# Ranking of 10 Global One-Arc-Second DEMs Reveals Limitations in Terrain Morphology Representation

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Test DEMs

#### 2.2. Pixel Origin Models

#### 2.3. Test Areas

#### 2.4. Comparison Criteria

#### 2.4.1. Statistical Measures from the Difference Distribution

#### 2.4.2. Fraction of Unexplained Variance (FUV)

^{2}, the squared Pearson coefficient, and ranges in value from 0 (best, r

^{2}= 1) to 1 (worst, r

^{2}= 0). The restricted range of evaluation values allows comparison of different tiles to generalize controls on the performance of one-arc-second DEMs, as well as facilitating the production of graphics showing the relationships present in our databases. The correlation coefficient, r

^{2}, or FUV are effective ways to compare grids and multiple land surface parameters [33], but their systematic use to evaluate the quality of DEMs with respect to a reference DTM is a novelty of this work.

#### 2.4.3. Landform Raster Classification and Vector Comparisons

^{2}areas. Some of the comparison criteria might more appropriately use a different test area such as drainage basins. Because we want to evaluate the ability of the test DEMs to match output created by a reference DTM of much higher quality, all the test DEMs face the same issues in dealing with a truncated drainage basin. The resulting stream network might misrepresent locations along the boundary, but is expected to be comparable with a network derived from the reference DTM and allow us to compare the test DEMs.

#### 2.5. DEMIX Database Version 3

## 3. Results

#### 3.1. Difference Distributions for FULL Elevation Range

#### 3.2. FUV Criteria

- CopDEM performs the best of the DEMs, with two caveats depending on slope (Figure 7). For slopes above 55%, ALOS performs almost as well, while for slopes below 5%, FABDEM performs better.
- In flat coastal areas, the vegetation and buildings, which still have an effect on CopDEM, have an undue influence on many of the parameters.
- In very flat terrain, SRTM and NASADEM slightly outperform CopDEM for elevation, but not for any of the other criteria.
- For average tile slope under 10%, FABDEM has the best average rank over the 17 FUV criteria.
- Between 10% and 60% slopes, CopDEM ranks best.
- Above a 60% slope, ALOS performs best, but below that point ALOS performs significantly worse than both FABDEM and CopDEM.

#### 3.3. Pixel Raster Classification Criteria

#### 3.4. Vector Mismatch Criteria

#### 3.5. Clustering to Evaluate Geomorphometric Controls on Results

- Except for cluster 9, elevation FUV is generally very low, indicating that the test DEMs compare closely to the reference DTM. Even for cluster 9, the elevation FUV is much lower than any of the other criteria.
- The parameters that require computation of multiple derived grids (LS, WETIN, and HAND) have higher values of FUV, meaning they compare poorly with the reference DTM. Each derived grid needed to compute the grid for a parameter increases the uncertainty in the final grid.
- The second-derivative parameters (e.g., curvatures) behave much worse than most of the others. TANGC and PROFC are better than PLANC and ROTOR.

