Validation of SRTM Elevations Over Vegetated and Non-vegetated Terrain Using Medium-Footprint Lidar

M. Hofton,R. Dubayah,J. Blair,D. Rabine

Published 2006 in Photogrammetric Engineering and Remote Sensing

ABSTRACT

The Shuttle Radar Topography Mission (SRTM) generated one of the most-complete high-resolution digital topographic data sets of the world to date. The elevations generated by the on-board C-band sensor represent surface elevations in “bare earth” regions, and the elevations of various scatterers such as leaves and branches in other regions. Elevations generated by a medium-footprint (� 10 m diameter) laser altimeter (lidar) system known as NASA’s Laser Vegetation Imaging Sensor (LVIS) were used to assess the accuracy of SRTM elevations at study sites of variable relief, and landcover. Five study sites in Maine, Massachusetts, Maryland, New Hampshire, and Costa Rica were chosen where coincident LVIS and SRTM data occur. Both ground and canopy top lidar elevations were compared to the SRTM elevations. In “bare earth” regions, the mean vertical offset between the SRTM elevations and LVIS ground elevations varied with study site and was approximately 0.0 m, 0.5 m, 3.0 m, 4.0 m, and 4.5 m at the Maine, Maryland, Massachusetts, New Hampshire, and Costa Rica study sites, respectively. In vegetated regions, the mean vertical offset increased, implying the phase center fell above the ground, and the offset varied by region. The SRTM elevations fell on average approximately 14 m below the LVIS canopy top elevations, except in Costa Rica where they were approximately 8 m below the canopy top. At all five study sites, SRTM elevations increased with increasing vertical extent (i.e., the difference between the LVIS canopy top and ground elevations and analogous to canopy height in vegetated regions). A linear relationship was found sufficient to describe the relationship between the SRTM-LVIS elevation difference and canopy vertical extent.

PUBLICATION RECORD

  • Publication year

    2006

  • Venue

    Photogrammetric Engineering and Remote Sensing

  • Publication date

    2006-03-01

  • Fields of study

    Geography, Environmental Science

  • Identifiers
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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