Master of Arts, Miami University, 2024, Geography

Rapid urbanization and the Urban Heat Island (UHI) effect are key global challenges, particularly in the Global South. Dhaka City, Bangladesh, one of the fastest growing and most densely populated cities in this region, facing increasing heat trends which threaten its public health, social and environmental conditions. Mitigating future UHI requires a geospatial analysis of urbanization and temperature trends at the neighborhood-scale. However, proprietary geospatial data and applications are often prohibitively costly for planners. This study combines cost-effective, publicly-accessible geospatial applications and time series satellite data from 2003 - 2023 to analyze land use and land cover (LULC) and land surface temperature (LST) changes in Dhaka City at regional and neighborhood-scales. For monitoring LULC, Landsat images were analyzed through supervised classification at a regional scale, and spectral mixture analysis (SMA) helped understand complex urban development patterns at the neighborhood-scale. This study analyzed MODIS daily LST images to understand diurnal temperature trends and found a strong positive correlation between urban development intensity and increased day and nighttime temperatures, contributing to neighborhood-specific UHI impacts. The study emphasizes the importance of developing publicly-accessible and inexpensive geospatial methods to support UHI mitigation planning that can benefit other similar cities.

Committee: David Prytherch Dr. (Advisor); Jessica McCarty Dr. (Committee Member); Robbyn Abbitt Ms. (Committee Member); John Maingi Dr. (Committee Member) Subjects: Climate Change; Environmental Studies; Geographic Information Science; Geography; Remote Sensing; Urban Planning

MS, University of Cincinnati, 2024, Engineering and Applied Science: Environmental Engineering

Harmful algae blooms in surface waters are a global environmental concern and threaten both human and environmental health. By outcompeting aquatic diversity, causing dissolved oxygen levels in surface waters to fall, and secreting toxins, algae blooms stress water treatment infrastructure and result in large economic losses. To control and manage the impact of harmful algae blooms, timely detection and monitoring is critical. However, current monitoring methods, such as permanent monitoring stations or water sampling, can be very costly or time-intensive, and require direct water access. The methods become dangerous or impractical in areas surrounded by cliffs or wetlands. In this study, a flexible, efficient, and cost-effective approach for monitoring surface water quality was developed by integrating water quality sensors and unmanned aerial vehicles (UAV). The integration platform was designed, constructed, and deployed through the summer of 2023 to monitor chlorophyll, phycocyanin, and turbidity in William H. Harsha Lake of Clermont County, Ohio. The water quality parameters, used as an indicator of algae blooms, were then correlated to reflectance from Landsat 8 and 9 and Sentinel 2 satellites through single and multiple linear regressions. Multiple linear regressions using reflectance from Sentinel 2 satellites yielded the highest correlations between reflectance and water quality, with R2 values of 0.70, 0.86, and 0.97 for chlorophyll, phycocyanin and turbidity, respectively. From the regressions, visible, near infrared, and red-edge bands were identified as useful for algae detection, and commercially available multispectral cameras capable of integration with UAVs were identified for future improvement of the UAV monitoring platform.

Committee: Dongmei Feng Ph.D. (Committee Chair); Richard Beck Ph.D. (Committee Member); Drew McAvoy Ph.D. (Committee Member) Subjects: Environmental Engineering

Doctor of Philosophy, The Ohio State University, 2023, Environmental Science

The Arctic region has been reported to have rising temperature at a much faster rate than the global average in response to recent warming climate, which coincides with a large-scale increase in satellite-observed vegetation indices (i.e., normalized difference vegetation index, NDVI), known as the “Arctic greening”. The positive feedbacks associated with the greening have been projected to override its cooling effect, and to amplify the regional warming in the near future as a result of accelerated carbon loss (net difference between carbon emission and carbon sequestration) through thawing of permafrost soils. However, not all areas in the Arctic region are greening. There have emerged studies highlighting a reversal or slowdown in the NDVI trend in some regions (i.e., “Arctic browning”) due to reduced water availability or extreme events. For a better projection of Arctic carbon fate, it is necessary to unravel the mechanical links between in-situ and satellite observations. In this regard, the Arctic greening/browning trends are often attributed to increased/decreased biomass, canopy cover, or canopy height. However, these attributions sometimes fail to be validated by in-situ observations due to several known issues associated with NDVI. Given this, this dissertation seeks to develop satellite-based approaches that enable regional-scale mapping of multi-dimensional components of boreal forests that can be validated by airborne measured counterparts. The mapped multi-dimensional forest components include the horizontal (forest cover), vertical extent (forest height) of forest canopy structure, and the geographical extent (timberline, i.e., frontiers of continuous forests). First, we proposed a multispectral unmixing based tree fractional cover (fCover) estimation approach using Landsat-8 imagery by further considering local endmember variability and spatial contextual information. Our results demonstrate the effectiveness of the proposed method in enhancing t (open full item for complete abstract)

