Master of Science (MS), Bowling Green State University, 2005, Geology

The work in this thesis used passive hyperspectral remote sensing analysis with data collected form the high altitude MODIS Airborne Simulator (MAS). This study examined multiple remote sensing band ratios designed to capitalize on methane's 3.314µm absorption feature. Other ratios were also developed to detect atmospheric gas changes associated with possible methane plumes. Much of the analysis utilized datasets covering two California regions known to contain active oil/gas seeps and production. It was determined that no single MAS ratio algorithm could be used to confidently detect a methane gas plume. The presence of other atmospheric gases has the potential to affect the algorithms and produce complications for interpretation. However, by using a concurrence of ratio algorithm results, a suspected plume was thought to be detected in one of the off-shore datasets. The analysis for the land datasets found the high degree of surface material and temperature variations dramatically interfered with the ability to interpret the algorithm results with any significant confidence.

Master of Science (MS), Ohio University, 2017, Geography (Arts and Sciences)

Lake Bonneville was the largest water body that accumulated in the Great Basin during the late Pleistocene. Its latest major lacustral cycle lasted from 30 ka to 12 ka and much evidence of the lake remains are still evident in the landscape today. This thesis investigates the use of Landsat-8 multispectral imagery for mapping the Quaternary deposits in the Tule Valley portion, and Hyperion (EO-1) hyperspectral data for mapping part of the adjacent Snake Valley of Lake Bonneville. Maximum likelihood classification was applied for Landsat 8 data, and the two spectral analysis approaches of linear spectral unmixing and spectral angle mapper (SAM) were applied to the Hyperion dataset. Furthermore, X-ray diffraction (XRD) analysis of a Lake Bonneville marl sediment sample characterized the dominant minerals in that sample. This investigation relied on Sack's (1990) Quaternary geologic map of Tule Valley as the reference for the remote sensing analysis. This study investigates if those sources of information can approach in quality and detail the traditional map that relies on fieldwork and air photo interpretation. Results illustrate that hyperspectral and multispectral data have potential value for Quaternary geological mapping. Maximum likelihood classification yielded overall accuracy of 51% with successful discrimination of Qlf, Qeg, Qes, Qlm, Qac, and bedrock. However, complete separation between several lacustrine and alluvial classes was not achieved. In general, the Hyperion spectral angle mapper (SAM) and spectral unmixing results discriminated relatively well among the three endmembers of calcite, gypsum, and quartz across portions of the Snake Valley study area. The high fraction abundance values on the fractional images reliably represented pixels dominated by calcite, gypsum, or quartz. Some confusion between classifications are attributeded to the local mixing of classes at the pixel scale, overlap in mineralogy, similarities in the nature of surface weathe (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2012, Electrical and Computer Engineering

Range-resolved interferometric phase and Doppler spectra are two subjects of interest with regard to the retrieval of sea surface height profiles from coherent marine radar measurements. The studies of this dissertation attempt to improve understanding of the properties and associated measurement errors of these quantities through the use of numerically simulated low-grazing-angle backscatter data. In the first part of the dissertation, studies of the interferometric phase are described. Backscattered fields computed using the method of moments for one dimensional ocean-like surface profiles are used to examine statistical properties of the single-look interferometric phase estimator, in order to investigate the applicability of standard expectations for height retrieval accuracy in this problem. The results show that shadowing and multipath propagation effects cause errors in interferometric phase estimation beyond those caused by speckle effects alone. In addition, the decorrelation between the fields received at two antennas is found to be impacted by shadowing and multipath propagation effects, making standard models for this quantity less applicable as well. These results show that modeling the expected performance of interferometric sea surface height retrieval approaches at low grazing angles is difficult. The second part of the dissertation involves studies of the range-resolved Doppler spectra at low-grazing-angles. Backscattered fields are computed for a single realization of a one-dimensional ocean-like surface profile as the realization evolves in time. Transformation into the range-Doppler domain enables examination of properties of the resulting Doppler spectra (for both HH and VV polarizations) and their relationship to properties of the surface profile. In general, a strong correspondence between the long wave orbital velocity of the surface and the Doppler centroid frequency is observed for visible portions of the surface, as well as some evidence (open full item for complete abstract)

