PhD

Congratulations to Dr. Jin Xing, who successfully defended his dissertation.

Scale handling for land Use/cover change in an era of big data

Abstract Big data promises numerous benefits for Land Use/Cover Change (LUCC) research, in terms of increased volume, velocity, and variety of remotely sensed imagery datasets. However, it challenges traditional approaches to identifying LUCC. The increased volume and velocity of big data mean that existing data handling frameworks may not be able to effectively distribute spatial data and computation across a large number of computers. Previous LUCC workflows are not designed for big data and they cannot be easily deployed on big data computing tools such as cloud computing or the Hadoop framework. High levels of scale heterogeneity mean that images can cover different spatial and temporal granularities and extents. Theoretically, it becomes difficult to handle the data because these multiple and conflicting scales exist contemporaneously. Because we are working with big data, geographic entities may be recorded at different granularities and extents than should be detected as LUCC, but cannot be. Finally, no one has yet combined each of these distinct problems to fully examine all of the big data challenges facing LUCC. I present six advances to address each of the big data challenge in LUCC: (1) a theoretical concept called Scope, (2) a spatially sensitive decomposition/recomposition method, (3) a scale invariant change detection method, (4) a spatial-temporal model for LUCC big data, (5) a change boundary optimization algorithm, and (6) a LUCC-specific Geospatial CyberInfrastructure. In this manuscript, I first propose Scope as a concept to model spatial-temporal scales by explicitly merging granularity, extent, time, and property. Second, I develop a new decomposition/recomposition framework to manage data decomposition, distribution, and recombination in a distributed computing environment. Third, a scale invariant change detection method identifies LUCC by combining regional and point features from datasets at multiple spatial granularities and extents. Fourth, I theorize a spatial-temporal object model to improve the integration of space and time within LUCC research. The spatial-temporal object model and, the fifth advance, a change boundary optimization algorithm handle data noise and better organize the spatial-temporal object changes. Finally, a Geospatial CyberInfrastructure combines these separate approaches with cloud computing and distributed computing frameworks as a holistic approach for the big data challenge in LUCC research. These six advances are tested in a series of case studies using datasets collected from 2005-2012, at the Greater Montreal Area.

Drew Bush successfully defended his dissertation on climate change and education. Congratulations, Dr. Bush!

Student Climate Change Education: The Role of Scientific Technologies in Improving Public Geoscience Understandings

Abstract: In North America, segments of the public misunderstand the physical science of anthropogenic global climate change (AGCC) and its connection to human society. Individuals have been shown to filter their scientific understandings through identification with specific worldviews, ecological paradigms, geographic identities or political leanings. To overcome this problem, prominent scientists and the Next Generation Science Standards (NGSS) have called for curricula and instructional approaches that emphasize learning about climate research using climate models. Using the techniques of educational research, this study presents unique empirical findings on how geoscientists can employ innovate instructional approaches and science education technologies to overcome sociocultural barriers and improve public understanding of AGCC.

The chapters of this dissertation present detailed analysis and statistically significant results on the educational impact of students learning to run a National Aeronautics and Space Administration (NASA) global climate model (GCM). Through a series of case studies, this study explored how a key technology of climate science—a GCM—impacted student learning compared to ubiquitous simple climate education technologies. The central hypothesis was that student use of the actual research methods and technologies of climate scientists will better improve AGCC understanding. This study utilized a pre/post, control/treatment experimental design that allowed for comparison between instructional strategies and climate education technologies used by two groups of students. To operationalize this work, it employed research instruments such as pre/post diagnostic exams, performance-based assessments, pre/post questionnaires and 536-minutes of classroom video recordings. It also utilized quantitative statistical analysis to determine the significance of differences and establish what educational and sociocultural factors impacted individual student learning gains across the whole sample. Findings from this work have shown that more students succeed at understanding AGCC when exposed to inquiry research processes using scientific technologies such as GCMs. In contrast, those who learned about GCMs through lecture only showed improvement in their recall of facts tested by multiple-choice questions. Individual students’ ecological paradigms and relationships to natural places also best predicted engagement (represented by class attendance) with course materials and larger learning gains.

Congratulations, Dante Torio, for passing your comprehensive exam! Dante is funded by our GEOIDE research.