Texas A&M University – Corpus Christi
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Impact of ocean acidification on Montipora capitata
Get PDFCollege of Science, Department of Life Sciences, Honors Program; Faculty Mentor: Keisha BahrIn Kāne‘ohe Bay Hawai‘i, the second most dominant coral species, Montipora capitata is an ecologically important reef-building coral that has shown resilience to environmental changes. However, ocean acidification (OA) may compromise the structural integrity of the coral's skeleton, threatening the species’ resiliency. Therefore, this project analyzed multiple biological response variables of M. capitata under ocean acidification conditions. OA is a change in ocean water chemistry due to an increase in the absorption of atmospheric carbon, which decreases seawater pH and aragonite saturation state. This also increases the concentration of hydrogen ions in the water, which will impact the total alkalinity, or the ability of the water to neutralize ions. Previous research has stated that a lower concentration of carbonate impacts the coral’s ability to calcify under OA conditions. Contrarily, the Proton Flux Hypothesis states that the increase in hydrogen ions limits coral calcification under OA. To better understand coral growth under OA conditions, corals were exposed to a control and three experimental treatments varying in pH and total alkalinity levels, over a month-long experiment. Following experimentation, biological response variables from each coral were measured. These variables include the density and chlorophyll concentrations of the symbiotic algae and changes in skeletal density. It is hypothesized that the combination of low pH and total alkalinity will have a synergistic effect on the coral's skeletal density. The result of this work aims to further our understanding of OA and its impacts on coral calcification
Funeral flowers Filling a Room
No full textA Room filled Funeral flowers Wreaths,Potted and Bouqets covering the corner of the roo
The price of negligence
No full textSince 2000, there have been incredible changes in data transmission and media. We now live in a world where we can voice opinions and express ourselves almost limitlessly. Although these developments have changed communication for the better, the covert impact of social media algorithms makes their justification questionable. Thus, this proposal aims to evaluate the effect that social media algorithms have on the general awareness of current events and social justice issues
A Table of Four people enjoying Dinner
No full textA Table of Four people enjoying Dinner smiling for a pictur
Topic 5.3: Multidimensional arrays and arraylists
No full textIn this module, you will learn about:
Multidimensional Arrays,
Variable-length argument list,
Arraylis
Application of UAS photogrammetry and geospatial AI techniques for palm tree detection and mapping
No full textA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Geospatial Systems EngineeringUncrewed aircraft systems (UAS), commonly known as drones, underwent significant advance ments in recent years, particularly in the development of improved sensors and cameras that enabled high-resolution imagery and precise measurements. This study utilized a UAS to capture aerial imagery of Texas A & M University-Corpus Christi (TAMUCC) main campus, which was then processed using Structure-from-Motion (SfM) photogrammetric software to generate orthomosaic imagery. The primary purpose of this study was to utilize the orthomosaic imagery acquired from UAS to detect, map, and quantify the number of palm trees. Initially, three deep-learning models were trained using the same set of training samples. The model exhibiting the highest performance in terms of precision, recall, and F1-Score was selected as the optimal model. The model obtained through the fine-tuning of a pre-trained GIS-based model with additional training samples was identified as the optimal choice, yielding the following values: precision=0.88, recall=0.95, and F1-score=0.91. This model successfully detected a total of 1414 sabal palm trees within our study area. The chosen optimal model was employed to examine the impact of ground sampling distance (GSD) on the deep learning model. GSD values were varied, namely 5 cm, 10 cm, 20 cm, and 40 cm. The findings revealed that the model’s performance deteriorated as the resolution decreased. Furthermore, the optimal model was subjected to an additional test using multi-temporal datasets with approximately the same GSD (1.5 cm). These datasets included one acquired a year prior to the model’s training datasets, and another obtained three months after the training datasets. Remarkably, the results demonstrated that the model maintained a comparable level of accuracy across all three testing datasets. The obtained results were verified using ground truth values taken in a small portion of the study area. This study concludes that deep learning models for object detection exhibit superior performance when fine-tuned with training samples specific to the area of interest. Furthermore, it is evident that the optimal model’s effectiveness diminishes significantly when the imagery resolution is reduced. Additionally, the performance of the deep learning model remains relatively consistent when applied to datasets acquired at different time frames, as long as the resolution of the testing data remains the same. In summary, the application of deep learning demonstrates its efficacy, user-friendliness, and time-saving capabilities for object detection. This study shows how we can use UAS and deep learning to detect palm trees. It helps us develop better ways to monitor and manage palm trees.Geospatial Surveying EngineeringCollege of Scienc
Determining Nitrogen sources and processing along the Texas Coast and potential impacts due to sea-level variations