Master of Science, The Ohio State University, 2005, Graduate School

Master of Science, The Ohio State University, 2006, Graduate School

MS, Kent State University, 2024, College of Arts and Sciences / Department of Geography

Wildfire and drought are key drivers of shrubland expansion in southwestern US landscapes. Stand-replacing fires in dry conifer forests induce shrub-dominated stages, and changing climatic patterns may cause a long-term shift from coniferous forests to deciduous shrublands. This study assessed recent changes in deciduous fractional shrub cover (DFSC) in the eastern Jemez Mountains from 2019-2023 using topographic and Sentinel-2 satellite data in a random forest model. Sentinel-2 provides multispectral bands at 10 and 20 meters, including three 20 meter red edge bands, which are highly sensitive to variation in vegetation. There is no consensus in the literature on whether upscaling imagery to 20 meters or downscaling to 10 meters is more advantageous. Therefore, an additional goal of this study was to evaluate the impact of spatial scale on DFSC model performance. Two random forest models were built, a 10 and 20 meter model. The 20 meter model outperformed the 10 meter model, achieving an R-squared value of 0.82 and an RMSE of 7.85, compared to the 10 meter model (0.76 and 9.99, respectively). The 20 meter model, built from 2020 satellite imagery, was projected to the other years of the study, by replacing the spectral variables with satellite imagery from the respective year, resulting in yearly predictions of DFSC from 2019-2023. DFSC decreased from 2019-2022, coinciding with severe drought and a 2022 fire, followed by a significant increase in 2023, particularly within the 2022 fire footprint. Overall trends showed a general increase in DFSC despite high interannual variability, with elevation being a key topographic variable influencing these trends. This study revealed yearly vegetation dynamics in a semi-arid system and provided a close look at post-fire regeneration patterns in deciduous resprouting shrubs. Understanding this complex system is crucial for informing management strategies as the landscape continues to shift from conifer forest to shrubland du (open full item for complete abstract)

Master of Science, The Ohio State University, 2006, Graduate School

Master of Science, The Ohio State University, 2005, Graduate School

Master of Science, The Ohio State University, 1960, Graduate School

Master of Science in Computer Engineering, University of Dayton, 2024, Electrical and Computer Engineering

Event-based sensors (EBS) report pixel-asynchronous changes in scene intensity called events. Hence, its sparse event stream is well-suited for many computer vision tasks— particularly object tracking. However, relative motion between the sensor and scene will generate extraneous events caused by the translation of static scene features across the sensor. We present OFE-EBS, an optical flow equation-inspired event-based sensor for low-Earth orbit (LEO) ground moving target indication (GMTI). Owing to the predictable velocity of a satellite platform in LEO, we augment the EBS pixel with additional cross-row subtraction hardware to remove static background features. Pixel adaptivity is modified to ensure dynamic foreground features generate fewer events, further reducing event rate. Finally, using our analytical sensor model, we show that OFE-EBS outperforms conventional EBS in spatial resolution and event rate, considering the effects of pixel nonuniformity.

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.

Master of Science, The Ohio State University, 2024, Food, Agricultural and Biological Engineering

Waste coal in abandoned mine lands poses significant environmental challenges, affecting nearby communities, rivers, and streams. Effective management of these piles is essential due to concerns such as acid mine drainage, soil and water contamination, coal fires, and methane emissions. Various strategies have been proposed for managing waste coal, including potential utilization for rare earth element recovery, soil amendment, construction aggregates, and energy generation. However, the implementation of these strategies remains uncertain due to the lack of precise location and volume data on waste coal piles. Traditional methods for gathering these data rely on field visits and Global Navigation Satellite System surveying, which are costly and labor-intensive. Advances in satellite technologies and the availability of digital elevation models (DEMs) offer an opportunity to estimate waste coal volume on a regional scale in a timely and cost-effective manner. Thus, the objective of this thesis was to develop a robust data analytical framework to locate and estimate the volume of waste coal piles on a regional scale, using the Muskingum River Basin (MRB) in Ohio as the study area. Initially, a prototype was developed to determine the most effective machine learning (ML) model to map waste coal piles in a historical coal mine site within the MRB. While all four ML models effectively identified dominant classes such as Grassland and Forest, the Random Forest (RF) model demonstrated superior performance in classifying the more complex waste coal class, with a precision of 86.15% and recall of 76.71%. Subsequently, the greatest disturbance and reclamation mapping of these waste coal piles were conducted using the LandTrendr algorithm to distinguish waste coal piles in abandoned mine lands from those in active mining areas. Moreover, this study utilized publicly available elevation models to estimate waste coal volume in the MRB. However, since historical terrain mo (open full item for complete abstract)

