dtm_idw: Spatial Interpolation Algorithm
In Jean-Romain/lidR: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications
dtm_idw R Documentation
Spatial Interpolation Algorithm
Description
This function is made to be used in rasterize_terrain or normalize_height. It implements an algorithm
for spatial interpolation. Interpolation is done using a k-nearest neighbour (KNN) approach with
an inverse-distance weighting (IDW).
Usage
knnidw(k = 10, p = 2, rmax = 50)
Arguments
k
integer. Number of k-nearest neighbours. Default 10.
p
numeric. Power for inverse-distance weighting. Default 2.
rmax
numeric. Maximum radius where to search for knn. Default 50.
See Also
Other dtm algorithms:
dtm_kriging,
dtm_tin
Examples
LASfile <- system.file("extdata", "Topography.laz", package="lidR")
las = readLAS(LASfile)
#plot(las)
dtm = rasterize_terrain(las, algorithm = knnidw(k = 6L, p = 2))
#plot(dtm)
#plot_dtm3d(dtm)
Jean-Romain/lidR documentation built on April 6, 2024, 9:41 p.m.
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| dtm_idw | R Documentation |
Spatial Interpolation Algorithm
Description
This function is made to be used in rasterize_terrain or normalize_height. It implements an algorithm for spatial interpolation. Interpolation is done using a k-nearest neighbour (KNN) approach with an inverse-distance weighting (IDW).
Usage
knnidw(k = 10, p = 2, rmax = 50)
Arguments
k |
integer. Number of k-nearest neighbours. Default 10. |
p |
numeric. Power for inverse-distance weighting. Default 2. |
rmax |
numeric. Maximum radius where to search for knn. Default 50. |
See Also
Other dtm algorithms:
dtm_kriging,
dtm_tin
Examples
LASfile <- system.file("extdata", "Topography.laz", package="lidR")
las = readLAS(LASfile)
#plot(las)
dtm = rasterize_terrain(las, algorithm = knnidw(k = 6L, p = 2))
#plot(dtm)
#plot_dtm3d(dtm)
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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