CoChat
Sign Up
Cited by 199

The DOI matches its Crossref record.

A multi-sensor approach towards a global vegetation corrected SRTM DEM product

O'Loughlin, F.E., Paiva, R.C.D., Durand, M., Alsdorf, D.E., Bates, P.D.·2016·Remote Sensing of Environment
DOI 10.1016/j.rse.2016.04.018
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.rse.2016.04.018

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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 [22]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 36

  1. Baldocchi, 2001, FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities, Bulletin of the American Meteorological Society, 82, 2415, 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2 · DOI
  2. Bamber, 1994, A Digital Elevation Model of the Antarctic ice sheet derived from ERSl altimeter data and comparison with terrestrial measurements, Annals of Glaciology, 20, 48, 10.3189/1994AoG20-1-48-54 · DOI
  3. Baugh, 2013, SRTM vegetation removal and hydrodynamic modeling accuracy, Water Resources Research, 49, 5276, 10.1002/wrcr.20412 · DOI
  4. Berry, 2007, Near-global validation of the SRTM DEM using satellite radar altimetry, Remote Sensing of Environment, 106, 17, 10.1016/j.rse.2006.07.011 · DOI
  5. Bhang, 2007, Verification of the vertical error in C-band SRTM DEM using ICESat and Landsat-7, Otter Tail County, MN, IEEE Transactions on Geoscience and Remote Sensing, 45, 36, 10.1109/TGRS.2006.885401 · DOI
  6. Broxton, 2014, A global land cover climatology using MODIS data, Journal of Applied Meteorology and Climatology, 53, 1593, 10.1175/JAMC-D-13-0270.1 · DOI
  7. Carabajal, 2005, ICESat validation of SRTM C-band Digital Elevation Models, Geophysical Research Letters, 32, L22S01, 10.1029/2005GL023957 · DOI · Paper page
  8. Farr, 2000, Shuttle Radar Topography Mission produces a wealth of data, EOS. Transactions of the American Geophysical Union, 81, 583, 10.1029/EO081i048p00583 · DOI
  9. Farr, 2007, The Shuttle Radar Topography Mission, Reviews of Geophysics, 45, 10.1029/2005RG000183 · DOI · Paper page
  10. Gallant, 2012, Removal of tree offsets from SRTM and other digital surface models, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1, 275, 10.5194/isprsarchives-XXXIX-B4-275-2012 · DOI
  11. Hall, 2012, Geodetic corrections to Amazon River water level gauges using ICESat altimetry, Water Resources Research, 48, 10.1029/2011WR010895 · DOI
  12. Hansen, 2003, Global percent tree cover at a spatial resolution of 500meters: First results of the MODIS Vegetation Continuous Fields Algorithm, Earth Interactions, 7, 1, 10.1175/1087-3562(2003)007<0001:GPTCAA>2.0.CO;2 · DOI
  13. Harding, 2005, ICESat waveform measurements of within-footprinttopographic relief and vegetation vertical structure, Geophysical Research Letters, 32, 10.1029/2005GL023471 · DOI
  14. Hirabayashi, 2013, Global flood risk under climate change, Nature Climate Change, 3, 816, 10.1038/nclimate1911 · DOI
  15. Jarihani, 2015, Satellite-derived Digital Elevation Model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low-gradient and data-sparse catchments, Journal of Hydrology, 524, 489, 10.1016/j.jhydrol.2015.02.049 · DOI
  16. Kenyi, 2009, Comparative analysis of SRTM–NED vegetation canopy height to LiDAR-derived vegetation canopy metrics, International Journal of Remote Sensing, 30, 2797, 10.1080/01431160802555853 · DOI
  17. Krabill, 1984, Airborne laser topographic mapping results, Photogrammetric Engineering and Remote Sensing, 685–694
  18. Lefsky, 2010, A global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system, Geophysical Research Letters, 37, 10.1029/2010GL043622 · DOI
  19. Milly, 2002, Increasing risk of great floods in a changing climate, Nature, 415, 514, 10.1038/415514a · DOI
  20. Moore, 1991, Digital terrain modelling: A review of hydrological, geomorphological, and biological applications, Hydrological Processes, 5, 3, 10.1002/hyp.3360050103 · DOI
  21. O'Loughlin, 2016, ICESat-derived inland water surface spot heights, Water Resources Research, 52, 10.1002/2015WR018237 · DOI
  22. O'Loughlin, 2013, Hydraulic characterization of the middle reach of the Congo River, Water Resources Research, 49, 5059, 10.1002/wrcr.20398 · DOI
  23. Paiva, 2013, Large-scale hydrologic and hydrodynamic modeling of the Amazon River basin, Water Resources Research, 49, 1226, 10.1002/wrcr.20067 · DOI
  24. Paiva, 2011, Large scale hydrologic and hydrodynamic modeling using limited data and a GIS based approach, Journal of Hydrology, 406, 170, 10.1016/j.jhydrol.2011.06.007 · DOI
  25. Peel, 2007, Updated world map of the Köppen-Geiger climate classification, Hydrology and Earth System Sciences, 11, 1633, 10.5194/hess-11-1633-2007 · DOI
  26. Pinel, 2015, Correction of interferometric and vegetation biases in the SRTMGL1 spaceborne DEM with hydrological conditioning towards improved hydrodynamics modeling in the Amazon Basin, Remote Sensing, 7, 16108, 10.3390/rs71215822 · DOI
  27. Rabus, 2003, The Shuttle Radar Topography Mission—A new class of Digital Elevation Models acquired by spaceborne radar, ISPRS Journal of Photogrammetry and Remote Sensing, 57, 241, 10.1016/S0924-2716(02)00124-7 · DOI
  28. Rodríguez, 2006, A global assessment of the SRTM performance, Photogrammetric Engineering and Remote Sensing, 72, 249, 10.14358/PERS.72.3.249 · DOI · Paper page
  29. Sanders, 2007, Evaluation of on-line DEMs for flood inundation modeling, Advances in Water Resources, 30, 1831, 10.1016/j.advwatres.2007.02.005 · DOI
  30. Schumann, 2014, Technology: Fight floods on a global scale, Nature, 507, 169, 10.1038/507169e · DOI
  31. Schutz, 2005, Overview of the ICESat mission, Geophysical Research Letters, 32, 10.1029/2005GL024009 · DOI
  32. 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, International Journal of Digital Earth, 6, 427, 10.1080/17538947.2013.786146 · DOI · Paper page
  33. Shimada, 2014, New global forest/non-forest maps from ALOS PALSAR data (2007–2010), Remote Sensing of Environment, 155, 13, 10.1016/j.rse.2014.04.014 · DOI
  34. Simard, 2011, Mapping forest canopy height globally with spaceborne LiDAR, Journal of Geophysical Research – Biogeosciences, 116, G04021, 10.1029/2011JG001708 · DOI
  35. Wilson, 2007, Modeling large-scale inundation of Amazonian seasonally flooded wetlands, Geophysical Research Letters, 34, 10.1029/2007GL030156 · DOI
  36. Zwally, 2002, ICESat's laser measurements of polar ice, atmosphere, ocean, and land, Journal of Geodynamics, 34, 405, 10.1016/S0264-3707(02)00042-X · DOI

Cited by

Sorted by citations

Metadata

Authors
O'Loughlin, F. E., Paiva, R. C. D., Durand, M., Alsdorf, D. E., & Bates, P. D.
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2016-09
Volume
182
Pages
49-59
ISSN
0034-4257
DOI
10.1016/j.rse.2016.04.018
Cited by
199
References
0
Language
en
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review