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 Earthʹs terrestrial environment. Such a goal involves processing (and reprocessing) thousands of Enhanced Thematic Mapper Plus (ETM+) scenes, but current analysis methodologies that 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 preprocessing and analysis of large volume of Landsat imagery. Users can submit arbitrary algorithms to the database using a query language to generate science results “on the fly.” The current prototype, running on a cluster of Linux-based PCs, has created a preliminary forest-cover map for the northeastern 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 large areas is now feasible.