API reference

This page provides a summary of xDEM’s API. For more details and examples, refer to the relevant chapters in the main part of the documentation.

Examples using xdem.DEM

DEM differencing

DEM differencing

Elevation error map

Elevation error map

Spatial correlation of errors

Spatial correlation of errors

Nuth and Kääb coregistration

Nuth and Kääb coregistration

Iterative closest point coregistration

Iterative closest point coregistration

Spatial propagation of elevation errors

Spatial propagation of elevation errors

Terrain attributes

Terrain attributes

Bias correction with deramping

Bias correction with deramping

Working with a collection of DEMs

Working with a collection of DEMs

Blockwise coregistration

Blockwise coregistration

Normalized regional hypsometric interpolation

Normalized regional hypsometric interpolation

Slope and aspect methods

Slope and aspect methods

Estimation and modelling of spatial variograms

Estimation and modelling of spatial variograms

Estimation and modelling of heteroscedasticity

Estimation and modelling of heteroscedasticity

Standardization for stable terrain as error proxy

Standardization for stable terrain as error proxy

DEM

Important

A DEM inherits all raster methods and attributes from the Raster object of GeoUtils. Below, we only repeat the core attributes and methods of GeoUtils, see the Raster API in GeoUtils for the full list.

Opening or saving a DEM

DEM(filename_or_dataset[, vcrs, silent])	The digital elevation model.
DEM.info([stats, verbose])	Print summary information about the raster.
DEM.save(filename[, driver, dtype, nodata, ...])	Write the raster to file.

Plotting a DEM

DEM(filename_or_dataset[, vcrs, silent])	The digital elevation model.
DEM.plot([bands, cmap, vmin, vmax, alpha, ...])	Plot the raster, with axes in projection of image.

Create from an array

DEM.from_array(data, transform, crs[, ...])

Create a DEM from a numpy array and the georeferencing information.

Unique attributes

Inherited from Raster

DEM.data	Array of the raster.
DEM.crs	Coordinate reference system of the raster.
DEM.transform	Geotransform of the raster.
DEM.nodata	Nodata value of the raster.

Specific to DEM

DEM.vcrs

Vertical coordinate reference system of the DEM.

Georeferencing

Inherited from Raster

DEM.reproject([ref, crs, res, grid_size, ...])	Reproject raster to a different geotransform (resolution, bounds) and/or coordinate reference system (CRS).
DEM.crop(crop_geom[, mode, inplace])	Crop the raster to a given extent.

Vertical referencing for DEM

DEM.set_vcrs(new_vcrs)	Set the vertical coordinate reference system of the DEM.
DEM.to_vcrs(vcrs[, force_source_vcrs])	Convert the DEM to another vertical coordinate reference system.

Raster-vector interface

Note

See the full list of vector methods in GeoUtils’ documentation.

DEM.polygonize([target_values])	Polygonize the raster into a vector.
DEM.proximity([vector, target_values, ...])	Compute proximity distances to the raster target pixels, or to a vector geometry on the raster grid.

Coregistration

Tip

To build and pass your coregistration pipeline to coregister_3d(), see the API of Coreg.

DEM.coregister_3d(reference_elev[, ...])

Coregister DEM to a reference DEM in three dimensions.

Terrain attributes

DEM.slope([method, degrees, use_richdem])	Generate a slope map for a DEM, returned in degrees by default.
DEM.aspect([method, degrees, use_richdem])	Calculate the aspect of each cell in a DEM, returned in degrees by default.
DEM.hillshade([method, azimuth, altitude, ...])	Generate a hillshade from the given DEM.
DEM.curvature([use_richdem])	Calculate the terrain curvature (second derivative of elevation) in m-1 multiplied by 100.
DEM.profile_curvature([use_richdem])	Calculate the terrain curvature parallel to the direction of the slope in m-1 multiplied by 100.
DEM.planform_curvature([use_richdem])	Calculate the terrain curvature perpendicular to the direction of the slope in m-1 multiplied by 100.
DEM.maximum_curvature([use_richdem])	Calculate the signed maximum profile or planform curvature parallel to the direction of the slope in m-1 multiplied by 100.
DEM.topographic_position_index([window_size])	Calculates the Topographic Position Index, the difference to the average of neighbouring pixels.
DEM.terrain_ruggedness_index([method, ...])	Calculates the Terrain Ruggedness Index, the cumulated differences to neighbouring pixels.
DEM.roughness([window_size])	Calculates the roughness, the maximum difference between neighbouring pixels, for any window size.
DEM.rugosity()	Calculates the rugosity, the ratio between real area and planimetric area.
DEM.fractal_roughness([window_size])	Calculates the fractal roughness, the local 3D fractal dimension.

