Globally, water bodies are increasingly affected by undesirable harmful algal blooms. This dissertation contributes to research methodology pertaining to quantification of the algal blooms in multiple water bodies of Mississippi using in situ, analytical, and remote sensing techniques. The main objectives of this study were to evaluate the potential of several techniques for phytoplankton enumeration and to develop remote sensing algorithms for several sensors and evaluate the performance of the sensors for quantifying phytoplankton in several water bodies. Analytical techniques such as “FlowCam”, an imaging flow cytometer; “HPLC”, high performance liquid chromatography with the chemical taxonomy program “ChemTax”; spectrofluorometric analyses; and “ELISA” assay were used to quantify a suite of parameters on algal blooms. Additionally, in-situ algal pigment biomass was measured using fluorescence probes. It was found that that each technique has unique potential. While some of the rapid and simpler techniques can be used instead of more involved techniques, sometimes use of several techniques together is beneficial for managing aquatic ecosystems and protecting human health. Algorithms were developed to quantify chlorophyll a using five remote sensing sensors including three currently operational satellite sensors and two popular sensors onboard the Unmanned Aerial Systems (UASs). Empirical band ratio algorithms were developed for each sensor and the best algorithms were chosen. Cluster analysis helped in differentiating the water types and linear regression was used to develop algorithms for each of the water types. The UAS sensor- Micasense was found to be most useful among the UAS sensors and the best overall with highest R2 value 0.75 with p<0.05 and minimum %RMSE of 28.22% and satellite sensor OLCI was found to be most efficient among the three satellite sensors used in the study for chlorophyll a estimation with R2 of 0.75 with p<0.05 and %RMSE 13.19%. The algorithms developed for these sensors in this study represent the best algorithms for chlorophyll a estimation in these water bodies based on R2 and %RMSE. The applicability of the algorithms can be extended to other water bodies directly or the approach developed in this study can be adopted for estimating Chl a in other water bodies.

Presently, scientific communities are confronting Earth’s foremost environmental issues using best management practices. However, an increase for need in the synthesis of socio-ecological principles using a multi- and trans-disciplinary approach is required for solutions that benefit both nature and humans. To examine whether a community perceives stormwater runoff as both a local resource and threat to coastal water quality, an online survey of the Ventura River watershed community probed local residents’ understanding of watershed knowledge, beliefs, and behavior with regards to their local environment as it pertained to water resources, especially as affected by human activity. Analysis of 144 participants’ responses and their self-reported water activity, water activity frequency, and perceptions of Ventura River’s discharge and stormwater runoff reveals the community’s behavior regarding exposure to poor water quality in a local coastal environment and, ultimately, the survey participants’ level of geoliteracy concerning their local watershed. A statistical analysis between categorical variables of the survey questions examines relationships between self-reported waterborne illness symptoms and the water activities that participants enjoy regularly and/or perform for work. The survey responses demonstrated common themes in water knowledge that exist throughout this particular coastal community. Additionally, through the use of an optical and historical classification system, the Ventura River’s sediment discharge was examined both remotely and in situ. Multispectral ocean color satellite sensors have been useful in monitoring the water quality of Case 2 waters. Particularly, after severe storm events contaminants can be carried along with storm runoff from urban storm drains and Mediterranean river mouths which then enter coastal and recreationally trafficked water. Earth scientists have observed poor water quality occurring offshore in Case 2 waters near major river mouths and urban areas causing the coastal water column to deteriorate in quality.

Classification of remotely sensed imagery for reliable land use and land cover (LULC) change information remains a challenge in areas where spectrally similar LULC features occur. Dissolved organic matter (DOM) influences the biogeochemistry of aquatic environments and its quantity and quality are due, in large part, to the surrounding LULC. Thus, objectives were to improve the accuracy of LULC classification and quantify seasonal variations of water quality in a watershed dominated by agriculture and determine the controls for the variations in water quality. Support vector machine classification scheme with post classification correction yielded highest accuracy for LULC classifications and four distinct DOM components were found that changed seasonally and were controlled by hydrology and LULC. The microbial component was the main fraction of the DOM pool due in large part to agricultural practices. This DOM can influence the water quality significantly as it moves downstream and causes increased biological activity.

A pixel in remotely sensed hyperspectral imagery is typically a mixture of multiple electromagnetic radiances from various ground cover materials. Spectral unmixing is a quantitative analysis procedure used to recognize constituent ground cover materials (or endmembers) and obtain their mixing proportions (or abundances) from a mixed pixel. The abundances are typically estimated using the least squares estimation (LSE) method based on the linear mixture model (LMM). This dissertation provides a complete investigation on how the use of appropriate features can improve the LSE of endmember abundances using remotely sensed hyperspectral signals. The dissertation shows how features based on signal classification approaches, such as discrete wavelet transform (DWT), outperform features based on conventional signal representation methods for dimensionality reduction, such as principal component analysis (PCA), for the LSE of endmember abundances. Both experimental and theoretical analyses are reported in the dissertation. A DWT-based linear unmixing system is designed specially for the abundance estimation. The system utilizes the DWT as a pre-processing step for the feature extraction. Based on DWT-based features, the system utilizes the constrained LSE for the abundance estimation. Experimental results show that the use of DWT-based features reduces the abundance estimation deviation by 30-50% on average, as compared to the use of original hyperspectral signals or conventional PCA-based features. Based on the LMM and the LSE method, a series of theoretical analyses are derived to reveal the fundamental reasons why the use of the appropriate features, such as DWT-based features, can improve the LSE of endmember abundances. Under reasonable assumptions, the dissertation derives a generalized mathematical relationship between the abundance estimation error and the endmember separabilty. It is proven that the abundance estimation error can be reduced through increasing the endmember separability. The use of DWT-based features provides a potential to increase the endmember separability, and consequently improves the LSE of endmember abundances. The stability of the LSE of endmember abundances is also analyzed using the concept of the condition number. Analysis results show that the use of DWT-based features not only improves the LSE of endmember abundances, but also improves the LSE stability.

This study looks at the changes to NDVI value in the Weeks Bay Reserve following the impact by Hurricane Katrina. Four Landsat images from March 24, 2005 (Pre-Katrina), September 16, 2005/ April 26, 2006 (Post-Katrina) and August 7, 2002 (Control) were classified into different landcover types and run with the NDVI vegetation index. Those images were compared against each other and showed that the September image had a NDVI value drop of 49% and the April image had a 47% drop as compared to the previous March. The emergent vegetation surrounding the shoreline was most susceptible to changes in NDVI value and recovered the slowest of the tested landcover types. Swift tracks, bay areas, and rivers in the study area where tested and showed that the rivers are the most susceptible change in NDVI value and recovered the slowest.

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