#### 3.6. Edited One-Arc-Second DTMs

#### 3.7. Hallucinations

## 4. Discussion

#### 4.1. DEM Comparison Methodology

#### 4.2. Spatial Patterns of One-Arc-Second Global DEM Quality

#### 4.3. Evaluating Reference DTMs

#### 4.4. Gaps and Data Fill in Global Arc-Second DEMs

#### 4.5. Parameter Ranking Based on FUV Performance

## 5. Conclusions: Which Global DEM to Use?

## Supplementary Materials

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

3DEP | 3D Elevation Program |

COP, CopDEM | Copernicus DEM |

DEM | Digital elevation model |

DEMIX | Digital Elevation Model Intercomparison Exercise |

DSM | Digital surface model |

DTM | Digital surface model |

FUV | Fraction of unexplained variance |

LE90 | Linear error 90th percentile |

MAE | Mean average error |

USGS | United States Geological Survey |

UTM | Universal Transverse Mercator-projected coordinate system |

## References

- Farr, T.G.; Rosen, P.A.; Caro, E.; Crippen, R.; Duren, R.; Hensley, S.; Kobrick, M.; Paller, M.; Rodriguez, E.; Roth, L.; et al. The Shuttle Radar Topography Mission. Rev. Geophys.
**2007**, 45, RG2004. [Google Scholar] [CrossRef] - Abrams, M.; Crippen, R.; Fujisada, H. ASTER Global Digital Elevation Model (GDEM) and ASTER Global Water Body Dataset (ASTWBD). Remote Sens.
**2020**, 12, 1156. [Google Scholar] [CrossRef] - Tadono, T.; Nagai, H.; Ishida, H.; Oda, F.; Naito, S.; Minakawa, K.; Iwamoto, H. Generation of the 30 M-Mesh Global Digital Surface Model by ALOS PRISM. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
**2016**, XLI-B4, 157–162. [Google Scholar] [CrossRef] - Crippen, R.; Buckley, S.; Agram, P.; Belz, E.; Gurrola, E.; Hensley, S.; Kobrick, M.; Lavalle, M.; Martin, J.; Neumann, M.; et al. NASADEM Global Elevation Model: Methods and Progress. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
**2016**, XLI-B4, 125–128. [Google Scholar] [CrossRef] - Wessel, B.; Huber, M.; Wohlfart, C.; Marschalk, U.; Kosmann, D.; Roth, A. Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data. ISPRS J. Photogramm. Remote Sens.
**2018**, 139, 171–182. [Google Scholar] [CrossRef] - Rizzoli, P.; Martone, M.; Gonzalez, C.; Wecklich, C.; Tridon, D.B.; Bräutigam, B.; Bachmann, M.; Schulze, D.; Fritz, T.; Huber, M.; et al. Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS J. Photogramm. Remote Sens.
**2017**, 132, 119–139. [Google Scholar] [CrossRef] - Strobl, P. The new Copernicus digital elevation model. GSICS Q.
**2020**, 14, 11–14. [Google Scholar] [CrossRef] - Guth, P.L. Geomorphometry from SRTM: Comparison to NED. Photogramm. Eng. Remote Sens.
**2006**, 72, 269–278. [Google Scholar] [CrossRef] - Hawker, L.; Uhe, P.; Paulo, L.; Sosa, J.; Savage, J.; Sampson, C.; Neal, J. A 30 m global map of elevation with forests and buildings removed. Environ. Res. Lett.
**2022**, 17, 024016. [Google Scholar] [CrossRef] - Neal, J.; Hawker, L. FABDEM V1-2. 2023. Available online: https://data.bris.ac.uk/data/dataset/s5hqmjcdj8yo2ibzi9b4ew3sn (accessed on 20 August 2024).
- Kolp, S.; Strauss, B. CoastalDEM v3.0: Improving Fully Global Coastal Elevation Predictions through a Convolutional Neural Network and Multi-Source DEM Fusion. 2024. Available online: https://24975331.fs1.hubspotusercontent-eu1.net/hubfs/24975331/CoastalDEM_3___Scientific_White_Paper_Mar2024-1.pdf# (accessed on 20 August 2024).