dDEM

dDEM(raster, start_time, end_time[, error])

A difference-DEM object.

DEMCollection

dDEM

DEMCollection(dems[, timestamps, outlines, ...])

A temporal collection of DEMs.

Coreg

Overview of co-registration class structure:

Coregistration, pipeline and blockwise

xdem.coreg.Coreg([meta])	Generic co-registration processing class.
xdem.coreg.CoregPipeline(pipeline)	A sequential set of co-registration processing steps.
xdem.coreg.BlockwiseCoreg(step, subdivision)	Block-wise co-registration processing class to run a step in segmented parts of the grid.

Fitting and applying transforms

xdem.coreg.Coreg.fit(reference_elev, ...[, ...])	Estimate the coregistration transform on the given DEMs.
xdem.coreg.Coreg.apply(elev[, bias_vars, ...])	Apply the estimated transform to a DEM.

Other functionalities

xdem.coreg.Coreg.residuals(reference_elev, ...)

Calculate the residual offsets (the difference) between two DEMs after applying the transformation.

Affine coregistration methods

Generic parent class:

xdem.coreg.AffineCoreg([subsample, matrix, meta])

Generic affine coregistration class.

Convenience classes for specific coregistrations:

xdem.coreg.VerticalShift([vshift_func, ...])	DEM vertical shift correction.
xdem.coreg.NuthKaab([max_iterations, ...])	Nuth and Kääb (2011) DEM coregistration: iterative registration of horizontal and vertical shift using slope/aspect.
xdem.coreg.ICP([max_iterations, tolerance, ...])	Iterative Closest Point DEM coregistration.
xdem.coreg.Tilt([subsample])	DEM tilting.

Bias-correction (including non-affine coregistration) methods

Generic parent class:

xdem.coreg.BiasCorr([fit_or_bin, fit_func, ...])

Parent class of bias correction methods: non-rigid coregistrations.

Classes for any 1-, 2- and N-D biases:

xdem.coreg.BiasCorr1D([fit_or_bin, ...])	Bias-correction along a single variable (e.g., angle, terrain attribute).
xdem.coreg.BiasCorr2D([fit_or_bin, ...])	Bias-correction along two variables (e.g., X/Y coordinates, slope and curvature simultaneously).
xdem.coreg.BiasCorrND([fit_or_bin, ...])	Bias-correction along N variables (e.g., simultaneously slope, curvature, aspect and elevation).

Convenience classes for specific corrections:

xdem.coreg.Deramp([poly_order, fit_or_bin, ...])	Correct for a 2D polynomial along X/Y coordinates, for example from residual camera model deformations.
xdem.coreg.DirectionalBias([angle, ...])	Bias correction for directional biases, for example along- or across-track of satellite angle.
xdem.coreg.TerrainBias([terrain_attribute, ...])	Correct a bias according to terrain, such as elevation or curvature.

Terrain attributes

xdem.terrain

Terrain attribute calculations, such as slope, aspect, hillshade, curvature and ruggedness indexes.

Volume integration methods

xdem.volume

Volume change calculation tools (aimed for glaciers).

Fitting methods

xdem.fit

Functions to perform normal, weighted and robust fitting.

Filtering methods

xdem.filters

Filters to remove outliers and reduce noise in DEMs.

Spatial statistics methods

xdem.spatialstats

Spatial statistical tools to estimate uncertainties related to DEMs