The Island of Tongatapu, Tonga, was struck by a tsunami on January 15, 2022. Internet was cut off from the island, which made remote sensing a valuable tool for the assessment of damages. Through land cover classification, change vector analysis and log-ratio image differencing, damages caused by the tsunami were assessed remotely in this thesis. Damage assessment is a vital part of both assessing the need for humanitarian aid after a tsunami, but also lays the foundation for preventative measurements and reconstruction. The objective of this thesis was to assess damage in terms of square kilometers and create damage maps. It was also vital to assess the different methods and evaluate their accuracy. Results from this study could theoretically be combined with other damage assessments to evaluate different aspects of damage. It was also important to evaluate which methods would be good to use in a similar event. In this study Sentinel-1, Sentinel-2 and high-resolution Planet Imagery were used to conduct a damage assessment. Evaluating both moderate and high-resolution imagery in combination with SAR yielded plausible, but flawed results. Land cover was computed for moderate and high-resolution imagery using three types of classifiers. It was found that the Random Forest classifier outperforms both CART and Support Vector Machine classification for this study area.  Land cover composite image differencing for pre-and-post tsunami Sentinel-2 images achieved an accuracy of around 85%. Damage was estimated to be about 10.5 km^2. Land cover classification with high-resolution images gave higher accuracy. The total estimated damaged area was about 18 km^2. The high-resolution image classification was deemed to be the better method of urban damage assessment, with moderate-resolution imagery working well for regional damage assessment.  Change vector analysis provided plausible results when using Sentinel-2 with NDVI, NDMI, SAVI and BSI. NDVI was found to be the most comprehensive change indicator when compared to the other tested indices. The total estimated damage using all tested indices was roughly 7.6 km^2. Using the same method for Sentinel-1's VV and VH bands, the total damage was estimated to be 0.4 and 2.6 km^2 respectively. Log ratio for Sentinel-1 did not work well compared to change vector analysis. Issues with false positives occurred. Both log-ratios of VV and VH gave a similar total estimated damage of roughly 5.2 km^2.  Problems were caused by cloud cover and ash deposits. The analysis could have been improved by being consistent with the choice of dates for satellite images. Also, balancing classification samples and using high-resolution land cover classification on specific areas of interest indicated by regional methods. This would circumvent problems with ash, as reducing the study area would make more high-resolution imagery available.

Change detection (CD) has, in recent years, shown promising results in remote sensing (RS). The development of deep learning CD (DLCD) has, in even more recent years, taken change detection to another level and it has become more widely researched. However, the research depends on publicly available datasets that have been manually annotated for the task of CD. This method is cumbersome and the resulting datasets do not often include all types of change. In this thesis, the generalizability to different areas and different change types of a model trained on a widely used public dataset is analyzed. Also, the thesis investigates how 3D information from Maxar Technologies 3D models can be used to automatically create new more general datasets for CD with both binary or non-binary outputs. The access to large amounts of satellite images together with 3D information enables the creation of more general datasets that can capture more types of change.The thesis concludes that a model trained on the publicly available dataset does not generalize to other areas or other types of change. Models trained on the automatically generated datasets yield relatively good results which indicates that using 3D information to automatically create large datasets is a valid method for CD. Even non-binary approaches show promising results which enable using to gain more practical information on the change of an area. While the thesis presents encouraging results, work can definitely be done to further improve the generalization of the models and improve the dataset generation.

