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A web-based lidar experimentation and data analysis system (LEDAS) was developed, with support from a National Science Foundation award, to support resource sharing of lidar equipment, datasets and data analysis routines and collaboration between members of the Connecticut State University System (CSUS) Lidar Collaboratory. The system allows users at different geographical locations to conduct remote sensing research and education over the Web through remote access and control of a single shared lidar system and web-based data analysis. Users need not have any specialized instrumentation or software at their institutions, thereby making real remote sensing research available to students and faculty from institutions which may not have the internal budgets for such facilities. An original structure providing basic functionality was developed and implemented. This paper describes the second generation data analysis system which provides significant new enhancements and capabilities. © 2008 IEEE.
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Temporal and spatial analysis was applied to a sequence of cloud top pressure (CTP) images and cloud optical thickness (TAU) images, and a storm tracking algorithm was proposed. A sequence of storm tracks from the satellite images was developed from the satellite images. Composite images were created by projecting ahead in time and substituting the first valid pixel for missing data, and a variety of CTP and TAU cut-off values were used to identify regions of interest. The region correspondences were determined from one time frame to another which yielded the storm center coordinates. The obtained tracks were compared to the storm tracks computed from sea level pressure data by matching the results first in time and then in spatial distance.
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The objective of this study is to compare geometric-based and evolutionary techniques for tracking storm systems from sequences of satellite images. Analysis was applied to the International Satellite Cloud Climatology Project low resolution D1 database for selected storm systems during the month of September, 1988. During this time period there were two exceptionally long tracks of major hurricane systems, Hurricanes Gilbert and Helene. Cloud top pressure and cloud optical thickness were used to identify storm systems. The ability of the geometric-based and evolutionary techniques to generate tracks through storm regions was assessed. Differences in final tracking results between the two techniques resulted not only from the differences in methodology but also form differences in the type of preprocessed input used by each of the techniques. Tracking results were compared to results disseminated by the Colorado State/Tropical Prediction Center and maintained by the National Hurricane Center in Miami, Florida. For the hurricanes investigated in this study, both techniques were able to generate tracks which followed either most or some of the portions of the hurricanes. The evolutionary algorithm was in general able to maintain good continuity along the tracks but, with no knowledge of overall region movement, was unable to discern which of two possible directions would be best to pursue in cases where there were tow or more equally close storm systems components. The geometric method was able to maintain a smooth track close to the course of the hurricane except for confusion primarily at the beginning and/or end of tracks.
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