Browsing by Subject "Earth science"

Now showing 1 - 3 of 3
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    Approximate search on massive spatiotemporal datasets.
    (2012-08) Brugere, Ivan
    Efficient time series similarity search is a fundamental operation for data exploration and analysis. While previous work has focused on indexing progressively larger datasets and has proposed data structures with efficient exact search algorithms, we motivate the need for approximate query methods that can be used in interactive exploration and as fast data analysis subroutines on large spatiotemporal datasets. This thesis formulates a simple approximate range query problem for time series data, and proposes a method that aims to quickly access a small number of high-quality results of the exact search resultset. We formulate an anytime framework, giving the user flexibility to return query results in arbitrary cost, where larger runtime incrementally improves search results. We propose an evaluation strategy on each query framework when the false dismissal class is very large relative to the query resultset and investigate the performance of indexing novel classes of time series subsequences.
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    Earth science teachers' knowledge of the water system and Its reflections in their lesson plans.
    (2011-08) Nam, Younkyeong
    Over the last two decades, scientists have recognized the necessity of studying the earth as an integrated system. Consequently, the knowledge of physical earth systems and human interactions was integrated to form a new discipline, Earth System Science (ESS). Given the acceleration of environmental change, such as that of the global climate system, understanding the earth as a system has become essential in order to create a scientifically literate citizenry. However, our understanding of teachers' and students' conceptual understanding of earth as a system is still in its infancy. Due to the interdisciplinary nature of the ESS discipline as well as the complexity of the ESS knowledge structure, there is no consensus about important ESS knowledge for teachers or students. This study presents an analytical framework, Earth System Knowledge Framework (ESKF), to assess teachers' conceptual understanding of earth systems using the concept of water. By utilizing the framework, this study investigates five secondary earth science teachers' conceptual understandings of water in earth system. This study also probes how the teachers' conceptual understanding of water in the earth system affects their selection and organization of the topics and related content knowledge for lesson planning. Through intensive interviews with the teachers, this study employs multiple case studies using inductive and qualitative analysis methods. The findings of this study demonstrate that the teachers' conceptual understandings of water in earth system are highly related to their Earth System Knowledge (ESK). Furthermore, the science teachers' conceptual understanding of water in earth system directly affects the topic choices and content knowledge used for teaching the concept of water. This study implies that the teachers not only need to possess knowledge of physical earth systems but also knowledge of earth's biosphere and ecosystems to understand earth as a system. This study also suggests a need to reform teacher preparation in a way that the teachers could gain basic and fundamental knowledge of earth system and elaborate their skills to apply earth system knowledge for teaching.
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    Time series change detection: algorithms for land cover change.
    (2010-04) Boriah, Shyam
    The climate and earth sciences have recently undergone a rapid transformation from a data-poor to a data-rich environment. In particular, climate and ecosystem related observations from remote sensors on satellites, as well as outputs of climate or earth system models from large-scale computational platforms, provide terabytes of temporal, spatial and spatio-temporal data. These massive and information-rich datasets offer huge potential for advancing the science of land cover change, climate change and anthropogenic impacts. One important area where remote sensing data can play a key role is in the study of land cover change. Specifically, the conversion of natural land cover into human-dominated cover types continues to be a change of global proportions with many unknown environmental consequences. In addition, being able to assess the carbon risk of changes in forest cover is of critical importance for both economic and scientific reasons. In fact, changes in forests account for as much as 20% of the greenhouse gas emissions in the atmosphere, an amount second only to fossil fuel emissions. Thus, there is a need in the earth science domain to systematically study land cover change in order to understand its impact on local climate, radiation balance, biogeochemistry, hydrology, and the diversity and abundance of terrestrial species. Land cover conversions include tree harvests in forested regions, urbanization, and agricultural intensification in former woodland and natural grassland areas. These types of conversions also have significant public policy implications due to issues such as water supply management and atmospheric CO2 output. In spite of the importance of this problem and the considerable advances made over the last few years in high-resolution satellite data, data mining, and online mapping tools and services, end users still lack practical tools to help them manage and transform this data into actionable knowledge of changes in forest ecosystems that can be used for decision making and policy planning purposes. In particular, previous change detection studies have primarily relied on examining differences between two or more satellite images acquired on different dates. Thus, a technological solution that detects global land cover change using high temporal resolution time series data will represent a paradigm-shift in the field of land cover change studies. To realize these ambitious goals, a number of computational challenges in spatio-temporal data mining need to be addressed. Specifically, analysis and discovery approaches need to be cognizant of climate and ecosystem data characteristics such as seasonality, non-stationarity/inter-region variability, multi-scale nature, spatio-temporal autocorrelation, high-dimensionality and massive data size. This dissertation, a step in that direction, translates earth science challenges to computer science problems, and provides computational solutions to address these problems. In particular, three key technical capabilities are developed: (1) Algorithms for time series change detection that are effective and can scale up to handle the large size of earth science data; (2) Change detection algorithms that can handle large numbers of missing and noisy values present in satellite data sets; and (3) Spatio-temporal analysis techniques to identify the scale and scope of disturbance events.