DispFieldSTbb: Displacement fields for spatiotemporal data using a bounding...
In ICvectorfields: Vector Fields from Spatial Time Series of Population Abundance

DispFieldSTbb

R Documentation

Displacement fields for spatiotemporal data using a bounding box

Description

This is an implementation of a novel algorithm that differs from more traditional digital image correlation implementations that are applied in the DispField and DispFieldbb functions. The function calculates a displacement field representing persistent movement based on the cross-covariance in a raster stack (in this case a sequential series of rasters) presumably representing spatial population abundance or density at more than two different instances of time. If analysis is restricted to only two time instances, DispFieldbb is more appropriate.

Usage

DispFieldSTbb(
  inputstack1,
  lag1,
  rowmn,
  rowmx,
  colmn,
  colmx,
  restricted = FALSE
)

Arguments

`inputstack1`	a raster stack with each raster layer representing an instance of time. The raster stack should be organized such that the first raster in the stack is the first observed spatial dataset and time progresses forward with the third dimension index of the raster stack. The raster stack should contain only numeric values. Any NA value will be converted to a zero
`lag1`	an integer time lag
`rowmn`	an integer for the minimum row index of the bounding box
`rowmx`	an integer for the maximum row index of the bounding box
`colmn`	an integer for the minimum column index of the bounding box
`colmx`	an integer for the maximum column index of the bounding box
`restricted`	logical (TRUE or FALSE)

Details

The input rasters in the raster stack are first converted to equivalent matrices, which together represent a three-dimensional array with two spatial dimensions and one time dimension. The prescribed lag is applied to the three dimensional array derived from the raster stack by first producing two equivalent arrays and then removing appropriate numbers of layers from the top of one and the bottom of the other. These are referred to as unlagged and lagged spatiotemporal arrays in the description that follows.

The function converts three dimensional lagged and unlagged spatiotemporal arrays to two-dimensional lagged and unlagged spatiotemporal matrices by averaging along one of the spatial dimensions (either rows or columns) to obtain two pairs of two-dimensional matrices in which one dimension is spatial (either rows or columns) and one dimension is temporal. One of each pair corresponds to the unlagged spatiotemporal array and the other corresponds to the lagged spatiotemporal array. Displacement in the vertical direction is computed using unlagged and lagged matrices that have been averaged along rows and displacement in the horizontal direction is computed using unlagged and lagged matrices that have been averaged along columns.

If restricted is set to FALSE (the default), the function computes cross-covariance between the values within the bounding box of the unlagged row-averaged spatiotemporal matrix and the whole row-averaged lagged spatiotemporal matrix and between the values within the bounding box of the unlagged column-averaged spatiotemporal matrix and the entirety corresponding lagged matrix.

If restricted is set to TRUE, the function uses cross-covariance between lagged and unlagged version of row-averaged and column averaged spatiotemporal matrices that have all been either row or column-averaged within the bounding box to estimate vertical and horizontal displacement.

Regardless of whether restricted is set to TRUE or FALSE, for each sub-grid, displacement in the x and y direction is divided by the shift in the time dimension to produce orthogonal velocity vetors. Note that for this reason, the lag1 argument of the function does not necessarily determine the time lag that is used to produce each orthoganal velocity vector.

Reference coordinates and cell size are extracted from the first raster stack such that the locations from whence displacement is estimated as well as displacement (or velocity) estimates can be expressed in the units of the projected coordinates.

The coordinates are assumed to increase vertically and horizontally from the lower left corner of the two-dimensional domain.

Caution is warranted when defining the sub-grid dimensions because the function can produce erroneous results when sub-grids are too small.

#' In addition, results can be quite sensitive to specification of the time lag. If velocities are highly variable in space or over time, avoid specifying a single time lag by calling the related DispFieldSTbball function.

Value

A data frame is returned with the following column names: rowcent, colcent, frowmin, frowmax, fcolmin, fcolmax, centx, centy, dispx, and dispy. The rowcent and colcent column names are the row and column indices for the center of each sub-grid; frowmin and frowmax are the sub-grid minimum and maximum row indices; fcolmin and fcolmax are the sub-grid minimum and maximum column indices; centx and centy are the projected coordinates of the centre of the subgrid derived from the raster input files; dispx and dispy are the orthoganal velocity vectors in units of space per timestep in the horizontal and vertical directions in the same spatial units as the projected coordinates of the raster input files.

Examples

rseq <- stats::runif(54)
Mat1 <- matrix(rep(0, 9*9), nrow = 9)
Mat2 <- Mat1; Mat3 <- Mat1; Mat4 <- Mat1
Mat1[1:9, 1:6] <- rseq
Mat1
Mat2[1:9, 2:7] <- rseq
Mat2
Mat3[1:9, 3:8] <- rseq
Mat3
Mat4[1:9, 4:9] <- rseq
Mat4

# rasterizing
rast1 <- terra::rast(Mat1)
terra::plot(rast1)
rast2 <- terra::rast(Mat2)
terra::plot(rast2)
rast3 <- terra::rast(Mat3)
terra::plot(rast3)
rast4 <- terra::rast(Mat4)
terra::plot(rast4)

teststack1 <- c(rast1, rast2, rast3, rast4)
(VFdf3 <- DispFieldSTbb(teststack1, lag1 = 1, 2, 8, 2, 8))
# block is moving rightward at a speed of 1 unit of space per unit of time
# dispx = 1

ICvectorfields documentation built on March 18, 2022, 7:34 p.m.