Thesis (M.S.)--Indiana University, 2007. / Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI). Advisor(s): Li Lin, Lenore P. Tedesco, Jeffrey S. Wilson. Includes vitae. Includes bibliographical references (leaves 145-149).

Thesis (M.S.)--Indiana University, 2007. / Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI). Advisor(s): Gabriel M. Filippelli, Catherine Souch, Lenore P. Tedesco. Includes vitae. Includes bibliographical references (leaves 80-83).

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis presents the results of an analysis of predicting phytoplankton pigment concentrations (chlorophyll a and phycocyanin) from remotely sensed imagery. Hyperspectral airborne and hand-held reflectance spectra were acquired on three reservoirs (Geist, Morse and Eagle Creek) in Central Indiana, USA. Concurrent with the reflectance acquisition, in situ samples were collected and analyzed in laboratories to quantify the pigment concentration and other water quality parameters. The resultant concentration was then linked to Airborne Imaging Spectrometer for Applications (AISA) reflectance spectra for the sampling stations to develop predictive models. AISA reflectance spectra were extracted from the imagery which had been processed for radiometric calibration and geometric correction. Several previously published algorithms were examined for the estimation of pigment concentration from the spectra. High coefficients of determination were achieved for predicting chlorophyll a in two of the three reservoirs (Geist R2 = 0.712, Morse R2 = 0.895 and Eagle Creek Reservoir R2 = 0.392). This situation was similar for PC prediction, where two of the three reservoirs had high coefficients of determination between pigment concentration and reflectance (Geist R2 = 0.805, Morse R2 = 0.878 and Eagle Creek Reservoir R2 = 0.316). The results of this study show that reflectance spectra collected with an airborne hyperspectral imager are statistically significant, p < 0.03, in predicting chlorophyll a and phycocyanin pigment concentration in all three reservoirs in this study without the consideration of other parameters. The algorithms were then applied to the AISA image to generate high spatial resolution (1 m2) maps of Chlorophyll a and Phycocyanin distribution for each reservoir.

Indiana University-Purdue University Indianapolis (IUPUI) / The focus of this study was to document phosphorus (P) and metal cycling in the Eagle Creek Reservoir (ECR), located in Indianapolis, central Indiana. Eagle Creek Reservoir serves the drinking water needs of over 80,000 residents. Within the last several years, algal blooms have created stress to the local treatment facility. The objective of this study was to examine how P cycling from oxygen deprived bottom sediments affects the algal bloom productivity. As such, cores were retrieved from different water depths (7 and 16 m) from portions of the reservoir where high surficial concentrations of organic matter and P were found to occur. The dried samples were analyzed for P, sulfur, iron, barium, cadmium, copper, lead, and zinc, using a strong acid digestion technique. The samples were also analyzed for iron-bound P (Fe-P), authigenic P (A-P), detrital P (D-P), organic P (O-P), reducible iron, and reducible manganese, using a sequential extraction technique. The results from the study showed moisture contents ranged from 16 to 76% and organic matter contents ranged from 2 to 12 wt%. The dry bulk densities were determined to be between 0.27 and 1.68 g cm3. The average percentages of P in ECS-1, as determined by the sequential extraction method, were as follows: Fe-P, 66.2%; A-P, 8.1%; D-P, 4.8%; and O-P, 20.9%. The average percentages of P in ECS-3, as determined by the sequential extraction method, were as follows: Fe-P, 77.0%; A-P, 6.5%; D-P, 2.8%; and O-P, 16.7%. To determine relationships between elements, correlations were calculated. When looking as the relationships between the P fractions and reducible Fe, differences were observed between the different water depths. There was less correlation between reducible Fe and Fe-P, and between O-P and Fe-P, in ECS-3, indicating that Fe-P is more efficiently dissolved and recycled in the deep portion of ECR. The study shows that the Fe-P flux, caused by the iron redox cycle, is persistent and will continue to influence algal bloom productivity in the deeper portions of ECR.

Indiana University-Purdue University Indianapolis (IUPUI) / This study explores relationships between temperate stream geochemistry and watershed land cover in two temperate streams, Fishback Creek and School Branch Creek, located in a rapidly urbanizing area on the northwest side of Indianapolis in Eagle Creek Watershed, Indiana. The temporal and spatial patterns of NO3-N, PO4, DOC, SiO2, Cl-, and Na+ were assessed to understand the influence of land cover on the magnitude and timing of water, chemical, and nutrient delivery to streams. Results of the study indicate that the influences of different land cover types on water delivery to streams and in-stream water quality vary seasonally and with respect to flow regime, that urbanization may result in decreased nitrate input, and that phosphate and dissolved organic carbon concentrations will likely remain constant as the watershed is developed. Results also indicate that riparian buffer downstream of intense agriculture lands dilutes high agricultural NO3-N concentrations, but not enough to return in-stream concentrations to natural levels.

Indiana University-Purdue University Indianapolis (IUPUI) / Environmental planning is very cumbersome work for environmentalists, government agencies like USDA and NRCS, and farmers. There are a number of conflicts and issues involved in such a decision making process. This research is based on the work to provide a common platform for environmental planning called WRESTORE (Watershed Restoration using Spatio-Temporal Optimization of Resources). We have designed a system that can be used to provide the best management practices for environmental planning. A distributed system was designed to combine high performance computing power of clusters/supercomputers in running various environmental model simulations. The system is designed to be a multi-user system just like a multi-user operating system. A number of stakeholders can log-on and run environmental model simulations simultaneously, seamlessly collaborate, and make collective judgments by visualizing their landscapes. In the research, we identified challenges in running such a system and proposed various solutions. One challenge was the lack of fast optimization algorithm. In our research, several algorithms are utilized such as Genetic Algorithm (GA) and Learning Automaton (LA). However, the criticism is that LA has a slow rate of convergence and that both LA and GA have the problem of getting stuck in local optima. We tried to solve the multi-objective problems using LA in batch mode to make the learning faster and accurate. The problems where the evaluation of the fitness functions for optimization is a bottleneck, like running environmental model simulation, evaluation of a number of such models in parallel can give considerable speed-up. In the multi-objective LA, different weight pair solutions were evaluated independently. We created their parallel versions to make them practically faster in computation. Additionally, we extended the parallelism concept with the batch mode learning. Another challenge we faced was in User Modeling. There are a number of User Modeling techniques available. Selection of the best user modeling technique is a hard problem. In this research, we modeled user's preferences and search criteria using an ANN (Artificial Neural Network). Training an ANN with limited data is not always feasible. There are many situations where a simple modeling technique works better if the learning data set is small. We formulated ways to fine tune the ANN in case of limited data and also introduced the concept of Deep Learning in User Modeling for environmental planning system.