David A. Bader Email address protected by JavaScript. Please enable JavaScript to contact me. Professor College of Computing Georgia Tech Atlanta, GA 30332
At regional scales, satellite-based sensors are the primary source of information to study the earth's environment, as they provide the needed dynamic temporal view of the earth's surface. Raw satellite orbit data have to be processed and mapped into a standard projection to produce multitemporal data sets which can then be used for regional or global earth science studies, such as land cover dynamics, global carbon cycle, planetary-scale climate dynamics and deforestation. For a given sensor, different applications may require different processing chains with the same few core steps. Application dependent processing steps include atmospheric correction, spatial and temporal subsetting, and output image projection. However, the data sets that are currently available to the scientific community are generated using a predetermined processing chain in a fixed projection. Generating products that are different than the standard ones can be difficult and will result in at least a re-sampling step and hence some loss of accuracy. In this paper, we describe a software system Kronos for the generation of custom-tailored data products from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board of the National Oceanic and Atmospheric Administration (NOAA) series polar orbiting satellites. Kronos allows the generation of a rich set of products that can be easily specified through a Java interface by scientists wishing to carry out earth system modeling or analysis based on Global Area Coverage (GAC) data from the AVHRR sensor. Kronos is based on a flexible methodology and consists of four major components: ingest and preprocessing, indexing and storage, search and processing engine, and a Java interface. We illustrate the power of our methodology by including a few special data products generated by Kronos.
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Last updated: July 25, 2004
Computational Biology Parallel Computing Combinatorics
