There is a newer version of this record available.

Dataset Open Access

Continental Europe Digital Terrain Model at 30 m resolution based on GEDI and background layers

Hengl, Tomislav; Leal Parente, Leandro; Krizan, Josip; Bonannella, Carmelo

Digital Terrain Model for Continental Europe based on the three publicly available Digital Surface Models and predicted using an Ensemble Machine Learning (EML). EML was trainined using GEDI level 2B points (column "elev.lowestmode"): about 7 million GEDI points were overlaid vs NASADEM, AW3D, EU DEM, canopy height, tree cover and surface water cover maps, then an ensemble prediction model was fitted using random forest, GLM with Lasso, Cubist and GLMnet, and used to predict most probable terrain height (bare earth). Input layers used to train the EML include:

Detailed processing steps can be found here. Read more about the processing steps here. Training data set can be obtained in the file "gedi_elev.lowestmode_2019_eumap.RDS". Summary results of the model training (mlr::makeStackedLearner) report:

Call:
stats::lm(formula = f, data = d)

Residuals:
    Min      1Q  Median      3Q     Max 
-65.580  -2.630   0.648   3.120 181.769 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)   -4.1448129  0.4663283  -8.888  < 2e-16 ***
regr.ranger    0.2667469  0.0009676 275.677  < 2e-16 ***
regr.glmnet   -4.7183974  0.6038334  -7.814 5.54e-15 ***
regr.cvglmnet  4.6966219  0.6042481   7.773 7.69e-15 ***
regr.cubist    0.7643997  0.0012860 594.378  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 6.729 on 6757726 degrees of freedom
Multiple R-squared:  0.9996,	Adjusted R-squared:  0.9996 
F-statistic: 4.644e+09 on 4 and 6757726 DF,  p-value: < 2.2e-16

Which indicates that the elevation errors are in average (2/3rd of pixels) between +2-3 m. The output predicted terrain model includes the following two layers:

  • "dtm_elev.lowestmode_gedi.eml_m": mean estimate of the terrain elevation,
  • "dtm_elev.lowestmode_gedi.eml_md": standard deviation of the independently fitted stacked predictors quantifying the prediction uncertainty,

The predicted elevations are based on the GEDI data hence the reference water surface (WGS84 ellipsoid) is about 43 m higher than the sea water surface for a specific EU country. All GeoTIFFs were prepared using Integer format (elevations rounded to 1 m) and have been converted to Cloud Optimized GeoTIFFs using GDAL.

Disclaimer: The output DTM still shows forest canopy (overestimation of the terrain elevation) and has not been hydrologically corrected for spurious sinks and similar. This data set is continuously updated. To report a bug or suggest an improvement, please visit here. To register for updates please subscribe to: https://twitter.com/HarmonizerGeo.

This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
Files (48.7 GB)
Name Size
001_preview_EU_DTM.png
md5:0f960837d2385943460e1721a82ba131
1.4 MB Download
dtm_canopy.height_glad.umd_m_30m_0..0cm_2019_eumap_epsg3035_v0.1.tif
md5:b7136655458433c798b064d22d089b69
3.7 GB Download
dtm_elev.dsm_alos.aw3d_m_30m_0..0cm_2016..2018_eumap_epsg3035_v1804.tif
md5:32692709cfbff1942a4020fa26b57719
13.9 GB Download
dtm_elev.dsm_eudem.eea_m_30m_0..0cm_2002..2014_eumap_epsg3035_v1.0.tif
md5:1c71a8782a4da623e6883d041b89f0b3
8.0 GB Download
dtm_elev.lowestmode_gedi.eml_m_100m_0..0cm_2000..2018_eumap_epsg3035_v0.1.tif
md5:a1fede759ea9ec9c76169a31b15131ac
623.9 MB Download
dtm_elev.lowestmode_gedi.eml_m_30m_0..0cm_2000..2018_eumap_epsg3035_v0.1.tif
md5:f2f08cf61704c5969ff55d24cfb220e8
8.0 GB Download
dtm_elev.lowestmode_gedi.eml_md_30m_0..0cm_2000..2018_eumap_epsg3035_v0.1.tif
md5:9451519c2f6bc98804a0bddc9956298b
2.7 GB Download
gedi_elev.lowestmode_2019_eumap.RDS
md5:7e3e3e2e398fa5fa9594a930d6e1095e
1.5 GB Download
hyd_surface.water_jrc.gswe_p_30m_0..0cm_1984..2019_eumap_epsg3035_v0.1.tif
md5:af37fef0c0e8ae693888c5bc737f90a5
998.2 MB Download
lcv_bare.earth_glcf.landsat_m_30m_0..0cm_2010_eumap_epsg3035_v0.1.tif
md5:a4554a120b89bf3230b8e1373ca0c7b8
3.5 GB Download
lcv_landcover.12_pflugmacher2019_c_1m_s0..0m_2014..2016_eumap_epsg3035_v0.1.tif
md5:e292d4800e8c8c4df40dea4b7f756478
1.3 GB Download
lcv_tree.cover_umd.landsat_m_30m_0..0cm_2010_eumap_epsg3035_v1.7.tif
md5:31ac1244c16de3a59c3e7f8b25769273
4.3 GB Download
  • Bischl, B., Lang, M., Kotthoff, L., Schiffner, J., Richter, J., Studerus, E., ... & Jones, Z. M. (2016). mlr: Machine Learning in R. The Journal of Machine Learning Research, 17(1), 5938-5942.

  • Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., ... & Kommareddy, A. (2013). High-resolution global maps of 21st-century forest cover change. science, 342(6160), 850-853.

  • Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418-422.

  • Pflugmacher, D., Rabe, A., Peters, M., & Hostert, P. (2019). Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote sensing of environment, 221, 583-595.

  • Potapov, P., X. Li, A. Hernandez-Serna, A. Tyukavina, M.C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C.E. Silva, J. Armston, R. Dubayah, J. B. Blair, M. Hofton (2020) Mapping and monitoring global forest canopy height through integration of GEDI and Landsat data. In review

  • Takaku, J., & Tadono, T. (2017). Quality updates of 'AW3D'global DSM generated from ALOS PRISM. In 2017 IEEE International Geoscience and Remote Sensing Symposium (Igarss) (pp. 5666-5669). IEEE.

1,195
421
views
downloads
All versions This version
Views 1,195580
Downloads 421220
Data volume 1.9 TB1.1 TB
Unique views 1,023530
Unique downloads 205144

Share

Cite as