EGU22-1237
https://doi.org/10.5194/egusphere-egu22-1237
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

rassta: Raster-based Spatial Stratification Algorithms

Bryan Fuentes1, Minerva Dorantes2, and John Tipton3
Bryan Fuentes et al.
  • 1University of Arkansas, Crop, Soil, and Environmental Sciences Department, United States of America (bafuente@uark.edu)
  • 2University of Arkansas, Crop, Soil, and Environmental Sciences Department, United States of America (mjdorant@uark.edu)
  • 3University of Arkansas, Department of Mathematical Sciences, United States of America (jrtipton@uark.edu)

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 R 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.

How to cite: Fuentes, B., Dorantes, M., and Tipton, J.: rassta: Raster-based Spatial Stratification Algorithms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1237, https://doi.org/10.5194/egusphere-egu22-1237, 2022.