| Title: | Calculate Surface/Image Texture Indexes |
| Version: | 0.0.1.2 |
| Description: | Methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) <doi:10.1016/j.catena.2023.106927> and Trevisani and Guth (2025) <doi:10.3390/rs17233864>). It provides various functions for the computation of surface texture indices (e.g., omnidirectional roughness and roughness anisotropy), including the ones based on the robust MAD estimator. The kernels included in the software permit also to calculate the surface/image texture indices directly from the input surface (i.e., without de-trending) using increments of order 2 and of order 4. It also provides the new radial roughness index (RRI), representing the improvement of the popular topographic roughness index (TRI). The framework can be easily extended with ad-hoc surface/image texture indices. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| BugReports: | https://github.com/strevisani/SurfRough/issues |
| LazyData: | true |
| Depends: | R (≥ 3.5.0), terra |
| Suggests: | tinytest |
| URL: | https://github.com/strevisani/SurfRough, https://doi.org/10.5281/zenodo.7132160 |
| NeedsCompilation: | yes |
| Packaged: | 2026-01-27 09:19:44 UTC; x |
| Maintainer: | Sebastiano Trevisani <strevisani@iuav.it> |
| LinkingTo: | Rcpp |
| Author: | Sebastiano Trevisani
 [aut, cre],
Ilich Alexander
[ctb],
Zakharko Taras
[ctb] |
| Repository: | CRAN |
| Date/Publication: | 2026-01-27 12:30:17 UTC |
Calculate the mean of absolute values raised to an exponent found in a search window
Description
With this you can compute variogram and madogram (but remember that for conventional geostatistical indices you need to divide the derived isotropic index by 2!)
Usage
CalcMeans(deltas, w, exponent)
Arguments
deltas |
The values from which calculate the median of absolute values (i.e., directional differences of order K) |
w |
The moving window used (e.g. w=KernelCircular(3)) |
exponent |
The exponent: increasing the exponent increase the sensitivity to outliers. Set 2 for Variogram and 1 for Madogram. |
Value
A raster with the mean of absolute values in the search window
Calculate the median of absolute values found in a search window for each raster in a list
Description
Calculate the median of absolute values found in a search window for each raster in a list
Usage
CalcMedians(deltas, w)
Arguments
deltas |
A list of rasters with the values from which calculate the median of absolute values (e.g., directional differences of order K) |
w |
The moving window used (e.g. w=KernelCircular(3)) |
Value
A list of rasters with the median of absolute values in the search window
Build a circular moving window
Description
Build a circular moving window
Usage
KernelCircular(radius)
Arguments
radius |
The radius of the moving window |
Value
A matrix with selected pixels
Examples
#A circular moving window with a radius of 3 pixels
w=KernelCircular(3)
w
Build a rectangular kernel of size X x Y
Description
Build a rectangular kernel of size X x Y
Usage
KernelRectangular(lenx, leny)
Arguments
lenx |
The size in pixels along x |
leny |
The size in pixels along y |
Value
The matrix (square/rectangular) with the selected pixels
Examples
#A rectangular moving window 5x5 pixels
w=KernelRectangular(5,5)
w
Calculate MAD basic indices
Description
Calculate MAD basic indices considering a specif lag and difference of order K. It computes 3 indices of roughness/image texture: isotropic/omnidirectional; direction of maximum continuity; anisotropy index. The anisotropy index is based on vector dispersion approach: 0 minimum anisotropy; 1 maximum anisotropy. The direction of anisotropy is in degrees according to geographical convention.
Usage
Madscan(inRaster, kernels, w)
Arguments
inRaster |
The DEM/residual-dem/image from which to compute the indices |
kernels |
The kernels to be used for computing the directional differences (e.g. order 1,2 and 4 for various lags) |
w |
The moving window adopted for computing the geostatistical index (i.e., MAD) |
Value
A list of 3 rasters: 1)isotropic roughness; 2) direction of anisotropy;3)index of anisotropy.
References
Trevisani, S. & Rocca, M. 2015. MAD: Robust image texture analysis for applications in high resolution geomorphometry. Computers and Geosciences, vol. 81, pp. 78-92, doi:10.1016/j.cageo.2015.04.003.
Trevisani, S. Teza, G., Guth, P., 2023. A simplified geostatistical approach for characterizing key aspects of short-range roughness. CATENA,Volume 223, ISSN 0341-8162,doi:10.1016/j.catena.2023.106927.
Trevisani, S., Guth, P.L., 2025. Surface Roughness in Geomorphometry: From Basic Metrics Toward a Coherent Framework. Remote Sensing 17, doi:10.3390/rs17233864.
