Climate change, with global temperatures rising over the past decades, is a primary driver of sea level rise through the thermal expansion of seawater and the melting of the Antarctic and Greenland Ice Sheets (AIS and GIS). These ice sheets are crucial for predicting future sea level changes, as increased melting forms supraglacial lakes. These lakes can induce hydrofracture, leading to ice shelf instability and accelerated ice flow into the ocean, further elevating sea levels and affecting global climate systems. This study focuses on the AIS and GIS, emphasizing the development and application of a deep learning model to detect and classify the behavior of summer supraglacial lakes using Sentinel-1 SAR satellite data.

The methodology involved normalizing SAR imagery data to enhance data consistency, training a deep learning model using a U-Net architecture on a labeled dataset of Greenland lakes for semantic segmentation, and evaluating its performance on both Greenland and Antarctic datasets using metrics such as accuracy, precision, recall, F1-score, and SSIM. The model is trained to distinguish between draining and refreezing lakes based on backscatter intensity patterns captured in the satellite images. Furthermore, a sensitivity analysis is conducted by creating ten different perturbations of the testing dataset, which included variations in intensity, rotation, and zoom levels. The trained model is then applied to Antarctic data to create an Antarctic-wide map of lake behavior.

The deep learning model exhibited high performance, achieving an accuracy of 90.6%, precision of 90.9%, recall of 90.6%, and an F1-score of 90.0%. It showed high classification accuracy for non-lake pixels (97.5%) and draining lake pixels (84.4%), but lower accuracy for refreezing lake pixels (55.4%) due to class imbalance. Sensitivity analysis revealed optimal performance on the 'zoomed out' dataset with an overall accuracy of 92.9%. Applying the model to Antarctic data successfully identified regions of draining and refreezing lakes, providing a starting point for monitoring ice sheet dynamics and their implications for climate change.

This study underscores the potential of deep learning models to enhance supraglacial lake monitoring, contributing to a better understanding of ice sheet stability and the impacts of climate change. Future work should address class imbalance and explore further model optimizations to improve classification accuracy across both lake types.

The Alps are experiencing a gradual reduction in snow cover due to rising temperatures, impacting the landscape and dependent ecosystems. While several models have been developed to study snow cover in the region, there is a lack of visual representations. This research employs a Conditional Generative Adversarial Network (cGAN) to generate a multispectral Landsat-8 image of the Alps using environmental data inputs. The study utilizes elevation, monthly precipitation, and monthly temperature data from November to March to produce an end-of-winter Landsat-8 image. The resulting multispectral image is then used to calculate the Normalised Difference Snow Index (NDSI) and determine the snow extent by counting pixels with NDSI values above 0.4. Two climate scenarios are considered, and the generated images are compared to actual Landsat-8 imagery. Findings indicate that while the generated imagery closely resembles the real imagery, the snow extent is generally underestimated in the current model configuration, and the snow reflectance is consistently overestimated across all training steps of the cGAN. Additionally, it is observed that increasing the spatial distance between the training and testing locations leads to increased error in the results. The thesis demonstrates the feasibility of using a cGAN to generate snow extent, but suggests that enhancements to the training dataset and cGAN architecture are necessary for improved accuracy. By leveraging the cGAN, future climate scenarios can be visually represented through multispectral imagery, enabling a more detailed understanding of potential future landscapes under different climate conditions.