No full textA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Coastal and Marine System ScienceThe Gulf Coast of Texas has had increased incidents of eutrophication and high concentrations of fecal bacteria since 2009, which are indicators of poor water quality. Nitrogen loading in the form of both dissolved organic nitrogen (DON) and dissolved inorganic nitrogen (DIN) sources can contribute to water quality decline. To determine the sources and processes contributing to nitrogen loading on Texas sandy barrier islands, ground-, pore- and surface waters from Galveston to Matagorda counties were analyzed for DON and DIN concentrations and isotopic composition. Additionally, groundwater elevations were monitored to determine if water table fluctuations were associated with increases/decreases in N loading. Average nitrate (NO3-), ammonium (NH4+), and dissolved organic nitrogen (DON) in surface and porewater samples were reminiscent of ocean processing. Surface water isotopic evidence indicated assimilation and competing subsurface denitrification, while porewater isotopic evidence suggested competing DNRA/denitrification coupled with nitrification processes. In contrast, NO3- was elevated at three groundwater sites, while most wells showed elevated NH4+ concentrations. Isotopic and nutrient concentration evidence indicated a septic signature with coupled denitrification/anammox and nitrification processes, along with possible saltwater intrusion. Regarding monitoring wells, depth to water (DTW) values had a positive correlation with NO3- concentrations and a negative correlation with NH4+ concentrations, indicating direct contamination from a NH4+ source at low DTW and NH4+ source processing to NO3- at high DTW. A Bayesian-type isotope mixing model estimated NO3- source contributions to Gulf surface water as septic/sewage (35.9 ± 20.5%), dog waste/gull guano (22.9 ± 17.5%), soil (22.9 ± 15.5%), and wet deposition (18.3 ± 7.8%). Nitrate source contributions to porewater were septic/sewage (35.3 ± 21.3%), soil (26.8 ± 18.7%), dog waste/gull guano (25.2 ± 19.0%), and wet deposition (12.7 ± 6.7%). Source contributions to groundwater were septic/sewage (62.6 ± 24.1%), dog waste/gull guano (15.8 ± 21.7%), wet deposition (12.8 ± 7.7%), and soil (8.8 ± 6.3%). Results indicate that there is substantial septic/sewage contamination throughout the study area and possible contamination from animal waste, with evidence of saltwater intrusion in wells. This is the first study to attempt to characterize nitrogen processing, loading, and source contributions to the area. A history of failing septic/sewage systems, along with sea level rise and the increase of more severe climatic events, is estimated to cause more saltwater intrusion and septic/sewage malfunctions to an already vulnerable coastal area. Stakeholders and decision makers can use this data as a starting point for contamination and saltwater intrusion mitigation strategies.Coastal and Marine System ScienceCollege of Scienc
Geospatial monitoring and assessment of coastal land subsidence
No full textSubsidence, the downward movement of the land, presents risks in coastal areas such as shoreline erosion and coastal flooding. The accurate estimation of subsidence and the identification of its underlying causes holds significant values for comprehending subsidence processes and guiding decision-making. However, both the subsidence estimation and interpretation are challenging due to its spatio-temporal variability, limited observability, and the complexity caused by natural processes and anthropogenic activities. The contributions of this dissertation were to 1) estimate subsidence at locations of tide gauge (TG) stations along the coastlines; 2) investigate coastal subsidence by integrating measurements from a variety of geodetic techniques such as global navigation satellite systems (GNSS), interferometric synthetic aperture radar (InSAR), TGs, and satellite radar altimtery (SRA); and 3) model subsidence with features related to natural processes and anthropogenic activities and identify potential drivers with machine learning (ML) techniques. These contributions were exemplified through case studies at the Texas Gulf Coast areas. First, two sea-level difference methods, through leveraging TG and SRA measurements, were developed to reconstruct subsidence time series at tide gauge (TG) locations along the Texas coastlines with observation periods exceeding ten years. In addition, synthetic aperture radar (SAR) imagery, continuously operating GNSS (cGNSS) observations, and sea-level measurements were harnessed to estimate the spatio-temporal patterns of subsidence spanning around three decades since the 1990s at the Eagle Point TG station, a prominent hotspot of sea-level rise in the United States. The results obtained from multiple geodetic techniques provided strong and consistent evidence of subsidence processes in the vicinity of Eagle Point. Moreover, a large-scale subsidence map along the Texas coastlines post-2016 was generated with SAR images, revealing that the Texas Gulf Coast experienced an average subsidence rate of -1 mm/yr near the shoreline with an increasing trend in magnitude inland. Attribution analysis indicated that hydrocarbon extraction and groundwater withdrawal were the predominant factors responsible for identified subsidence hotspots in the Texas Gulf Coast. ML demonstrated an impressive performance (with an 2 of 0.56) in modeling the observed large-scale subsidence, by incorporating a range of features related to natural terrain variations and anthropogenic activities. Explainable artificial intelligence (XAI) methods provided quantitative estimates of feature contributions of the ML model, and the data-driven results revealed that the digital elevation model (DEM) and anthropogenic factors were contributing features in relation to subsidence.Computing SciencesCollege of Engineerin