Research environment for advanced landsat monitoring (REALM) a computational approach for Landsat-7 global science

Landsat-7 science mission requires large-area analysis of land to cover to quantify anthropogenic and natural changes in Earth's terrestrial environme nt. Such a goal involves processing (and reprocessing) thousands of Enhance d Thematic Mapper Plus (ETM+) scenes, but current analysis methodologies th at rely on "handcrafting" individual scenes cannot scale to this data flow. As an alternative, we have constructed a prototype computer system, REALM (Research Environment for Advanced Landsat Monitoring), to automate preproc essing and analysis of large volume of Landsat imagery. Users can submit ar bitrary algorithms to the database using a query language to generate scien ce results "on the fly." The current prototype, running on a cluster of Lin ux-based PCs, has created a preliminary forest-cover map for the northeaste rn US from 9 GB of ETM+ data in just 25 min, for an aggregate throughput of 6 MB/s. The exercise demonstrates the processing Landsat data over very la rge areas is now feasible. (C) 2001 Published by Elsevier Science Inc. All rights reserved.