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HANOVER, N.H. – ERDC’s Cold Regions Research and Engineering Laboratory (CRREL) and Topographic Engineering Center (TEC) recently held the first of a series of coordinated experiments under the Exploiting Sensing for Patterns (ESP) work unit within the Geospatial Research and Engineering focus area. The experiments were conducted over a two-day period and used the field test facility at CRREL for a variety of experiments including persistent ISR (Intelligence, Surveillance, and Reconnaissance) from LiDAR (Light Detection And Ranging), distribution of optical marking materials for disturbance and perimeter indication and data gathered on soil and vegetation for evaluating the efficacy of optical materials used with LiDAR sensors. The CRREL facility (Figure 1), located near the Connecticut River, is approximately 1-square kilometer in area and features a complex of soils, test pits and lanes. The site features a vegetated perimeter and has been fully instrumented with meteorological and physical environmental data collection instruments. The site also has been recently outfitted with two observation towers for the mounting of terrestrial LiDARs and other instruments.
ESP tests focus on establishing a baseline of data involving the derivation of performance metrics for various optical materials in soils and the subsequent effects on remote sensing and of these materials over time (Figure 2a). The tests also included disturbance and tamper marking and perimeter monitoring for evaluating LiDAR as a persistent ISR technique (Figure 2b). To accomplish this, CRREL and TEC are collecting time series LiDAR scans over the period of the experiment. CRREL will continue to scan periodically (as weather permits) over the next year to capture seasonal effects, when another experiment will be conducted in spring 2013.
As the ESP program enters its third year, the results of these experiments will help drive models for environmental effects and influences on materials, optical recovery of materials over time (persistence), and ancillary data necessary for sensor placement and performance prediction.