Examples
# MAD for lag 2 with differences of order 2 using a circular search window of radius 3.
# Using differences of order 1, you should
# apply these on a detrended surface/image.
library(terra)
dem=rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
w=KernelCircular(3)
rough2c=Madscan(dem,k2ck2, w)
#Plot isotropic roughness
plot(rough2c$IsoRough)
#Plot anisotropy index/strenght
plot(rough2c$AnisoR)
Calculate MAD basic indices (version for large files)
Description
Calculate MAD basic indices considering a specif lag and difference of order K. It computes 3 indices of roughness/image texture: isotropic/omnidirectional; direction of maximum continuity; anisotropy index. The anisotropy index is based on vector dispersion approach: 0 minimum anisotropy; 1 maximum anisotropy. The direction of anisotropy is in degrees according to geographical convention. This version clean and remove unused files so as to reduce the consumption of disk space. When non necessary prefer Madscan(). Work is in progress to create a still more memory (ram and disk) efficient function.
Usage
MadscanL(inRaster, kernels, w)
Arguments
inRaster |
The DEM/residual-dem/image from which to compute the indices |
kernels |
The kernels to be used for computing the directional differences (e.g. order 1,2 and 4 for various lags) |
w |
The moving window adopted for computing the geostatistical index (i.e., MAD) |
Value
A list of 3 rasters: 1)isotropic roughness; 2) direction of anisotropy;3)index of anisotropy.
References
Trevisani, S. & Rocca, M. 2015. MAD: Robust image texture analysis for applications in high resolution geomorphometry. Computers and Geosciences, vol. 81, pp. 78-92, doi:10.1016/j.cageo.2015.04.003.
Trevisani, S. Teza, G., Guth, P., 2023. A simplified geostatistical approach for characterizing key aspects of short-range roughness. CATENA,Volume 223, ISSN 0341-8162,doi:10.1016/j.catena.2023.106927.
Trevisani, S., Guth, P.L., 2025. Surface Roughness in Geomorphometry: From Basic Metrics Toward a Coherent Framework. Remote Sensing 17, doi:10.3390/rs17233864.
Examples
# MAD for lag 2 with differences of order 2 using a circular search window of radius 3.
# Using differences of order 1, you should
# apply these on a detrended surface/image.
library(terra)
dem=rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
w=KernelCircular(3)
rough2c=MadscanL(dem,k2ck2, w)
#Plot isotropic roughness
plot(rough2c$IsoRough)
#Plot anisotropy index/strenght
plot(rough2c$AnisoR)
Calculate less robust geostatistical indices (mean of absolute differences raised to an exponent)
Description
With this you can compute variogram and madogram (but remember that for conventional geostatistical indices you need to divide the derived isotropic index by 2!). Moreover you can calibrate the exponent in order to filter or enhance hotspots and discontinuities
Usage
Meanscan(inRaster, kernels, w, exponent)
Arguments
inRaster |
The DEM/residual-dem/image from which to compute the indices |
kernels |
The kernels to be used for computing the directional differences (e.g. order 1 or 2 for various lags) |
w |
The moving window adopted for computing the geostatistical index (i.e., MAD) |
exponent |
The exponent: increasing the exponent increase the sensitivity to outliers. Set 2 for Variogram and 1 for Madogram. |
Value
A SpatRaster with 3 layers: 1)isotropic roughness; 2) direction of anisotropy; 3)index of anisotropy.
Examples
#' Variogram-like for lag 2 with differences of order 2 using a circular search window of radius 3.
# Using differences of order 1, you should
# apply these on a detrended surface/image.
library(terra)
dem=rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
w=KernelCircular(3)
rough2c=Meanscan(dem,k2ck2, w,2)
#(divide by two if you need conventional estimator)
plot(rough2c$IsoRough)
RRI: Radial Roughness index
Description
Modified TRI, based on increments of order 2 (reducing/removing slope dependence) and correcting for diagonal distance. RRI modifies TRI (topographic ruggedness index) using increments of order 2, symmetrical to the central pixel, so as to reduce/remove the effect of local slope. This version corrects for the diagonal distance using bilinear interpolation. It uses a 5x5 kernel, consequently 12 directional differences of order k=2 are used in the estimation. One could also use a 3x3 kernel using only the 4 differences centered on the central pixel but the metric would be very noisy (see RRIcore()). The input is the DEM/image (no need to detrend).
Usage
RRI(x, ...)
## S3 method for class 'numeric'
RRI(x, ...)
## S3 method for class 'SpatRaster'
RRI(x, ..., .method = c("rcpp", "r"))
Arguments
x |
A DEM/image as a SpatRaster or a vector of numeric values from a focal window in a DEM from which to compute the index |
... |
reserved for future use |
.method |
Either |
Value
RRI (in the same units of input)
References
#' 1) Riley, S. J., S. D. DeGloria, and R. Elliott. 1999. A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain Journal of Science 5:23. 2) Wilson, M.F.J., O'Connell, B., Brown, C., Guinan, J.C. & Grehan, A.J. 2007. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope". Marine Geodesy, vol. 30, no. 1-2, pp. 3-35. 3) Trevisani S., Teza G., Guth P.L., 2023. Hacking the topographic ruggedness index. Geomorphology doi:10.1016/j.geomorph.2023.108838.
