Automated methods for atmospheric correction and fusion of multispectral satellite data for national monitoring

The Earth Observation for Sustainable Development of Canada´s forests (EOSD) project monitors Canada´s forests from space. Canada contains ten-percent of the world´s forests. Initial EOSD products are land cover, forest change, forest biomass, and automated methods. There are more than 500 LANDSAT TM or ETM+ scenes required for a single coverage of Canada´s forests. Multi-temporal analysis using satellite data requires automation for conversion of these data to common units of exoatmospheric radiance or ground reflectance. During the next ten years the EOSD project will use a variety of Landsat optical and Radarsat sensors. A diverse set of ancillary and satellite data formats exist which require the development of adaptable data ingest and processing streams. Legacy LANDSAT TM and ETM+ data are available in a number of different formats from several national and US suppliers. In this paper, we present an automated system for managing processing streams for calibration and atmospheric correction of LANDSAT TM and ETM+ data to create data sets ready to analyze for EOSD products. Using known forest attributes from GIS data and field measurements, we validated our results of studies undertaken to assess spectral signal variability using both at-sensor radiance and ground reflectance for LANDSAT TM and ETM+ for a test site on Vancouver Island, BC. We present a strategy for correcting and fusing multi-source and multitemporal satellite data for meeting EOSD requirements.