Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Dash, Padmanava

Committee Member

Ezell, John

Committee Member

Ambinakudige, Shrinidhi

Committee Member

Paul, Varun

Date of Degree

8-7-2025

Original embargo terms

Visible MSU Only 2 Years

Document Type

Dissertation - Campus Access Only

Major

Earth and Atmospheric Sciences

Degree Name

Doctor of Philosophy (Ph.D.)

College

College of Arts and Sciences

Department

Department of Geosciences

Abstract

Ocean acidification results from atmospheric CO₂ absorption, while coastal acidification is more localized, influenced by nutrient runoff, freshwater input, and organic matter decomposition. Due to its complexity, specialized monitoring is essential. The present research estimated two key carbonate system parameters total alkalinity (TA) and partial pressure of carbon dioxide (pCO₂) using uncrewed aircraft systems (UAS) imagery and autonomous surface vessel (ASV) observations over an oyster reef in the Western Mississippi Sound (WMS). Field campaigns were conducted from 2018 to 2022 to collect high resolution aerial imagery over the largest oyster reef in WMS, utilizing a multispectral sensor mounted on a drone. An ASV was deployed during June, July, and September 2021 UAS missions over the same sites to collect in situ data, including pH, partial pressure of carbon dioxide (pCO2), sea surface temperature (SST), sea surface salinity (SSS), colored dissolved organic matter (CDOM), and chlorophyll-a (Chl-a). Random forest models developed and accurately estimated TA and pCO₂ (R² > 0.91). Time-series maps were generated using Chl-a images derived from UAS imagery and SSS images derived from CDOM maps, employing salinity-CDOM linear regression model developed in this study. Results demonstrate UAS effectiveness in small-scale coastal monitoring due to its high spatial resolution. However, UAS lacks spatial coverage needed for broader regions like Mississippi Sound. To address this, MODIS imagery and HYCOM model outputs were integrated with ASV data collected in June and August 2023 in this research. Random forest models using SST, SSS, and Chl-a performed well (R² = 0.81 for TA, 0.87 for pCO₂). By incorporating MODIS Level 3 SST and Chl-a (1 km) and HYCOM SSS (downscaled 4 km to 1 km), this research generated annual and monthly time-series maps of mean TA and pCO₂ over the entire Mississippi Sound for the period 2002–2020. These maps reveal spatial seasonal dynamics and long-term trends. This research also investigated how land use and land cover (LULC) changes influenced TA and pCO₂ across the entire Mississippi Sound from 2002 to 2020. Spatial correlation and trend maps revealed associations between eight LULC class type changes and TA and pCO₂ patterns. The findings suggest connections between environmental changes and carbonate system responses but do not confirm causation, instead providing a basis for hypothesis generation and further study of biogeochemical processes. Overall, this dissertation highlights how combining remote sensing, in situ measurements, machine learning technique, and LULC analysis improves coastal acidification assessment in the Mississippi Sound.

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