rassta: Raster-Based Spatial Stratification Algorithms

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.32614/RJ-2022-036. This is version 3 of this Preprint.

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Bryan Andre Fuentes, Minerva Justine Dorantes, John Robert Tipton


Spatial stratification of landscapes allows for the development of efficient sampling surveys, the inclusion of domain knowledge in data-driven modeling frameworks, and the production of information relating the spatial variability of response phenomena to that of landscape processes. This work presents the rassta package as a collection of algorithms dedicated to the spatial stratification
of landscapes, the calculation of landscape correspondence metrics across geographic space, and the application of these metrics for spatial sampling and modeling of environmental phenomena. The theoretical background of rassta is presented through references to several studies which have benefited from landscape stratification routines. The functionality of rassta is presented through code
examples which are complemented with the geographic visualization of their outputs.




Agriculture, Ecology and Evolutionary Biology, Forest Sciences, Life Sciences


stratification, modeling, Sampling, Spatial signature, Similarity, Hierarchical


Published: 2021-11-17 16:12

Last Updated: 2022-07-26 22:37

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CC BY Attribution 4.0 International