- Dusseau, D.; Zobel, Z.; Schwalm, C.R. DiluviumDEM: Enhanced accuracy in global coastal digital elevation models. Remote Sens. Environ.
**2023**, 298, 113812. [Google Scholar] [CrossRef] - Dusseau, D.; Zobel, Z.; Schwalm, C.R. DiluviumDEM. 2023. Available online: https://zenodo.org/records/8384665 (accessed on 20 August 2024).
- Pronk, M.; Hooijer, A.; Eilander, D.; Haag, A.; de Jong, T.; Vousdoukas, M.; Vernimmen, R.; Ledoux, H.; Eleveld, M. DeltaDTM: A global coastal digital terrain model. Sci. Data
**2024**, 11, 273. [Google Scholar] [CrossRef] [PubMed] - Pronk, M. DeltaDTM: A Global Coastal Digital Terrain Model. Version 2. 4TU.ResearchData. Dataset. 2024. Available online: https://data.4tu.nl/datasets/1da2e70f-6c4d-4b03-86bd-b53e789cc629/2 (accessed on 15 August 2024).
- López-Vázquez, C.; Ariza-López, F.J. Global digital elevation model comparison criteria: An evident need to consider their application. ISPRS Int. J.-Geo-Inf.
**2023**, 12, 337. [Google Scholar] [CrossRef] - Bielski, C.; López-Vázquez, C.; Grohmann, C.H.; Guth, P.L.; Hawker, L.; Gesch, D.; Trevisani, S.; Herrera-Cruz, V.; Riazanoff, S.; Corseaux, A.; et al. Novel approach for fanking DEMs: Copernicus DEM improves one arc second open global topography. IEEE Trans. Geosci. Remote Sens.
**2024**, 62, 1–22. [Google Scholar] [CrossRef] - Guth, P.L.; Van Niekerk, A.; Grohmann, C.H.; Muller, J.P.; Hawker, L.; Florinsky, I.V.; Gesch, D.; Reuter, H.I.; Herrera-Cruz, V.; Riazanoff, S.; et al. Digital elevation models: Terminology and definitions. Remote Sens.
**2021**, 13, 3581. [Google Scholar] [CrossRef] - Landsat Missions. Differences between Pixel-Is-Area and Pixel-Is-Point Designations. Available online: https://www.usgs.gov/media/images/differences-between-pixel-area-and-pixel-point-designations (accessed on 15 August 2024).
- Florinsky, I.V. Digital Terrain Analysis in Soil Science and Geology, 2nd ed.; Elsevier: Amsterdam, The Netherlands, 2016; p. 432. [Google Scholar]
- Guth, P.; Kane, M. Slope, aspect, and hillshade algorithms for non-square digital elevation models. Trans. GIS
**2021**, 25, 2309–2332. [Google Scholar] [CrossRef] - Guth, P.L.; Strobl, P.; Gross, K.; Riazanoff, S. DEMIX 10k Tile Data Set (1.0). Dataset Zenodo 2023. Available online: https://zenodo.org/records/7504791 (accessed on 15 August 2024).
- Stoker, J.; Miller, B. The accuracy and consistency of 3D Elevation Program data: A systematic analysis. Remote Sens.
**2022**, 14, 940. [Google Scholar] [CrossRef] - Guth, P.L. DEMIX GIS Database (3.0). 2024. Available online: https://zenodo.org/records/13331458 (accessed on 20 August 2024).
- MICRODEM: Open-Source GIS with a Focus on Geomorphometry. Available online: https://microdem.org/ (accessed on 13 June 2024).
- prof-pguth-git_microdem. Available online: https://github.com/prof-pguth/git_microdem (accessed on 13 June 2024).
- Lindsay, J. Whitebox GAT: A case study in geomorphometric analysis. Comput. Geosci.
**2016**, 95, 75–84. [Google Scholar] [CrossRef] - WhiteboxTools Open Core. Available online: https://www.whiteboxgeo.com/geospatial-software/ (accessed on 13 June 2024).
- Whitebox Workflows for Python. Available online: https://www.whiteboxgeo.com/whitebox-workflows-for-python/ (accessed on 13 June 2024).
