strevisani/MADSurfaceTexture: V1.1 plus R package

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:

• 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

• 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"