Browsing by Author "Jeffrey G. White, Committee Co-Chair"

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Application of Ground-Penetrating Radar to Map Stratigraphy of a Drained Carolina Bay and Aid Its Wetland Restoration
(2005-01-14) Szuch, Ryan Paul; C. W. Zanner, Committee Member; Rodney L. Huffman, Committee Member; Michael J. Vepraskas, Committee Co-Chair; Jeffrey G. White, Committee Co-Chair
Carolina Bays (bays) are a wetland type found along the Atlantic Coastal Plain that occur as oval-shaped depressions. Knowledge of bay stratigraphy might improve inconclusive theories on bay formation and aid attempts at wetland restoration of drained bays. Ground-penetrating radar (GPR) provides high-resolution and continuous profiles of the subsurface but has seldom been used for large-scale investigations or in Carolina Bays. A GPR survey was performed at Juniper Bay, a 300 ha drained bay in Robeson County, North Carolina. The survey included 23.2 km of GPR transects and soil borings at 174 locations. The broad objective of the survey was to map Juniper Bay's stratigraphy, particularly to determine the depth, extent, and continuity of clayey horizons likely to act as aquitards. To prevent ambiguity in identifying the ground surface on GPR transects, a 'lift-test' was developed that delineated the surface. Spatial variation in wave velocity was addressed using a "reflector-interface matching" technique to determine velocity at multiple locations and at various depths within the bay. A linear calibration equation relating travel time of GPR waves to depth of soil interface was developed. The average deviation between observed and predicted depth to clayey horizons was 0.25 m (16% error). Error was mainly attributed to the survey's large scale, subsurface complexity, presence of organic soils, and depth of the horizons. The lift-test improved accuracy by 10%, and the use of multiple calibration points limited extrapolation of the calibration equation. These calibration methods should prove valuable for future study of GPR accuracy on large-scale, complex sites. Information obtained during the GPR survey and associated coring was used to describe Juniper Bay's stratigraphy and develop theories on its formation. The bay's stratigraphy consists of alternating layers of sands and clays. Clayey layers appear continuous over much of the bay except where truncated by features that seem to be paleochannels. Historic geomorphic events at Juniper Bay have varied spatially and temporally but have included repeated lacustrine deposition, fluvial deposition, and fluvial incision. The original bay probably expanded and incorporated a smaller bay and fluvial feature. Future GPR work and integration of geologic and hydrologic studies may aid our assessment of Juniper Bay's stratigraphy and evolution. Many bays have been drained for conversion to agriculture. Clayey subsurface strata commonly occurs in bays and act as aquitards, restricting vertical water flow. Modification to drainage systems could lead to restoration of wetland conditions. The North Carolina Department of Transportation (NCDOT) intends to restore Juniper Bay for wetland mitigation credit. Ground-penetrating radar interpretation found that clayey aquitards underlay most of the bay at an average depth of 1.64 m. An anomalous GPR reflection in the southeast corner of the bay was interpreted as a fluvial deposit that does not contain aquitards until 3 to 5 m. NCDOT should consider alternative restoration plans for this area. By comparing the depths of aquitards and drainage ditches, several areas were identified as likely locations of ditch-induced aquitard discontinuity. NCDOT should fill or line suspect ditches to prevent potential water losses. Hypothetical proposals by professional firms indicated that GPR could provide large volumes of data with cost and time efficiency. Ground-penetrating radar surveys are suggested as a useful tool for determining suitability of potential wetland restoration sites.
