Abinash Agrawal (Committee Member), Songlin Cheng (Committee Member), Doyle Watts (Advisor)
Master of Science (MS)
The purposes of the following studies were to investigate natural and human influences on several spatial and temporal aspects of a local and regional environment. The decreasing discharge rate of the ground water supplied Yellow Spring may be caused by the increase in population of the nearby Village of Yellow Springs, Ohio. Periodic measurements of Yellow Spring's discharge rate compared to changes in the town's population showed an inverse relationship, where spring discharge declined as population grew. A sharp decrease in discharge occurred during a period when the spring's facade was modified and an airport was built partially overlying the spring's recharge area. These events are believed to have had a greater impact on spring discharge rate than changing population because discharge rate remained relatively constant after its sharp decline, while population began to decline. Aquifer volume change was determined by calculating the volume difference between decadal average water tables that were modeled with ArcMap from water well depth to water measurements and LiDAR elevation data. Counterintuitively, aquifer volume generally increased with population then fell sharply as the population gradually decreased. A slight increase in aquifer volume after withdrawal wells were installed suggests that human consumption had little impact on aquifer volume. When compared to the average Palmer Hydrological Drought Index, aquifer volume generally lowered during dry periods, and rose during wet periods. Minor variations in climate can greatly impact aquifer volume because precipitation only needed to have decreased by 0.26 percent over a 40 year period to account for the lowest calculated aquifer volume. Determining the composition and spatial extent of land uses through land use classification increases our understanding of processes that are harmful to the environment. Because of LiDAR's high spatial resolution, the ability to classify marginally rural land uses of Greene County, Ohio with LiDAR intensity data was assessed to improve the accuracy of land uses previously classified from lower resolution satellite images. Trends in frequency distributions of intensity values extracted from sample sites of six major land uses reveal that LiDAR, measuring in the near-infrared (1064 nm), is spectrally insufficient to distinguish between land use elements (grass, trees, pavement, buildings, etc.), where each intensity value identifies between 3 and 6 land use elements. Land use elements with the same intensity values can be distinguished when remotely sensed data of other wavelengths are added to create spectral variation. The ability to classify land uses with LiDAR intensity data is further reduced by its poor temporal resolution and large file size. LiDAR surveys are typically conducted in early spring when trees are leafless to allow for ground elevation measurements in forested areas. LiDAR .las files are large because of its high spatial resolution, and require significant computing resources to process.
Department or Program
Department of Earth and Environmental Sciences
Year Degree Awarded
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