dtwSat (version 0.2.8)

plotMapSamples: Plotting maps

Description

Method for plotting maps and samples.

Usage

plotMapSamples(x, samples = "all", ...)

Value

A ggplot object.

Arguments

x

An object of class twdtwAssessment.

samples

A character defining the samples to plot "correct", "incorrect", "all". Default is "all".

...

Other arguments to pass to twdtwRaster

Author

Victor Maus,

References

Maus:2019dtwSat

Maus:2016dtwSat

See Also

twdtwAssessment, plotAccuracy, and plotAdjustedArea.

Examples

if (FALSE) {
  
# Example of TWDTW analysis using raster files 
library(dtwSat)
library(caret) 

# Load raster data 
evi  <- brick(system.file("lucc_MT/data/evi.tif",  package = "dtwSat"))
ndvi <- brick(system.file("lucc_MT/data/ndvi.tif", package = "dtwSat"))
red  <- brick(system.file("lucc_MT/data/red.tif",  package = "dtwSat"))
blue <- brick(system.file("lucc_MT/data/blue.tif", package = "dtwSat"))
nir  <- brick(system.file("lucc_MT/data/nir.tif",  package = "dtwSat"))
mir  <- brick(system.file("lucc_MT/data/mir.tif",  package = "dtwSat"))
doy  <- brick(system.file("lucc_MT/data/doy.tif",  package = "dtwSat"))
timeline <- 
  scan(system.file("lucc_MT/data/timeline", package = "dtwSat"), what="date")

# Create raster time series 
rts <- twdtwRaster(evi, ndvi, red, blue, nir, mir, timeline = timeline, doy = doy)

# Load field samples and projection 
field_samples <- 
  read.csv(system.file("lucc_MT/data/samples.csv", package = "dtwSat"))
proj_str <- 
  scan(system.file("lucc_MT/data/samples_projection", package = "dtwSat"), 
       what = "character")

# Split samples for training (10%) and validation (90%) using stratified sampling 
set.seed(1)
I <- unlist(createDataPartition(field_samples$label, p = 0.1))
training_samples <- field_samples[I, ]
validation_samples <- field_samples[-I, ]

# Get time series form raster
training_ts <- getTimeSeries(rts, y = training_samples, proj4string = proj_str)
validation_ts <- getTimeSeries(rts, y = validation_samples, proj4string = proj_str)

# Create temporal patterns 
temporal_patterns <- createPatterns(training_ts, freq = 8, formula = y ~ s(x))

# Set TWDTW weight function 
log_fun <- logisticWeight(-0.1, 50)

# Run TWDTW analysis 
system.time(
  r_twdtw <-
    twdtwApply(x = rts, y = temporal_patterns, weight.fun = log_fun, progress = 'text') 
)

# Plot TWDTW distances for the first year 
plot(r_twdtw, type = "distance", time.levels = 1)

# Classify raster based on the TWDTW analysis 
r_lucc <- twdtwClassify(r_twdtw, progress = 'text')

# Plot TWDTW classification results 
plot(r_lucc, type = "map")

# Assess classification 
twdtw_assess <- 
  twdtwAssess(object = r_lucc, y = validation_samples, 
              proj4string = proj_str, conf.int = .95) 

# Plot map accuracy 
plot(twdtw_assess, type = "accuracy")

# Plot area uncertainty 
plot(twdtw_assess, type = "area")

# Plot misclassified samples  
plot(twdtw_assess, type = "map", samples = "incorrect") 

# Get latex table with error matrix 
twdtwXtable(twdtw_assess, table.type = "matrix")

# Get latex table with error accuracy 
twdtwXtable(twdtw_assess, table.type = "accuracy")

# Get latex table with area uncertainty 
twdtwXtable(twdtw_assess, table.type = "area")

}