1,720,999 research outputs found
Interoperability of Digital Geograhic Information in the Domain of Maritime Navigation
Full text linkThis thesis examines the interoperability of digital geographic information (DGI) in the context of maritime navigation. Maritime navigation is a complex domain that draws on a wide variety of information resources. The diversity and multitude of such information creates challenges with respect to interoperability – the ability of software and hardware systems on multiple machines from multiple vendors to communicate with each other meaningfully. The Open Geospatial Consortium (OGC) is continually developing a series of standards for DGI that may provide solutions for DGI interoperability in the domain of maritime navigation. This thesis examines the OGC Web Services (OWS) and how the Web Feature Service (WFS) can be adopted to serve Notice to Mariners (NTM)
Geophysical Feature Extraction and Spatiotemporal Analysis of Polar Sea Ice Using High Spatial Resolution Imagery
Full text linkThe Arctic sea ice region has become an increasingly important study area since it is not only a key driver of the Earth’s climate, but also a sensitive indicator of climate change. To model and validate sea ice changes, it is crucial to extract geophysical features of sea ice from high-resolution remote sensing data. We collected a large volume of remote sensing images from multiple platforms such as airborne Digital Mapping System (DMS) and Worldview series satellite in the Arctic region during melting season. Processing such a large volume of imagery poses a significant challenge for extracting sea ice spatiotemporal patterns in a timely manner. Additionally, high spatial resolution (HSR) has been largely ignored due to its complex and heterogeneous nature in both space and time, and Arctic operational missions can routinely produce hundreds of gigabytes of data. The advancement of drone technologies keeps adding rapidly to the volume of sea ice aerial-survey-based observations. In summary, processing such big sea ice data includes challenges such as: 1) the big data challenges in HSR image product, e.g., the big data volume and the heterogeneous formats of a variety of sea ice HSR image data collected by different platforms or agencies; 2) the lack of standard sea ice feature extraction procedure from HSR imagery; 3) the ability for managing, visualizing, and processing HSR sea ice image data, and extracting geophysical properties or attributes. I propose a reliable and effective high-accuracy and high-performance approach to extract sea ice geophysical features from a large amount of HSR remote sensing data to support scientists and allow them to gain new insights from the spatiotemporal analysis on big data process. The objectives of this research are to 1) develop an efficient geophysical feature extraction workflow based on object-based image analysis (OBIA) method for HSR image data to classify different sea ice features and extract the relevant geophysical parameters such as sea ice leads, sea ice floe, melt pond and ice ridge; 2) design a practice workflow to analyze spatiotemporal patterns of sea ice geophysical features; and 3) design and develop a prototype of an on-demand web service for the cyberinfrastructure, providing a publicly available portal for various data owners and users. In order to achieve these objectives, an on-demand sea ice HSR imagery management and processing service is developed, and a scientific case study is demonstrated for geophysical feature extraction and spatiotemporal analysis of sea ice leads. This research on geophysical feature extraction and spatiotemporal analysis of sea ice from high spatial resolution data is innovative for: 1) the practical OBIA classification workflow in a distributed environment for large datasets; 2) the extracted geophysical features could serve as ground references in sea ice research; 3) the developed arctic cyberinfrastructure provides a data service prototype for polar community. The results of this research can be helpful for the understanding of sea ice processing and utilization of climate modeling and verification at different scales
Geovisualisation Mashup Tool to Provide Better Situation Awareness for Earthquakes
Full text linkImportant information pertaining to earthquake and its response is spread all over the Internet. In the event of a disaster like an earthquake, rapid access to information is critical. The public usually has a hard time retrieving and combining information from various sources spread all across the Internet thus preventing them from making quick decisions. Most of the current earthquake mashups do not provide relevant information like location of first responders and routing to important facilities like hospitals, emergency operation centers, police stations and fire stations which could save important time and lives. To address the challenges, I developed an Earthquake Information Mashup Tool which demonstrates a mashup approach to provide a web visualization tool that provides real-time monitoring of earthquakes and traffic conditions, the location of important facilities and routing to them, and the ability for users to depict a description of the situation around them. Users are thus able to integrate information from various near real time sources and get better situation awareness of the environment around them and are able to make important decisions. The Earthquake Information Mashup tool demonstrates an effective means to achieve situation awareness and a routing tool to important facilities in the vicinity of the user
Optimizing Geospatial Cyberinfrastructure to Improve the Computing Capability for Climate Studies
