rasterCVA: Change Vector Analysis

In RStoolbox: Remote Sensing Data Analysis

Change Vector Analysis

Description

Calculates angle and magnitude of change vectors. Dimensionality is limited to two bands per image.

Usage

rasterCVA(x, y, tmf = NULL, nct = NULL, ...)

Arguments

x	SpatRaster with two layers. This will be the reference/origin for the change calculations. Both rasters (y and y) need to correspond to each other, i.e. same resolution, extent and origin.
y	SpatRaster with two layers. Both rasters (y and y) need to correspond to each other, i.e. same resolution, extent and origin.
tmf	Numeric. Threshold median factor (optional). Used to calculate a threshold magnitude for which pixels are considered stable, i.e. no change. Calculated as tmf * mean(magnitude[magnitude > 0]).
nct	Numeric. No-change threshold (optional). Alternative to tmf. Sets an absolute threshold. Change magnitudes below nct are considered stable and set to NA.
...	further arguments passed to writeRaster

Details

Change Vector Analysis (CVA) is used to identify spectral changes between two identical scenes which were acquired at different times. CVA is limited to two bands per image. For each pixel it calculates the change vector in the two-dimensional spectral space. For example for a given pixel in image A and B for the red and nir band the change vector is calculated for the coordinate pairs: (red_A | nir_A) and (red_B | nir_B).

The coordinate system is defined by the order of the input bands: the first band defines the x-axis and the second band the y-axis, respectively. Angles are returned *in degree* beginning with 0 degrees pointing 'north', i.e. the y-axis, i.e. the second band.

Value

Returns a SpatRaster with two layers: change vector angle and change vector magnitude

Examples

library(terra)
pca <- rasterPCA(lsat)$map

## Do change vector analysis
cva <- rasterCVA(pca[[1:2]], pca[[3:4]])
cva

RStoolbox documentation built on May 29, 2024, 6:34 a.m.