Estimating Forage Biomass and Nitrogen Concentration Using False Color Infrared Photography
(2002-12-04) Morgan, Sarah Paige; James T. Green, Jr., Committee Co-Chair; Ronnie W. Heiniger, Committee Member; Noah N. Ranells, Committee Member; Jeffrey G. White, Committee Co-Chair
The objective of this research was to investigate the utility of using nonnormalized (raw) digital counts and vegetation indices (VIs) derived from false color infrared (FCIR) photography to estimate biomass (dry), nitrogen (N) concentration, and N uptake of several warm season forage canopies at several locations. In July 2000, FCIR aerial photography was obtained at an altitude of 854 meters from an experiment established in 1998 at the Caswell Farm in Kinston, NC to investigate realistic yield expectations (RYE) from warm season forages fertilized with swine (Sus scrofa domesticus) effluent and ammonium nitrate (NH₄NO₃). The experiment consisted of three forage canopies (bermudagrass [Cynodon dactylon L. 'Coastal'], crabgrass [Digitaria sanguinalis L. 'Red River'], and volunteer warm season [80% native crabgrass, 20% forbs]) fertilized at five N rates (0, 224, 449, and 674 kg ha⁻¹ yr⁻¹) with either effluent or NH₄NO₃ in a stripped split plot design. Biomass, N concentration, and N uptake were measured and regressed against green (G [490 to 550 nm]), red (R [550 to 700 nm]), and near infrared (NIR [700 to 900 nm]) digital counts and seven VIs (NDVI, Green NDVI [GNDVI], DVI, RVI, Normalized NIR [NormNIR], Normalized Green [NG], and Normalized Red [NR]). There was an N source x N rate interaction for N uptake in bermudagrass (BG) and crabgrass (CG) canopies and for biomass and N concentration in BG. Differences due to N source (N source x VI) affected the relationship between biomass and GNDVI in BG canopies and many of the relationships between crop response variables and VIs in VWS canopies. Biomass was best estimated by NIR digital counts in BG (R² = 0.82), NDVI in CG (R² = 0.54), and NormNIR in VWS (R2 = 0.86). Nitrogen concentration was best estimated by NDVI in BG (R² = 0.62), NIR digital counts in CG (R² = 0.56), and G digital counts in VWS (R² = 0.63). Green NDVI was a consistently strong estimator (R² > 0.76) of N uptake for all forage canopies and was unaffected by N source. In September, 2000, an experiment was established in Raleigh, NC to (i) test the utility of FCIR ground-based photography in estimating N concentration differences in bermudagrass ('Coastal') canopies grown at similar biomass, and (ii) to investigate how different soil moisture levels would affect image interpretation. The experiment consisted of three irrigation levels (0, 25 minutes, and 90 minutes) applied 24 hours before harvest split across three replications of five N rates (0, 11, 22, 45, and 90 kg ha⁻¹) applied eleven days before harvest on a well-established bermudagrass sod at early heading. False color infrared photographs were obtained from a height of 1.83 meters above the ground and represented a harvest area of 0.25 m². Biomass (dry), N concentration, N uptake, and soil moisture were measured at each harvest area and regressed against nonnormalized raw digital counts and VIs. Differences among N rates were found for N concentration and N uptake but not for biomass, indicating that biomass levels were similar across all treatments. Irrigation rates only affected soil moisture. Significant, but weak correlations (R² < 0.28) were found for the relationships among N concentration, N uptake, NIR digital counts, NormNIR, DVI, NG and GNDVI. When replications were analyzed as 'sites' and irrigation blocks as 'replications within sites', there was a site x VI interaction for N concentration, NG, and GNDVI, whereby two of the three 'sites' were more strongly correlated (R² = 0.38 to 0.57) with a VI than the combined relationship. Relationships were generally stronger within site 1 versus site 2 and site 3. In July and August of 2000 and 2001, ground-based FCIR photographs were acquired from six harvests of bermudagrass canopies ('Coastal') from four different locations throughout eastern North Carolina which were part of a larger experiment examining realistic yield expectations (RYE) on three soil types fertilized at five rates of nitrogen. Similar photographic methods were used, however, each harvest area consisted of an average of three photographs, each representing a ground area of 0.25 m². Relationships between Red, Green, and NIR digital counts and crop response variables for most of the sites were weak, however, normalization generally improved correlations. Moderate or strong correlations between spectral and crop response variables, such as between Green NDVI and N uptake (R² = 0.89), could be found among all sites and cuttings except one. There were cutting, year, and site interactions with VIs for all three comparisons among sites, cuttings, and years. Despite statistics indicating that harvests were best modeled individually, combined relationships usually resulted in higher coefficients of determination (R² = 0.66). Taking into account location, photography method, and environmental conditions, Green NDVI and DVI were best to estimate N concentration across four sites (R² = 0.40).
Spatial Analysis of In-Season Site-Specific Nitrogen Management Effects on Groundwater Nitrate and Agronomic Performance
(2004-10-21) Hong, Nan; Marcia L. Gumpertz, Committee Member; Hugh Devine, Committee Member; Jeffrey G. White, Committee Co-Chair; D. Keith Cassel, Committee Chair
In-season, site-specific (SS) N management based on remote sensing (RS) has been suggested as a way of reducing groundwater NO3-N contamination. In-season N management seeks to match the temporal variability of crop N needs by applying appropriate amounts of N at critical crop growth stages. Site-specific N management attempts to match the spatial variability of crop N requirements by applying appropriate, spatially variable N rates within fields. We evaluated the environmental and agronomic benefits of two in-season, RS-informed N management strategies applied on a uniform field-average (FA) or SS basis. We compared these to current uniform N recommendations based on "Realistic Yield Expectations" (RYE) in a typical coastal plain cropping system. We also sought to understand the spatial and temporal dynamics of shallow groundwater NO3-N. An additional objective was to develop a statistical procedure for the analysis of spatially dense, georeferenced subsample data in randomized complete block designs, a common characteristic of precision agriculture research. The experiment was established in a 12-ha North Carolina field with a 2-yr winter wheat double-crop soybean-corn rotation. The three N management treatments were applied to 0.37 ha plots in a randomized complete block design with 10 replications. Groundwater NO3-N and water table depth were measured every two weeks at 60 well nests (two per plot) sampling 0.9- to 1.8-, 1.8- to 2.7-, and 2.7- to 3.7-m depths from 2001 to 2003. We developed a statistical procedure for selecting an appropriate covariance model in randomized complete block analyses in the presence of spatial correlation. When warranted, incorporating spatial covariance in the statistical analysis provides greater efficiency in estimating treatment effects. Elevations, soil organic matter (SOM), and water table elevations (WTE) were spatial covariates used for explaining NO3-N spatial correlation. Compared to RYE, SS achieved: (i) less groundwater NO3-N by reducing fertilizer N and increasing the harvest N ratio (the ratio of N harvested in grain or forage to the total fertilizer N applied) for wheat in 2001; (ii) increased yield associated with higher N applied and decreased harvest N ratio for corn in 2002; and (iii) increased yield associated with similar fertilizer N and increased harvest N ratio for wheat in 2003. Overall, FA performed similarly to SS for wheat, but differed greatly for corn due to an overapplication of N at tasselling. These results indicate that RS-informed SS and FA might improve groundwater quality with no sacrifice in yield, or increase grain yield with similar fertilizer N compared to RYE-based N recommendations in the Coastal Plain. Mean NO3-N concentrations averaged over sampling depth at each well nest showed clear temporal fluctuations and were positively correlated with WTE. Groundwater NO3-N was frequently spatially correlated and spatial covariance structure changed periodically. The spatial correlation range varied over time from 46 to 551 m, and appeared to follow the trend of the mean water table depth. Blocking alone or together with elevation, SOM, and WTE frequently explained NO3-N spatial correlation. Our data suggest that to assess the environmental efficacy of N management, frequent and periodic monitoring of groundwater NO3-N, especially after significant rainfall, is essential to capture in-season treatment effects. Simultaneous measurement of precipitation and water table depth facilitate understanding of these effects. The traditional sampling of NO3-N only at or after harvest is likely to be insufficient to capture the entirety of treatment effects throughout the growing season. This is especially true in coastal plain and other coarse-textured soils where in-season NO3-N leaching may be pronounced. Our data also suggest that residual effects of differential N management may appear long after N application, even on these coarse-textured soils, indicating a need for longitudinal sampling.