Technical Methodology for ASTER Global DEM

• Published in: IEEE transactions on geoscience and remote sensing Vol. 50; no. 10; pp. 3725 - 3736
• Main Authors: Fujisada, H., Urai, M., Iwasaki, A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.2012; Institute of Electrical and Electronics Engineers
• Subjects: Applied geophysics; ASTER instrument; Containers; digital elevation model (GDEM); Earth; Earth sciences; Earth, ocean, space; Exact sciences and technology; global database; Internal geophysics; NASA; optical sensor; Stacking; stereo; Telescopes; water body detection; algorithms; models; detection; water body; water body detection; optical sensor; latitude; coordinates; global; outliers; clouds; digital elevation model (GDEM); residual anomalies; thermal emission; Earth; drill cores; instruments; corrections; ASTER instrument; stereo; data bases; global database; filtering; Pixel
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2012.2187300

At the core of the technical methodology for creating the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) global digital elevation model (GDEM) is the procedure for generating a global set of 1 ° latitude-by-1 ° longitude tiles containing DEM data in geographic latitude and longitude coordinates and with one arc second postings from scene-based ASTER DEMs. The ASTER GDEM is comprised of all tiles, which include at least 0.01% land in them, each containing 3601-by-3601 elevation data points. The tiles are created by stacking all observed scene DEM data matched geographically to the tile container, selecting valid data for each pixel, removing abnormal data values, and then averaging the remaining selected valid data to assign as the tile elevation data. Valid Earth surface elevation values typically clump within a ± 40-m range and are assumed to be lower in elevation than residual cloud outliers. The filtering process, which assigns the tile elevation data, is one of the most important parts of the GDEM generation system. The median-based selection method is designed to efficiently select the valid data for each pixel. The combination of cloud-masked and non-cloud-masked data is another important part of the process to assign accurate elevation data for each pixel, because the cloud masking capability is not perfect. The algorithm used to combine both data is described. The postprocessing for inland water bodies is successfully carried out to yield a flattened elevation value. This postprocessing is essential to assign unique elevation values for each inland water body. GDEM tile elevation data include some residual anomalies, mostly in areas with fewer than three valid stacked input scenes. The correction method using existing reference data also is described.