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Digital terrain model elevation corrections using space-based imagery and ICESat-2 laser altimetry

Magruder, Lori, Neuenschwander, Amy, Klotz, Brad·2021·Remote Sensing of Environment
DOI 10.1016/j.rse.2021.112621
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  • To correct the effect of vegetation on most pixels, the adopted solution is to develop an empirical model that relates vegetation height, or the magnitude of the error vegetation causes in DEMs, to the correction.

    “To correct the effect of vegetation on most pixels, the solution that has been adopted is to develop an empirical model that relates the height of the vegetation, or more precisely, the magnitude of the error that the vegetation causes in DEMs [22][23][24].”

    From ANADEM: A Digital Terrain Model for South America (2024) · cites this paper as [24]

    Section 2.1 describes a regression model that predicts SRTM height differences from canopy cover and terrain slope, supporting the claim that an empirical model is used to correct elevation errors. It does not use vegetation height as a model input. The Discussion reports improved agreement at 70% of SRTM grid locations in the study site, rather than establishing a solution for most pixels across DEMs generally; page numbers are unavailable in our copy. (LLM, conf=0.86)

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Research works printed but not linked: 31

  1. Berry, 2007, Near global validation of the SRTM DEM using satellite radar altimetry, Remote Sens. Environ., 106, 17, 10.1016/j.rse.2006.07.011 · DOI
  2. Brunt, 2019, Assessment of ICESat-2 ice sheet surface heights, based on comparisons over the interior of the Antarctic ice sheet, Geophys. Res. Lett., 46, 13072, 10.1029/2019GL084886 · DOI
  3. Carabajal, 2006, SRTM C-band and ICESat laser altimetry elevation comparisons as a function of tree cover and relief, Photogramm. Eng. Remote. Sens., 72, 287, 10.14358/PERS.72.3.287 · DOI
  4. Crippen, 2016, NASADEM global elevation model: methods and progress, Int. Soc.Photogrammetry and Remote Sensing, July, 12-19, 2016
  5. Farr, 2007, The shuttle radar topography mission, Rev. Geophys., 45, 10.1029/2005RG000183 · DOI · Paper page
  6. Gruber, 2012, Operational TanDEM-X DEM calibration and first validation results, ISPRS J. Photogramm. Remote Sens., 73, 39, 10.1016/j.isprsjprs.2012.06.002 · DOI
  7. Hansen, 2002, Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data, Remote Sens. Environ., 83, 303, 10.1016/S0034-4257(02)00079-2 · DOI
  8. Kenyi, 2009, Comparative analysis of SRTM–NED vegetation canopy height to LiDAR-derived vegetation canopy metrics, Int. J. Remote Sens., 30, 2797, 10.1080/01431160802555853 · DOI
  9. Magruder, 2021, Passive ground-based optical techniques for monitoring the on-orbit ICESat-2 altimeter geolocation and footprin diameter, Earth and Space Science, 10.1029/2020EA001414 · DOI
  10. Magruder, 2016, Evaluation of terrain and canopy height products in central African tropical forests, Int. J. Remote Sens., 37, 5365, 10.1080/01431161.2016.1232870 · DOI
  11. Magruder, 2020, Early ICESat-2 geolocation validation using ground-based corner cube retro-reflectors, Remote Sens., 12, 3653, 10.3390/rs12213653 · DOI
  12. Magruder, 2021, ICESat-2 early mission synopsis and observatory performance, Earth and Space Science, 8, 10.1029/2020EA001555 · DOI
  13. Markus, 2017, The ice, cloud, and land elevation satellite-2 (ICESat-2): science requirements, concept, and implementation, Remote Sens. Environ., 190, 260, 10.1016/j.rse.2016.12.029 · DOI · Paper page
  14. Neuenschwander, 2019, Canopy and terrain height retrievals with ICESat-2: a first look, Remote Sens., 11, 1721, 10.3390/rs11141721 · DOI
  15. Neuenschwander, 2019, The ATL08 land and vegetation product for the ICESat-2 mission, Remote Sens. Environ., 221, 247, 10.1016/j.rse.2018.11.005 · DOI
  16. Neuenschwander, 2020, Validation of ICESat-2 terrain and canopy heights in boreal forests, Remote Sens. Environ., 251, 10.1016/j.rse.2020.112110 · DOI
  17. Neumann, 2019, The ice, cloud, and land elevation Satellite-2 mission: a global geolocated photon product derived from the advanced topographic laser altimeter system, Remote Sens. Environ., 233, 111325, 10.1016/j.rse.2019.111325 · DOI
  18. O’Loughlin, 2016, A multi-sensor approach towards a global vegetation corrected SRTM DEM product, Remote Sensing of the Environment, 182
  19. Reuter, 2007, An evaluation of void-filling interpolation metods for SRTM data, International Journal of Geographical Information Science, 21, 983, 10.1080/13658810601169899 · DOI
  20. Schumann, 2018, The need for a high-accuracy, open access global DEM, Front. Earth Sci., 04
  21. Schutz, 2005, Overview of the ICESat mission, Geophys. Res. Lett., 32, 10.1029/2005GL024009 · DOI
  22. Sexton, 2013, Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error, Int. J. of Digital Earth, 6, 427, 10.1080/17538947.2013.786146 · DOI · Paper page
  23. Slater, J. A., Garvey, G., Johnston, C., Haase, J., Heady, B., Kroenung, G., & Little, J. (2006). The SRTM data “finishing” process and products. Photogramm. Eng. Remote. Sens., 3, 237-247. Doi: 10.14358/PERS.72.3.237. · DOI
  24. Su, 2014, A practical method for SRTM DEM correction over vegetated mountain areas, ISPRS J. Photogrammetry and Remote Sensing., 87, 216, 10.1016/j.isprsjprs.2013.11.009 · DOI
  25. Su, 2015, SRTM DEM correction in vegetated mountain areas through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery, Remote Sens., 7, 11202, 10.3390/rs70911202 · DOI
  26. Tadono, 2014, Precise global DEM generation by ALOS PRISM, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 11
  27. Uuemaa, 2020, Vertical accuracy of freely available global digital elevation models (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM), Remote Sens., 12, 3482, 10.3390/rs12213482 · DOI · Paper page
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  29. Yamazaki, 2017, A high accuracy map of global terrain elevations, Geophys. Res. Lett., 44, 10.1002/2017GL072874 · DOI · Paper page
  30. Zhao, 2018, A global corrected SRTM DEM product for vegetated areas, Remote Sensing Letters, Vol. 9, 10.1080/2150704X.2018.1425560 · DOI
  31. Zhou, 2020, An adaptive terrain-dependent method for SRTM DEM correction over mountainous areas, IEEE Access

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Metadata

Authors
Magruder, L., Neuenschwander, A., & Klotz, B.
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2021-10
Volume
264
Pages
112621
ISSN
0034-4257
DOI
10.1016/j.rse.2021.112621
Cited by
116
References
0
Language
en
Licence
Funders
  • National Geospatial-Intelligence Agency (HM047618C0040)
  • British Archaeological Association (047618C0040)

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