Published April 6, 2021 | Version v0.1.2
Dataset Open

Digital Terrain Model for British Columbia at 25-m spatial resolution based on GEDI, ICESat-2, AW3D, CDED, GLAD layers, MERIT DEM and GLO-30

  • 1. EnvirometriX Ltd
  • 2. LandMapper Inc

Description

We used GEDI and ICESat-2 space-based LiDAR survey data (cca 7M points) as ground control points to train a model to predict bare surface terrain height using as covariates: AW3D, GLO-30, MERIT DEM, CDED-25, GLAD canopy height, surface water occurrence probability, GLAD (Global Forest Watch) bare earth probability and tree cover fractions for 2000, 2010 and 2014. The training points were extracted using the lowest observed altitudes from the GEDI (Level 2A; “elev_lowestmode”) and ICESat-2 (ATL08; “h_te_mean”) surveys. Prior to regression modeling the WGS84 ellipsoid altitudes were converted to EGM2008 using the global difference grids. The Ensemble Models were then fitted using spatial 5-fold cross-validation with refitting, as implemented in the mlr package for Machine Learning, and random forest (ranger), cubist and glmnet as the base learners. The results achieved a reported RMSE of 8.6-m with an R-squared of 0.9998, with random forest and cubist identified as equally important learners.

Data description:

  • dtm_elev.lowestmode_gedi.cded.eml_mf_25m_0..0cm_2000..2018_bc.epsg3005_v0.1.2.tif: Ensemble estimate of the terrain height in decimeter (dm).

  • dtm_elev.lowestmode_gedi.cded.eml_md_25m_0..0cm_2000..2018_bc.epsg3005_v0.1.2.tif: Error of the ensemble estimate of terrain heights in meter (m).

Model summary:

Variable: elev_lowestmode.f 
R-square: 1 
Fitted values sd: 610 
RMSE: 8.61 

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

Residuals:
    Min      1Q  Median      3Q     Max 
-59.725  -3.772   0.291   3.769 267.910 

Coefficients: (1 not defined because of singularities)
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)   -0.2358210  0.0189646  -12.44   <2e-16 ***
regr.ranger    0.5504095  0.0013799  398.89   <2e-16 ***
regr.glmnet    0.0074061  0.0005931   12.49   <2e-16 ***
regr.cubist    0.4422925  0.0014321  308.83   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 8.605 on 5385151 degrees of freedom
Multiple R-squared:  0.9998,	Adjusted R-squared:  0.9998 
F-statistic: 9.02e+09 on 3 and 5385151 DF,  p-value: < 2.2e-16

The variable importance table shows:

               variable   importance
3      bc_CDED_DEM_30m_ 653702254033
4      bc_MERITDEM_30m_ 488290057186
1     bc_AW3Dv2012_30m_ 434604342980
2         bc_GLO30_30m_ 363673956969
7      bc_bare2010_30m_  43879671456
6 bc_treecover2010_30m_  13043530952
8 bc_treecover2000_30m_   5833643430
9 bc_canopy_height_30m_    854769631

 

Notes

Created for the purpose of the Predictive Ecosystem Mapping in British Columbia project / Government of British Columbia; Natural Resources.

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Additional details

References

  • Dubayah, R., Blair, J. B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S., ... & Silva, C. (2020). The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth's forests and topography. Science of remote sensing, 1, 100002. https://doi.org/10.1016/j.srs.2020.100002
  • Pavlis, N. K., Holmes, S. A., Kenyon, S. C., & Factor, J. K. (2012). The development and evaluation of the Earth Gravitational Model 2008 (EGM2008). Journal of geophysical research: solid earth, 117(B4). https://doi.org/10.1029/2011JB008916
  • Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C., Kommareddy, A., ... & Hofton, M. (2021). Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 253, 112165. https://doi.org/10.1016/j.rse.2020.112165
  • Uuemaa, E., Ahi, S., Montibeller, B., Muru, M., & Kmoch, A. (2020). Vertical Accuracy of Freely Available Global Digital Elevation Models (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM). Remote Sensing, 12(21), 3482. https://doi.org/10.3390/rs12213482
  • Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., & Pavelsky, T. M. (2019). MERIT Hydro: a high‐resolution global hydrography map based on latest topography dataset. Water Resources Research, 55(6), 5053-5073. https://doi.org/10.1029/2019WR024873