Scientists now recognize how quickly environmental conditions are changing, yet to monitor and understand these spatially distributed changes more dispersed quantitative and qualitative data are needed than ever before. The need for more comprehensive and robust data has created the burgeoning field of citizen science, which engages volunteers to monitor environmental changes and report this information to scientists. Precipitation monitoring networks like RainLog.org are considered one of the oldest types of citizen science with many networks in existence for over 100 years. RainLog.org is a more modern version of these original networks and was developed in response to a need to better characterize precipitation events and provide stakeholders with more robust precipitation totals and distributions throughout Arizona.RainLog.org is a statewide precipitation monitoring network that relies on volunteers across Arizona to report daily precipitation into an online reporting system. To ensure that these data are reliable, a quality assurance and quality control analysis (QA/QC) was completed on a subset of gauges in the Tucson area. Results indicate that although there are many errors inherent with any precipitation network, whether volunteer or scientist driven, these errors are for the most part identified using basic interpolation methods. This paper analyzes a range of user reporting and gauge type errors, discusses the significance of each error type and provides recommendations for mitigating reporting errors in any citizen science network.

A wind-wave model for Lake Pontchartrain has been developed. This model uses the probability data obtained from the frequency analysis of wind information from the four weather monitoring stations in Lake Pontchartrain. For any given season and any given location, this model generates statistical results of wave heights, wave periods and long-shore sediment transport in 10 degree directional bins along the shoreline of the Lake. This model can be used as an effective tool for planning, construction and maintenance of beaches along the shores of Lake Pontchartrain.

There is shift in the Architectural / Engineering / Construction and Facility Management (AEC&FM) industry toward performance-driven projects. Assuring good performance requires efficient and reliable performance control processes. However, the current state of the AEC&FM industry is that control processes are inefficient because they generally rely on manually intensive, inefficient, and often inaccurate data collection techniques. Critical performance control processes include progress tracking and dimensional quality control. These particularly rely on the accurate and efficient collection of the as-built three-dimensional (3D) status of project objects. However, currently available techniques for as-built 3D data collection are extremely inefficient, and provide partial and often inaccurate information. These limitations have a negative impact on the quality of decisions made by project managers and consequently on project success. This thesis presents an innovative approach for Automated 3D Data Collection (A3dDC). This approach takes advantage of Laser Detection and Ranging (LADAR), 3D Computer-Aided-Design (CAD) modeling and registration technologies. The performance of this approach is investigated with a first set of experimental results obtained with real-life data. A second set of experiments then analyzes the feasibility of implementing, based on the developed approach, automated project performance control (APPC) applications such as automated project progress tracking and automated dimensional quality control. Finally, other applications are identified including planning for scanning and strategic scanning.

There is shift in the Architectural / Engineering / Construction and Facility Management (AEC&FM) industry toward performance-driven projects. Assuring good performance requires efficient and reliable performance control processes. However, the current state of the AEC&FM industry is that control processes are inefficient because they generally rely on manually intensive, inefficient, and often inaccurate data collection techniques. Critical performance control processes include progress tracking and dimensional quality control. These particularly rely on the accurate and efficient collection of the as-built three-dimensional (3D) status of project objects. However, currently available techniques for as-built 3D data collection are extremely inefficient, and provide partial and often inaccurate information. These limitations have a negative impact on the quality of decisions made by project managers and consequently on project success. This thesis presents an innovative approach for Automated 3D Data Collection (A3dDC). This approach takes advantage of Laser Detection and Ranging (LADAR), 3D Computer-Aided-Design (CAD) modeling and registration technologies. The performance of this approach is investigated with a first set of experimental results obtained with real-life data. A second set of experiments then analyzes the feasibility of implementing, based on the developed approach, automated project performance control (APPC) applications such as automated project progress tracking and automated dimensional quality control. Finally, other applications are identified including planning for scanning and strategic scanning.