MS, Kent State University, 2024, College of Arts and Sciences / Department of Geography

In 2018, the largest wildfire to ever occur in the Santa Monica Mountains National Recreation Area burned 88% of National Park Service (NPS) land. Located near Los Angeles, this park is the largest urban national park in the U.S. and home to mountain lions (Puma concolor) that are severely threatened. High levels of urbanization force them to live in overlapping and too small of home ranges, leading to intraspecific conflicts and inbreeding. The frequent wildfires add another threat, killing pumas directly or damaging their habitat. Current research conflicts as to how pumas select habitat post-fire, and most do not incorporate remote sensing metrics or consider how movements change with time since fire. In this study, I used global positioning system (GPS) collar data supplied by the NPS to analyze post-fire puma habitat selection. I conducted integrated step selection functions (iSSFs) at individual and population levels, for every 6-month seasonal period following the 2018 fire through 2023. I analyzed nine static variables to account for abiotic landscape variability and three variables derived from multi-temporal remote sensing to capture the dynamic, biotic environment, mainly focused on burn severity and vegetation condition and structure metrics. Habitat selection and variable importance were compared within each time period, as well as throughout the study period. I focused results on the population level analyses only. Results indicated that pumas consistently selected for increased vegetation vigor and selected for higher landscape heterogeneity and structure for the majority of time periods. Vegetation vigor also appeared as one of the most important variables to movement, along with terrain ruggedness and slope. Seasonal trends emerged for some variables post-fire. This study suggests that pumas are considerate of vegetation condition and fire impacts when selecting habitat, highlighting key habitat characteristics that pumas prefer post-fire. The influ (open full item for complete abstract)

Master of Science (MS), Bowling Green State University, 2024, Geology

Precise estimation of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) is essential for effective monitoring of crop growth conditions. The field of satellite imaging spectroscopy, known as hyperspectral imaging, offers a non-destructive, large-area, and real-time approach to monitoring of CCC and CNC over large areas. With the introduction of recent science-driven mission such as the Environmental Mapping and Analysis Program (EnMAP), imaging spectroscopy has emerged as a crucial component of empirical and physical modelling. This study introduces two approaches for retrieving CCC and CNC using EnMAP hyperspectral imagery acquired over Kellogg Biological Station, Michigan in the summer 2023: i) Machine learning regression algorithms (MLRAs), and ii) a hybrid model that combines a radiative transfer model with machine learning. We assessed the performances of six regression techniques including kernel ridge regression (KRR), least squares linear regression (LSLR), partial least squares regression (PLSR), Gaussian process regression (GPR), neural network (NN), and random forest (RF) for retrieving CCC and CNC. In the hybrid model workflow, each of the six techniques were integrated with PROSAIL, a widely known radiative transfer model. For both the approaches, the final CCC and CNC models were validated against field data. For CCC retrieval, KRR demonstrated the best performance in the MLRA approach (RMSE = 10.01, NRMSE = 9.81%, R2 = 0.93), while GPR exhibited the best performance in the hybrid approach (RMSE = 9.62, NRMSE = 9.43%, R2 = 0.93). Regarding CNC retrieval, KRR outperformed other models in both the MLRA (RMSE = 10.10, NRMSE = 8.13%, R2 = 0.94) and hybrid approach (RMSE = 16.98, NRMSE = 13.67%, R2 = 0.83). While the hybrid model outperformed the standalone MLRA approach for CCC retrieval, the MLRA approach surpassed the hybrid approach in CNC retrieval. In CCC retrieval, the most significant bands of EnMAP were identified in the visible to (open full item for complete abstract)

Master of Science in Electrical Engineering, University of Dayton, 2024, Electrical Engineering

Remote sensing techniques are capable of capturing large scenes of data over several sensing domains. Hyperspectral imagery (HSI), often accompanied with lIDAR and orthoimagery sensors during collection, can provide deeper contextual information for a wide range of applications in many different fields. Complex characteristics across spectral bands in addition to high-dimensionality of HSI data present challenges to accurate classification. Generally, dimensionality reduction of the input hyperspectral data cube is performed through multi-phase analytical algorithms as a pre-processing step before further analysis to include machine learning networks. These networks commonly rely on labeled training data for segmentation. Annotating ground truth aerial data can prove to be a cumbersome endeavor that may require specific expertise for accurate assessment. This inspires exploratory research for useful unsupervised feature-learning approaches that can withdraw essential information from HSI data to map scenes without labeled data thereby providing a start-to-finish scene segmentation process.