- Welcome to the SAGA Homepage. Available online: https://saga-gis.sourceforge.io/en/index.html (accessed on 13 June 2024).
- Buchhorn, M.; Lesiv, M.; Tsendbazar, N.E.; Herold, M.; Bertels, L.; Smets, B. Copernicus Global Land Cover Layers—Collection 2. Remote Sens.
**2020**, 12, 1044. [Google Scholar] [CrossRef] - Maxwell, A.E.; Shobe, C.M. Land-surface parameters for spatial predictive mapping and modeling. Earth-Sci. Rev.
**2022**, 226, 103944. [Google Scholar] [CrossRef] - Zhong, Y.; Xiong, L.; Zhou, Y.; Tang, G. Quantifying the spatial associations among terrain parameters from digital elevation models. Trans. GIS
**2024**, 28, 746–768. [Google Scholar] [CrossRef] - Evans, I.S. An integrated system of terrain analysis and slope mapping. Z. Geomorphol.
**1980**, 36, 274–295. [Google Scholar] - Guisan, A.; Weiss, S.B.; Weiss, A.D. GLM versus CCA spatial modeling of plant species distribution. Plant Ecol.
**1999**, 143, 107–122. [Google Scholar] [CrossRef] - Pelton, C. A computer program for hill-shading digital topographic data sets. Comput. Geosci.
**1987**, 13, 545–548. [Google Scholar] [CrossRef] - Yokoyama, R.; Shirasawa, M.; Pike, R.J. Visualizing topography by openness: A new application of image processing to digital elevation models. Photogramm. Eng. Remote Sens.
**2002**, 68, 257–266. [Google Scholar] - Grohmann, C.H.; Smith, M.J.; Riccomini, C. Multiscale analysis of topographic surface roughness in the Midland Valley, Scotland. Geosci. Remote Sens. IEEE Trans.
**2011**, 49, 1200–1213. [Google Scholar] [CrossRef] - Trevisani, S.; Teza, G.; Guth, P.L. Hacking the topographic ruggedness index. Geomorphology
**2023**, 439, 108838. [Google Scholar] [CrossRef] - Wilson, J.P. Environmental Applications of Digital Terrain Modeling; John Wiley & Sons: Hoboken, NJ, USA, 2018; p. 355. [Google Scholar]
- Shary, P.A.; Sharaya, L.S.; Mitusov, A.V. Fundamental quantitative methods of land surface analysis. Geoderma
**2002**, 107, 1–32. [Google Scholar] [CrossRef] - Florinsky, I.V. An illustrated introduction to general geomorphometry. Prog. Phys. Geogr. Earth Environ.
**2017**, 41, 723–752. [Google Scholar] [CrossRef] - Rennó, C.D.; Nobre, A.D.; Cuartas, L.A.; Soares, J.V.; Hodnett, M.G.; Tomasella, J. HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia. Remote Sens. Environ.
**2008**, 112, 3469–3481. [Google Scholar] [CrossRef] - Beven, K.J.; Kirkby, M.J. A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol. Sci. Bull.
**1979**, 24, 43–69. [Google Scholar] [CrossRef] - Moore, I.D.; Grayson, R.; Ladson, A. Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrol. Process.
**1991**, 5, 3–30. [Google Scholar] [CrossRef] - Böhner, J.; McCloy, K.R.; Strobl, J. SAGA-Analysis and Modelling Applications; Göttinger Geographische Abhandlungen; University of Goettingen: Goettingen, Germany, 2006; p. 120. [Google Scholar]
- Claps, P.; Fiorentino, M.; Oliveto, G. Informational entropy of fractal river networks. J. Hydrol.
