Master of Science (MS), Bowling Green State University, 2018, Geology

Evapotranspiration (ET) is a hydrologically and eco-agronomically important process that can be altered by soil properties, crop type and mechanisms of photosynthesis (e.g. C3 and C4), crop status, agricultural practices (crop rotation and monoculture), and meteorology. In particular, corn monoculture, which is widely used in the U.S, may affect over agricultural fields differently than soybean and wheat deu to the different (C4) photosynthesis mechanism, and thus can have an impact on local hydrologic cycle and climate. Satellite observations are the most sophisticated technology to monitor different rates of ET at large scale. This study used data from two satellites, Landsat 8 and Sentinel 2, to examine the capability of combining those data in ET time series to explore the differences between ET rates for C3 (soybean and winter wheat) and C4 (corn) crops. ET was estimated for a study area located in the Western Lake Erie basin for 2016 and 2017 using satellite data and the Boreal Ecosystem Productivity simulator (BEPS), a process based ecosystem model, modified for the agricultural ecosystem. Satellite images (from which land cover/land use data, and leaf area index were generated), weather (Gridmet data), and soil data (SSURGO data) were main inputs to BEPS. In addition, a sensitivity analysis was conducted to estimate ET for different percent increments of the total area covered by corn to the point of becoming a monoculture using synthetically developed land covers and LAI images. For both years, corn and soybean reach the maximum ET rate in the mid-growing season as expected with the peak being somewhat later in the season for soybean. The ET relationship between two sensors was strong during the mid-season (r = 0.95 for July) when LAI was high, and at the end of the season, when many crops were harvested and soil exposed (r = 0.98 for iv October). A high correlation was also observed when data were acquired within a short period of time (open full item for complete abstract)

Committee: Anita Simic Dr. (Advisor); Peter Gorsevski Dr. (Committee Member); Ganming Liu Dr. (Committee Member) Subjects: Agriculture; Agronomy; Earth; Ecology; Environmental Geology; Geobiology; Geology; Remote Sensing

PhD, University of Cincinnati, 2010, Arts and Sciences : Geography

Unchecked human development has ravaged the region between Dominical and Uvita, Costa Rica. Much of the development transition has been driven by tourism and further foreign direct investment in residential, service and commercial enterprises. The resulting land-use/land-cover change has removed traditional forest cover in exchange for impervious surfaces, physical structures, and bare ground which is no longer mechanically supported by woody vegetation. Combined with a tropical climate, deeply weathered soils and lithography which are prone to erosion, land cover change has led to an increase in slope failure occurrences. Given the remoteness of the Dominical-Uvita region, its rate of growth and the lack of monitoring, new techniques for monitoring land use and slope failure susceptibility are needed. Two new indices are presented here that employ a Digital Elevation Model (DEM) and widely available Landsat imagery to assist in this endeavor. The first index, or Vegetation Influenced Landslide Index (VILI), incorporates slope derived from a DEM and Lu et al.'s (2007) Surface Cover Index to quantify vegetative cover as a means of mechanical stabilization in landslide prone areas. The second index, or Slope Multiplier Index (SMI), uses individual Landsat data bands and basic Landsat band ratios as environmental proxies to replicate soil, vegetative and hydrologic properties. Both models achieve accuracy over 70% and rival results from more complicated published literature. The accuracy of the indices was assessed with the creation of a landslide inventory developed from field observations occurring in December 2007 and November 2008. The creation of these indices represents an efficient and accurate way of determining landslide susceptibility zonation in data poor areas where environmental protection practitioners may be overextended, under-trained or both.

Committee: Nicholas Dunning PhD (Committee Chair); Kenneth Hinkel PhD (Committee Member); Robert Frohn PhD (Committee Member); Mark Bowers PhD (Committee Member) Subjects: Geography

Doctor of Philosophy, The Ohio State University, 2005, Agricultural, Environmental and Development Economics

As smallholder farmers in Zimbabwe face inevitable drought, the need to develop drought-mitigation strategies and risk transfer mechanisms becomes an important and challenging task for policy makers. Rather than treating drought as a natural disaster that warrants emergency declarations whenever it strikes, countries in Southern Africa could alter their policy to embrace drought as an integral part of their national policy framework. Although drought cannot be eliminated, its impact can be reduced through implementation of pro-active and pro-poor risk management policy programs. This study explored two potential policy programs. One program proposes wide-scale adoption of improved seasonal forecasts by smallholder farmers as a drought mitigation strategy, and the second program proposes implementation of area-yield drought-indexed insurance as a risk-transfer and risk-protection mechanism for the smallholder farmers. To investigate whether adoption of seasonal forecasts and drought insurance is possible in Zimbabwe this dissertation explored three hypotheses: First, do seasonal forecasts really matter to smallholder farmers in Zimbabwe? Second, given the prevalence of food-aid in Zimbabwe, does drought insurance really matter for smallholder farmers? Third, given drought is a catastrophic risk, will a drought-index insurance scheme intended for smallholder farmers be viable and/or feasible? The first two questions were empirically investigated via surveys based on the contingency valuation method (CVM). More than 1,000 smallholder farmers were surveyed throughout Zimbabwe's agro-ecological regions II-V where willingness-to-pay (WTP) for the proposed programs was elicited. With respect to the first hypothesis, results showed that for the improved seasonal forecasts program, estimated WTP (Z$) based on a single-bound model ranged from Z$2,427 to Z$4,676. For a double-bound model, WTP ranged from Z$2,532 to Z$4,225. A distinct differential pattern in WTP was observed a (open full item for complete abstract)

