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Development and application of a new comprehensive image-based classification scheme for coastal and benthic environments along the southeast Florida continental shelf
Title
Development and application of a new comprehensive image-based classification scheme for coastal and benthic environments along the southeast Florida continental shelf.
Creator
Makowski, Christopher, Finkl, Charles W., Florida Atlantic University, Charles E. Schmidt College of Science, Department of Geosciences
Abstract/Description
The coastal (terrestrial) and benthic environments along the southeast Florida continental shelf show a unique biophysical succession of marine features from a highly urbanized, developed coastal region in the north (i.e. northern Miami-Dade County) to a protective marine sanctuary in the southeast (i.e. Florida Keys National Marine Sanctuary). However, the establishment of a standard bio-geomorphological classification scheme for this area of coastal and benthic environments is lacking. The...
Show moreThe coastal (terrestrial) and benthic environments along the southeast Florida continental shelf show a unique biophysical succession of marine features from a highly urbanized, developed coastal region in the north (i.e. northern Miami-Dade County) to a protective marine sanctuary in the southeast (i.e. Florida Keys National Marine Sanctuary). However, the establishment of a standard bio-geomorphological classification scheme for this area of coastal and benthic environments is lacking. The purpose of this study was to test the hypothesis and answer the research question of whether new parameters of integrating geomorphological components with dominant biological covers could be developed and applied across multiple remote sensing platforms for an innovative way to identify, interpret, and classify diverse coastal and benthic environments along the southeast Florida continental shelf. An ordered, manageable hierarchical classification scheme was developed to incorporate the categories of Physiographic Realm, Morphodynamic Zone, Geoform, Landform, Dominant Surface Sediment, and Dominant Biological Cover. Six different remote sensing platforms (i.e. five multi-spectral satellite image sensors and one high-resolution aerial orthoimagery) were acquired, delineated according to the new classification scheme, and compared to determine optimal formats for classifying the study area. Cognitive digital classification at a nominal scale of 1:6000 proved to be more accurate than autoclassification programs and therefore used to differentiate coastal marine environments based on spectral reflectance characteristics, such as color, tone, saturation, pattern, and texture of the seafloor topology. In addition, attribute tables were created in conjugation with interpretations to quantify and compare the spatial relationships between classificatory units. IKONOS-2 satellite imagery was determined to be the optimal platform for applying the hierarchical classification scheme. However, each remote sensing platform had beneficial properties depending on research goals, logistical restrictions, and financial support. This study concluded that a new hierarchical comprehensive classification scheme for identifying coastal marine environments along the southeast Florida continental shelf could be achieved by integrating geomorphological features with biological coverages. This newly developed scheme, which can be applied across multiple remote sensing platforms with GIS software, establishes an innovative classification protocol to be used in future research studies.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004135, http://purl.flvc.org/fau/fd/FA00004135
Subject Headings
Benthos -- Habitat -- Maps, Environmental mapping, Marine ecology -- Florida, Marine ecology -- Florida, Ubiquitous computing
Format
Document (PDF)
Integrated modeling approach for enery alternatives and green house gas mitigation in the state of Florida
Title
Integrated modeling approach for enery alternatives and green house gas mitigation in the state of Florida.
Creator
Thakkar, Kuntal, Ghenai, Chaouki, Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description
The objective of the research is to develop various green-house gas (GHG) mitigations scenarios in the energy demand and supply sectors for state of Florida through energy and environment modeling tool called LEAP (Long Range Energy Alternative Planning System Model) for 2010-2050. The GHG mitigation scenarios consist of various demand and supply side scenarios. One of the GHG mitigation scenarios is crafted by taking into account the available renewable resources potential for power...
