniques, including the algorithms that had been developed to process LiDAR data. This dissertation introduces novel algorithms and frameworks based on statistical techniques and image analysis in order to segment and extract surfaces from sparse noisy point clouds. We introduce an adaptive method for mapping point clouds onto an image grid followed by a contour detection approach that is based on an enhanced version of region-based Active Contours Without Edges (ACWE). We also proposed a noise reduction method using Bayesian approach and incorporated it, along with other noise reduction approaches, into a joint framework that produces robust results. We combined the aforementioned techniques with a statistical surface refinement method to introduce a novel framework to detect ground and canopy surfaces in micropulse photon-counting LiDAR data. The algorithm is fully automatic and uses no prior elevation or geographic information to extract surfaces. Moreover, we propose a novel segmentation framework for noisy point clouds in the plane based on a Markov random field (MRF) optimization that we call Point Cloud Densitybased Segmentation (PCDS). We also developed a large synthetic dataset of in plane point clouds that includes either a set of randomly placed, sized and oriented primitive objects (circle, rectangle and triangle) or an arbitrary shape that forms a simple approximation for the LiDAR point clouds. The experiment performed on a large number of real LiDAR and synthetic point clouds showed that our proposed frameworks and algorithms outperforms the state-of-the-art algorithms in terms of segmentation accuracy and surface RMSE. Advisors/Committee Members: Abbott, Amos L. (committeechair), Parikh, Devi (committee member), Nelson, Ross Frederick (committee member), Hsiao, Michael S. (committee member), Ghanem, Ahmed M. (committee member), Wynne, Randolph H. (committee member).
Awadallah, Mahmoud Sobhy Tawfeek. “Image Analysis Techniques for LiDAR Point Cloud Segmentation and Surface Estimation.” 2016. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/73055.
Awadallah, Mahmoud Sobhy Tawfeek. “Image Analysis Techniques for LiDAR Point Cloud Segmentation and Surface Estimation.” 2016. Web. 19 Apr 2019.
Awadallah MST. Image Analysis Techniques for LiDAR Point Cloud Segmentation and Surface Estimation. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/73055.
27. Waiswa, Daniel. Dynamics of Forest Cover Extent, Forest Fragmentation and Their Drivers in the Lake Victoria Crescent, Uganda From 1989 to 2009.
▼ Despite the important values forests play in the tropics, sustainable forest management still remains a challenge as manifested through continued forest loss. The objective of this study was to provide information on the dynamics of forest cover and their drivers vital for enhancing sustainable forest management in the Lake Victoria crescent, Uganda. Several methodologies including remote sensing and Geographic Information Systems techniques, analysis of landscape patterns and various social science techniques were integrated in working towards the stated goal. Results showed that the Lake Victoria crescent, Uganda covering an area of about 1,509,228 ha, experienced a decline in forest cover from 9.0% in 1989 to 4.4% in 2009. This was in comparison with non-forest cover which increased from 58.7% in 1989 to 63.5% in 2009 while open water coverage generally remained unchanged averaging 32.3% from 1989 to 2009. Mean annual deforestation rate from 1989 to 2009 decreased with a weighted mean rate of 2.56%. Both deforestation and afforestation declined between 1989 and 2009 although deforestation still exceeded afforestation. In addition to deforestation, the Lake Victoria crescent also experienced forest fragmentation from 1989 to 2009. Forests greater than 100 ha in size were the most vulnerable to forest fragmentation yet they still constituted a big proportion of forest cover in 2009. Deforestation was a consequence of proximate causes which were triggered by a number of underlying drivers acting singly or in combination, with underlying drivers being more influential. In a bid to promote sustainable forest management, there is a need to continue with efforts to curb deforestation and forest fragmentation, especially amongst forests greater than 100 ha. This could be achieved through empowerment of local communities to take a core role in sustainable management of forest resources. Advisors/Committee Members: Prisley, Stephen P. (committeechair), Wynne, Randolph H. (committee member), Copenheaver, Carolyn A. (committee member), Stern, Marc J. (committee member), Radtke, Philip J. (committee member).
Waiswa, Daniel. “Dynamics of Forest Cover Extent, Forest Fragmentation and Their Drivers in the Lake Victoria Crescent, Uganda From 1989 to 2009.” 2011. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/26803.
Waiswa, Daniel. “Dynamics of Forest Cover Extent, Forest Fragmentation and Their Drivers in the Lake Victoria Crescent, Uganda From 1989 to 2009.” 2011. Web. 19 Apr 2019.
