DOI: 10.5281/zenodo.4483227
title: Continental Europe Digital Terrain Model at 30 m resolution based on GEDI and background layers

description: 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 MERITDEM, AW3D30, GLO-30, EU DEM, GLAD 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: "lcv_bare.earth_glcf.landsat": UMD GLAD bare earth estimate for year 2010 based on Landsat time series, "dtm_elev.dsm_alos.aw3d": Digital Surface Model based on ALOS AW3D, "dtm_canopy.height_glad.umd": UMD GLAD canopy height for 2019 based on GEDI data, "dtm_elev.dsm_eudem.eea": Copernicus EU DEM based on the SRTM and ASTER DEMs, "hyd_surface.water_jrc.gswe": JRC Global Surface Water Explorer surface water probability based on the Landsat time-series, "lcv_landcover.12_pflugmacher2019": land cover map of Europe at 30 based on Pflugmacher et al. (2019), "lcv_tree.cover_umd.landsat_2000": forest tree cover for year 2000 based on the Global Forest Change data, "lcv_tree.cover_umd.landsat_2010": forest tree cover for year 2010 based on the Global Forest Change data, Detailed processing steps can be found <strong>here</strong>. Read more about the processing steps <strong>here</strong>. Training data set can be obtained in the file "gedi_elev.lowestmode_2019_eumap.RDS". Summary results of the model training (mlr::makeStackedLearner) report: <pre><code>Variable: elev_lowestmode R-square: 1 Fitted values sd: 353 RMSE: 6.45 Ensemble model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -78.880 -2.474 0.523 2.817 223.228 Coefficients: Estimate Std. Error t value Pr(&gt;|t|) (Intercept) 2.086691 0.078138 26.70 &lt;2e-16 *** regr.ranger 0.586053 0.001123 522.01 &lt;2e-16 *** regr.glmnet 3.108060 0.101530 30.61 &lt;2e-16 *** regr.cvglmnet -3.104559 0.101658 -30.54 &lt;2e-16 *** regr.cubist 0.404570 0.001146 353.10 &lt;2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 6.448 on 6756251 degrees of freedom Multiple R-squared: 0.9997, Adjusted R-squared: 0.9997 F-statistic: 5.051e+09 on 4 and 6756251 DF, p-value: &lt; 2.2e-16</code></pre> Which indicates that the elevation errors are in average (2/3rd of pixels) between +2-3 m. The variable importance based on Random Forest package ranger shows: <pre><code>Variable importance: variable importance 4 eu_MERITv1.0.1_30m_ 339179872235 2 eu_GLO30_30m_ 195444455910 1 eu_AW3Dv2012_30m_ 177308698430 3 eu_dem25m_ 118993789329 9 eu_canopy_height_30m_ 4605735928 7 bare2010_ 3360985239 6 treecover2010_ 801394531 8 treecover2000_ 314790601</code></pre> 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 <strong>reference water surface (WGS84 ellipsoid) is about 43 m higher than the sea water surface for a specific EU country</strong>. All GeoTIFFs were prepared using Integer format (elevations rounded to 1 m) and have been converted to Cloud Optimized GeoTIFFs using GDAL. <strong>Disclaimer</strong>: 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 <strong>here</strong>. To access DTM derivatives at 30-m, 100-m and 250-m please visit <strong>here</strong>. To register for updates please subscribe to: https://twitter.com/HarmonizerGeo.
description: 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).
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