Committee: Brent Sohngen (Advisor) Subjects: Economics, Agricultural

Master of Science (MS), Ohio University, 2020, Geography (Arts and Sciences)

In this study it was investigated if ecoregion type and hurricane-induced vegetation damage are related to recovery period in landfall areas by observing similar and different intensity hurricanes making landfall in different and similar ecoregions. Understanding of the interaction between hurricane intensity and its effects on vegetation could potentially benefit hurricane management plans and policies by observing the trend in damage and recovery period. To analyze the relation between ecoregion and hurricane, this research analyzed two comparative case studies utilizing remote sensing-based satellite images and geographic information system (GIS) tools. Results from the considered cases indicate that there is not a one-to-one relation between ecoregion type and the damage-recovery pattern of hurricanes. It cannot be generalized that hurricanes would affect vegetation similarly in similar ecoregions or differently in different ecoregions. Rather, it was found that pre-existing conditions associated with local weather and climate events and storm-scale meteorological parameters were playing a more dominant role in the characteristics of the damage footprint on vegetation in the studied cases.

Committee: Jana Houser (Advisor) Subjects: Geographic Information Science; Geography; Remote Sensing

Master of Science (MS), Bowling Green State University, 2014, Geology

Leaf area index (LAI) is an important indicator of ecosystem conditions and an important key biophysical variable to many ecosystem models. The LAI in this study was measured by Leica ScanStation C 10 Terrestrial Laser Scanner (TLS) and a hand-held Li-Cor LAI-2200 Plant Canopy Analyzer for understanding differences derived from the two sensors. A total of six different LAI estimates were generated using different methods for the comparisons. The results suggested that there was a reasonable agreement (i.e., the correlations r > 0.50) considering a total of 30 plots and limited land cover types sampled. The predicted LAI from spectral vegetation indices including WDVI, DVI, NDVI, SAVI, and PVI3 which were derived from Landsat TM imagery were used to identify statistical relationships and for the development of the Bayesian inference model. The Bayesian Linear Regression (BLR) approach was used to scale up LAI estimates and to produce continuous field surfaces for the Oak Openings Region in NW Ohio. The results from the BLR provided details about the parameter uncertainties but also insight about the potential that different LAIs can be used to predict foliage that has been adjusted by removing the wooden biomass with reasonable accuracy. For instance, the modeled residuals associated with the LAI estimates from TLS orthographic projection that consider only foliage had the lowest overall model uncertainty with lowest error and residual dispersion range among the six spatial LAI estimates. The deviation from the mean LAI prediction map derived from the six estimates hinted that sparse and open areas that relate to vegetation structure were associated with the highest error. However, although in many studies TLS has been shown to hold a great potential for quantifying vegetation structure, in this study the quantified relationship between LAI and the vegetation indices did not yield any statistical relationship that needs to be further explore.

Committee: Peter Gorsevski PhD (Advisor); Anita Simic PhD (Committee Member); Kurt Panter PhD (Committee Member); Jeff Snyder PhD (Committee Member) Subjects: Environmental Geology; Geology

Doctor of Philosophy, University of Akron, 2013, Integrated Bioscience

Climate change threatens to alter the current distribution, productivity, and community composition of wetlands in the Midwestern United States. Increasing rainfall variability and rising temperatures will yield unique stresses for wetland vegetation, including an increase in flooding severity and a higher frequency of potentially harmful heat events. This dissertation explores the interactions and impacts of climate warming and hydrologic variability on productivity, morphological plasticity, reproduction, and functional composition within wetland communities, followed by an evaluation of the connection between wetland distribution and climate on a regional scale. Climate warming led to depressions in productivity during the warmest months while hydrologic variation consistent with climate projections yielded decreases in spring production and peak biomass. Reproductive allocation and other functional trait differences suggested that the future climate will limit productivity in many wetland ecosystems in the Midwest. A distribution model based on Artificial Neural Networks projected significant increases in flooding leading to wetland expansion concentrated in the Midwestern Corn Belt and potential declines in wetland area in Minnesota and northern Michigan. These results suggest that, though wetland area is projected to increase for the Midwest, without hydrologic management, many wetland systems are at risk of community turnover and degradation resulting from a shifting climate.