Waiswa D. Dynamics of Forest Cover Extent, Forest Fragmentation and Their Drivers in the Lake Victoria Crescent, Uganda From 1989 to 2009. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/26803.
28. Sen, Susmita. Characterizing Impacts of and Recovery from Surface Coal Mining in Appalachian Forested Landscapes Using Landsat Imagery.
▼ This dissertation describes research investigating the potential for using Landsat data to identify and characterize woody canopy cover on reclaimed coal-mined lands through three separate studies. The objective of the first study was to assess whether surface coal mines in the forested central Appalachian regions of the US can be separated from the other prevalent forest-replacing disturbances through analysis of an interannual chronosequence of Landsat images. Disturbances were classified using descriptors of the disturbance/recovery trajectories: disturbance minimum, recovery slope and recovery maximum. Three vegetation indices (VIs) (normalized difference vegetation index, NDVI; tasseled cap greenness/brightness ratio, TC G/B; and inverse of Landsat band 3, B3I) were used to analyze multitemporal trajectories generated using both pixels and objects. Classification accuracies using objects were better than those obtained using pixels for all VIs. The highest object-based classification accuracy was achieved using TC G/B (89%), followed by NDVI (88%) and B3I (80%). The objective of the second study was to evaluate performance of a woody canopy cover (including both native and invasive species) estimation method based on the 2011 National Land Cover Database (NLCD) protocol for both mined and non-mined areas of the central Appalachians. Potential explanatory variables included raw and derived bands from leaf-on and leaf-off Landsat scenes plus terrain descriptors. Results show that the model developed to estimate canopy cover for mines (R2 = 0.78, Adj. R2 = 0.77, RMSE = 16%) is more robust than the models developed for non-mines, mixed, and all areas combined. The objective of the third study was to determine whether four disturbance/recovery parameters (recovery time, disturbance minimum, recovery slope, and recovery maximum), alone or in combination with variables identified in the second study, enable robust estimation of woody canopy cover on reclaimed surface coal mines. Of the disturbance/recovery parameters, only recovery time made a significant contribution to the model (R2 0.45, Adj. R2 0.44, RMSE 14%). Addition of leaf-on and leaf-off NDVI improved the R2 to 0.54 (Adj. R2 0.53, RMSE 13%). Analysis of Landsat data has strong potential for identifying reclaimed mines and characterizing the extent to which woody canopy has recovered post-reclamation. Advisors/Committee Members: Thomas, Valerie A. (committee member), Masek, Jeffrey G. (committee member), Campbell, James B. Jr. (committee member), Zipper, Carl E. (committeecochair), Wynne, Randolph H. (committeecochair).
Sen, Susmita. “Characterizing Impacts of and Recovery from Surface Coal Mining in Appalachian Forested Landscapes Using Landsat Imagery.” 2011. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/38737.
Sen, Susmita. “Characterizing Impacts of and Recovery from Surface Coal Mining in Appalachian Forested Landscapes Using Landsat Imagery.” 2011. Web. 19 Apr 2019.
Sen S. Characterizing Impacts of and Recovery from Surface Coal Mining in Appalachian Forested Landscapes Using Landsat Imagery. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/38737.
29. Banskota, Asim. The discrete wavelet transform as a precursor to leaf area index estimation and species classification using airborne hyperspectral data.
▼ The need for an efficient dimensionality reduction technique has remained a critical challenge for effective analysis of hyperspectral data for vegetation applications. Discrete wavelet transform (DWT), through multiresolution analysis, offers oppurtunities both to reduce dimension and convey information at multiple spectral scales. In this study, we investigated the utility of the Haar DWT for AVIRIS hyperspectral data analysis in three different applications (1) classification of three pine species (Pinus spp.), (2) estimation of leaf area index (LAI) using an empirically-based model, and (3) estimation of LAI using a physically-based model. For pine species classification, different sets of Haar wavelet features were compared to each other and to calibrated radiance. The Haar coefficients selected by stepwise discriminant analysis provided better classification accuracy (74.2%) than the original radiance (66.7%). For empirically-based LAI estimation, the models using the Haar coefficients explained the most variance in observed LAI for both deciduous plots (cross validation R2 (CV-R2) = 0.79 for wavelet features vs. CV-R2 = 0.69 for spectral bands) and all plots combined (CV R2 = 0.71 for wavelet features vs. CV-R2 = 0.50 for spectral bands). For physically-based LAI estimation, a look-up-table (LUT) was constructed by a radiative transfer model, DART, using a three-stage approach developed in this study. The approach involved comparison between preliminary LUT reflectances and image spectra to find the optimal set of parameter combinations and input increments. The LUT-based inversion was performed with three different datasets, the original reflectance bands, the full set of the wavelet extracted features, and the two wavelet subsets containing 99.99% and 99.0% of the cumulative energy of the original signal. The energy subset containing 99.99% of the cumulative signal energy provided better estimates of LAI (RMSE = 0.46, R2 = 0.77) than the original spectral bands (RMSE = 0.69, R2 = 0.42). This study has demonstrated that the application of the discrete wavelet transform can provide more accurate species discrimination within the same genus than the original hyperspectral bands and can improve the accuracy of LAI estimates from both empirically- and physically-based models. Advisors/Committee Members: Wynne, Randolph H. (committeechair), Nelson, Ross F. (committee member), Thomas, Valerie A. (committee member), Campbell, James B. Jr. (committee member), Huemmrich, Karl F. (committee member).
Banskota, Asim. “The discrete wavelet transform as a precursor to leaf area index estimation and species classification using airborne hyperspectral data.” 2011. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/39188.
Banskota, Asim. “The discrete wavelet transform as a precursor to leaf area index estimation and species classification using airborne hyperspectral data.” 2011. Web. 19 Apr 2019.
Banskota A. The discrete wavelet transform as a precursor to leaf area index estimation and species classification using airborne hyperspectral data. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/39188.
30. Walker, Jessica. Analysis of Dryland Forest Phenology using Fused Landsat and MODIS Satellite Imagery.
▼ This dissertation investigated the practicality and expediency of applying remote sensing data fusion products to the analysis of dryland vegetation phenology. The objective of the first study was to verify the quality of the output products of the spatial and temporal adaptive reflectance fusion method (STARFM) over the dryland Arizona study site. Synthetic 30 m resolution images were generated from Landsat-5 Thematic Mapper (TM) data and a range of 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance datasets and assessed via correlation analysis with temporally coincident Landsat-5 imagery. The accuracy of the results (0.61 < R2 < 0.94) justified subsequent use of STARFM data in this environment, particularly when the imagery were generated from Nadir Bi-directional Reflectance Factor (BRDF)-Adjusted Reflectance (NBAR) MODIS datasets. The primary objective of the second study was to assess whether synthetic Landsat data could contribute meaningful information to the phenological analyses of a range of dryland vegetation classes. Start-of-season (SOS) and date of peak greenness phenology metrics were calculated for each STARFM and MODIS pixel on the basis of enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) time series over a single growing season. The variability of each metric was calculated for all STARFM pixels within 500 m MODIS extents. Colorado Plateau Pinyon Juniper displayed high amounts of temporal and spatial variability that justified the use of STARFM data, while the benefit to the remaining classes depended on the specific vegetation index and phenology metric. The third study expanded the STARFM time series to five years (2005-2009) to examine the influence of site characteristics and climatic conditions on dryland ponderosa pine (Pinus ponderosa) forest phenological patterns. The results showed that elevation and slope controlled the variability of peak timing across years, with lower elevations and shallower slopes linked to higher levels of variability. During drought conditions, the number of site variables that controlled the timing and variability of vegetation peak increased. Advisors/Committee Members: Thomas, Valerie A. (committee member), Seiler, John R. (committee member), Resler, Lynn M. (committee member), de Beurs, Kirsten M. (committeecochair), Wynne, Randolph H. (committeecochair).
Walker, Jessica. “Analysis of Dryland Forest Phenology using Fused Landsat and MODIS Satellite Imagery.” 2012. Doctoral Dissertation, Virginia Tech. Accessed April 19, 2019. http://hdl.handle.net/10919/39403.
Walker, Jessica. “Analysis of Dryland Forest Phenology using Fused Landsat and MODIS Satellite Imagery.” 2012. Web. 19 Apr 2019.
Walker J. Analysis of Dryland Forest Phenology using Fused Landsat and MODIS Satellite Imagery. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2019 Apr 19]. Available from: http://hdl.handle.net/10919/39403.

industry_corpus2: Agriculture & Fisheries


text:
J. L. Lewis: Bit Error Probability of a Large-Jammer Cancellation System. NTC 78 Birmingham Conf. Rec, IEEE, 1978, V. 3, 35.3.1-35.3.5.
 V. P. Afanasjev: Digital algorithms of discrimination of phase-manipulated wide-band signals. (in Russian). Izv. VUZov SSSR - Radioelektronika 22 (1979), 8, 65-72.

industry_corpus2: Computing & Telecommunications


text:
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