Committee: Desheng Liu (Advisor); Rongjun Qin (Committee Member); Ian Howat (Committee Member); Gil Bohrer (Committee Member) Subjects: Environmental Science; Remote Sensing

Master of Science (MS), Wright State University, 2021, Earth and Environmental Sciences

The cumulative number versus floe area distribution of seasonal ice floes from four satellite images covering the Summer season (November - February) in the Weddell Sea Antarctica during the summer breakup and melting is fit by two scale-invariant power scaling regimes for the floe areas ranging from 7 to 20 x 108 m2. Scaling exponents, β, for larger floe areas range from -1.5 to -1.7 with an average of -1.6 for floe areas ranging from 6 x 106 to 55 x 107 m2. Scaling exponents, β, for smaller floe areas range from -0.8 to -0.9 with an average of -0.85 for floe areas ranging from 3 x 106 to 1.55 x 106 m2. The inflection point between the two scaling regimes ranges from 62 x 106 to 151 x 106 m2 and generally moves from larger to smaller floe areas through the summer season. We propose that the two power scaling regimes and the inflection between them are defined during the initial breakup of sea ice solely by the process of fracturing. The distributions of floe size regimes retain their scaling exponents as the floe pack evolves from larger to smaller floe areas from the initial breakup through the summer season, due to scale-independent processes including grinding, crushing, fracture, and melting. The scaling exponents for floe area distribution are in the same range as those reported in previous studies of Antarctic and Arctic floes. A probabilistic model of fragmentation is presented that generates a single power scaling distribution of fragment size.

Committee: Christopher Barton Ph.D. (Advisor); Sarah Tebbens Ph.D. (Committee Member); Doyle Watts Ph.D. (Committee Member) Subjects: Earth; Remote Sensing

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

Doctor of Philosophy, The Ohio State University, 2020, Geography

Remote Sensing offers a unique perspective of our Earth and is crucial for managing its resources. Currently, there is no single satellite data product that is suitable for all applications. Satellite data are limited by their spatial, spectral, and temporal resolution. Additionally, satellite images can be affected by sensor noise and cloud cover. One of the solutions to overcome these limitations is by combining existing satellite products to minimize the drawbacks of a dataset. In this dissertation, we improve the spatial and temporal resolution of satellite data products, minimize sensor noise, and remove cloud cover from satellite images by combining data from multiple satellite sensors using deep learning methods. Deep learning has been successful in natural image superresolution, denoising, and translation and these methods perform efficiently given sufficiently large datasets and computational resources. Therefore, publicly available satellite datasets and recent computational advancements provide an ideal opportunity for applying deep learning for our tasks. In our first study, we downscale low resolution optical and thermal spectral bands of MODIS to match higher resolution NIR and Red bands. Information extraction from satellite data often requires the combined use of multiple spectral bands. Usually, the low-resolution bands are downscaled using naive interpolation methods or high-resolution bands are upscaled to create spectral indices. We train a deep learning model for downscaling MODIS spectral to create a spatially consistent MODIS dataset. Our model is compared to a state-of-the-art satellite image downscaling method and a deep learning image superresolution method. Additionally, we investigate the importance of prior natural images towards downscaling satellite images. Next, we increase the effective spatial resolution and denoise MODIS spectral bands with the help of Landsat 8 images. MODIS and Landsat 8 have similar measurement principles and (open full item for complete abstract)

Committee: Desheng Liu Dr (Advisor); Alvaro Montenegro Dr (Committee Member); Srinivasan Parthasarathy Dr (Committee Member); Rongjun Qin Dr (Committee Member) Subjects: Geographic Information Science; Geography; Remote Sensing

