The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), the successor mission to ICESat, is planned to launch in 2017. The ICESat-2 spacecraft will carry the Advanced Topographic Laser Altimeter System (ATLAS). ATLAS will be the most precise space-based photon-counting laser altimeter to date, and its measurement strategy requires the development of sophisticated onboard receiver algorithms to ensure success in downlinking the science data in the telemetry and the subsequent development of science data products. A set of databases, the digital elevation model and digital relief map (DRM), has been developed for use in ATLAS onboard signal processing. A number of elevation data sets were combined to create the global elevation and relief databases, and a method for calculating along-track relief from raster elevation data sets was devised. A technique for deriving the accuracy of the DRM relative to the magnitude of relief was developed to inform the selection of DRM margin values.

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Light detection and ranging (LIDAR) technology offers the capability to rapidly capture high-resolution, 3-dimensional surface data with centimeter-level accuracy for a large variety of applications. Due to the foliage-penetrating properties of LIDAR systems, these geospatial data sets can detect ground surfaces beneath trees, enabling the production of highfidelity bare earth elevation models. Precise characterization of the ground surface allows for identification of terrain and non-terrain points within the point cloud, and facilitates further discernment between natural and man-made objects based solely on structural aspects and relative neighboring parameterizations. A framework is presented here for automated extraction of natural and man-made features that does not rely on coincident ortho-imagery or point RGB attributes. The TEXAS (Terrain EXtraction And Segmentation) algorithm is used first to generate a bare earth surface from a lidar survey, which is then used to classify points as terrain or non-terrain. Further classifications are assigned at the point level by leveraging local spatial information. Similarly classed points are then clustered together into regions to identify individual features. Descriptions of the spatial attributes of each region are generated, resulting in the identification of individual tree locations, forest extents, building footprints, and 3-dimensional building shapes, among others. Results of the fully-automated feature extraction algorithm are then compared to ground truth to assess completeness and accuracy of the methodology.