Taxon composition and biodiversity analyses are known powerful parameters for environmental site status and environment diagnosis. Many ecological studies assess taxon composition through traditional species identification and use bioindicator species to evaluate environmental conditions. The recent breakthrough in bulk sample sequencing combined with DNA barcoding has created a new era for environmental monitoring. Metabarcoding approaches are more robust in studying alpha, and beta diversity compare to the DNA barcoding and the conventional method of species identification, particularly for rare and cryptic species. Here we built upon ecological studies of bioindicator species and transferred the traditionally named taxa to DNA-based approaches. We developed a small customized DNA database for biodiversity assessment and taxonomic identification of environmental DNA samples using high-throughput amplicon sequences. It contains macroinvertebrate species that are known as indicators of specific environmental conditions. By implementing this small database into the KRAKEN algorithm for the first time, we were able to assess environmental biodiversity compared to other popular methods of taxonomic classification, especially in polluted environments where the taxonomic composition globally change by the presence of anthropogenic drivers. Our method is incredibly faster, and it requires significantly less computational power in contrast to common homology-based techniques. To evaluate our approach, we have also studied the importance of database’s size and the depth of sequencing in taxonomic classification of high-throughput DNA sequences. / Thesis / Master of Science (MSc) / We developed a small customized DNA database for biodiversity assessment and taxonomic identification of environmental DNA samples using high-throughput amplicon sequences. It contains macroinvertebrate species that are known as indicators of specific environmental conditions. By implementing this small database into the KRAKEN algorithm for the first time, we were able to assess environmental biodiversity compared to other popular methods of taxonomic classification, especially in polluted environments where the taxonomic composition globally change by the presence of anthropogenic drivers. Our method is incredibly faster, and it requires significantly less computational power in contrast to common homology-based techniques.

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There is new evidence that global earth resources satellite net will be practical. This paper weighs recent advances in remote sensing to pinpoint the dominant constraints. The data and sensor systems interfacing requirements are critically reviewed. It is shown that conventional optics constraints can be relaxed, with the newer systems, based on multi-spectral imagery and statistical processing methods. The most powerful computational methods use algorithms based on a Gaussian assumption for the species vector in feature space, but biases in the imagery limit their efficiency. A rationale is proposed: improving the observational network calibrating efficiency will also improve the photogrammetric removal of imagery biases, and thereby increase signature detection efficiency. The author discloses an unexpected finding: while conventional resolution degrades with satellite altitude, signature detectability should improve since calibration improves dramatically with altitude. A unique global network is then described than can exploit these new developments. The scope of this subject is so broad that despite the paper's length (sixty pages), a quantitative treatment is not practical; the author uses a combination of classical analysis, bibliographic research, and conservative technological assumptions based on the current state-of-the-art.

This study was designed to test the reliability of avifauna information retrieved from Biota of Virginia, BOVA, a computerized fish and wildlife information system developed at Virginia Polytechnic Institute and State. University. Reliability was defined as the percent of species that were detected during field surveys that also were listed by BOVA to occur. Six habitat types were selected for study within the Blacksburg Ranger District of the Jefferson National Forest in Southwestern Virginia. These were Mixed (oak-pine) Seedling/Sapling, Mixed Pole, Mixed Mature, Deciduous Pole, Deciduous Mature, and Coniferous Pole. Three stands of each type, each 8-20 hectares in area, were chosen randomly for study: Each stand was surveyed with 12 90-minute survey periods over 2 years. The random-walk technique was used to determine species occurrence. BOVA species lists were determined for comparison with lists of species detected in the field. All species detected in the field and(or) listed by BOVA were placed into at least one status category defined by BOVA (e.g., Federal Migratory, Game, Accidental) or the Virginia Society of Ornithology (e.g., Abundant, Permanent Resident, Transient Breeder). / Master of Science