MA, Kent State University, 2019, College of Arts and Sciences / Department of Geography

Deforestation is a major driver of global climate change and the causes and consequences of deforestation are largely societal. Forested areas in the Russian Federation have a particularly important role, mainly due to the size, location, and growth periods of the boreal, coniferous, and deciduous forests. Understanding the causes of deforestation also requires a comprehension of the changes that have occurred since the collapse of the Soviet Union. In the nearly twenty-five years, which have passed since the dissolution of the Union of Soviet Socialist Republics (USSR), a number of political, economic, and social dynamics have changed the landscape of the country both physically and institutionally. Two Russian cities, Moscow and Vladivostok, will serve as comparative case studies of the human environment dynamics across different natural environments, economic industries, and population centers in the country. In order to assess how human dimensions like urban expansion, supply and demand, and national/regional forest sector legislation have influenced land cover change; a mixed methods investigation is deployed. The investigation depends on both spatial evidence of land cover changes via remote sensing and analysis of human drivers such as policy, markets, and agriculture. Landsat images will be analyzed using normalized difference vegetation index (NDVI) and other classification queries. Content analysis of national forest policy will also serve to bolster where and why deforestation occurred. It is expected that deforestation is an outcome of complex social processes and in most cases the drivers of land cover change are multi-dimensional and require moving beyond analysis of single causal mechanisms such as urban expansion through the clearing of forested land. In other words, deforestation is not simply driven by proximate causes such as the cutting down of trees for usage elsewhere or the opening of new land for use. The clearing of forests in Russia is one (open full item for complete abstract)

Committee: James Tyner (Advisor); V. Kelly Turner (Committee Member); Mandy Munro-Stasiuk (Committee Member) Subjects: Geography

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

BS, Kent State University, 2018, College of Arts and Sciences / Department of Earth Sciences

Nearly a quarter of ocean species are confined to coral reefs, making reefs important environmental resources. Increases in development and changes in land use have created an influx of sediment and nutrients entering the coastal waters of the US Virgin Islands (USVI), causing detrimental effects on water quality. As a consequence, coral reefs have started to degrade. We employ remote sensing as a method of water quality monitoring, which offers a spatial advantage and cost effective alternative over traditional water quality monitoring. This study integrates NASA Landsat 8 (L8), Landsat 7 (L7), and Landsat 5 (L5) data with field spectroscopy in order to determine bio-optical properties and to quantify changes in water quality parameters that affect coral reef health in the USVI. Surface reflectance imagery was collected for clear days from January 1985 through May 2015 and from August 2016 through May 2017. The images were systematically analyzed by taking the derivative of the measured visible/near infrared spectra and then using Varimax-rotated Principal Component Analysis (VPCA) decomposition to identify color producing agents (CPAs) in the water column. VPCA loadings were standardized and results were matched to libraries of reflectance derivative spectra for known pigment and mineral standards. Research campaigns were conducted to provide in-situ Chlorophyll-a (Chl-a), Colored Dissolved Organic Matter (CDOM), turbidity, and Total Suspended Solid (TSS) measurements for comparison with the VPCA-decomposed imagery. Water samples were gravimetrically filtered and analyzed by colleagues at the College of Charleston, South Carolina. Results characterize present water quality trends and show changes in the spatial distribution of CPAs over time, which may suggest changes in coastal water quality. The detection and analysis of water quality parameters is a necessity in current and future remediation efforts and the KSU spectral decomposition method will likely prove (open full item for complete abstract)

Committee: Joseph Ortiz (Advisor); Alison Smith (Committee Member); Daniel Holm (Committee Member); Mahbobeh Vezvaei (Committee Member) Subjects: Remote Sensing

Doctor of Philosophy, The Ohio State University, 2017, Geodetic Science and Surveying