Natural disasters like flooding have always been a big problem for countries around the world, but as the global climate changes and the number of people living in cities keeps growing, the threat of flooding has become a lot worse. Even though many studies have been conducted on flood mapping and vulnerability assessment in urban areas, this research addresses a significant knowledge gap in this domain. First, we used a flood depth estimation approach has been used to address the overestimation of urban flood mapping areas using Sentinel-1 images. Ten different combinations of the two initial VH and VV polarizations were used to rapidly and accurately map urban floods within open-source Google Earth Engine platforms using four different methods. The inclusion of flood depth has improved the accuracy of these methods by 7% on average. Next, we focused our research to find out who is most at risk in the floodplain areas. Minority communities, such as African Americans, as a result of socioeconomic constraints, face more difficulties. So, next we conducted an analysis of spatial and temporal changes of demographic patterns (Race) in five southern cities in US. From our analysis we have found that in majority of cities, the minority population within the floodplain has increased over the past two decades, with the exception of Charleston, South Carolina, where the white population has increased while the minority population has decreased. Building upon these insights, we have included more socio-economic and demographic variables in our analysis to find out the more holistic view of the vulnerable people in two of these cities (Jackson and Birmingham). Due to high autocorrelation between explanatory variables, we used Principal Component Analysis (PCA) along with global and local regression techniques to find out how much these variables can explain the vulnerability. According to our findings, the spatial components play a significant role in explaining vulnerabilities in greater detail. The findings of this research can serve as an important resource for policymakers, urban planners, and emergency response agencies to make informed decisions in future events and enhancing overall resilience.

This study aims to conduct an assessment of the land cover change of the Mississippi and Alabama coastal region, an integral part of the Gulf Coast ecological makeup. Landsat satellite data were used to perform a supervised classification using the imagery captured by Landsat sensors including Landsat 1-2 Multispectral Scanner (MSS), Landsat 4-5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI) from 1973 to 2015. The objective of this study is to build a long-term assessment of urban development and land cover change over the past four decades for the Alabama and Mississippi Gulf Coast and to characterize these changes using Landscape Metrics (LM). The findings of this study indicate that the urban land cover doubled in size between 1973 and 2015. This expansion was accompanied by a high degree of urban fragmentation during the first half of the study period and then a gradual leveling off. Local, state, and federal authorities can use the results of this study to build mitigation plans, coastal development planning, and serve as the primary evaluation of the current urban development for city planners, environmental advocates, and community leaders to reduce degradation for this environmentally sensitive coastal region.

No description available.

No description available.

No description available.

No description available.

<p>When an observer detects changes in a scene from a viewpoint that is different from the learned viewpoint, viewpoint change caused by observer’s locomotion would lead to better recognition performance compared to a situation where the viewpoint change is caused by equivalent movement of the scene. While such benefit of observer locomotion could be caused by spatial updating through body-based information (Simons and Wang 1998), or knowledge of change of reference direction gained through locomotion (Mou et al, 2009). The effect of such reference direction information have been demonstrated through the effect of a visual cue (e.g., a chopstick) presented during the testing phase indicating the original learning viewpoint (Mou et al, 2009).</p> <p>In the current study, we re-examined the mechanisms of such benefit of observer locomotion. Six experiments were performed using a similar change detection paradigm. Experiment 1 & 2 adopted the design as that in Mou et al. (2009). The results were inconsistent with the results from Mou et al (2009) in that even with the visual indicator, the performance (accuracy and response time) in the table rotation condition was still significantly worse than that in the observer locomotion condition. In Experiments 3-5, we compared performance in the normal walking condition with conditions where the body-based information may not be reliable (disorientation or walking over a long path). The results again showed a lack of benefit with the visual indicator. Experiment 6 introduced a more salient and intrinsic reference direction: coherent object orientations. Unlike the previous experiments, performance in the scene rotation condition was similar to that in the observer locomotion condition.</p> <p>Overall we showed that the body-based information in observer locomotion may be the most prominent information. The knowledge of the reference direction could be useful but might only be effective in limited scenarios such as a scene with a dominant orientation.</p> / Master of Science (MSc)

As social networks become more prevalent, there is significant interest in studying these network data, the focus often being on detecting anomalous events. This area of research is referred to as social network surveillance or social network change detection. While there are a variety of proposed methods suitable for different monitoring situations, two important issues have yet to be completely addressed in network surveillance literature. First, performance assessments using simulated data to evaluate the statistical performance of a particular method. Second, the study of aggregated data in social network surveillance. The research presented tackle these issues in two parts, evaluation of a popular anomaly detection method and investigation of the effects of different aggregation levels on network anomaly detection. / Ph. D.