Advance Personalized Learning is one of the 14 grand challenges of engineering as identified by the National Academy of Engineering. One possible approach for this advancement is to deploy systems that allow an investigator to understand the differences in the learning process of individuals. In this context, cyberlearning systems that use networked computing and communication technology to reach a large number of learners offer the affordance to uniquely identify learners and track their learning process in real-time. Motivated by this idea, this doctoral research aims to investigate personalized learning and engagement within a cyberlearning system, called the Online Watershed Learning System (OWLS). This cyberlearning system utilizes learning resources generated by a real-time high-frequency environmental monitoring system, called the Learning Enhanced Watershed Assessment System (LEWAS). The goals include advancing the OWLS with a user tracking system and data availability and visualization features and investigating personalized learning and engagement within the OWLS. A user-tracking system is developed utilizing a Node.js-based Express framework and deployed in the LEWAS server, which identifies individual users across devices such as laptops, tablets, and desktops, and detects their interaction within the OWLS, and stores the interaction data in a PostgreSQL database. HTML, CSS, and JavaScript technologies are used for the client-side development. Informed by the situative theory of learning and engagement theory, an investigation was carried out with 52 students from a junior-level civil engineering class. They completed an OWLS-based in-class task focused on concepts related to the environmental monitoring. Pre and post-surveys and the user-tracking system were utilized to collect data on individual student's perceived and conceptual learning, perceived and behavioral engagement, and perception towards the learning value of the OWLS. Results provide several insights into individual student's learning and engagement with the OWLS. For example, students gained knowledge using the OWLS, and their learning varied with the design of the in-class task, which, however, did not impact their engagement. Further, students' learning (scores on in-class task) had a significant negative relationship with their behavioral engagement (frequency of resource utilization of the OWLS). Additionally, temporal navigational strategies of 52 students were identified on an individual basis. Finally, variations in learning and engagement were analyzed in terms of factors such as gender and background knowledge. The study has implications for designing effective cyberlearning systems and learning activities that can utilize cyberlearning systems for leveraging technology-enhanced teaching and learning. / Ph. D. / Individuals differ in their approaches to learning. For the success of diverse group of learners, the National Academy of Engineering has identified “Advance Personalized Learning” as one of the 14 grand challenges. One possible approach for this advancement is to utilize online learning technologies, such as cyberlearning systems that provide the affordance to uniquely identify each learner and track his/her learning progress allowing an investigator to understand the differences in the learning process of individuals. Motivated by this idea, an interactive cyberlearning system, called the Online Watershed Learning System (OWLS) has been utilized in this study. It contains learning resources generated by a real-time high-frequency environmental monitoring system, called the Learning Enhanced Watershed Assessment System (LEWAS). The goals of the study include: 1) advancing the OWLS with a user tracking system and data availability and visualization features and 2) investigating personalized learning and engagement within the OWLS. For goal 1, cutting-edge technologies were utilized so that OWLS with its user-tracking system can be accessible by large number of users using modern web browsers on devices, such as laptops, tablets and cell phones. For goal 2, classroom implementation was carried out with 52 junior-level civil engineering students, who completed an OWLS-based environmental monitoring task within the class time. Results provide several insights into variation of individual student’s learning and engagement with the OWLS. For example, students gained knowledge using the OWLS, and their learning varied with the design of the in-class task, which, however, did not impact their engagement. Additionally, temporal navigational strategies of 52 students were identified on an individual basis. Variations in learning and engagement were also analyzed in terms of factors such as gender and background knowledge. The study has implications for designing effective cyberlearning systems and learning activities that can utilize cyberlearning system for leveraging technology-enhanced teaching and learning.

In this thesis, we propose algorithms designed for monitoring hazardous agents. Because hazardous environmental monitoring is either tedious or dangerous for human operators, we seek a fully automated robotic system that can help humans. However, there are still many challenges from hardware design to algorithm design that restrict robots to be applied to practical applications. Among these challenges, we are particularly interested in dealing with algorithmic challenges primarily caused by sensing and communication limitations of robots. We develop algorithms with provable guarantees that map and track hazards using a team of robots. Our contributions are as follows. First, we address a situation where the number of hazardous agents is unknown and varies over time. We propose a search and tracking framework that can extract individual target tracks as well as estimate the number and the spatial density of targets. Second, we consider a team of robots tracking individual targets under limited bandwidth. We develop distributed algorithms that can find solutions in bounded amount of time. Third, we propose an algorithm for aerial robots that explores a translating hazardous plume of unknown size and shape. We present a recursive depth-first search-based algorithm that yields a constant competitive ratio for exploring a translating plume. Last, we take into account a heterogeneous team of robots to map and sample a translating plume. These contributions can be applied to a team of aerial robots and a robotic boat monitoring and sampling a translating hazardous plume over a lake. In this application, the aerial robots coordinate with each other to explore the plume and to inform the robotic boat while the robotic boat collects water samples for offline analysis. We demonstrate the performance of our algorithms through simulations and proof-of-concept field experiments for real-world environmental monitoring. / Doctor of Philosophy / Quick response to hazards is crucial as the hazards may put humans at risk and thorough removal of hazards may take a substantial amount of time. Our vision is that the introduction of a robotic solution would be beneficial for hazardous environmental monitoring. Not only the fact that humans can be released from dangerous or tedious tasks, but we also can take advantage of the robot's agile maneuverability and its precise sensing. However, the development on both hardware and software is not yet ripe to be able to deploy autonomous robots in real-world scenarios. Moreover, partial and uncertain information of hazards impose further challenges. In this these, we present various research problems addressing these challenges in hazardous environmental monitoring. Particularly, we are interested in overcoming challenges from the perspective of software by designing planning and decision-making algorithms for robots. We validate our proposed algorithms through extensive simulations and real-world experiments.