Committee: Randall Mitchell Dr. (Advisor); Linda Barrett Dr. (Committee Member); Lauchlan Fraser Dr. (Committee Member); Stephen Weeks Dr. (Committee Member); Gregory Smith Dr. (Committee Member); John Senko Dr. (Committee Member) Subjects: Ecology; Geographic Information Science; Geography

Doctor of Philosophy (PhD), Wright State University, 2010, Environmental Sciences PhD

We investigated water loss by evapotranspiration (ET) from the Palo Verde Irrigation District (PVID) and the Cibola National Wildlife Refuge (CNWR) in southern California bordering the Colorado River collaborating with the United States Bureau of Reclamation (U.S.B.R.). We developed an empirical model to estimate ET for the entire PVID using satellite derived MODIS enhanced vegetation index (EVI), and ground based measurements of solar radiation and vapor pressure. We compared our predictions with U.S.B.R. estimates through statistical cross validation and showed they agree with an error less than 8%. We tested the same model for an alfalfa field inside PVID to check its applicability at a smaller spatial scale. We showed that the same model developed for PVID is the best model for estimating ET for the alfalfa field. We collected data from three Bowen ratio energy balance (BREB) towers installed in the invasive saltcedar (Tamarix spp) dominated riparian zone in the CNWR and a fourth tower in the alfalfa field in PVID. The riparian sites were selected according to different densities of vegetation. We collected data from these sites at various intervals during the period between June 2006 to November 2008. We reduced the errors associated with the Bowen ratio data using statistical procedures taking into account occasional instrument failures and problems inherent in the BREB method. Our results were consistent with vegetation density and estimates from MODIS EVI images. To estimate ET for larger patches of mixed vegetation we modified the crop coefficient equation and represented it in terms of EVI. Using this approach, we scaled the alfalfa field data to the entire PVID and compared the results with U.S.B.R. (2001-2007) estimates. We predicted ET well within the acceptable range established in the literature. We empirically developed ET models for the riparian tower sites to provide accurate point scale ET estimation and scaled for the entire riparian region in CN (open full item for complete abstract)

Committee: Doyle Watts PhD (Committee Chair); Subramania Sritharan PhD (Committee Co-Chair); Thaddeus Tarpey PhD (Committee Member); Abinash Agrawal PhD (Committee Member); G. Allen Burton PhD (Committee Member) Subjects: Atmosphere; Environmental Science

Master of Arts (MA), Ohio University, 2002, Geography (Arts and Sciences)

Remote sensing and GIS offer useful tools for monitoring vegetation health and riparian coverage in the Monday Creek Watershed. Monitoring programs evaluate environmental plan success and measure indicators to identify change. This research demonstrates how remote sensing can be integrated into a local watershed management process and applies the technology as a tool to effectively monitor selected indicators that direct plan implementation. Data includes 2 anniversary date Landsat satellite images and digitized GIS watershed layers. A 3-phase methodology involves land use/cover mapping, normalized difference vegetation index (NDVI) analysis and change detection through cross tabulation. Forty meter stream buffers assist riparian habitat analysis. Image Classification utilized an unsupervised K-means algorithm. Analysis of regional and subwatershed conditions shows that vegetation overall remains healthy although a slight decline in regional vegetation health is visible, especially along roads and settlements. Forest cover increased, forest fragmentation declined and trouble spots in subwatersheds are identified.

Committee: James Lein (Advisor) Subjects: Geography

Master of Environmental Science, Miami University, 2008, Environmental Sciences

Remote sensing has been used to directly detect and map invasive plants, but has not been used for forest understory invaders because they are obscured by a canopy. However, if the invasive species has a leaf phenology distinct from native forest species, then temporal opportunities exist to detect the invasive. Lonicera maackii, an Asian shrub that invades North American forests, expands leaves earlier and retains leaves later than native woody species. I explored whether Landsat 5 TM and Landsat 7 ETM+ imagery could predict L. maackii cover across woodlots in Darke and Preble Counties in south western Ohio and Wayne County, Indiana. The best predictor of L. maackii cover was Normalized Difference Vegetation Index (NDVI) from November 2005, with a quadratic function providing a better fit (R2 = 0.75) than a linear function. This predictive model was verified with 15 other woodlots. With refinement, this approach can map understory invasion by L. maackii.

Committee: David L. Gorchov PhD (Advisor); Mary C. Henry PhD (Committee Member); Jerry E. Green PhD (Committee Member) Subjects: Ecology; Forestry; Remote Sensing