strevisani/MADSurfaceTexture: V1.1 plus R package
Creators
Description
Geostatistical-based tools for surface roughness or image texture analysis based on the robust estimator MAD and on differences of order 2. These prototypes aim to promote the use and further development of these approaches in geomorphometry and remote sensing. In this new version we provide also a new roughness radial index that can be viewed as an evolution of TRI (topographic ruggedness index.
For information look at:
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Trevisani S., Teza G., Guth P.L., 2023. Hacking the topographic ruggedness index. Geomorphology,
Volume 439, 2023, 108838, ISSN 0169-555X, https://doi.org/10.1016/j.geomorph.2023.108838 Preprint also in Zenodo: https://doi.org/10.5281/zenodo.7716785 -
Trevisani, S., Teza, G. & Guth, P. (2023). A Simplified Geostatistical Approach for Characterizing Key Aspects of Short-Range Roughness. CATENA, Volume 223, https://doi.org/10.1016/j.catena.2023.106927
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Trevisani, S., Teza, G. and Guth, P., A Simplified Geostatistical Approach for Characterizing Key Aspects of Short-Range Roughness. Available at SSRN: https://ssrn.com/abstract=4223135 or http://dx.doi.org/10.2139/ssrn.4223135
- Trevisani S., Rocca M.,MAD: robust image texture analysis for applications in high resolution geomorphometry. Computer & Geosciences, 2015 https://doi.org/10.1016/j.cageo.2015.04.003
In this version, a package for R "SurfRough" (file SurfRough_0.0.1.000.tar.gz) has been added to facilitate the use of the tool in R.
Go to https://zenodo.org/doi/10.5281/zenodo.13216348 for source code of the R package linked to the Github repository for updates.
This package has been created in windows 11. You have also a tutorial both as pdf as well as in Rmarkdown.
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See "readmeUpdateSept2022" for updated information.
The new staff is in the folder "RImplementation" and "ArcmapNewTools"
Notes
Last update (October 2025)
In the link below you will find an implementation of MADk2 and RRI for ArcGis Pro. For R consider always the Cran repository and the dedicatede Github page. The code for ArcGis has been minimally tested, comparing the results with the R version. To use in ArcGis you need to open the scripts via Catalog. The zipped file in github contains all the files.
https://github.com/strevisani/MADSurfaceTexture/tree/master/ArcGisProScripts
I also added a small script for computing RRI in qgis
https://github.com/strevisani/MADSurfaceTexture/tree/master/Qgis
Update (19 March 2025)!
The new version R 1.1 published in Cran has been subject to the following updates:
- Cpp version of RRI, 24 times faster than the R version. The interface of the function has been also simplified, there is no more the need to define the search window, so you can call the function as RRI(input) (in which “input” is a raster).
- Added new function RRIK3 (R and cpp version), computing radial roughness using differences (increments) of order 3. This function permits to filter out also some curvature (in the next update, the function with differences of order 4 will be added).
- Functions RRIMin() and RRIMax() have been added. These compute the minimum and maximum radial roughness (for experimental purposes and future developments)
Some manual entries and examples have been improved and corrected.
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Last update (6 Sept. 2024): the R package is now available on the Cran repository:
https://cran.r-project.org/package=SurfRough
In this version the package R "SurfRough" (file SurfRough_0.0.1.000.tar.gz) has been updated: - documentation corrected - Madscan and Meanscan functions return the layers with proper names - The radial roughness index RRI has been added
Files
strevisani/MADSurfaceTexture-RoughnessV1.0.zip
Additional details
Identifiers
Related works
- Is derived from
- 10.5281/zenodo.7132161 (DOI)
- Is supplement to
- https://github.com/strevisani/MADSurfaceTexture/tree/RoughnessV1.0 (URL)
- Journal article: 10.1016/j.catena.2023.106927 (DOI)
References
- -Trevisani, S., Teza, G. and Guth, P., A Simplified Geostatistical Approach for Characterizing Key Aspects of Short-Range Roughness. Available at SSRN: https://ssrn.com/abstract=4223135 or http://dx.doi.org/10.2139/ssrn.4223135
- Trevisani S., Rocca M.,MAD: robust image texture analysis for applications in high resolution geomorphometry. Computer & Geosciences, 2015 https://doi.org/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, https://doi.org/10.1016/j.catena.2023.106927. Free download link by 3 March 2023: https://authors.elsevier.com/a/1gPQa1Dk5AZEPy