As the largest low-lying river delta in the world, located at the confluence of the mighty Ganges-Brahmaputra-Meghna rivers, and as one of the most densely populated countries with more than 163 million people, Bangladesh already faces tremendous vulnerability. Accelerated sea-level rise, along with tectonic, sediment load and groundwater extraction induced land uplift/subsidence, have exacerbated Bangladesh's coastal vulnerability. Climate change has further intensified these risks with increasing temperatures, greater rainfall volatility, and increased incidence of intensified cyclones and cyclone-induced storm surges, in addition to its seasonal transboundary monsoonal flooding, tides, large seasonal river discharges along with the associated sediment transport causing load/compaction of the coastal regions. As a result, Bangladesh coastal region has become the most dynamic region with the highest erosion and accretion rate in the world. For decades, the shape of the shoreline has changed greatly affecting millions of people living in the region. Our objective is to quantify the long-term or multi-decadal, seasonal shoreline changes for coastal Bangladesh to assess the impacts of the complex geophysical and climatic processes. In this study, the shoreline from 1970's to the year 2017 are extracted from a four-decade time-series of Landsat imagery. An automated shoreline extraction method based on Google Earth Engine (GEE) Application Programming Interface (API) is developed and applied to quantify Bangladesh coastal shoreline changes. This method involves Normalized Difference Water Index/Modified Normalized Water Index (NDWI/MNDWI) and the Otsu Threshold Method to enhance the accuracy of the digital imagery processing. The extracted Landsat imagery shorelines in three example regions are validated by comparing with independent DigitalGlobe and with CNES/Airbus higher resolution imagery at several m using Google Earth (GE). We concluded that the extracted Land (open full item for complete abstract)

Committee: Che-Kwan Shum (Advisor); Michael Thomas Durand (Committee Member); Alan John Saalfeld (Committee Member) Subjects: Earth; Geophysical

Master of Science (MS), Bowling Green State University, 2016, Geology

The oil and gas booms of the late 19th century left tens of thousands of wells in Wood County, Ohio abandoned and improperly capped. This allows hydrocarbons to seep into the surrounding soil. Detection of these wells proves difficult because many of the wells are buried and their locations lost. To be able to detect the oil wells over large areas, different remote sensing techniques can be used to detect changes in soil properties caused by the presence of hydrocarbons. However, the capability of this technology depends on spatial and spectral resolution of a sensor and in situ data are often necessary. In this study, in-situ hyperspectral reflectance data and thermal imaging are used in conjunction with clay mineral X-ray diffraction analysis to identify soil properties around abandoned wells located in an agricultural area in Wood County, Ohio. This study is confirmation of previous finding and it serves to indicate uncertainties related to a limited sampling effort, and to address the importance of field sampling strategies and adequate remote sensing techniques. Non-commercial satellite based remote sensors of medium, spatial resolution, such as Landsat, are inadequate for detection of the small abandoned wells in Wood County, Ohio. In situ hyperspectral reflectance measurements, used to simulate WorldView-3 spectral and spatial resolution, suggest that this high spatial resolution commercial satellite is optimal for detecting small abandoned oil wells. It is confirmed that a spectral band ratio in the spectral range between 2.185-2.225 µm and 2.295-2.365 µm (WorldView-3 shortwave bands 6 and 8, respectively) is effective. The clay mineral X-ray diffraction analysis suggests that these changes in the spectral information occur predominately due to the hydrocarbons; clay mineral content changes in the soil did not affect the soil spectral signature to a greater extent. Thermal imaging identified higher surface temperatures in soil with higher hydrocarbon content (open full item for complete abstract)

Committee: Anita Simic (Advisor); Jeff Snyder (Committee Member); John Farver (Committee Member) Subjects: Geology; Remote Sensing

