In hrvg/statisticalRoughness: Estimation of roughness exponents across scale for large areas

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 8, 
  fig.height = 8,
  warning = FALSE,
  message = FALSE
)

Purpose

This vignette showcases the functions used to derive the roughness and anisotropy exponents for a collection of tiles over a landscape.

Loading data

First, we perform all the steps from this vignette.

library("statisticalRoughness")

First, we load one of the DEM.

rstr <- raster::raster(file.path(system.file("extdata/rasters/", package = "statisticalRoughness"), "gabilan_mesa.tif"))
raster_resolution <- 10

Next we create tiles over which we will calculate the roughness and anisotropy exponents. This is done for simplicity here by using raster::aggregate().

tiles <- raster::aggregate(rstr, fact = 100)

Tiled workflow

Then, get_zeta_df() basically checks, conversions and parallelization to be able to efficiently run get_zeta(). It is also possible to directly to pass a polygon to get_zeta_df(). If passing a raster, the conversion from raster to polygons is handled internally.

zeta_df <- get_zeta_df(
  rstr, tiles, 
  raster_resolution, vertical_accuracy = 1.87
)

get_zeta_df() returns a data.frame with no spatial information. get_zeta_raster() takes care to re-introduce the spatial information from the initial raster (here, rstr) and from the tiles. If the .zeta_df argument is set to NULL, get_zeta_raster() first runs get_zeta()

zeta_raster <- get_zeta_raster(
  rstr, tiles, 
  .zeta_df = zeta_df, raster_resolution, vertical_accuracy = 1.87
)

For example here are the raster layers corresponding to $\alpha_{1,x}$ and $\alpha_{1,y}$.

sp::spplot(zeta_raster[["alpha1.x"]])
sp::spplot(zeta_raster[["alpha1.y"]])

hrvg/statisticalRoughness documentation built on March 12, 2021, 4:55 p.m.