Show moreThe objective of the research is to develop various green-house gas (GHG) mitigations scenarios in the energy demand and supply sectors for state of Florida through energy and environment modeling tool called LEAP (Long Range Energy Alternative Planning System Model) for 2010-2050. The GHG mitigation scenarios consist of various demand and supply side scenarios. One of the GHG mitigation scenarios is crafted by taking into account the available renewable resources potential for power generation in the state of Florida and then the comparison has been made for transformation sector and corresponding GHG emissions through this newly developed mitigation scenario versus Business As Usual and Florida State Policy scenario. Moreover two master mitigation scenarios (Electrification and Efficiency and Lifestyle) were crafted through combination of certain GHG mitigation scenarios. The energy demand and GHG emissions assessment is performed for both master mitigation scenarios versus business As Usual scenario for 2010 – 2050.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004166, http://purl.flvc.org/fau/fd/FA00004166
Subject Headings
Climate change mitigation -- Florida, Greenhouse gas mitigation -- Florida, Renewable energy sources -- Florida, Ubiquitous computing
Format
Document (PDF)
Sensitivity analysis of predictive data analytic models to attributes
Title
Sensitivity analysis of predictive data analytic models to attributes.
Creator
Chiou, James, Zhu, Xingquan, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Classification algorithms represent a rich set of tools, which train a classification model from a given training and test set, to classify previously unseen test instances. Although existing methods have studied classification algorithm performance with respect to feature selection, noise condition, and sample distributions, our existing studies have not addressed an important issue on the classification algorithm performance relating to feature deletion and addition. In this thesis, we...
Show moreClassification algorithms represent a rich set of tools, which train a classification model from a given training and test set, to classify previously unseen test instances. Although existing methods have studied classification algorithm performance with respect to feature selection, noise condition, and sample distributions, our existing studies have not addressed an important issue on the classification algorithm performance relating to feature deletion and addition. In this thesis, we carry out sensitive study of classification algorithms by using feature deletion and addition. Three types of classifiers: (1) weak classifiers; (2) generic and strong classifiers; and (3) ensemble classifiers are validated on three types of data (1) feature dimension data, (2) gene expression data and (3) biomedical document data. In the experiments, we continuously add redundant features to the training and test set in order to observe the classification algorithm performance, and also continuously remove features to find the performance of the underlying classifiers. Our studies draw a number of important findings, which will help data mining and machine learning community under the genuine performance of common classification algorithms on real-world data.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004274, http://purl.flvc.org/fau/fd/FA00004274
Subject Headings
Data mining, Forecasting -- Mathematical models, Social sciences -- Statistical methods, Ubiquitous computing
Format
Document (PDF)
Smart campus
Title
Smart campus.
Creator
Danda, Naga Prakash, Agarwal, Ankur, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
The Smart Campus project envisions a university campus where technology assists faculty, staff, students and visitors to improve and more efficiently accomplish their daily activities. The objective of this project is to develop a smart phone application that assists users in finding a certain location on campus, locating their friends and professors, interacting with any student or professors of the campus, get the count of users at certain locations and remain updated about all the events...
Show moreThe Smart Campus project envisions a university campus where technology assists faculty, staff, students and visitors to improve and more efficiently accomplish their daily activities. The objective of this project is to develop a smart phone application that assists users in finding a certain location on campus, locating their friends and professors, interacting with any student or professors of the campus, get the count of users at certain locations and remain updated about all the events and campus news. Through this project, an idea of ‘Futuristic Social Network’ in a Campus is modeled and developed on Android platform.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004098, http://purl.flvc.org/fau/fd/FA00004098
Subject Headings
Mobile communication systems -- Security measures, Technological innovations -- Social aspects, Ubiquitous computing, Universities and colleges -- Design
Format
Document (PDF)
Stereo vision-based target tracking system for USV operations
Title
Stereo vision-based target tracking system for USV operations.
Creator
Sinisterra, Armando Jose, Dhanak, Manhar R., Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description
A methodology to estimate the state of a moving marine vehicle, defined by its position, velocity and heading, from an unmanned surface vehicle (USV), also in motion, using a stereo vision-based system, is presented in this work, in support of following a target vehicle using an USV.
Date Issued
2015
PURL
http://purl.flvc.org/fau/fd/FA00004466, http://purl.flvc.org/fau/fd/FA00004466
Subject Headings
Adaptive control systems, Adaptive signal processing, Computer vision, Inertial navigation systems, Intelligent control systems, Motion segmentaton, Oceanographic instruments -- Development, Ubiquitous computing
Format
Document (PDF)