**1996**, 187, 145–156. [Google Scholar] [CrossRef] - O’Callaghan, J.F.; Mark, D.M. The extraction of drainage networks from digital elevation data. Comput. Vision Graph. Image Process.
**1984**, 28, 323–344. [Google Scholar] [CrossRef] - Jasiewicz, J.; Stepinski, T.F. Geomorphons—A pattern recognition approach to classification and mapping of landforms. Geomorphology
**2013**, 182, 147–156. [Google Scholar] [CrossRef] - Iwahashi, J.; Pike, R.J. Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology
**2007**, 86, 409–440. [Google Scholar] [CrossRef] - Cohen, J. A Coefficient of Agreement for Nominal Scales. Educ. Psychol. Meas.
**1960**, 20, 37–46. [Google Scholar] [CrossRef] - Foody, G.M. Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sens. Environ.
**2020**, 239, 111630. [Google Scholar] [CrossRef] - Guth, P.L. DEMIX GIS Database Version 2. 2023. Available online: https://zenodo.org/records/8062008 (accessed on 20 August 2024).
- Trevisani, S.; Skrypitsyna, T.N.; Florinsky, I.V. Global digital elevation models for terrain morphology analysis in mountain environments: Insights on Copernicus GLO-30 and ALOS AW3D30 for a large Alpine area. Environ. Earth Sci.
**2023**, 82, 198. [Google Scholar] [CrossRef] - Guth, P.L.; Geoffroy, T.M. LiDAR point cloud and ICESat-2 evaluation of 1 second global digital elevation models: Copernicus wins. Trans. GIS
**2021**, 25, 2245–2261. [Google Scholar] [CrossRef] - Guth, P.L.; Grohmann, C.H.; Trevisani, S. Subjective criterion for the DEMIX wine contest: Hillshade maps. In Proceedings of the Geomorphometry 2023 Conference, Iasi, Romania, 10–14 July 2023. [Google Scholar] [CrossRef]
- Reis, L.; Polidori, L. Challenges of relief modeling in flat areas: A case study in the Amazon coast floodplains. Bol. Ciênc. Geod.
**2024**, 30, e2024009. [Google Scholar] [CrossRef] - Michael Meadows, S.J.; Reinke, K. Vertical accuracy assessment of freely available global DEMs (FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM) in flood-prone environments. Int. J. Digit. Earth
**2024**, 17, 2308734. [Google Scholar] [CrossRef] - Gesch, D.B. Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure. Front. Earth Sci.
**2018**, 6, 230. [Google Scholar] [CrossRef] - Purinton, B.; Bookhagen, B. Validation of digital elevation models (DEMs) and comparison of geomorphic metrics on the southern Central Andean Plateau. Earth Surface Dyn.
**2017**, 5, 211–237. [Google Scholar] [CrossRef] - Purinton, B.; Bookhagen, B. Beyond vertical point accuracy: Assessing inter-pixel consistency in 30 m global DEMs for the Arid Central Andes. Front. Earth Sci.
**2021**, 9, 758606. [Google Scholar] [CrossRef] - Rubel, F.; Kottek, M. Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol. Z.
**2010**, 19, 135. [Google Scholar] [CrossRef] - World Maps of KÖPPEN-GEIGER Climate Classification. Available online: https://koeppen-geiger.vu-wien.ac.at/shifts.htm (accessed on 18 June 2024).
- World Bank-ImageCat Inc. RIT Haiti Earthquake LiDAR Dataset. Available online: https://portal.opentopography.org/datasetMetadata?otCollectionID=OT.072010.32618.1 (accessed on 20 June 2024).
- Corseaux, A.; Gross, K.; Riazanoff, S.; Strobl, P. DEM Intercomparison eXercise (DEMIX)—Maps of Completeness Criteria Scores for Global DEMs. 2024. Available online: https://zenodo.org/records/11389298 (accessed on 20 August 2024).

