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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

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 a 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. 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

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 shows forest tops 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 (47.1 GB)
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001_preview_EU_DTM.png
md5:0f960837d2385943460e1721a82ba131
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dtm_canopy.height_glad.umd_m_30m_0..0cm_2019_eumap_epsg3035_v0.1.tif
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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
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dtm_elev.lowestmode_gedi.eml_m_100m_0..0cm_2000..2018_eumap_epsg3035_v0.1.tif
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dtm_elev.lowestmode_gedi.eml_m_30m_0..0cm_2000..2018_eumap_epsg3035_v0.1.tif
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dtm_elev.lowestmode_gedi.eml_md_30m_0..0cm_2000..2018_eumap_epsg3035_v0.1.tif
md5:9451519c2f6bc98804a0bddc9956298b
2.7 GB Download
hyd_surface.water_jrc.gswe_p_30m_0..0cm_1984..2019_eumap_epsg3035_v0.1.tif
md5:af37fef0c0e8ae693888c5bc737f90a5
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lcv_bare.earth_glcf.landsat_m_30m_0..0cm_2010_eumap_epsg3035_v0.1.tif
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lcv_landcover.12_pflugmacher2019_c_1m_s0..0m_2014..2016_eumap_epsg3035_v0.1.tif
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lcv_tree.cover_umd.landsat_m_30m_0..0cm_2010_eumap_epsg3035_v1.7.tif
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