Title of article :
EarthEnv-DEM90: A nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data
Author/Authors :
Robinson، نويسنده , , Natalie and Regetz، نويسنده , , James and Guralnick، نويسنده , , Robert P.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2014
Abstract :
A variety of DEM products are available to the public at no cost, though all are characterized by trade-offs in spatial coverage, data resolution, and quality. The absence of a high-resolution, high-quality, well-described and vetted, free, global consensus product was the impetus for the creation of a new DEM product described here, ‘EarthEnv-DEM90’. This new DEM is a compilation dataset constructed via rigorous techniques by which ASTER GDEM2 and CGIAR-CSI v4.1 products were fused into a quality-enhanced, consistent grid of elevation estimates that spans ∼91% of the globe. EarthEnv-DEM90 was assembled using methods for seamlessly merging input datasets, thoroughly filling voids, and smoothing data irregularities (e.g. those caused by DEM noise) from the approximated surface. The result is a DEM product in which elevational artifacts are strongly mitigated from the input data fusion zone, substantial voids are filled in the northern-most regions of the globe, and the entire DEM exhibits reduced terrain noise. As important as the final product is a well defined methodology, along with new processing techniques and careful attention to final outputs, that extends the value and usability of the work beyond just this single product. Finally, we outline EarthEnv-DEM90 acquisition instructions and metadata availability, so that researchers can obtain this high-resolution, high-quality, nearly-global new DEM product for the study of wide-ranging global phenomena.
Keywords :
Void-filling , Multi-scale smoothing , ASTER , Global Digital Elevation Model (DEM) , SRTM , DEM blending
Journal title :
ISPRS Journal of Photogrammetry and Remote Sensing
Journal title :
ISPRS Journal of Photogrammetry and Remote Sensing