Examples
library(terra)
dem= rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
w <- matrix(1, nrow=5, ncol=5)
roughRRI_v1=focal(dem, w=w, fun=RRI)
roughRRI_v2=RRI(dem)
plot(c(roughRRI_v1, roughRRI_v2))
RRIK3: Radial Roughness index with differences of order 3
Description
Extension of RRI using differences of order 3, with a 5x5 kernel. Accordingly, this version filters out a trend of order 2, so it reduces still more the dependence on slope and partially on curvature (for filtering of curvature better to select RRIk4()). The input is the DEM/image (no need to detrend).
Usage
RRIK3(x, ...)
## S3 method for class 'numeric'
RRIK3(x, ...)
## S3 method for class 'SpatRaster'
RRIK3(x, ..., .method = c("rcpp", "r"))
Arguments
x |
A DEM/image as a SpatRaster or a vector of numeric values from a focal window in a DEM from which to compute the index |
... |
reserved for future use |
.method |
Either |
Value
isotropic roughness (in the same units of input)
References
Trevisani S., Teza G., Guth P.L., 2023. Hacking the topographic ruggedness index. Geomorphology, doi:10.1016/j.geomorph.2023.108838.
Examples
library(terra)
dem= rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
roughRRIK3=RRIK3(dem)
plot(roughRRIK3)
RRIMax: Maximum Radial Roughness index
Description
Same as RRI but instead of computing the mean of the absolute differences of order 2, the maximum is computed. The input is the DEM/image (no need to detrend).
Usage
RRIMax(x, ...)
## S3 method for class 'numeric'
RRIMax(x, ...)
## S3 method for class 'SpatRaster'
RRIMax(x, ..., .method = c("rcpp", "r"))
Arguments
x |
A DEM/image as a SpatRaster or a vector of numeric values from a focal window in a DEM from which to compute the index |
... |
reserved for future use |
.method |
Either |
Value
isotropic roughness (in the same units of input)
References
Trevisani S., Teza G., Guth P.L., 2023. Hacking the topographic ruggedness index. Geomorphology, doi:10.1016/j.geomorph.2023.108838.
Examples
library(terra)
dem= rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
roughRRIMax=RRIMax(dem)
plot(roughRRIMax)
RRIMin: Minimum Radial Roughness index
Description
Same as RRI but instead of computing the mean of the absolute differences of order 2, the minimum is computed. The input is the DEM/image (no need to detrend).
Usage
RRIMin(x, ...)
## S3 method for class 'numeric'
RRIMin(x, ...)
## S3 method for class 'SpatRaster'
RRIMin(x, ..., .method = c("rcpp", "r"))
Arguments
x |
A DEM/image as a SpatRaster or a vector of numeric values from a focal window in a DEM from which to compute the index |
... |
reserved for future use |
.method |
Either |
Value
isotropic roughness (in the same units of input)
References
Trevisani S., Teza G., Guth P.L., 2023. Hacking the topographic ruggedness index. Geomorphology, doi:10.1016/j.geomorph.2023.108838.
Examples
library(terra)
dem= rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
roughRRImin=RRIMin(dem)
plot(roughRRImin)
RRIcore: RRI using only the four inner second order directional differences of the RRI kernel
Description
RRIcore is like RRI, but it just uses the four inner second order directional differences, using a 3x3 kernel. There are some analogies with Casorati curvature. The input is the DEM/image (no need to detrend).
Usage
RRIcore(x, ...)
## S3 method for class 'numeric'
RRIcore(x, ...)
## S3 method for class 'SpatRaster'
RRIcore(x, ..., .method = c("rcpp", "r"))
Arguments
x |
A DEM/image as a SpatRaster or a vector of numeric values from a focal window in a DEM from which to compute the index |
... |
reserved for future use |
.method |
Either |
Value
RRIcore (in the same units of input)
References
Trevisani S., Teza G., Guth P.L., 2023. Hacking the topographic ruggedness index. Geomorphology, doi:10.1016/j.geomorph.2023.108838.
Examples
library(terra)
dem= rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
roughRRIcore=RRIcore(dem)
plot(roughRRIcore)
RRIk4: Radial Roughness index with fourth order differences
Description
RRI based on increments of order 4, permits the filtering of curvature (filters a polynomial of order 3), always using a 5x5 kernel. The input is the DEM/image (no need to detrend).
Usage
RRIk4(x, ...)
## S3 method for class 'numeric'
RRIk4(x, ...)
