rassta: Raster-based Spatial Stratification Algorithms

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Authors

Bryan Andre Fuentes, Minerva Justine Dorantes, John Robert Tipton

Abstract

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.

DOI

https://doi.org/10.31223/X50S57

Subjects

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

Keywords

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

Dates

Published: 2021-11-17 09:12

Last Updated: 2021-11-17 14:12

License

CC BY Attribution 4.0 International

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