Meat and poultry slaughtering and processing practices have been associated with the microbial contamination with Listeria spp. Ready-to-eat poultry products have been considered as a primary agent associated with Listeria monocytogenes illness outbreaks. Developing environmental monitoring programs (EMPs) that are based on product and/or process risk level analysis is a useful approach to reduce contamination in poultry processing plants and enhance food safety. Sampling criteria that is based on product risk levels and process control in ready-to-eat poultry processing facilities was developed to allow users to design and conduct appropriate sampling plans to target Listeria spp. After developing the criteria, an internet-based environmental monitoring program ("EZSafety") was developed to allow poultry producers to enhance their sample collection and analysis of test results over time and conduct appropriate sampling plans for Listeria spp. and other microbiological indicators. The frontend of the program website was built using React Native (an open-source JavaScript library for building user interfaces). The backend of the program website was built using Node.js which executes JavaScript code outside a web browser. MongoDB was used as a document-oriented database for the website. The program was evaluated by 20 food safety professionals to assess its ability to develop appropriate sampling plans to target Listeria spp. The majority of these participants believed that EZSafety has several tools that are effective for targeting Listeria spp. and other indicators and enhancing environmental monitoring. Additionally, most participants agreed that EZSafety is organized and user-friendly. EMPs can play a significant role in improving the detection rate and the prevention of Listeria spp. and other indicators in poultry processing plants. / Master of Science in Life Sciences / Meat and poultry slaughtering and processing practices have been associated with the microbial contamination with a bacterium known as Listeria. Cooked poultry products during the manufacturing process have been considered as a primary agent associated with Listeria monocytogenes (disease causing type of bacteria) sickness outbreaks. Developing environmental monitoring plans to detect and prevent this bacterium in poultry processing establishments is a useful approach to reduce contamination and enhance food safety. Several guidelines and baselines were developed to allow users to design and conduct appropriate environmental monitoring plans to target this bacterium. After developing these guidelines and baselines, an internet-based environmental monitoring program ("EZSafety") was developed to allow poultry processors to enhance their sample collection and analysis of test results over time. The program was developed using several kinds of computer platforms (JavaScript, React Native, and MongoDB) . These open-source platforms were used to design, develop, and store the program over the internet. In order to validate its usefulness, the program was evaluated by 20 users who are majored in food safety and familiar with poultry processing plants hygiene to assess its ability to suggest appropriate monitoring plans. Most of the participants believed that EZSafety has several tools that are effective for targeting Listeria and other kinds of bacteria and enhancing environmental monitoring plans. Additionally, most participants agreed that EZSafety is organized and user-friendly. Such automated monitoring programs can play a significant role in enhancing the detection rate and the prevention of Listeria and other organisms in poultry processing facilities.