## S3 method for class 'SpatRaster'
RRIk4(x, ..., .method = c("rcpp", "r"))
Arguments
x |
A DEM/image as a SpatRaster or a vector of numeric values from a focal window in a DEM from which to compute the index |
... |
reserved for future use |
.method |
Either |
Value
RRIk4 (in the same units of input)
References
Trevisani S., Teza G., Guth P.L., 2023. Hacking the topographic ruggedness index. Geomorphology, doi:10.1016/j.geomorph.2023.108838.
Trevisani, S., Guth, P.L., 2025. Surface Roughness in Geomorphometry: From Basic Metrics Toward a Coherent Framework. Remote Sensing 17, doi:10.3390/rs17233864.
Examples
library(terra)
dem= rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
roughRRIk4=RRIk4(dem)
plot(roughRRIk4)
TRIbi: TRI with bilinear interpolation along diagonals
Description
TRI is based on DDs of first order (at least in its original definition), however, it does not take into account the different distances between the pixels on the diagonals with respect to the cardinal ones. This version corrects with bilinear interpolation to get the right distance. As TRI, it is a proxy of slope!
Usage
TRIbi(x, ...)
## S3 method for class 'numeric'
TRIbi(x, ...)
## S3 method for class 'SpatRaster'
TRIbi(x, ..., .method = c("rcpp", "r"))
Arguments
x |
A DEM/image as a SpatRaster or a vector of numeric values from a focal window in a DEM from which to compute the index |
... |
reserved for future use |
.method |
Either |
Value
TRI (in the same units of input)
References
Trevisani S., Teza G., Guth P.L., 2023. Hacking the topographic ruggedness index. Geomorphology, doi:10.1016/j.geomorph.2023.108838.
Examples
library(terra)
dem= rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
roughTRIbi=TRIbi(dem)
plot(roughTRIbi)#proxy of slope!
Improved TRI (with differences of order 2), reducing/removing slope dependence (for testing purposes)
Description
It is essentially a radial roughness index.This has been created for explaining the derivation of RRI, and it is not supposed to be used for doing real analysis. TRIk2 modifies TRI (topographic ruggedness index) using increments of order 2, symmetrical to central pixel, so as to remove/reduce the effect of local slope. This version does not correct for diagonal distance and therefore is mainly for testing/simulation purposes, so in practice the Radial Roughness Index calculated by the RRI function should be used instead. It uses a 5x5 kernel, consequently 12 directional differences of order k (2) are used in the estimation. One could also use a 3x3 kernel using only the 4 differences centered on the central pixel but the metric would be very noisy. The input is the DEM (no need to detrend).
Usage
Trik2(x)
Arguments
x |
A DEM/image as a SpatRaster or a vector of numeric values from a focal window in a DEM from which to compute the index |
Value
isotropic roughness (in the same units of input)
References
Riley, S. J., S. D. DeGloria, and R. Elliott. 1999. A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain Journal of Science 5:23.
Wilson, M.F.J., O'Connell, B., Brown, C., Guinan, J.C. & Grehan, A.J. 2007. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope". Marine Geodesy, vol. 30, no. 1-2, pp. 3-35.
Trevisani S., Teza G., Guth P.L., 2023. Hacking the topographic ruggedness index. Geomorphology doi:10.1016/j.geomorph.2023.108838.
Examples
library(terra)
dem=rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
w <- matrix(1, nrow=5, ncol=5)
roughTrik5x5_v1=focal(dem, w=w, fun=Trik2)
roughTrik5x5_v2=Trik2(dem)
plot(c(roughTrik5x5_v1,roughTrik5x5_v2))
Improved TRI (with differences of order 2), reducing/removing slope dependence (for testing purposes).
Description
It is essentially a radial roughness index.This has been created for explaining the derivation of RRI, and it is not supposed to be used for doing real analysis. TRIk2 modifies TRI (topographic ruggedness index) using increments of order 2, symmetrical to central pixel, so as to reduce/remove the effect of local slope. This version does not correct for diagonal distance and therefore is mainly for testing/simulation purposes, so in practice the Radial Roughness Index calculated by the RRI function should be used instead. It uses a 5x5 kernel, consequently 12 directional differences of order k (2) are used in the estimation. One could also use a 3x3 kernel using only the 4 differences centered on the central pixel but the metric would be very noisy. The input is the DEM (no need to detrend).
Usage
## S3 method for class 'SpatRaster'
Trik2(x)
Arguments
x |
A DEM as a SpatRaster or a vector of numeric values from a focal window in a DEM from which to compute the index |
Value
isotropic roughness (in the same units of input)
References
Riley, S. J., S. D. DeGloria, and R. Elliott. 1999. A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain Journal of Science 5:23.
Wilson, M.F.J., O'Connell, B., Brown, C., Guinan, J.C. & Grehan, A.J. 2007. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope". Marine Geodesy, vol. 30, no. 1-2, pp. 3-35.