Master of Science (MS), Bowling Green State University, 2015, Geology

The aim of this study is to evaluate the effectiveness of remote sensing in distinguishing organic and conventional corn. The hypothesis of the research is that the difference between organic and conventional corn can be detected based on the dissimilarities in their vigor and maturity, which are commonly altered by different agricultural management and nutrient application to soil. Hyperspectral in situ measurements as well as multispectral reflectance along with narrow and wide band vegetation indices were assessed. Two available cloud-free Landsat 7 and Landsat 8 data sets were used in the analysis, one for the mid- growing season and another for the pre-harvest (maturity) season. Overall, the organic corn demonstrates higher values of chlorophyll- and nitrogen- related narrow band indices at the mid-season and at maturity stage, based on the in situ measurements. The results indicate a significant difference between two types of corn, particularly at 410, 545, 710, and 760 nm. A 750/550 ratio and MSR705 index, calculated from reflectances at 445, 705, and 740 nm, are the most effective indices for the corn separation. The wide band indices, calculated from the satellite data in the blue, red, NIR, and MIR regions, are effective in corn type determination. Landsat 8 color composite images with indices 1/4, 6/2, and 5/7 (Coastal Aerosol/red, SWIR1/blue, NIR/SWIR2) as well as 1/4, 6/2, 5/4 (Coastal Aerosol/red, SWIR1/blue, NIR/red) indices are able to separate the organic and conventional parcels in the mid-season and pre-harvest time in the study. Landsat 8 has more potential than Landsat 7 to discriminate corn type (organic vs. conventional) in both the mid-season and pre-harvest time. More research should be conducted in order to understand the factors that cause the differences between the two types of corn. Chlorophyll measurements, soil chemistry data, soil textural analysis, and soil moisture records were not available for this study.

Committee: Anita Simic Ph.D. (Advisor); Peter Gorsevski Ph.D. (Committee Member); Enrique Gomezdelcampo Ph.D. (Committee Member) Subjects: Remote Sensing

Doctor of Philosophy, The Ohio State University, 2015, Geodetic Science and Surveying

The flow velocity of glacier and its fluctuation are valuable data to study the contribution of sea level rise of ice sheet by understanding its dynamic structure. Repeat-image feature tracking (RIFT) is a platform-independent, feature tracking-based velocity measurement methodology effective for building a time series of velocity maps from optical images. However, limited availability of perfectly-conditioned images motivated to improve robustness of the algorithm. With this background, we developed an improved RIFT algorithm based on multiple-image multiple-chip algorithm presented in Ahn and Howat (2011). The test results affirm improvement in the new RIFT algorithm in avoiding outlier, and the analysis of the multiple matching results clarified that each individual matching results worked in complementary manner to deduce the correct displacements. LANDSAT 8 is a new satellite in LANDSAT program that has begun its operation since 2013. The improved radiometric performance of OLI aboard the satellite is expected to enable better velocity mapping results than ETM+ aboard LANDSAT 7. However, it was not yet well studied that in what cases the new will sensor will be beneficial, and how much the improvement will be obtained. We carried out a simulation-based comparison between ETM+ and OLI and confirmed OLI outperforms ETM+ especially in low contrast conditions, especially in polar night, translucent cloud covers, and bright upglacier with less texture. We have identified a rift on ice shelf of Pine island glacier located in western Antarctic ice sheet. Unlike the previous events, the evolution of the current started from the center of the ice shelf. In order to analyze this unique event, we utilized the improved RIFT algorithm to its OLI images to retrieve time series of velocity maps. We discovered from the analyses that the part of ice shelf below the rift is changing its speed, and shifting of splashing crevasses on shear margin is migrating to the center o (open full item for complete abstract)

Committee: Ian Howat Dr. (Advisor); Alper Yilmaz Dr. (Committee Member); Michael Durand Dr. (Committee Member) Subjects: Climate Change; Earth; Geography; Remote Sensing

Master of Arts, University of Toledo, 2014, Geography

Modern urbanization changes the albedo, temperature and hydrography of the natural landscape resulting in an increase in surface temperature of urban areas compared to the surrounding rural areas. This urban-rural temperature difference is called Urban Heat Island (UHI). This research utilizes GLOBE student surface temperature data in the study of UHIs and provides a critical analysis of the viability of GLOBE data. The GLOBE Program is a worldwide program that engages students in scientific observation by providing protocols for the collection and reporting of environmental observations to a public database. The first objective of this research was to establish focus areas using climate and physiographic regions, for study of UHI using available GLOBE data in the Great Lakes region. The second objective compared GLOBE surface temperature data to Landsat thermal imagery in order to determine validity of GLOBE measurements and ability to detect UHI. Previous research has established an expected temperature difference between Landsat Thermal Imagery and in-situ ground measurements to be within 2.7°C (Goetz, 1997). Inherent to the student data is the potential for errors such as temperature reported in Fahrenheit rather than Celsius, local time instead of UTC time, inaccurate GPS coordinates of study site, and accuracy of surface temperature measured by the student. It is also difficult to find GLOBE data that was collected at the exact same time as the Landsat overpass time. Within these limitations, GLOBE data is most comparable to Landsat on vegetated surface cover within 1 hour of overpass time. Generally where Landsat detects UHI, GLOBE schools detect a UHI with the same magnitude. Finally, this research utilized GLOBE data in a comparison of surface temperature by cover type. This research found that impervious surface and urban location had the greatest warming influence on surface temperature. Urban areas tended to have a warming effect and when coupled with i (open full item for complete abstract)