Full text linkClimate simulation has significant uncertainties due to our current limited understanding of the processes and interactions between different components of the Earth. Model sensitivity analysis, which tests the sensitivity of model output to the input parameter values, is a standard practice for determining the model uncertainties and improving model accuracy. A common approach for climate model sensitivity analysis is to run a model many times by sweeping a large number of adjustable parameters. However, this approach is hampered by three computational challenges: computing intensity, data intensity, and procedure complexity. This dissertation proposes three optimization methodologies to address these challenges respectively, including 1) tackling the computing intensity challenge posed by climate simulation using Model as a Service, a new service model in the context of cloud computing; 2) managing and processing the big model output – “data intensity” – using a scalable big spatiotemporal data analytics framework; 3) solving the procedure complexity issue using a service-oriented cloud-based scientific workflow framework
Developing a Generic Framework to Support Multi-dimensional Earth Observing System Data in GIS Applications
Full text linkEarth Observing System (EOS) data are expanding at an unprecedented rate due to the fast development of advanced data acquisition technologies. These data provide valuable, long-term record of change and dynamics about our Earth, and therefore are paramount in addressing key national and global challenges in climate change, water use and quality, natural disasters, weather forecasting and warnings, renewable energy, agriculture, forestry and natural ecosystems, coasts and oceans, and national security. As a result, they have been increasingly used in various GIS applications by both government and science communities. However, many varied formats and standards have been defined to organize and store the EOS data that are highly tailored for different applications by different organizations over the past decades. Many of these data are in old formats, and specialized geospatial tools are required to interpret and use them. This makes it difficult to incorporate EOS into GIS and it very ineffectively to analyze in either commercial or open-source GIS tools. On the other hand, most GIS systems cannot comprehensively process and utilize all types of EOS data, and there are always unexpected issues and errors while importing and manipulating EOS data. To reconcile the conflicts between EOS data and GIS systems, initiatives have been made for developing a general methodology to solve EOS data compatibility in GIS using common standards. However, no solutions are currently available to support the processing of all types of EOS data products. The objective of this research is to explore the barriers and strategies of integrating various types of EOS data in GIS applications. Specifically, the research investigates and solves three key technical problems including: (i) designing a generic and heuristic plug-in framework for consuming different types of EOS data; (ii) developing a series of functions to fix the problem occurring when using EOS data in GIS applications; (iii) optimizing HDF4/HDF5 data drivers of GDAL for enhancing its capability of handle EOS data; and (iv) developing an open source GIS extension to enhance the capability of GIS systems in accessing EOS data. One research result of this thesis is optimized source code of Geospatial Data Abstraction Library (GDAL) commonly used in most GIS systems for handling geospatial raster and vector data, without impacting the original function on reading non-EOS data products. The optimized GDAL fixes the issues of HDF4 and HDF5 data drivers used to access HDF datasets and overcome limitations in processing multiple dimensional datasets posed by the current GDAL version. Finally based on the optimized GDAL, an open source extension is developed to support the access of more EOS data of different types and fill in the gap between GDAL and commercial GIS software (e.g. ArcGIS) or open source GIS projects (e.g. QGIS). A series of EOS data products collected from NASA’s Atmospheric Scientific Data Center (ASDC) are selected as study cases for demonstrating the effectiveness and applicability of the proposed framework and tools. The enhanced GDAL and GIS extension enable and encourage more GIS users to use EOS data in GIS software for different research or applications. Additionally, a series of Application Programming Interfaces (APIs) are provided to allow other developers in GIS communities to integrate these interfaces into their GIS applications. It is concluded that the proposed plug-in framework can be effectively applied to different domains for handling the current problems or limitations of interpreting multi-dimensional dataset, without compromising their original functions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
An Interoperable Framework for Planetary Defense Data Integration and Visualization to Support the Mitigation of Potential Hazardous Asteroids
Full text linkA large asteroid impact can cause catastrophic environmental effects, as was shown by the Chicxulub impact some 66 million years ago (Pope et al. 1997). In order to protect our planet from future near-Earth objects (NEOs), it is crucial to efficiently and seamlessly integrate data, discoveries, and resources. However, planetary defense information remains scattered throughout multiple branches, organizations, and countries.The challenges that come with dispersed planetary defense information are manifold. First, the heterogeneity of planetary defense situations requires unique responses from various organizations. Second, there is a lack of structured integration, and interoperability among planetary defense stakeholders. This hampers effective communication and collaboration. Third, the diversity of data and information for planetary defense research creates discrepancy between PD data formats for individual researchers. Finally, future