Within the past 40 years a large number of unique Earth observation datasets have been acquired and archived by space agencies and other Earth observation data producers worldwide. These time series, and in particular those based on radiometrically similar sensors, constitute a valuable humankind asset. They allow for the first time to perform long-term analyses of environmental phenomena based on Earth observation. However, in order for the data to be useful now and for future generations, archive owners, in addition to sustainable funding, need specific and precise requirements from data users in order to design and operate archives which serve well current and future generations of users.

TELEIOS is a recent European project that addresses the need for scalable access to petabytes of Earth Observation data and the discovery and exploitation of knowledge that is hidden in them. TELEIOS builds on scientific database technologies (array databases, SciQL, data vaults) and Semantic Web technologies (stRDF and stSPARQL) implemented on top of a state of the art column store database system (MonetDB). We demonstrate a first prototype of the TELEIOS Virtual Earth Observatory (VEO) architecture, using a forest fire monitoring application as example.

Similar to many advanced approaches in multisensor image analysis, image information mining (IIM) is hampered by sensor-specific differences in spatial resolution and spectral response. This study examines the representation of semantic categories integrating Ikonos and Quickbird imagery in the knowledge-based information mining system KIM. A processing sequence is presented, which accounts for sensor-related differences along with an evaluation of the application of IIM technologies in operational rapid-mapping scenarios.

The results shown in this paper highlights the usefulness of a recently proposed index to extract hints of built-up areas in remotely sensed images. The novelty of this work is in the application of the approach to a very different data set than the one for which the index was originally developed, i.e. SAR instead of optical data. Due to the different approaches (active vs. passive sensors), wavelengths (optical vs. microwave) and distortion/noise effects (additive vs. speckle noise), it is valuable to find out the advantages and limits of the index results on these new data sets. Moreover, due to the different geometry of acquisition for radar sensors, two different implementations of the same index are considered and compared, adding insights on the suitability of slant-range vs. ground-range analysis of SAR data for built-up area recognition.

In this study we used differential InSAR techniques to monitor current post slide activity at several landslides along transportation and energy corridors. The landslide materials vary from rock debris, glacial till to permafrost alluvium. Our results show that motion is triggered by spring melt and heavy rainfall events. In the northern Mackenzie Valley pipeline corridor seasonal landslide activity is related to permafrost melt during warm summer months.

Interferometric synthetic aperture radar (InSAR) data from RADARSAT-1 have been examined to assess their potential for mapping terrain and changes in snow cover characteristics, relative to the limiting effects of snow on foraging by endangered Peary caribou (Rangifer tarandus). Radar is one of the few observational tools that can provide information on the changing snow pack during the dark winter months. The goal of this research is to characterize the general ensemble of terrain characteristics that may affect winter foraging patterns of Peary caribou and the inter-annual consistency of snow cover patterns within our specific study area.

High-resolution multidate SAR spring images with similar incidence angles were used to update permeability maps over recharge areas on glacial aquifers. From a difference image produced from two dates in early spring we interpreted high medium and low permeability zones. These SAR permeability zones are related to the distribution and behavior of soil moisture on different surficial deposits and slopes. Permeability thematic maps will be useful to identify nutrient infiltration patterns and accumulation on farmed recharge areas.

Our results have shown that selective processing of RADARSAT-1 images has assisted in regional terrain mapping of large areas the northern boreal forest region in Canada. Terrain roughness information derived from RADARSAT-1 when fused with high resolution DEM data provided image maps that are useful for landform interpretation.

The purpose of using RADARSAT-1 for this mineral potential evaluation was to provide a means for acquiring quick and relatively inexpensive information about the study area. The problems faced in carrying out the mineral potential evaluation of this area were two-fold: (1) very limited geologic information exists to base a mineral assessment on and (2) the rocks with the greatest mineral potential are overlain by younger flat-lying sedimentary rocks. The Lac Grandin area has never been flown by regional or industry geophysics, it has only seen very limited reconnaissance geological mapping, no geochemical data exists and no mineral exploration has been carried out over the area. Flat-lying sedimentary rocks of the Phanerozoic Interior Platform underlie the Lac Grandin area. The Great Bear magmatic zone (GBmz) of the Proterozoic Bear Geological province outcrops 30-50 km to the east and underlies the Phanerozoic sedimentary cover rocks of the Lac Grandin area. Depth to Proterozoic crystalline basement (GBmz) rocks in the study area has been determined to be less than 200 m. The GBmz is known to host significant mineral deposits and some past producing mines. An industry aeromagnetic survey exists for the area immediately adjacent to the study area and was flown to fill in a gap in the regional geophysical database. It was hoped that structural patterns recognized from the aeromagnetic data could be extrapolated into the study area using the RADARSAT-1 images. The RADARSAT-1 images acquired were Fine Mode, Beam 1 descending (f1d) and Standard Mode, Beam 4 descending (s4d), which partially overlap the study area and the adjacent area with industry aeromagnetic coverage. The images proved very effective in demonstrating that basement feature lineaments could be recognized through the sedimentary cover rock and continued beyond the aeromagnetic coverage area into the study area. The RADARSAT-1 images proved very effective as an ancillary tool to the aeromag maps by aiding in locating and identifying structures thought to be important to the determination of areas of high mineral potential.

This paper provides some preliminary results of the use of RADARSAT fine mode image for characterizing the debris size and distribution of a 30/spl times/10/sup 6/ m/sup 3/ rock avalanche. From the image we were able to classify coarse, medium and fine debris based on their SAR texture. Such simple textural classification will be useful to plan more detail field and aerial surveys on large landslides so as to understand landslide processes, post failure mechanism and mobility.