**Figure 2.**Average ranks for the difference distribution and FUV criteria and evaluations of the FUV criteria for average slope, average roughness, percentage of tile barren, and percentage forested.

**Figure 3.**CopDEM win/loss record for difference distribution criteria. Solid color wins, white ties, and cross-hatch losses. Criteria defined by [17].

**Figure 4.**Best evaluation percentiles versus the FUV for all criteria used in the study, for all tiles and 5 filters. DEM performance increases to the right. The best/easiest criteria to match are listed in order from the top of the legend. Criteria names given in Table 4.

**Figure 5.**FUV for three criteria, sorted by the best tile evaluations for four test DEMs; for all seven test DEMs see Figure S8.

**Figure 6.**Effect of tile slope and percent barren on the best evaluation from the test DEMs on 3 FUV criteria. Number of tiles indicated for each category.

**Figure 7.**CopDEM head-to-head comparison to other test DEMs for the FULL elevation range, FUV criteria. Solid color wins, white ties, and cross-hatch (which may appear just as a light color) losses. Criteria names given in Table 4.

**Figure 9.**Clusters for FULL-elevation-range FUV criteria, with the number of tiles in each cluster. Criteria names given in Table 4.

**Figure 10.**Cluster characteristics for CopDEM, with single points showing outliers. Colors for the clusters are the same as in the previous section. The box extent includes the 25th to the 75th percentiles, the middle line shows the mean, the whiskers go from the 5th to the 95th percentiles, and the data points show outliers.

**Figure 12.**Test DEM comparisons to CopDEM for all FUV criteria for the U10, U80, U120, and FULL elevation range. Supplementary figures use FABDEM (Figure S9) and CoastalDEM (Figure S10) as the base comparison. Solid color wins, white ties, and cross-hatch (which may appear just as a light color) losses. Criteria names given in Table 4.

**Figure 13.**FUV criteria performance for all elevation ranges. Criteria names given in Table 4.

**Figure 15.**Average evaluations by slope category for the FULL elevation, U120, U80, and U10 data sets.

**Figure 16.**Edited DTM changes to CopDEM on barren coast of southwest Africa, with the CopDEM hillshade and the GLCS LC100 land cover. Differences greater than 1 m highlighted.

**Figure 17.**Slope for FUV for three representative criteria for four best test DEMs. Criteria names given in Table 4.

Elevation Band | Edited DTM | License | Source Data | Methods | Validation |
---|---|---|---|---|---|

FULL: covers entire Earth | FABDEM [9,10] | Restricted | CopDEM | Random forest | Split-sample, lidar, ICESat-2 |

U120, <120 m, but 1-degree tiles filled | CoastalDEM 3.0 [11] | Restricted | Several recent and advanced global DEMs | Convolutional neural networks | ICESat-2 |

U80, <80 m | DiluviumDEM [12,13] | Creative Commons Attribution | CopDEM | Decision tree | Local DTMs from airborne lidar in 10 countries |

U10, <10 m | DeltaDTM [14,15] | Creative Commons Attribution | CopDEM | Filtering and co-registration | Local DTMs from airborne lidar in 9 countries |

DEM | Pixel-Is (GeoTIFF Tag #1025) | Model Tie Point (GeoTIFF tag #33922) | Nominal DEM Corner | Pixel Origin Model |
---|---|---|---|---|

CopDEM, TanDEM-X, FABDEM, SRTM, and NASADEM | Point | DEM nominal corner from file name | Pixel centroid | SRTM |

ASTER and CoastalDEM | Area | Half-pixel offset from DEM nominal corner | Pixel centroid | SRTM |

ALOS, USGS 3DEP and DiluviumDEM | Area | DEM nominal corner from file name | Pixel corner | ALOS |

Country | Test Areas | DEMIX Tiles |
---|---|---|

United States | 71 | 2139 |

Spain | 12 | 346 |

France | 7 | 243 |

Italy | 3 | 214 |

Switzerland | 4 | 118 |

Haiti | 1 | 116 |

Canada | 10 | 92 |

UK | 1 | 51 |

Australia | 3 | 34 |

Netherlands | 4 | 20 |

Denmark | 1 | 11 |

Brazil | 2 | 9 |

Norway | 1 | 3 |

Uruguay | 1 | 1 |

Criterion | Meaning | Computing Category | Geomorphometric Category | Computation Area | Additional Grids Required | Algorithm | Computation Software |
---|---|---|---|---|---|---|---|

ELEV | Elevation | Grid FUV | Grid value | Single grid cell | N/A | N/A | |

SLOPE | Slope | Grid FUV | First derivative | 3 × 3 neighborhood | [34] | MICRODEM | |

TPI | Topographic position index | Grid FUV | First derivative | 7 × 7 neighborhood | [35] | MICRODEM | |