Doctor of Philosophy, The Ohio State University, 2024, Civil Engineering

With the development of remote sensing technology in recent decades, spaceborne sensors with sub-meter and meter spatial resolution (Worldview & PlanetScope) have achieved a considerable image quality to generate 3D geospatial data via a stereo matching pipeline. These achievements have significantly increased the data accessibility in 3D, necessitating adapting these 3D geospatial data to analyze human and natural environments. This dissertation explores several novel approaches based on stereo and multi-view satellite image-derived 3D geospatial data, to deal with remote sensing application issues for built-up area modeling and natural environment monitoring, including building model 3D reconstruction, glacier dynamics tracking, and lake algae monitoring. Specifically, the dissertation introduces four parts of novel approaches that deal with the spatial and temporal challenges with satellite-derived 3D data. The first study advances LoD-2 building modeling from satellite-derived Orthophoto and DSMs with a novel approach employing a model-driven workflow that generates building rectangular 3D geometry models. By integrating deep learning for building detection, advanced polygon extraction, grid-based decomposition, and roof parameter computation, we accurately computed complex building structures in 3D, culminating in the development of SAT2LoD2—a popular open-source tool in satellite-based 3D urban reconstruction. Secondly, we further enhanced our building reconstruction framework for dense urban areas and non-rectangular purposes, we implemented deep learning for unit-level segmentation and introduced a gradient-based circle reconstruction for circular buildings to develop a polygon composition technique for advanced building LoD2 reconstruction. This approach refines building 3D modeling in complex urban structures, particularly for challenging architectural forms. Our third study utilizes high-spatiotemporal resolution PlanetScope satellite imagery for (open full item for complete abstract)

Master of Science, The Ohio State University, 2023, Civil Engineering

Semantic segmentation plays a fundamental and indispensable role in the field of image processing, computer vision, and remote sensing, finding extensive applications across diverse domains such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others (Minaee et al., 2021). It involves the partitioning of data into well-defined regions or groups, facilitating the extraction of valuable information and informed decision-making based on the segmented data. This study aims to investigate the utilization of deep neural networks in the Urban Building Classification (UBC) dataset (Huang et al., 2022) for fine-grained semantic segmentation in the context of urban environments. The UBC dataset comprises RGB images captured by the SuperView and Gaofen, featuring urban areas from cities in China and Germany. Unlike conventional approaches that typically treat semantic segmentation as a single stage task, this research investigates the coarse-to-fine manner to utilize the hierarchical relationship of categories in the dataset. The proposed methodology for this study advocates for a combined approach involving deep convolutional neural networks (DCNNs), classifier ensembles and fusion techniques to attain the desired results. Multiple classifiers are trained independently on both datasets, and a meticulous classifier selection process is conducted, encompassing a comparison between state-of-the-art segmentation methods and traditional approaches. Subsequently, checkpoint ensemble methods are explored to facilitate model fine-tuning, culminating in the refinement of the final model through joint probability fusion. The outcomes of this investigation hold the potential to contribute significantly to the understanding of urban city structures and their potential for reconstruction.