Trevisani S., Teza G., Guth P.L., 2023. Hacking the topographic ruggedness index. Geomorphology doi:10.1016/j.geomorph.2023.108838.
Examples
library(terra)
dem=rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
roughTrik5x5=Trik2(dem)
plot(roughTrik5x5)
Improved TRI (with differences of order 2), reducing/removing slope dependence (for testing purposes)
Description
It is essentially a radial roughness index.This has been created for explaining the derivation of RRI, and it is not supposed to be used for doing real analysis. TRIk2 modifies TRI (topographic ruggedness index) using increments of order 2, symmetrical to central pixel, so as to remove the effect of local slope. This version does not correct for diagonal distance and therefore is mainly for testing/simulation purposes, so in practice the Radial Roughness Index calculated by the RRI function should be used instead. It uses a 5x5 kernel, consequently 12 directional differences of order k (2) are used in the estimation. One could also use a 3x3 kernel using only the 4 differences centered on the central pixel but the metric would be very noisy. The input is the DEM (no need to detrend).
Usage
## S3 method for class 'numeric'
Trik2(x)
Arguments
x |
A vector of numeric values from a focal window in a DEM from which to compute the index |
Value
isotropic roughness (in the same units of input)
References
Riley, S. J., S. D. DeGloria, and R. Elliott. 1999. A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain Journal of Science 5:23.
Wilson, M.F.J., O'Connell, B., Brown, C., Guinan, J.C. & Grehan, A.J. 2007. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope". Marine Geodesy, vol. 30, no. 1-2, pp. 3-35.
Trevisani S., Teza G., Guth P.L., 2023. Hacking the topographic ruggedness index. Geomorphology doi:10.1016/j.geomorph.2023.108838.
Examples
library(terra)
dem=rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
w <- matrix(1, nrow=5, ncol=5)
roughTrik5x5=focal(dem, w=w, fun=Trik2)
plot(roughTrik5x5)
Calculate the direction of maximum continuity considering 4 directions
Description
The input is represented by four rasters with the spatial variability index (e.g., MAD, variogram, etc.) computed in four directions (N-S, NE-SW, E-W, SE-NW)
Usage
anisoDir(N, NE, E, SE)
Arguments
N |
Spatial variability along N-S direction |
NE |
Spatial variability along NE-SW direction |
E |
Spatial variability along E-W direction |
SE |
Spatial variability along SE-NW direction |
Value
A raster with the direction (in degrees, geographical) of maximum continuity
Calculate the direction of maximum continuity considering 4 directions
Description
The input is represented by a list of rasters with the spatial variability index (e.g., MAD, variogram, etc.) computed in four directions (N-S, NE-SW, E-W, SE-NW)
Usage
anisoDirL(x)
Arguments
x |
A list of rasters with the spatial variability along 4 directions (see function anisoDir()) |
Value
A raster with the direction (in degrees, geographical) of maximum continuity
Calculate the index of anisotropy considering the spatial variability along 4 directions
Description
The input is represented by four rasters with the spatial variability index (e.g., MAD, variogram, etc.) computed in four directions (N-S, NE-SW, E-W, SE-NW)
Usage
anisoR(N, NE, E, SE)
Arguments
N |
Spatial vairability along N-S direction |
NE |
Spatial vairability along NE-SW direction |
E |
Spatial vairability along E-W direction |
SE |
Spatial vairability along SE-NW direction |
Value
A raster with the index of anisotropy (min=0 max=1)
Calculate the index of anisotropy considering the spatial variability along 4 directions
Description
The input is represented by a list of rasters with the spatial variability index (e.g., MAD, variogram, etc.) computed in four directions (N-S, NE-SW, E-W, SE-NW)
Usage
anisoRL(x)
Arguments
x |
A list of rasters with the spatial variability along 4 directions (see function anisoR()) |
Value
A raster with the index of anisotropy (min=0 max=1)
Compute circular variance of aspect (i.e. of the gradient vector)
Description
Compute circular variance of aspect (i.e. of the gradient vector)
Usage
circularDispersionGV(inraster, window)
Arguments
inraster |
The DEM/image from which compute the index |
window |
The moving window adopted for computing the index |
Value
The raster with the computed index
Examples
# Gradient vector dispersion using a circular search window of radius 3.
library(terra)
dem=rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
w=KernelCircular(3)
roughGrad=circularDispersionGV(dem,w)
plot(roughGrad)
Compute circular variance of normal vectors to surface
Description
Compute circular variance of normal vectors to surface, using the resultant vector length
Usage
circularDispersionNV(inraster, window)
Arguments
inraster |
The DEM/image from which compute the index |
window |
The moving window adopted for computing the index |
Value
The raster with the computed index
Examples
#
#Normal vector dispersion using a circular search window of radius 3.
library(terra)
dem=rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
w=KernelCircular(3)
roughVDR=circularDispersionNV(dem,w)
plot(roughVDR)
Compute circular variance of normal vectors to surface
Description
Compute circular variance of normal vectors to surface, using the eigen values (only for testing, very slow)
Usage
circularEigenNV(inraster, window)
Arguments
inraster |
The DEM from which compute the index |
window |
The moving window adopted for computing the index |
Value
The raster with the computed index
iqrST: interquartile range in a moving window
Description
A function to compute IQR with r method type 7 in a search window. It uses the implemented rcpp version that is many times faster that using focal with IQR() base R function. It provides the same result as ArcGis Pro.It is intended for computing roughness indices expressed as a robust (differently from standard deviation) estimate of dispersion of local surface parameters (e.g., slope, profile curvature, residual surface, etc.).