Station siting, exposure, instrumentation, and time of observations influence longterm climatic records. This thesis compared and analyzed temperature data from four Kentucky Mesonet stations located in Fayette (LXGN), Franklin (LSML), Clark (WNCH), and Bullitt (CRMT) counties to two nearby Automated Surface Observation Systems (ASOS) stations in Kentucky. The ASOS stations are located at Louisville International Airport (Standiford Field - KSDF) and at Lexington Airport (Blue Grass Field - KLEX). The null hypothesis states that there is no significant difference in temperature measurements between the two types of stations. To quantify the differences in temperature measurements, geoprofiles and the following statistical procedures were used: coefficient of determination (R2), coefficient of efficiency (E), index of agreement (d), root mean square error (RMSE), and mean absolute error (MAE). Geoprofiles were developed using GIS, and take into account elevation, slope, hillshading, land use, and aspect for each site to help better understand the influence of local topography. It was found that temperature differences could be related to the advancement of weather patterns, vegetation growth and decay, and changes in the landscape at the stations. KSDF consistently recorded higher temperatures than those at CRMT. The positive bias ranged between 0.27 and 2.41 ºC during the time period of September 2009 to August 2010. KLEX was found to be warmer or cooler, with temperature differences that ranged from -1.42 to 0.22 ºC for LXGN, LSML, and WNCH. The index of agreement at KSDF for mean hourly temperatures, when compared to the Bullitt County mesonet station, ranged from 0.88 to 0.99. Meanwhile, the index of agreement at KLEX was 0.96 to 1.00 when compared to the Franklin, Fayette, and Clark mesonet stations. KLEX recorded temperatures that were higher or lower compared to the Franklin, Fayette, and Clark mesonet stations. At the seasonal scale, fall and summer showed larger differences between the Mesonet and ASOS observations. KSDF consistently recorded higher temperatures ranging up to 2.41 °C during the summer. The index of agreement at KSDF in the fall, when compared to the Bullitt County mesonet station average temperatures, ranged from 0.89 to 0.95, while in the summer it was 0.88 to 0.96. The d index indicates a good agreement between ASOS and mesonet stations in winter. KLEX indicates that the index of agreement, RMSE, and MAE are best during winter for all three stations, while in the fall and summer the agreement was not as strong when compared to the Franklin, Fayette, and Clark mesonet stations. In summary, results indicate that the Kentucky Mesonet and ASOS temperature measurements show significant differences throughout the year; therefore, the alternative hypothesis is accepted. These differences are attributed to biases associated with ASOS observations, nearby artificial sources of heating, equipment/maintenance procedures, and land use and land cover at the site.

Thesis (MBA)--Stellenbosch University, 2014. / Using the case of an environmental monitoring service envisaged by the South African Environmental Observation Network (SAEON), this research represents a comprehensive systematic approach to strategising for and justifying the adoption and implementation of a technical service by a public entity. The conceptual design of the research followed the strategic triangle for public management. Strategic analyses applied the Internal-Market-External (IME) model to understand the current and future business contexts of SAEON. Schemas for a generic business cycle and a value chain for the environmental research sector were developed. These new schemas aided strategic thinking about environmental research as a line of work within the public sector. Key findings were that competition is prevalent in the publicfunded environmental research sector and the key competitive force driving the sector is the availability of resources, which is largely determined by political financiers. A survey of the potential market has provided sufficient evidence that a market for environmental monitoring services does exist. This market was described in terms of market segmentation, drivers of decision-making, specific requirements of service providers, perceptions about current service providers and preferences for the type of service provider organisation. The results of the survey elucidated the public value and legitimacy of an environmental monitoring service and should have meaning beyond just South Africa in the context of the International Long-Term Ecological Research Network (ILTER). Analyses of the external environment of the proposed service confirmed that rising environmental pressures and uncertainty are globally concerning governments, society and the business sector. Since the service will eventually have to be fully paid for, the most powerful competitive force will be the clients. Conversely to the high competition of the public funded environmental research sector, current market failure was identified in the environmental monitoring services market since all the market segments expressed their general dissatisfaction with the services they received from a range of service provider categories. This leaves the door wide open for SAEON to enter the environmental monitoring market legitimately to deliver on the market’s expectations as a form of Blue Ocean Strategy in the public sector. The research found that the service will be an extension of SAEON’s core competencies, but should apply a low-cost strategy. Application of business tools such as business model design and key success factors provided clear guidelines on how the service should be implemented. Evidence was found that key theoretical constructs and management tools abstracted from commercial enterprises may be usefully applied, either individually or in combination, in the context of the public sector, albeit with some modification. The research demonstrated how core business tools such as the Five Competitive Forces, the Balanced Scorecard and Strategy Mapping can be made amenable to the public sector by replacing ‘profitability’ with ‘public value’ as the key objective for a public entity.