HILL | Hillshade | Grid FUV | Perceptive index or First derivative | 3 × 3 neighborhood | Originally based on [36] | MICRODEM | |

OPEND | Downward openness | Grid FUV | Perceptive index | 8 radials out to 250 m | [37] | MICRODEM | |

OPENU | Upward openness | Grid FUV | Perceptive index | 8 radials out to 250 m | [37] | MICRODEM | |

RUFF | Roughness (standard deviation of slope) | Grid FUV | Second derivative | 5 × 5 slopes (7 × 7 elevations) | [38] | MICRODEM | |

RRI | Radial roughness index | Grid FUV | Second derivative | 5 × 5 neighborhood | [39] | MICRODEM | |

PROFC | Profile curvature | Grid FUV | Second derivative | 3 × 3 neighborhood | [20] | WhiteboxTools | |

TANGC | Tangent curvature | Grid FUV | Second derivative | 3 × 3 neighborhood | [40] | WhiteboxTools | |

ROTOR | Rotor | Grid FUV | Second derivative | 3 × 3 neighborhood | [41] | Whitebox Workflows | |

PLANC | Plan curvature | Grid FUV | Second derivative | 3 × 3 neighborhood | [42] | WhiteboxTools | |

HAND | Height above nearest drainage (elevation above stream) | Grid FUV | Hydrology related | Entire test area | Flow accumulation, streams | [43] | Whitebox Workflows |

WETIN | Wetness index | Grid FUV | Hydrology related | Entire test area | Flow accumulation, slope | [44] | WhiteboxTools |

LS | Sediment transport (slope length factor) | Grid FUV | Hydrology related | Point and downslope neighbors | Flow accumulation, slope | [45,46] | Whitebox Workflows |

CONIN | Convergence index | Grid FUV | Hydrology related | 3 × 3 neighborhood | [47] | Whitebox Workflows | |

ACCUM | Flow accumulation, log transform | Grid FUV | Hydrology related | Entire test area | [48] | Whitebox Workflows | |

GEOM | Gemorphons | Per-pixel raster classification | Point classification | Local neighborhood | [49] | WhiteboxTools + MICRODEM | |

IP12 | Iwahashi and Pike 12 category classification | Per-pixel raster classification | Point classification | 10 cell neighborhood | [50] | SAGA | |

CHAN_MISS1 | Channel network mismatch, 1 pixel wide channels | Vector comparison | Hydrology related | Entire test area | [17] | Whitebox Workflows + MICRODEM | |

CHAN_MISS3 | Channel network mismatch, 3 pixel wide channels | Vector comparison | Hydrology related | Entire test area | [17] | Whitebox Workflows + MICRODEM |

Database Table | Data Set | Areas | DEMIX Tiles | Difference Distribution Records | FUV Records | Raster Classification Records | Vector Comparison Records |
---|---|---|---|---|---|---|---|

DEMIX DB v3 | Full | 124 | 3462 | 50,319 | 58,854 | 27,603 | 5838 |

DEMIX DB v3 | U120 | 69 | 1569 | 23,249 | |||

DEMIX DB v3 | U80 | 48 | 727 | 1041 | |||

DEMIX DB v3 | U10 | 26 | 285 | 4159 | |||

DEMIX DB v2 [17] | Full | 24 | 234 | 55,699 | N/A | N/A | N/A |

Cluster | CopDEM | TanDEM-X | FABDEM | ALOS | NASADEM | SRTM | ASTER |
---|---|---|---|---|---|---|---|

