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The output predicted terrain model includes the following two layers: The variable importance based on Random Forest package ranger shows: Variable importance: Which indicates that the elevation errors are in average (2/3rd of pixels) between +2-3 m. Residual standard error: 6.448 on 6756251 degrees of freedom Summary results of the model training ( mlr::makeStackedLearner) report: Variable: elev_lowestmode Training data set can be obtained in the file "gedi_elev.lowestmode_2019_eumap.RDS". Read more about the processing steps here. "lcv_ver_umd.landsat_2010": forest tree cover for year 2010 based on the Global Forest Change data,ĭetailed processing steps can be found here.
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"lcv_ver_umd.landsat_2000": forest tree cover for year 2000 based on the Global Forest Change data,."lcv_landcover.12_pflugmacher2019": land cover map of Europe at 30 based on Pflugmacher et al."hyd_surface.water_jrc.gswe": JRC Global Surface Water Explorer surface water probability based on the Landsat time-series,."dtm_elev.dsm_eudem.eea": Copernicus EU DEM based on the SRTM and ASTER DEMs,."dtm_canopy.height_glad.umd": UMD GLAD canopy height for 2019 based on GEDI data,."dtm_elev.dsm_alos.aw3d": Digital Surface Model based on ALOS AW3D,."lcv_bare.earth_glcf.landsat": UMD GLAD bare earth estimate for year 2010 based on Landsat time series,.Input layers used to train the EML include: 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). Digital Terrain Model for Continental Europe based on the three publicly available Digital Surface Models and predicted using an Ensemble Machine Learning (EML).