The USGS currently posesses several nation-wide raster datasets of similar spatial resolution, such as the 30-meter National Elevation Dataset (NED), National Land Cover Dataset (NLCD), Landsat-7, and Multi-Resolution Land Cover Dataset-2000 (MRLC-2000). Preliminary research shows promise that these datasets can be vertically integrated so as to provide a height component to the sensor-derived imager or interpolated raster data, but further investigation is needed. These satellite-derived datasets with added elevation components would benefit researchers who currently rely on separate, if any, elevation data in modeling and prediction. Research will include analyzing approaches for assigning true height values from the NED, LIDAR, IFSAR, or highest-resolution-available elevation data. Procedures will be developed to create geo-rectified raster datasets from disparate high-resolution source raster data, having appropriate image file formats, projections, cell resolutions, and scales. The goal of the project is to provide preliminary fused datasets (raster and vector) for hydrographically correct elevation.
Project Lead: John Kosovich