Cluster 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |

Cluster 2 | 109 | 100 | 78 | 1 | 0 | 0 | 0 |

Cluster 3 | 106 | 98 | 106 | 1 | 0 | 0 | 0 |

Cluster 4 | 350 | 306 | 280 | 211 | 0 | 0 | 0 |

Cluster 5 | 388 | 336 | 432 | 645 | 12 | 8 | 0 |

Cluster 6 | 747 | 634 | 826 | 768 | 873 | 830 | 47 |

Cluster 7 | 580 | 673 | 646 | 477 | 1245 | 1283 | 1407 |

Cluster 8 | 549 | 633 | 635 | 512 | 657 | 665 | 825 |

Cluster 9 | 570 | 618 | 397 | 785 | 613 | 614 | 1121 |

Country | Test Areas | DEMIX Tiles |
---|---|---|

USA | 18 | 164 |

Switzerland | 1 | 1 |

Spain | 7 | 68 |

Haiti | 1 | 1 |

Koppen | DEMIX Tiles | Name |
---|---|---|

As | 2 | Tropical savanna—dry summer |

BSk | 107 | Mid-latitude cold steppe |

BWh | 87 | Low-latitude hot desert |

BWk | 55 | Mid-latitude cold desert |

Cfa | 2 | Humid subtropical no dry season hot summer |

Cfb | 43 | Marine west coast no dry season warm to cool summer |

Csa | 146 | Mediterranean summer dry and hot |

Csb | 67 | Mediterranean summer dry and warm |

Dfa | 3 | Humid continental hot summer |

Dfb | 43 | Humid continental mild summer |

Dfc | 6 | Subarctic 1–4 mild months |

Dsb | 37 | Subarctic summer dry mild summer |

ET | 3 | Tundra |

Field | Meaning | Mean FUV | ${\mathit{r}}^{2}$ |
---|---|---|---|

ELEV FUV | Elevation | 0.0001 | 0.9999 |

HILL FUV | Hillshade | 0.0093 | 0.9907 |

SLOPE FUV | Slope | 0.0202 | 0.9798 |

OPEND FUV | Downward openness | 0.0279 | 0.9721 |

TPI FUV | Terrain position index | 0.0279 | 0.9721 |

OPENU FUV | Upward openness | 0.0285 | 0.9715 |

RUFF FUV | Roughness | 0.0358 | 0.9642 |

CONIN FUV | Convergence index | 0.0367 | 0.9633 |

HAND FUV | Height above nearest drainage | 0.0789 | 0.9211 |

RRI FUV | Radial roughness index | 0.0832 | 0.9168 |

TANGC FUV | Tangential curvature | 0.0875 | 0.9125 |

PROFC FUV | Profile curvature | 0.1169 | 0.8831 |

WETIN FUV | Wetness index | 0.1271 | 0.8729 |

LS FUV | LS factor | 0.2096 | 0.7904 |

ROTOR FUV | Rotor | 0.2867 | 0.7133 |

ACCUM FUV | Flow accumulation | 0.5040 | 0.4960 |

PLANC FUV | Plan curvature | 0.5129 | 0.4871 |

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## Share and Cite

**MDPI and ACS Style**

Guth, P.L.; Trevisani, S.; Grohmann, C.H.; Lindsay, J.; Gesch, D.; Hawker, L.; Bielski, C.
Ranking of 10 Global One-Arc-Second DEMs Reveals Limitations in Terrain Morphology Representation. *Remote Sens.* **2024**, *16*, 3273.
https://doi.org/10.3390/rs16173273

**AMA Style**

Guth PL, Trevisani S, Grohmann CH, Lindsay J, Gesch D, Hawker L, Bielski C.
Ranking of 10 Global One-Arc-Second DEMs Reveals Limitations in Terrain Morphology Representation. *Remote Sensing*. 2024; 16(17):3273.
https://doi.org/10.3390/rs16173273

**Chicago/Turabian Style**

Guth, Peter L., Sebastiano Trevisani, Carlos H. Grohmann, John Lindsay, Dean Gesch, Laurence Hawker, and Conrad Bielski.
2024. "Ranking of 10 Global One-Arc-Second DEMs Reveals Limitations in Terrain Morphology Representation" *Remote Sensing* 16, no. 17: 3273.
https://doi.org/10.3390/rs16173273