threats mitigation efforts are often hindered by a lack of comprehensive understanding of the problem. Consequently, an interoperable framework for planetary defense data integration and visualization is needed to support the mitigation of potentially hazardous asteroids. This dissertation has presented a data-fusion framework that can be used to support the detection, characterization, and mitigation of potentially hazardous asteroids. The data-fusion framework was used to develop the Planetary Defense Knowledge Gateway (PDKG), a platform that enables users to access, visualize, and analyze integrated, and interoperable planetary defense data. This dissertation also focused on multiprocessing techniques, comprehensive data modeling, and data inaccuracies verification. The implemented multiprocessing techniques provides three main advantages: (1) a data pre-fetching technique to minimize data retrieval latency, (2) an in-memory caching technique to improve data access performance, and (3) a query parallelization technique to speed up the execution of complex queries. The comprehensive data modeling considered the different types of information that needed to be integrated, such as observational data, catalog data, and expert knowledge. The data inaccuracies verification was performed using a set of heuristics that were designed to identify errors in the data. This research provides a foundation upon which the planetary defense community can build to mitigate the effects of dispersed information and aid in the overall decision-making strategies
Optimizing Access to Big Earth Observation Data with Spatiotemporal Patterns -- An Example with the GEOSS Clearinghouse
Full text linkBig Data becomes increasingly important in almost all scientific domains, especially in geographical studies where millions to billions of sensors are collecting data of the Earth continuously. Recognizing the importance of managing the Big Earth observation Data, Group on Earth Observations selected the Global Earth Observation System of Systems Clearinghouse (CLH) to harvest, manage and share Earth observation metadata. Building a CLH to support global operation is very challenging, because it is essential for CLH to effectively manage and index Big Earth observation Data, provide accurate data service evaluation, and execute these services using fast provision computing resources to different space and time locations to support dynamic global user access. Although various optimization mechanisms (e.g., index, workload balancing, service model, cache) have been proposed, few approaches optimize the Earth observation data access with the spatiotemporal patterns of the data utilization. This dissertation investigates a variety of spatiotemporal optimizations to better support Big Earth observation Data access using the CLH as an example. Specifically, the objectives are the following: (1) develop a new indexing mechanism to accelerate Big Data access. The new indexing mechanism integrates the spatiotemporal user access patterns into traditional index structures. The experiment result showed that the new index yields 9-20% performance gain for the data access compared to a classic R*-tree index; (2) develop a new service performance model to improve the service evaluation accuracy. The new service model collects globally distributed service information with cloud services and volunteers, and integrates the spatiotemporal service characteristics to provide evaluation end users at different space-time locations. The proposed spatiotemporal service model yields 3-18% accuracy improvements gains, thereby helping end users better choose service for data access; and (3) develop a cloud computing adoption framework to better support global user access and spiking access. The cloud framework automatically provisions and delivers computing resources for different data access tasks with spatiotemporal computing workloads, and globally deploys system instances to different regions. The experiment result showed that the cloud framework helps the CLH achieve about 10 seconds’ performance gains for global and spiking user access. The significance of this research is that it provides a potential solution for optimizing access to Big Earth observation data using spatiotemporal data utilization patterns, thereby better supporting various Big Data related studies with faster data access
Utilizing Model Interoperability and Spatial Cloud Computing to Enable the Computability of Dust Storm Forecasting
Full text linkBoth environmental and human challenges, such as deforestation and desertification, require scientifically sound simulations of physical phenomena to better understand the past and to better predict future trends for improved decision support. However, many scientific problems cannot be processed using a single computer and require computing capability from many distributed computers. The problems should be solved by interdisciplinary efforts instead of by a single science community. Using dust storm forecasting as a case study, I investigate how interoperability technologies can facilitate data access service, model input integration, model coupling, and output utilization and dissemination. Additionally, the research will explore how to utilize spatiotemporal patterns of phenomena, models and computing resources to improve the performance of dust storm forecasting. Finally, I adopt and optimize cloud computing platforms through spatiotemporal patterns to enable the computability of dust storm forecasting over a large area with high resolution to support geospatial decision-making. This research eventually reduce the execution time and communication for two heterogeneous models, Eta-8bin 10 and NMM- dust storm models by enabling the interoperable and loosely-coupling execution of the two models
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