Remote sensing helps many applications like precision irrigation, habitat mapping, and traffic monitoring. However, due to shortcomings of current remote sensing platforms - like high cost, low spatial, and temporal resolution - many applications do not have access to useful remote sensing data. A team at the Center for Self-Organizing and Intelligent Systems (CSOIS) together with the Utah Water Research Laboratory (UWRL) at Utah State University has been developing a new remote sensing platform to deal with these shortcomings in order to give more applications access to remote sensing data. This platform (AggieAir) is low cost, fully autonomous, easy to use, independent of a runway, has a fast turnover time, and a high spatial resolution. A program called the Geospatial Real-Time Aerial Image Display (gRAID) has also been developed to process the images taken from AggieAir. gRAID is able to correct the camera lens distortion, georeference, and display the images on a 3D globe, and export them in a conventional Geographic Information System (GIS) format for further processing. AggieAir and gRAID prove to be innovative and useful tools for remote sensing.

Electronically collected animal movement data has been analyzed either statistically or visually using generic geographical information systems. The area of statistical analysis in this field has made progress over the last decade. However, visualizing the movement and behavior remains an open research problem. We have designed and implemented an interactive visualization system, MarineVis, to visualize geospatial uncertainty in the trajectories of marine animals. Using MarineVis, researchers are able to access, analyze and visualize marine animal data and oceanographic data with a variety of approaches. In this thesis, we discuss the MarineVis design structure, rendering techniques, and other visualization techniques which are used by existing software such as IDV to which we compare and contrast the visualization features of our system. Finally, directions of future work related to MarineVis are proposed which will inspire others to further study the challenging but amazingly interesting and exciting research field of marine visualization. / Marine animal movement is a fundamental yet poorly understood process. One of the reasons is because our understanding of movement is affected by the measurement error during the observation and process noise. Differentiating real movement behavior from observation error in data remains difficult and challenging. Methods that acknowledge uncertainty in movement pathways when estimating constantly changing animal movement have been lacking until this time. However with the arrival of state-space models, this problem is partially solved as SSMs acknowledge this problem by allowing unobservable true states to be estimated from data observed with errors which arise from imprecise observations. State-space models use Markov Chain Monte Carlo methods which generate samples from a distribution by constructing a Markov Chain where the current state only depends on the immediately preceding state. The task of fitting SSMs to data is challenging and requires large computational effort and expertise in statistics. With the arrival of the WinBUGs software, this formidable task becomes relatively easy. Though using the WinBUGs software researchers try to visualize the tracks and behaviors, new problems appear. One of the problems is that when marine animals come back to certain places or animals' tracks cross each other several times, the tracks become cluttered and users are not able to understand the direction. Another problem of visualizing the confidence intervals generated using SSMs is that images generated using other systems are static in nature and therefore lack interactivity. Information becomes cluttered when too much data appear. Users are not able to differentiate tracks, confidence intervals or the information they would like to visualize. Acknowledging these, we have designed and implemented an interactive visualization system, MarineVis, where these problems are overcome. Using our system the confidence intervals generated using the SSMs, can be visualized more clearly and the direction of the turtle tracks can be understood easily. Our system does not occlude the underlying terrain as much because the glyphs are localized at the sample points rather than being spread out around the entire path. Our system encodes both direction and position rather than just position. Users can interactively limit the view of data points as a subset of available data points on a path, in clustered regions, to reduce congestion, and can animate the progression of the animal along its trajectory which is absent in existing approaches. All these results are visualized over NASA World Wind maps that facilitates the understanding of the tracks.