Usage
iqrST(x, w = 5, ...)
Arguments
x |
A DEM/image as a SpatRaster |
w |
Search window (e.g., kernelCircular(3)), default 5x5 window |
... |
for further use |
Value
the IQR of the selected property in the search window (same units of the input)
References
Trevisani, S., Guth, P.L., 2025. Surface Roughness in Geomorphometry: From Basic Metrics Toward a Coherent Framework. Remote Sensing 17, doi:10.3390/rs17233864.
Examples
dem= rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
# iqr of slope in degrees
slope=terrain(dem, v="slope")
w=KernelCircular(3)
w
iqrSlope=iqrST(slope,w=w)
plot(iqrSlope)
basic kernels
Description
Kernels for computing directional differences for specific directions and lag distances. These have been constructed using bilinear interpolation for directions out of main axes. The kernels are intended to be used with "Terra" focal functions (i.e., convolution).
Usage
k05ck2
Format
just matrices.
Source
Sebastiano Trevisani
References
Trevisani, S. & Rocca, M. 2015. MAD: Robust image texture analysis for applications in high resolution geomorphometry. Computers and Geosciences, vol. 81, pp. 78-92.
Trevisani, S. Teza, G., Guth, P., 2023. A simplified geostatistical approach for characterizing key aspects of short-range roughness. CATENA,Volume 223, ISSN 0341-8162,https://doi.org/10.1016/j.catena.2023.106927
Examples
#to see kernels (each one is a list with 4 kernels) of order 1
#These should be used with a detrended "surface"
#lag 1 pixel
k1c
#lag 2 pixels
k2c
#lag 4 pixels
k4c
#lag 6 pixels
k6c
#lag 8 pixels
k8c
#kernels of order 2 (differences of differences)
#these can be applied directly without detrending
#lag 05 pixel
k05ck2
#lag 1 pixel
k1ck2
#lag 2 pixels
k2ck2
#kernels of order 4,for filtering curvature
#these can be applied directly without detrending
k1ck4
basic kernels
Description
Kernels for computing directional differences for specific directions and lag distances. These have been constructed using bilinear interpolation for directions out of main axes. The kernels are intended to be used with "Terra" focal functions (i.e., convolution).
Usage
k1c
Format
just matrices.
Source
Sebastiano Trevisani
References
Trevisani, S. & Rocca, M. 2015. MAD: Robust image texture analysis for applications in high resolution geomorphometry. Computers and Geosciences, vol. 81, pp. 78-92.
Trevisani, S. Teza, G., Guth, P., 2023. A simplified geostatistical approach for characterizing key aspects of short-range roughness. CATENA,Volume 223, ISSN 0341-8162,https://doi.org/10.1016/j.catena.2023.106927
Examples
#to see kernels (each one is a list with 4 kernels) of order 1
#These should be used with a detrended "surface"
#lag 1 pixel
k1c
#lag 2 pixels
k2c
#lag 4 pixels
k4c
#lag 6 pixels
k6c
#lag 8 pixels
k8c
#kernels of order 2 (differences of differences)
#these can be applied directly without detrending
#lag 05 pixel
k05ck2
#lag 1 pixel
k1ck2
#lag 2 pixels
k2ck2
#kernels of order 4,for filtering curvature
#these can be applied directly without detrending
k1ck4
basic kernels
Description
Kernels for computing directional differences for specific directions and lag distances. These have been constructed using bilinear interpolation for directions out of main axes. The kernels are intended to be used with "Terra" focal functions (i.e., convolution).
Usage
k1ck2
Format
just matrices.
Source
Sebastiano Trevisani
References
Trevisani, S. & Rocca, M. 2015. MAD: Robust image texture analysis for applications in high resolution geomorphometry. Computers and Geosciences, vol. 81, pp. 78-92.
