Master of Science, University of Toledo, 2019, Geology

Understanding wetland water inundation and vegetation changes over time is an important aspect of wetland research and management. Remote sensing offers a way to not only monitor changes in wetland water storage over time, but to classify emergent macrophyte and terrestrial vegetation habitats. In the past, satellite and aerial imagery have been used to classify wetland and upland areas with success; however, satellite imagery must often be refined and paired with other datasets due to lower spatial resolution, and aerial imagery is costly and often unavailable to wetland managers. The advancement of small unmanned aerial systems (sUAS) presents an opportunity to acquire frequent high-resolution imagery of wetland areas for a lower cost. SUAS can be deployed at the study area quickly, and with less intensive crew and equipment requirements than aerial imagery. Mapping wetland water storage and emergent vegetation at a barrier-protected estuarine wetland along the coast of Lake Erie in north-central Ohio was accomplished using a commercial sUAS platform paired with a multispectral MAPIR Survey 3W camera. The objectives of this study were to assess the effectiveness of this low-cost sensor for wetland monitoring applications, namely measuring vegetation extent and density changes and mapping short-term wetland water inundation over time to derive water storage parameters. To derive vegetation extent and density, the Normalized Difference Vegetation Index (NDVI) was utilized using a ratio of the Survey 3W's red (660 nanometers) and near-infrared (850 nanometers) bands. Water inundation was derived using the Normalized Difference Water Index (NDWI), which uses the ratio between the green (550 nanometers) and near-infrared bands. The method discussed in this study produced seven calibrated red/green/near-infrared (RGN) maps, from each of which an NDWI and NDVI map were created, and seven red/green/blue (RGB) maps. The RGN maps were calibrated to bottom-of-atmosphere (open full item for complete abstract)

Committee: Richard Becker (Committee Chair); Song Qian (Committee Member); James Martin-Hayden (Committee Member); Kevin Czajkowski (Committee Member) Subjects: Aquatic Sciences; Earth; Environmental Science; Geographic Information Science; Hydrologic Sciences; Hydrology; Natural Resource Management; Remote Sensing; Technology; Water Resource Management

Master of Science (MS), Bowling Green State University, 2019, Geology

The application of remote sensing techniques in mapping, classifying and monitoring land cover, land use and vegetation are popular among the researchers and scientists for several decades. It became more productive and economical in recent years with the advancement of information technology in a sophisticated and revolutionary manner. Currently, remote sensing is a widely used effective technique that provides spatial and temporal information about vegetation and invasive species in wetlands. The first objective of this study was to assess the effectiveness of the data obtained via Unmanned Ariel Vehicles (UAV) to identify invasive Phragmites australis in the Old Woman Creek (OWC) in Ohio. Secondly, the study aimed to determine the most suitable algorithm to distinguish between Phragmites australis and other vegetation types using pixel based and object based classification methods and a combination of feature layers derived from the UAV images. Pixel based classification found to be performing better than object based classification. Pixel based Neural Network (NN) was identified as the best classifier to map Phragmites in OWC with the least error of omission of 1.59% and the overall accuracy of 94.80% based on the Sequoia image acquired in August that was stacked with Canopy Height Model (CHM) from August and NDVI, which was derived using UAV data acquired in October (NDVIOct). The study emphasizes the necessity of a suitable sampling method and the use of optimum parameters of non-parametric classifiers. The study provides future directions for data acquisition to map Phrgamites at early and mid-summer to find data to eradicate Phragmites effectively in OWC estuary.

Committee: Anita Simic Milas Ph.D. (Advisor); Andrew Gregory Ph.D. (Committee Member); Angelica Vazquez-Ortega Ph.D. (Committee Member) Subjects: Ecology; Environmental Science; Geography; Remote Sensing

PHD, Kent State University, 2016, College of Arts and Sciences / Department of Earth Sciences

Various techniques for assessing, monitoring, and predicting algal blooms in an estuarine ecosystem are analyzed. In one section, routine water samples are collected at previously established monitoring sites in Old Woman Creek, filtered onto a 47 mm, 0.7 µm glass-fiber filter (GF/F), and then measured using a visible/near-infrared spectrophotometer. Varimax-rotated principal component analysis (VPCA) is applied to reflectance data and then used to quantify and identify pigments, phytoplankton taxa, and sediments by comparing the measured spectral signatures to known standards. Common assemblages that are reported throughout the three-year study include: bacillariophyceae (diatoms), chlorophyta (green algae), cyanobacteria (blue-green algae), and illite. A similar approach is taken in the next section by applying multivariate statistics to Landsat 8 satellite imagery in order to determine the distribution of in-water constituents at a high spatial resolution. Only four bands in the visible range are available for this analysis, but it is possible to identify several of the same groups of algae and sediments, providing a useful complement to the hyperspectral work. Finally, a bloom prediction model based on springtime discharge is created by applying VPCA to in-water sonde data from one of the monitoring sites at Old Woman Creek during a recent 11-year time period. In this model, a proxy for net community production (NCP) is determined using oxygen and pH dynamics and then compared to daily rates of streamflow. Possible monthly sequences between January and June are considered in order to determine which timeframe is the best indicator of the average annual NCP. Time of day (daytime versus nighttime) and mouth bar conditions (barrier beach present versus absent) are important factors in determining production in the estuary. Based on the results, the best predictor for NCP is stream discharge from March through May, which produces correlations that are significant at (open full item for complete abstract)

Committee: Joseph Ortiz (Advisor); Anne Jefferson (Committee Member); Alison Smith (Committee Member); Darren Bade (Committee Member) Subjects: Aquatic Sciences; Biological Oceanography; Environmental Geology; Environmental Science; Geology; Limnology; Water Resource Management

