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Glaciers have experienced a global trend of recession within the past century. Quantification of glacier variations using satellite imagery has been of great interest due to the importance of glaciers as freshwater resources and as indicators of climate change. Spatiotemporal glacier dynamics must be monitored to quantify glacier variations. The potential methods to quantify spatiotemporal glacier dynamics with increasing complexity levels include detecting the terminus location, measuring the length of the glacier from the accumulation zone to the terminus, quantifying the glacier surface area, and measuring glacier volume. Although some deep learning methods designed purposefully for glacier boundary segmentation have achieved acceptable results, these models are often localized to the region where their training data were acquired and further rely on the training sets that were often curated manually to highlight glacial regions. Due to the very large number of glaciers, it is practically impossible to perform a worldwide study of glacier dynamics using manual methods. As a result, an automated or semi-automated method is highly desirable. The current study has built upon our previous works moving towards identification methods of the 2D glacier profile for glacier area segmentation. In this study, a deep learning method is proposed for segmentation of temporal Landsat images to quantify the glacial region within the Mount Cook/Aoraki massif located in the Southern Alps/Kā Tiritiri o te Moana of New Zealand/Aotearoa. Segmented glacial regions can be further utilized to determine the relationship of their variations due to climate change. This model has demonstrated promising performance while trained on a relatively small dataset. The permanent ice and snow class was accurately segmented at a 92% rate by the proposed model. 

Within the last century, the global sea level has risen between 16 and 21 cm and will likely accelerate into the future. Projections from the Intergovernmental Panel on Climate Change (IPCC) show the global mean sea level (GMSL) rise may increase to up to 1 m (1000 mm) by 2100. The primary cause of the sea level rise can be attributed to climate change through the thermal expansion of seawater and the recession of glaciers from melting. Because of the complexity of the climate and environmental systems, it is very difficult to accurately predict the increase in sea level. The latest estimate of GMSL rise is about 3 mm/year, but as GMSL is a global measure, it may not represent local sea level changes. It is essential to obtain tailored estimates of sea level rise in coastline Florida, as the state is strongly impacted by the global sea level rise. The goal of this study is to model the sea level in coastal Florida using climate factors. Hence, water temperature, water salinity, sea surface height anomalies (SSHA), and El Niño southern oscillation (ENSO) 3.4 index were considered to predict coastal Florida sea level. The sea level changes across coastal Florida were modeled using both multiple regression as a broadly used parametric model and the generalized additive model (GAM), which is a nonparametric method. The local rates and variances of sea surface height anomalies (SSHA) were analyzed and compared to regional and global measurements. The identified optimal model to explain and predict sea level was a GAM with the year, global and regional (adjacent basins) SSHA, local water temperature and salinity, and ENSO as predictors. All predictors including global SSHA, regional SSHA, water temperature, water salinity, ENSO, and the year were identified to have a positive impact on the sea level and can help to explain the variations in the sea level in coastal Florida. Particularly, the global and regional SSHA and the year are important factors to predict sea level changes. 

Glaciers are important indictors of climate change as changes in glaciers physical features such as their area is in response to measurable evidence of fluctuating climate factors such as temperature, precipitation, and CO2. Although a general retreat of mountain glacier systems has been identified in relation to centennial trends toward warmer temperatures, there is the potential to extract a great deal more information regarding regional variations in climate from the mapping of the time history of the terminus position or surface area of the glaciers. The remote nature of glaciers renders direct measurement impractical on anything other than a local scale. Considering the sheer number of mountain glaciers around the globe, ground measurements of terminus position are only available for a small percentage of glaciers and ground measurements of glacier area are rare. In this project, changes in the terminal point and area of Franz Josef and Gorner glaciers were quantified in response to climate factors using satellite imagery taken by Landsat at regular intervals. Two supervised learning methods including a parametric method (multiple regression) and a nonparametric method (generalized additive model) were implemented to identify climate factors that impact glacier changes. Local temperature, CO2, and precipitation were identified as significant factors for predicting changes in both Franz Josef and Gorner glaciers. Spatiotemporal quantification of glacier change is an essential task to model glacier variations in response to global and local climate factors. This work provided valuable insights on quantification of surface area of glaciers using satellite imagery with potential implementation of a generic approach. 

Coastal communities are growing globally, promoted by the ocean’s abundant opportunity for food, recreation, tourism, and green energy. Erosion and accretion along the coast significantly affect the safety of these communities and longevity of coastal infrastructure. To better predict rates of erosion and accretion, sediment transport models and active-bed thickness prediction techniques are of particular importance. Two dimensionless parameters, the Shields and Ursell parameters, are often used to predict rates of sediment transport and wave linearity. The goal of this project is to analyze sediment movement in a laboratory wave flume using particle image velocimetry (PIV). From the analysis we estimate the dimensionless parameters and instantaneous active-bed thickness to predict volumetric sediment transport rates as waves propagate shoreward. 

Like many estuaries worldwide, the Indian River Lagoon (IRL), has seen a decline in resources and overall water quality due to human activities. One method to help restore water quality and benthic habitats is to construct and deploy oyster restoration mats on dock pilings, known as the Living Docks program. This community-driven program was founded to promote the growth of filter-feeding benthic organisms and improve local water quality. The purpose of this study was to assess the growth and performance at four of the Living Dock locations and to provide feedback to the citizens who were involved in the initial process and deployments. Four docks were biologically assessed for temporal changes during three-time points throughout the year, as denoted by changes in temperature in October, February, and June. The back of each mat was also analyzed for organism cementation to the piling. The presence of filter-feeding organisms was found to vary both spatially and temporally, especially for the eastern oyster (Crassostrea virginica), encrusting bryozoan (Schizobrachiella verrilli), sponges (Demospongiae), and barnacles (Amphibalanus amphitrite, Amphibalanus eburneus). A greater diversity in the sessile benthic flora and fauna was seen during the June sampling period. Cementation on the pilings was due to a combination of barnacles and sponge growth. Cementation was observed to increase from October and decrease for all but one dock for the June sampling period. The results demonstrate this restoration project to be successful in promoting the growth of benthic organisms, while also providing understanding into seasonal trends amongst species. Hopefully, the positive output will encourage more community members and citizen scientists to participate in the ongoing effort to help restore water quality in the IRL.