Trevisani, S. Teza, G., Guth, P., 2023. A simplified geostatistical approach for characterizing key aspects of short-range roughness. CATENA,Volume 223, ISSN 0341-8162,https://doi.org/10.1016/j.catena.2023.106927
Examples
#to see kernels (each one is a list with 4 kernels) of order 1
#These should be used with a detrended "surface"
#lag 1 pixel
k1c
#lag 2 pixels
k2c
#lag 4 pixels
k4c
#lag 6 pixels
k6c
#lag 8 pixels
k8c
#kernels of order 2 (differences of differences)
#these can be applied directly without detrending
#lag 05 pixel
k05ck2
#lag 1 pixel
k1ck2
#lag 2 pixels
k2ck2
#kernels of order 4,for filtering curvature
#these can be applied directly without detrending
k1ck4
basic kernels
Description
Kernels for computing directional differences for specific directions and lag distances. These have been constructed using bilinear interpolation for directions out of main axes. The kernels are intended to be used with "Terra" focal functions (i.e., convolution).
Usage
k1ck4
Format
just matrices.
Source
Sebastiano Trevisani
References
Trevisani, S. & Rocca, M. 2015. MAD: Robust image texture analysis for applications in high resolution geomorphometry. Computers and Geosciences, vol. 81, pp. 78-92.
Trevisani, S. Teza, G., Guth, P., 2023. A simplified geostatistical approach for characterizing key aspects of short-range roughness. CATENA,Volume 223, ISSN 0341-8162,https://doi.org/10.1016/j.catena.2023.106927
Trevisani, S., Guth, P.L., 2025. Surface Roughness in Geomorphometry: From Basic Metrics Toward a Coherent Framework. Remote Sensing 17. https://doi.org/10.3390/rs17233864
Examples
#to see kernels (each one is a list with 4 kernels) of order 1
#These should be used with a detrended "surface"
#lag 1 pixel
k1c
#lag 2 pixels
k2c
#lag 4 pixels
k4c
#lag 6 pixels
k6c
#lag 8 pixels
k8c
#kernels of order 2 (differences of differences)
#these can be applied directly without detrending
#lag 05 pixel
k05ck2
#lag 1 pixel
k1ck2
#lag 2 pixels
k2ck2
#kernels of order 4,for filtering curvature
#these can be applied directly without detrending
k1ck4
basic kernels
Description
Kernels for computing directional differences for specific directions and lag distances. These have been constructed using bilinear interpolation for directions out of main axes. The kernels are intended to be used with "Terra" focal functions (i.e., convolution).
Usage
k2c
Format
just matrices.
Source
Sebastiano Trevisani
References
Trevisani, S. & Rocca, M. 2015. MAD: Robust image texture analysis for applications in high resolution geomorphometry. Computers and Geosciences, vol. 81, pp. 78-92.
Trevisani, S. Teza, G., Guth, P., 2023. A simplified geostatistical approach for characterizing key aspects of short-range roughness. CATENA,Volume 223, ISSN 0341-8162,https://doi.org/10.1016/j.catena.2023.106927
Examples
#to see kernels (each one is a list with 4 kernels) of order 1
#These should be used with a detrended "surface"
#lag 1 pixel
k1c
#lag 2 pixels
k2c
#lag 4 pixels
k4c
#lag 6 pixels
k6c
#lag 8 pixels
k8c
#kernels of order 2 (differences of differences)
#these can be applied directly without detrending
#lag 05 pixel
k05ck2
#lag 1 pixel
k1ck2
#lag 2 pixels
k2ck2
#kernels of order 4,for filtering curvature
#these can be applied directly without detrending
k1ck4
basic kernels
Description
Kernels for computing directional differences for specific directions and lag distances. These have been constructed using bilinear interpolation for directions out of main axes. The kernels are intended to be used with "Terra" focal functions (i.e., convolution).
Usage
k2ck2
Format
just matrices.
Source
Sebastiano Trevisani
References
Trevisani, S. & Rocca, M. 2015. MAD: Robust image texture analysis for applications in high resolution geomorphometry. Computers and Geosciences, vol. 81, pp. 78-92.
Trevisani, S. Teza, G., Guth, P., 2023. A simplified geostatistical approach for characterizing key aspects of short-range roughness. CATENA,Volume 223, ISSN 0341-8162,https://doi.org/10.1016/j.catena.2023.106927
Examples
#to see kernels (each one is a list with 4 kernels) of order 1
#These should be used with a detrended "surface"
#lag 1 pixel
k1c
#lag 2 pixels
k2c
#lag 4 pixels
k4c
#lag 6 pixels
k6c
#lag 8 pixels
k8c
#kernels of order 2 (differences of differences)
#these can be applied directly without detrending
#lag 05 pixel
k05ck2
#lag 1 pixel
k1ck2
#lag 2 pixels
k2ck2
#kernels of order 4,for filtering curvature
#these can be applied directly without detrending
k1ck4
basic kernels
Description
Kernels for computing directional differences for specific directions and lag distances. These have been constructed using bilinear interpolation for directions out of main axes. The kernels are intended to be used with "Terra" focal functions (i.e., convolution).
Usage
k4c
Format
just matrices.