MS, Kent State University, 2013, College of Arts and Sciences / Department of Earth Sciences

This study provides a useful comparison between traditional fluorometric methods of testing for algal contamination and a newer analytical technique that has been developed for assessing water quality. This new technique, referred to as visible/near-infrared (VNIR) derivative spectroscopy, uses multivariate statistics to rapidly identify and quantify the distribution of phytoplankton in aquatic systems. Unlike traditional methods, VNIR derivative spectroscopy does not require chemical reagents can thus be considered easier, quicker, and more cost-effective to use. Samples are filtered onto a 47 mm, 0.4 um glass-fiber filter (GF/F), dried, and measured using a VNIR spectrophotometer. This results in a hyper-spectral reflectance recording for each sample (400-2500 nm). Statistics then provide a means by which to separate out important pigment classes. Reflectance data may also be converted to chlorophyll a concentrations using wavelength index numbers, allowing for independent techniques of measurement to be compared. In this study, an in-situ Hach hydrolab sensor and a Trilogy laboratory fluorometer were used to obtain direct chlorophyll a concentrations. A correlation value of 0.90 was found between these two methods. Comparison of reflectance data with Hach and Trilogy measurements also produced good results, with correlation values of 0.82 and 0.64, respectively. This study took place over the summer of 2011 in a dynamic estuary on the southern central shore of Lake Erie. A dramatic change occurred halfway through the study period when water passage into Lake Erie was prohibited due to sediment accumulation at the mouth bar. Multivariate statistics of reflectance data suggest there was a shift in which in-water constituents were responsible for determining the optical variability of the estuary when this change took place. Clay, chlorophyceae (green algae) and bacillariophyceae (diatoms) were found to be the most important in-water constituents during the fu (open full item for complete abstract)

Committee: Joseph Ortiz Ph.D. (Advisor); Elizabeth Griffith Ph.D. (Committee Member); David Hacker Ph.D. (Committee Member) Subjects: Aquatic Sciences; Environmental Studies; Geology; Limnology; Water Resource Management

Master of Science in Civil Engineering, University of Toledo, 2011, Civil Engineering

The Old Woman Creek watershed, located in the south-western basin of Lake Erie is one of its concerns for the amounts of sediments, nutrients, and other chemical discharges into Lake Erie. BASINS, a watershed-scale model, was used along with its internal models, HSPF and PLOAD, to simulate flow and pollutant loads from the Old Woman Creek watershed. Physical properties of soil and land use, meteorological data and, observed flow data were collected for a 6-year period from 2001 to 2006 and are used in the model development and validation. The model was calibrated for four years (2000-01 to 2003-04) and validated for another two years (2004-05 and 2005-06). For the calibration period, the correlation between the observed and simulated daily runoff was strongly accurate, as shown by the coefficient of determination value of 0.77. The coefficient of determination was 0.81 for the validation period. The Nash-Sutcliffe coefficients obtained were 75.5% and 79.7% for the calibration and validation period, respectively. The model was run and calibrated by adjusting lower zone evapotranspiration parameter (LZETP), fraction of groundwater inflow to deep recharge (DEEPFR), lower zone nominal soil moisture storage (LZSN), index to mean soil infiltration rate (INFILT), groundwater recession rate (AGWRC), interflow coefficient (INTFW) and, interflow recession coefficient (IRC). Calibration of these parameters improved the model simulation of flow as compared to the initial model run. The calibrated model was applied to the validation data set. The developed watershed model predicts the flow and pollutant loads and concentration. The model had a commendable success in the prediction of flow, which was calibrated and validated. The pollutant loads and concentration could not be validated because of the lack of observed data for the time series. The developed model was a strong success since all the model performance statistical parameters displayed strong accuracy comparable to the (open full item for complete abstract)

Committee: Andrew Heydinger Dr. (Advisor); Ashok Kumar Dr. (Committee Member); Brian Randolph Dr. (Committee Member) Subjects: Civil Engineering; Environmental Engineering

PHD, Kent State University, 2007, College of Arts and Sciences / Department of Earth Sciences

This study determined the capability of moderate resolution satellite imagery of 30 meter pixel dimension to investigate the spatial and temporal changes of Old Woman Creek National Estuarine Research Reserve, which is a dynamic coastal wetland of Lake Erie. Water quality and land cover reflectance data is interpreted with respect to in-situ sample measurements collected every 16 days in coincidence with the Landsat-5 TM over passing days mainly in summer 2005 and 2006. The study involved a variety of qualitative and quantitative, physical and remote sensing measurements generated from surface water and its constituents, aquatic emergent and terrestrial macrophytes, exposed mudflats, and radiometrically corrected Landsat-5 TM imagery. The prevailing environmental and climatic conditions of the area regulated the spatial and temporal variability of those land cover types.The study developed a suspended sediment concentration calibration method and two land cover variability mapping methods using Landsat-5 TM data. The two wetland mapping methods are based on principal component analysis (PCA) and scattergram segmentation of selected normalized difference remote sensing indices. In addition, the mineralogy and morphology of suspended particulates were investigated using an X-Ray diffraction (XRD) technique and environmental scanning electron microscopy (ESEM) which revealed the dominance of silica and calcite in surface water.The surface water samples provided total suspended particulate concentration (TSP) measurements which reported 0.7 correlation against normalized difference water index (NDWI) of bands 1 and 5, establishing a model to quantify TSP concentration in surface water. The principal component analysis (PCA) extracted endmember land covers reporting 87 % of total variance and their spatial and temporal distribution was mapped in order to identify the seasonal variability of macrophytes, open-water, and exposed ground. One dimensional spaces of normalized (open full item for complete abstract)

Committee: Joseph Ortiz (Advisor) Subjects: