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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. 

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.