Source
Sebastiano Trevisani
References
Trevisani, S. & Rocca, M. 2015. MAD: Robust image texture analysis for applications in high resolution geomorphometry. Computers and Geosciences, vol. 81, pp. 78-92.
Trevisani, S. Teza, G., Guth, P., 2023. A simplified geostatistical approach for characterizing key aspects of short-range roughness. CATENA,Volume 223, ISSN 0341-8162,https://doi.org/10.1016/j.catena.2023.106927
Examples
#to see kernels (each one is a list with 4 kernels) of order 1
#These should be used with a detrended "surface"
#lag 1 pixel
k1c
#lag 2 pixels
k2c
#lag 4 pixels
k4c
#lag 6 pixels
k6c
#lag 8 pixels
k8c
#kernels of order 2 (differences of differences)
#these can be applied directly without detrending
#lag 05 pixel
k05ck2
#lag 1 pixel
k1ck2
#lag 2 pixels
k2ck2
#kernels of order 4,for filtering curvature
#these can be applied directly without detrending
k1ck4
basic kernels
Description
Kernels for computing directional differences for specific directions and lag distances. These have been constructed using bilinear interpolation for directions out of main axes. The kernels are intended to be used with "Terra" focal functions (i.e., convolution).
Usage
k6c
Format
just matrices.
Source
Sebastiano Trevisani
References
Trevisani, S. & Rocca, M. 2015. MAD: Robust image texture analysis for applications in high resolution geomorphometry. Computers and Geosciences, vol. 81, pp. 78-92.
Trevisani, S. Teza, G., Guth, P., 2023. A simplified geostatistical approach for characterizing key aspects of short-range roughness. CATENA,Volume 223, ISSN 0341-8162,https://doi.org/10.1016/j.catena.2023.106927
Examples
#to see kernels (each one is a list with 4 kernels) of order 1
#These should be used with a detrended "surface"
#lag 1 pixel
k1c
#lag 2 pixels
k2c
#lag 4 pixels
k4c
#lag 6 pixels
k6c
#lag 8 pixels
k8c
#kernels of order 2 (differences of differences)
#these can be applied directly without detrending
#lag 05 pixel
k05ck2
#lag 1 pixel
k1ck2
#lag 2 pixels
k2ck2
#kernels of order 4,for filtering curvature
#these can be applied directly without detrending
k1ck4
basic kernels
Description
Kernels for computing directional differences for specific directions and lag distances. These have been constructed using bilinear interpolation for directions out of main axes. The kernels are intended to be used with "Terra" focal functions (i.e., convolution).
Usage
k8c
Format
just matrices.
Source
Sebastiano Trevisani
References
Trevisani, S. & Rocca, M. 2015. MAD: Robust image texture analysis for applications in high resolution geomorphometry. Computers and Geosciences, vol. 81, pp. 78-92.
Trevisani, S. Teza, G., Guth, P., 2023. A simplified geostatistical approach for characterizing key aspects of short-range roughness. CATENA,Volume 223, ISSN 0341-8162,https://doi.org/10.1016/j.catena.2023.106927
Examples
#to see kernels (each one is a list with 4 kernels) of order 1
#These should be used with a detrended "surface"
#lag 1 pixel
k1c
#lag 2 pixels
k2c
#lag 4 pixels
k4c
#lag 6 pixels
k6c
#lag 8 pixels
k8c
#kernels of order 2 (differences of differences)
#these can be applied directly without detrending
#lag 05 pixel
k05ck2
#lag 1 pixel
k1ck2
#lag 2 pixels
k2ck2
#kernels of order 4,for filtering curvature
#these can be applied directly without detrending
k1ck4
stdST: standard deviation in a moving window
Description
A function to compute standard deviation in a moving window. By default it uses the implemented rcpp version that is many times faster that using focal with var() base R function. It provides the same result as ArcGis Pro. It is intended for computing roughness indices expressed as dispersion of local surface parameters (e.g., slope, profile curvature, residual surface, etc.). Whenever there is a NA in the kernel the result is NA. R base var() function uses n-1 at the denominator, here we use n.
Usage
stdST(x, w = 5, ...)
Arguments
x |
A DEM/image as a SpatRaster |
w |
Search window (e.g., kernelCircular(3)), default 5x5 window |
... |
for further use |
Value
the STD of the selected property in the search window (same units of the input)
References
Trevisani, S., Guth, P.L., 2025. Surface Roughness in Geomorphometry: From Basic Metrics Toward a Coherent Framework. Remote Sensing 17, doi:10.3390/rs17233864.
Examples
library(terra)
dem= rast(paste(system.file("extdata", package = "SurfRough"), "/trento1.tif",sep=""))
# std of slope in degrees
slope=terrain(dem, v="slope")
w=KernelCircular(3)
w
stdSlope=stdST(slope,w=w)
plot(stdSlope)