Master of Science, The Ohio State University, 2023, Food, Agricultural and Biological Engineering

The proliferation of harmful algal blooms (HABs) in Ohio and worldwide has posed serious threats to aquatic and human health—a transdisciplinary issue hard to tackle without integrating tools from across disciplines and sectors. Satellite remote sensing is recognized as a useful technology to map, monitor, and predict HABs. However, the effective use of satellite images is hindered by many practical and technical factors, which include but are not limited to the complex nature of satellite data, the uncertainties associated with satellite data processing, inadequate product validation, and misconception of data quality, among others. This study leverages environmental data alongside a suite of satellite data, including Sentinel-3A OLCI, Sentinel-2, and Landsat-8, to identify patterns and potential drivers of HAB. Specifically, to explore the diverse nature of HAB and environmental data, we use multiple machine learning techniques, including Random Forest, Support Vector Regression, and Extreme Gradient Boosting (XGB), each complemented with SHapley Additive exPlanations (SHAP) models. Based on the Random Forest (RF) model curated for each of the four HAB proxies, Chlorophyll-a (Chl-a), Phycocyanin, Microcystin, and Secchi Depth, Chl-a showed better optical sensitivity with R2 = 0.55 and RMSE = 20.84 µg/L while the sensitivity of Phycocyanin, Microcystin, and Secchi depth to spectral bands were less pronounced. When the variability in Chl-a concentration was explored using XGB, including various combinations of spectral information alongside physicochemical and meteorological variables, Chl-a was better explained by physicochemical variables such as phosphorous and spectral band indices with R2 = 0.69 and RMSE = 9.06 µg/L. A majority of meteorological variables, such as precipitation, wind direction, and solar radiation, were found less effective in explaining variability in Chl-a, indicating the need to explore their potential lagged response. Based on 12 models de (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Earth Sciences

Ice shelves regulate ice sheet contribution to sea level rise by buttressing ice flow. Many ice shelves around Antarctica are thinning and retreating, with the most significant changes occurring in West Antarctica. Many factors influence ice shelf stability, including changes at the grounding line, basal melt, stress transfer, and the incidence and advection of surface and basal features. The work presented in this dissertation elucidates connections between narrow ice shelf melt features (basal channels) and structural changes on ice shelves and challenges the assumptions used to examine ice shelf change. Chapter 2 presents work published in the Journal of Geophysical Research: Earth Surface (Chartrand & Howat, 2020). Here, we use high spatiotemporal-resolution digital surface models (DSMs) from the Reference Elevation Model of Antarctica (REMA), satellite laser altimetry, and airborne laser and radar altimetry to track the position of a basal channel on the Getz Ice Shelf and to quantify thickness changes and basal melt rates. We show that the basal channel incised at rates up to 22 m a-1 and migrated independently of ice flow at rates of 70-80 m a-1. Lastly, we show that parts of the basal channel are not freely floating, which reduces confidence in the assumption of hydrostatic equilibrium. These results provide new insights into basal channel evolution, demonstrate the value of REMA DSMs in examining ice shelf change, and inspired the work in subsequent chapters. In Chapter 3, we explore the implications of assuming hydrostatic equilibrium to estimate ice shelf thickness from surface height measurements. We compare hydrostatic ice thickness estimates from airborne surface height measurements with contemporaneous ice thickness measurements. We find that the hydrostatic thickness overestimates ice thickness by an average of 17 m, with significant variability at multiple spatial scales. We show that the hydrostatic thickness is highly sensitive to which corr (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Electrical and Computer Engineering

Rough surface scattering is an essential aspect of modern remote sensing research, as virtually all real-world surfaces exhibit some degree of roughness whose effects cannot be adequately accounted for using simple planar surfaces. However, knowledge of what each rough surface model is capable of is critical, as choosing an appropriate model will aid in providing accurate results while minimizing the computational cost incurred. To explore these capabilities, four studies were conducted to assess how various rough surface scattering models fare in scenarios of current interest to the remote sensing community. The first two studies involve the Kirchhoff approximation (KA), with the first study assessing its applicability when the normalized coherent reflected field (which the KA is commonly used to model) is -20dB or lower, and the second study comparing it to a second-order correction term based on the second-order small slope approximation (SSA2) for ocean surface scattering. The first study shows that the KA continues to be applicable for such low amplitude cases, and the second study shows that the second-order correction shows no marked improvement over the base KA overall. The third study uses the SSA2 to validate retrieved zero-Doppler delay waveforms as part of a campaign to explore off-specular ocean scattering, and found the model waveforms to match the retrieved waveforms well in most cases considered. The fourth and final study uses simulated SAR imagery to determine under what conditions a monostatic radar system will observe the same surface scattering as a bistatic radar system, and revealed that cases with near-normal incidence angles and minor roughness yield the best agreement, with effects such as shadowing and multiple reflections accounting for most of the disagreements.