Incorporate vertical error into a Digital Elevation Model
add_dem_error(x, rmse, type = "u", samples = NULL)spatRaster
numeric. Vertical Root Mean Square Error of the Digital Elevation Model
character type 'u' (unfiltered), 'n' (neighbourhood autocorrelation), and 'd' (mean spatial dependence) implemented. See details for more information
numeric number of random spatial data locations sampled when using type 'd'. This can be used to overcome issues with computing time and memory limits
Joseph Lewis
Digital Elevation Models (DEMs) are representations of the earth's surface and are subject to error (Wechsler and Kroll, 2006)
The add_dem_error function incorporates vertical error into the supplied DEM. Three methods are implemented:
Unfiltered: Random error based on DEM RMSE range. Autocorrelation between random error is not accounted for. This can be interpreted as the worst case scenario
Neighbourhood autocorrelation: Random error is spatially autocorrelated by passing a mean low pass filter in a 3x3 neighbourhood over the surface
Mean Spatial Dependence: Random error is spatially autocorrelated by passing a DxD kernel over each cell. The centre cell of each kernel is replaced by the mean of the surrounding DxD cells. Distance of spatial dependence (D) is estimated by calcualting the semi-variogram nugget using the gstat package
Examples of RMSE for various datasets:
Shuttle Radar Topography Mission (SRTM) has a RMSE of 9.73m
Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) has a RMSE of 10.20m
Ordnance Survey OS Terrain 5 has a maximum RMSE of 2.5m
Ordnance Survey OS Terrain 50 has a maximum RMSE of 4m
TINITALY DEM has a RMSE of 4.3m
Wechsler, S. P., & Kroll, C. N. (2006). Quantifying DEM Uncertainty and its Effect on Topographic Parameters. Photogrammetric Engineering & Remote Sensing, 72(9), 1081-1090. https://doi.org/10.14358/PERS.72.9.1081
Fisher, P., & Tate, N. J. (2006). Causes and consequences of error in digital elevation models. Progress in Physical Geography: Earth and Environment, 30(4), 467-489. https://doi.org/10.1191/0309133306pp492ra
r <- terra::rast(system.file("extdata/SICILY_1000m.tif", package="leastcostpath"))
r2 <- add_dem_error(x = r, rmse = 4.3, type = "u")
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