This function visualizes time series patterns from the "twdtw_knn1" model.
It produces a multi-faceted plot, where each facet represents a different time series
label from the model's data. Within each facet, different bands or indices (attributes)
are plotted as distinct lines, differentiated by color.
Usage
# S3 method for twdtw_knn1
plot(x, bands = NULL, ...)Arguments
- x
A model of class
"twdtw_knn1".- bands
A character vector specifying the bands or indices to plot. If NULL (default), all available bands or indices in the data will be plotted.
- ...
Additional arguments passed to
ggplot. Currently not used.
Value
A ggplot object displaying the time series patterns.
Examples
if (FALSE) {
# Read training samples
samples_path <-
system.file("mato_grosso_brazil/samples.gpkg", package = "dtwSat")
samples <- st_read(samples_path, quiet = TRUE)
# Get satellite image time sereis files
tif_path <- system.file("mato_grosso_brazil", package = "dtwSat")
tif_files <- dir(tif_path, pattern = "\\.tif$", full.names = TRUE)
# Get acquisition dates
acquisition_date <- regmatches(tif_files, regexpr("[0-9]{8}", tif_files))
acquisition_date <- as.Date(acquisition_date, format = "%Y%m%d")
# Create a 3D datacube
dc <- read_stars(tif_files,
proxy = FALSE,
along = list(time = acquisition_date),
RasterIO = list(bands = 1:6))
dc <- st_set_dimensions(dc, 3, c("EVI", "NDVI", "RED", "BLUE", "NIR", "MIR"))
dc <- split(dc, c("band"))
# Create a knn1-twdtw model
m <- twdtw_knn1(x = dc,
y = samples,
cycle_length = 'year',
time_scale = 'day',
time_weight = c(steepness = 0.1, midpoint = 50),
formula = band ~ s(time))
print(m)
# Visualize model patterns
plot(m)
# Classify satellite images
system.time(lu <- predict(dc, model = m))
# Visualise land use classification
ggplot() +
geom_stars(data = lu) +
theme_minimal()
}