Committee: Kevin Czajkowski (Committee Chair); Beth Schlemper (Committee Member); Patrick Lawrence (Committee Member) Subjects: Climate Change; Geographic Information Science; Geography

Master of Science in Mathematics, Youngstown State University, 2011, Department of Mathematics and Statistics

Remote Sensing engages electromagnetic sensors to measure and monitor changes in the earth's surface and atmosphere. Remote Sensing Satellites are currently the fastest growing source of geographical area. Using data-mining techniques enables more opportunistic use of data banks of remote sensing satellite images. This thesis focuses on supervised and unsupervised classification, the two data mining techniques on the high resolution satellite Imagery from satellite IKONOS and satellite LANDSAT taken of the area around Kent State University, Ohio. The image was classified into ten distinct class: 1) Water, 2) Forested, 3) Agriculture, 4) Urban Development, 5) Vegetation1, 6) Vegetation2, 7) Vegetation3, 8) Vegetation4, 9) Grass, 10)Road. ERDAS Imagine was used in manipulating the images and creating the classification and analysis. The result obtained in form of accuracy helps to decide which image and classification technique is better to identify geographical patterns related to land use.

Committee: John Sullins PhD (Committee Chair); Bradley Shellito PhD (Committee Member); Jamal Tartir PhD (Committee Member) Subjects: Computer Science; Geographic Information Science; Remote Sensing

Master of Science (MS), Wright State University, 2008, Earth and Environmental Sciences

Multi-spectral satellite data from the Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER) and Landsat Thematic Mapper (TM) were used to make interpretations regarding the surface lithology and structure of the Tariat, Egiin Dawaa, and Orhon regions in Central Mongolia. These areas experienced widespread Cenozoic volcanism. This study mapped the locations and extents of the volcanic flows, located cinder cones and faults evident in the areas, and identified a possible exposure of the Mongolian granitic batholith in the Orhon area. ER Mapper, a geospatial imaging software, was used to generate and run algorithms. Image transforms and supervised classifications were used to identify the features of interest in this study. Samples from the area were examined spectrally and petrologically to assist in developing effective image transforms. Band ratios were the predominant image transform used. A greater than/less than algorithm was developed to allow a more precise identification of those pixels in a satellite image that correspond to the laboratory spectrum of a sample.

Committee: Doyle Watts PhD (Committee Chair); David Dominic PhD (Committee Member); Abinash Agrawal PhD (Committee Member) Subjects: Geology

Master of Science (MS), Wright State University, 2008, Earth and Environmental Sciences

This study used sap flow measurements and satellite imagery to estimatewater use by cottonwood (Populus fremontii S. Wats. ssp) trees in an irrigated restoration plot at Cibola National Wildlife Refuge on the Lower Colorado River. Several thousand hectares of irrigated plots of this type are planned to improve riparian habitat on the river, hence it is important to know how much water the trees require. In this study, the ET rates for 20 Freemont cottonwood trees, from an 8 ha plot, were monitored over a 30-day period. ET rates were estimated by measuring sap flow through branches of the trees. Biometric scaling was used to project ET at branch to ET at tree and plot level through the ratio of basal trunk area with the cross-sectional area of the branches. The mean biometric ratio exhibited a 1:1 relationship. Sap flow ET results showed that the cottonwood tree consumed 6-11 mm day-1 of water. My main contribution in this project was working with vegetation indices from MODIS and Landsat 5 TM (TM) time-series imagery and air temperature data. I developed projected ET rates over annual cycles, based on an empirical method calibrated against moisture flux tower data in previous studies. ET estimates from satellite data were similar to concurrent measurements of ET by sap flow methods. Annual estimates of ET from satellite data were approximately 1,200 mm yr-1, with an error or uncertainty of 20-30% inherent in both the ground and remote sensing methods.

