10th International Conference on Geomorphology
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Introducing a new and simplified geostatistical-based roughness algorithm

Sebastiano Trevisani
Sebastiano Trevisani
  • Università IUAV di Venezia (sede Terese), Venice, Italy (strevisani@iuav.it)

The first aim of this work is to introduce a new geostatistical-based algorithm that permits to detect specific aspects of short-range surface roughness (or of image texture) which does not require user defined choices, except for the radius of the search window, and provides a high interpretability of the results.  In particular, differently from usual geostatistical approaches, this algorithm does not require the derivation of a residual digital elevation model. The new proposed algorithm, despite its simplicity, permits to detect relevant aspects of surface texture, including anisotropy. Moreover, adopting approaches based of digital elevation model smoothing it can also be applied in the context of multiscale analysis. A second aim, functional to the introduction of the new algorithm, is to furnish a general overview of the key aspects of the geostatistical methodologies, highlighting analogies and differences with other approaches. In presenting the algorithm, a comparison with the roughness computed by means of dispersion of vectors normal to surface is performed.


ATKINSON, P.M. and LEWIS, P., 2000. Geostatistical classification for remote sensing: An introduction. Computers and Geosciences, 26(4), pp. 361-371.

BALAGUER, A., RUIZ, L.A., HERMOSILLA, T. and RECIO, J.A., 2010. Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification. Computers and Geosciences, 36(2), pp. 231-240.

GUTH, P.L., 2001. Quantifying terrain fabric in digital elevation models. GSA Reviews in Engineering Geology, 14, pp. 13-25.

HERZFELD, U.C. and HIGGINSON, C.A., 1996. Automated geostatistical seafloor classification - Principles, parameters, feature vectors, and discrimination criteria. Computers and Geosciences, 22(1), pp. 35-41.

TREVISANI, S., CAVALLI, M. and MARCHI, L., 2009. Variogram maps from LiDAR data as fingerprints of surface morphology on scree slopes. Natural Hazards and Earth System Science, 9(1), pp. 129-133.

TREVISANI, S., CAVALLI, M. and MARCHI, L., 2012. Surface texture analysis of a high-resolution DTM: Interpreting an alpine basin. Geomorphology, 161-162, pp. 26-39.

TREVISANI, S. and ROCCA, M., 2015. MAD: Robust image texture analysis for applications in high resolution geomorphometry. Computers and Geosciences, 81, pp. 78-92.

TREVISANI, S. and CAVALLI, M., 2016. Topography-based flow-directional roughness: Potential and challenges. Earth Surface Dynamics, 4(2), pp. 343-358.


How to cite: Trevisani, S.: Introducing a new and simplified geostatistical-based roughness algorithm, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-384, https://doi.org/10.5194/icg2022-384, 2022.