Committee: Doyle Watts PhD (Committee Chair); Allen Burton PhD (Committee Member); Pamela Nagler PhD (Committee Member); Subramania Sritharan, PhD (Committee Member) Subjects: Agriculture; Biogeochemistry; Botany; Earth; Environmental Science; Geology; Geophysics

Master of Science (MS), Wright State University, 2003, Geological Sciences

Hurley, Angela Lorraine. M.S., Department of Geological Sciences, Wright State University, 2003. Identification of Gypsy Moth Defoliation in Ohio Using Landsat Data. The gypsy moth is one of the most devastating forest pests in North America. In late spring, gypsy moth larvae hatch from eggs laid the previous summer. During the next forty days, tens of thousands of these caterpillars eat up to one square foot of foliage each. The gypsy moth has established populations in several states, and dangerously fast-growing populations in several others. The state of Ohio is a critical area in the suppression of the gypsy moth because the front of gypsy moth advance passes through the state. Besides diminishing the aesthetic value of Ohio's forests, gypsy moths also cause substantial economic damage to the Ohio timber industry, which is estimated to be a $7 billion per year industry. The Ohio Department of Agriculture currently uses aerial sketchmapping each year to assess the damage done by the gypsy moth. This procedure is difficult, time-consuming, and somewhat imprecise. The results obtained from Landsat 5 and Landsat 7 data can be compared to locations determined by aerial sketchmapping to locate gypsy moth infestations in Ohio. Since vegetation reflects infrared light and absorbs visible light, the health of vegetation can be assessed using a haze-adjusted ratio of Landsat spectral band 4 (near-infrared) to Landsat spectral band 3 (visible red). To determine the change that has occurred between two dates, the ratio values from two dates are subtracted. To identify change that has been caused by the gypsy moth, an area should exhibit defoliation between early June and late June and subsequent refoliation between late June and late July. This type of change results in large positive ratio subtraction values between early June and late June and large negative ratio subtraction values between late June and late July. Pixels that exhibit these attributes are candidates for (open full item for complete abstract)

Committee: Doyle Watts (Advisor) Subjects: Geology

MA, University of Cincinnati, 2009, Arts and Sciences : Geography

The aim of this research was to study the seasonal and annual changes in the water quality based on the landsat measures of turbidity and chlorophyll-a. The indices were applied to a 95 km segment of the Ohio River where the USEPA had collected actual turbidity and chlorophyll-a samples the same day as the Landsat-7 overpass. Pearson correlation coefficient was calculated for the chlorophyll-a and turbidity indices, which was -0.938. A regression model was also developed to quantify chlorophyll-a (dependent variable) from turbidity (independent variable). The regression model had R2 value of 0.879, indicating a good fit. For the annual analysis of water quality, only the turbidity index was taken into consideration. The turbidity level is constant in the years 2000 and 2001. There has been a significant decrease in the concentration of turbidity from the year 2002 indicating improvement in the water quality. Efforts taken by the government and other agencies to improve the water quality could be the reason for constant turbidity index.

Committee: Robert C Frohn (Committee Chair); Robert B South (Committee Member); Richard Beck (Committee Member) Subjects: Geography

PhD, University of Cincinnati, 2007, Arts and Sciences : Geography

The purpose of this research was to develop an object oriented approach to land cover analysis and evaluate this approach along with five other classifiers for accuracy in classifying Level II land-cover categories in Ohio. These methods consist of (1) USGS National Land Cover Data; (2) the spectral angle mapper; (3) the maximum likelihood classifier; (4) the maximum likelihood classifier with texture analysis; and (5) a recently introduced hybrid artificial neural network; The segmentation object-oriented processing (SOOP) classifier outperformed all others with an overall accuracy of 93.8% and Kappa of 0.93. SOOP was the only classifier to have by-class producer and user accuracies of 90% or higher for all categories. An artificial neural network (ANN) classifier had the second highest overall accuracy of 87.6% and Kappa of 0.85. The four remaining classifiers had overall accuracies less than 85%. The SOOP classifier has been applied to Landsat-7 data to perform a level II land-cover classification for the state of Ohio.

Committee: Dr. Robert Frohn (Advisor) Subjects: