diff --git a/.Rbuildignore b/.Rbuildignore
index a7d7e9a..6cf7893 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -24,3 +24,8 @@ examples_x64
 ^LICENSE\.md$
 ^dtwSat\.Rproj$
 ^\.github$
+^doc$
+^Meta$
+
+vignettes.awk
+_pkgdown.yml
\ No newline at end of file
diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
new file mode 100644
index 0000000..ed7650c
--- /dev/null
+++ b/.github/workflows/pkgdown.yaml
@@ -0,0 +1,48 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+  release:
+    types: [published]
+  workflow_dispatch:
+
+name: pkgdown
+
+jobs:
+  pkgdown:
+    runs-on: ubuntu-latest
+    # Only restrict concurrency for non-PR jobs
+    concurrency:
+      group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+        shell: Rscript {0}
+
+      - name: Deploy to GitHub pages 🚀
+        if: github.event_name != 'pull_request'
+        uses: JamesIves/github-pages-deploy-action@v4.4.1
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs
diff --git a/.gitignore b/.gitignore
index 527b631..29aaef1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -37,3 +37,5 @@ CRAN-SUBMISSION
 src/symbols.rds
 *.o
 *.so
+/doc/
+/Meta/
diff --git a/DESCRIPTION b/DESCRIPTION
index 4e08634..f4ba85e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: dtwSat
 Type: Package
 Title: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis
 Version: 1.0.0
-Date: 2023-09-03
+Date: 2023-09-21
 Authors@R:
     c(person(given = "Victor",
              family = "Maus",
@@ -24,18 +24,19 @@ Authors@R:
              )
 Description: Provides a robust approach to land use mapping using multi-dimensional 
     (multi-band) satellite image time series. By leveraging the Time-Weighted Dynamic 
-    Time Warping (TWDTW) distance metric in tandem with a 1-NN classifier, the package 
-    provides functions to produce land use maps based on distinct seasonality patterns, 
-    typically observed in the phenological cycles of vegetation. The TWDTW algorithm is 
-    described in Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and 
-    Maus et al. (2019) <doi:10.18637/jss.v088.i05>. A key strength of TWDTW is its ability 
-    to recognize patterns with only a minimal training set, achieving notable accuracy. 
-    The package features tools for generating temporal patterns for various land cover types, 
-    conducting land use mapping, and visualizing the outcomes.
+    Time Warping (TWDTW) distance metric in tandem with a 1 Nearest-Neighbor (1-NN) Classifier,
+    this package offers functions to produce land use maps based on distinct seasonality patterns, 
+    commonly observed in the phenological cycles of vegetation. The approach is described in 
+    Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and Maus et al. (2019) <doi:10.18637/jss.v088.i05>.
+    A primary advantage of TWDTW is its capability to handle irregularly sampled and noisy time series, 
+    while also requiring minimal training sets. The package includes tools for training the 1-NN-TWDTW model, 
+    visualizing temporal patterns, producing land use maps, and visualizing the results.
 License: GPL (>= 3)
 URL: https://github.com/vwmaus/dtwSat/
 BugReports: https://github.com/vwmaus/dtwSat/issues/
 Maintainer: Victor Maus <vwmaus1@gmail.com>
+VignetteBuilder: 
+    knitr
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.3
@@ -47,11 +48,17 @@ Depends:
 Imports:
     mgcv,
     stats,
-    scales,
-    reshape2,
-    rlang
+    tidyr,
+    proxy
 Suggests:
-    rbenchmark,
-    stringr,
+    knitr,
+    rmarkdown,
     testthat (>= 3.0.0)
 Config/testthat/edition: 3
+Collate: 
+    'plot.R'
+    'predict.R'
+    'prepare_time_series.R'
+    'shift_dates.R'
+    'train.R'
+    'zzz.R'
diff --git a/NAMESPACE b/NAMESPACE
index fb17b02..4c99aae 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,8 +1,10 @@
 # Generated by roxygen2: do not edit by hand
 
-export(create_patterns)
-export(plot_patterns)
+S3method(plot,twdtw_knn1)
+S3method(predict,twdtw_knn1)
+S3method(print,twdtw_knn1)
 export(shift_dates)
+export(twdtw_knn1)
 import(ggplot2)
 import(sf)
 import(stars)
@@ -10,11 +12,11 @@ import(twdtw)
 importFrom(mgcv,gam)
 importFrom(mgcv,predict.gam)
 importFrom(mgcv,s)
-importFrom(reshape2,melt)
-importFrom(rlang,.data)
-importFrom(scales,date_format)
-importFrom(scales,percent)
-importFrom(scales,pretty_breaks)
+importFrom(proxy,dist)
 importFrom(stats,as.formula)
 importFrom(stats,predict)
 importFrom(stats,setNames)
+importFrom(tidyr,nest)
+importFrom(tidyr,pivot_longer)
+importFrom(tidyr,pivot_wider)
+importFrom(tidyr,unnest)
diff --git a/R/create_patterns.R b/R/create_patterns.R
deleted file mode 100644
index 856d1dd..0000000
--- a/R/create_patterns.R
+++ /dev/null
@@ -1,146 +0,0 @@
-#' Create a Pattern Using GAM
-#'
-#' This function creates a pattern based on Generalized Additive Models (GAM).
-#' It uses the specified formula to fit the model and predict values.
-#'
-#' @param x A three dimensions stars object (x, y, time) with the satellite image time series.
-#' @param y An sf object with the coordinates of the training points.
-#' @param formula A formula for the GAM. Default is \code{band ~ \link[mgcv]{s}(time)}.
-#' @param start_column Name of the column in y that indicates the start date. Default is 'start_date'.
-#' @param end_column Name of the column in y that indicates the end date. Default is 'end_date'.
-#' @param label_colum Name of the column in y that contains land use labels. Default is 'label'.
-#' @param sampling_freq The time frequency for sampling including unit, e.g '16 day'. If NULL, the function will infer it.
-#' @param ... Additional arguments passed to the GAM function.
-#'
-#' @return A list containing the predicted values for each label.
-#'
-#' 
-#'
-#' @export
-create_patterns = function(x, y, formula = band ~ s(time), start_column = 'start_date', 
-                          end_column = 'end_date', label_colum = 'label', 
-                          sampling_freq = NULL, ...){
-  
-  # Check if x is a stars object with a time dimension
-  if (!inherits(x, "stars") || dim(x)['time'] < 1) {
-    stop("x must be a stars object with a 'time' dimension")
-  }
-
-  # Check if y is an sf object with point geometry
-  if (!inherits(y, "sf") || !all(st_is(y, "POINT"))) {
-    stop("y must be an sf object with point geometry")
-  }
-
-  # Check for required columns in y
-  required_columns <- c(start_column, end_column, label_colum)
-  missing_columns <- setdiff(required_columns, names(y))
-  if (length(missing_columns) > 0) {
-    stop(paste("Missing required columns in y:", paste(missing_columns, collapse = ", ")))
-  }
-
-  # Check if formula has two 
-  if(length(all.vars(formula)) != 2) {
-    stop("The formula should have only one predictor!")
-  }
-
-  # Convert columns to date-time
-  y[ , start_column] <- to_date_time(y[[start_column]])
-  y[ , end_column] <- to_date_time(y[[end_column]])
-
-  # Extract time series from stars
-  y_ts <- extract_time_series(x, y)
-  y_ts$geom <- NULL
-
-  # Shift dates
-  unique_ids <- unique(y_ts$id)
-  for (id in seq_along(unique_ids)) {
-    idx <- y_ts$id == unique_ids[id]
-    y_ts[idx, 'time'] <- shift_dates(y_ts[idx, 'time'])
-  }
-
-  # Split data frame by label and remove label column
-  y_ts <- lapply(split(y_ts, y_ts$label), function(df) {
-    df$label <- NULL
-    return(df)
-  })
-
-  # Determine sampling frequency
-  if (is.null(sampling_freq)) {
-    sampling_freq <- get_stars_time_freq(x)
-    cat("Sampling frequency inferred from the stars object:", as.numeric(sampling_freq), attr(sampling_freq, "units"), "\n")
-  }
-
-  # Define GAM function
-  gam_fun <- function(y, t, formula, ...){
-    df <- data.frame(y, t = as.numeric(t))
-    df <- setNames(list(y, as.numeric(t)), all.vars(formula))
-    fit <- mgcv::gam(data = df, formula = formula, ...)
-    pred_t <- setNames(list(as.numeric(seq(min(t), max(t), by = sampling_freq))), all.vars(formula)[2])
-    predict(fit, newdata = pred_t)
-  }
-
-  # Apply GAM function
-  patterns <- lapply(y_ts, function(ts){
-    y_time <- ts$time
-    ts$time <- NULL
-    ts$id <- NULL
-    sapply(as.list(ts), function(y) {
-      gam_fun(y, y_time, formula, ...)
-    })
-  })
-
-  return(patterns)
-
-}
-
-
-
-#' Extract Time Series from a Stars Object for Specified Points
-#'
-#' This function extracts a time series from a stars object for each specified point in the sf object. 
-#' Each extracted sample is then labeled with an ID and the label from the sf object.
-#'
-#' @param x A stars object containing the raster time series data.
-#' @param y An sf object containing the point geometries and their associated labels.
-#'
-#' @return A data.frame with the extracted time series for each point in the sf object, 
-#'         with additional columns for the ID and label of each sample.
-#'
-#'
-extract_time_series <- function(x, y) {
-  ts_samples <- st_extract(x, y)
-  ts_samples$id <- 1:dim(ts_samples)["geom"]
-  ts_samples$label <- y$label
-  as.data.frame(ts_samples)
-}
-
-
-
-#' Compute the Most Common Sampling Frequency in a Stars Object
-#'
-#' This function calculates the most common difference between consecutive time points in a stars object. 
-#' This can be useful for determining the sampling frequency of the time series data.
-#'
-#' @param x A stars object containing time series data.
-#'
-#' @return A difftime object representing the most common time difference between consecutive samples.
-#'
-#'
-get_stars_time_freq <- function(x) {
-
-  # Extract the time dimension
-  time_values <- st_get_dimension_values(x, "time")
-
-  # Compute the differences between consecutive time points
-  time_diffs <- diff(time_values)
-
-  # Convert differences to days (while retaining the difftime class)
-  time_diffs <- as.difftime(time_diffs, units = "days")
-
-  # Identify the mode
-  mode_val_index <- which.max(tabulate(match(time_diffs, unique(time_diffs))))
-  freq <- diff(time_values[mode_val_index:(mode_val_index+1)])
-
-  return(freq)
-}
-
diff --git a/R/plot.R b/R/plot.R
new file mode 100644
index 0000000..70a2fc9
--- /dev/null
+++ b/R/plot.R
@@ -0,0 +1,48 @@
+#' Plot Patterns from twdtw-knn1 model
+#'
+#' This function visualizes time series patterns from the \code{"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.
+#'
+#' @param x A model of class \code{"twdtw_knn1"}.
+#'
+#' @param 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.
+#'
+#' @param ... Additional arguments passed to \code{\link[ggplot2]{ggplot}}. Currently not used.
+#'
+#' @return A \code{\link[ggplot2]{ggplot}} object displaying the time series patterns.
+#'
+#' @seealso twdtw_knn1
+#'
+#' @inherit twdtw_knn1 examples
+#'
+#' @export
+plot.twdtw_knn1 <- function(x, bands = NULL, ...) {
+
+  # Convert the list of time series data into a long-format data.frame
+  df <- x$data
+  df$id <- 1:nrow(df)
+  df <- unnest(df, cols = 'observations')
+
+  # Select bands
+  if(!is.null(bands)){
+    df <- df[c('id', 'time', 'label', bands)]
+  }
+
+  # Pivote data into long format for ggplot2
+  df <- pivot_longer(df, !c('id', 'label', 'time'), names_to = "band", values_to = "value")
+
+  # Construct the ggplot
+  gp <- ggplot(df, aes(x = .data$time, y = .data$value, colour = .data$band, group = interaction(.data$id, .data$band))) +
+    geom_line() +
+    facet_wrap(~label) +
+    theme(legend.position = "bottom") +
+    guides(colour = guide_legend(title = "Bands")) +
+    ylab("Value") +
+    xlab("Time")
+
+  return(gp)
+
+}
diff --git a/R/plot_patterns.R b/R/plot_patterns.R
deleted file mode 100644
index dd983b8..0000000
--- a/R/plot_patterns.R
+++ /dev/null
@@ -1,41 +0,0 @@
-#' Plot Patterns from Time Series Data
-#'
-#' This function takes a list of time series data and creates a multi-faceted plot
-#' where each facet corresponds to a different time series from the list. 
-#' Within each facet, different attributes (columns of the time series) are 
-#' plotted as lines with different colors.
-#'
-#' @param x A list where each element is a data.frame representing a time series.
-#'          Each data.frame should have the same number of rows and columns, 
-#'          with columns representing different attributes (e.g., bands or indices)
-#'          and rows representing time points.
-#'          The name of each element in the list will be used as the facet title.
-#' 
-#' @param ... Not used.
-#' 
-#' @return A ggplot object displaying the time series patterns.
-#'
-#' @export
-plot_patterns = function(x, ...) {
-   
-  # Convert the list of time series data into a long-format data.frame
-  df.p = do.call("rbind", lapply(names(x), function(p) {
-    ts = x[[p]]
-    # Create a new data.frame with a 'Time' column and a 'Pattern' column
-    # representing the name of the current time series (facet name).
-    data.frame(Time = 1:nrow(ts), ts, Pattern = p) # Assuming the time series are evenly spaced
-  }))
-  
-  # Melt the data into long format suitable for ggplot2
-  df.p = melt(df.p, id.vars = c("Time", "Pattern"))
-  
-  # Construct the ggplot
-  gp = ggplot(df.p, aes(x = .data$Time, y = .data$value, colour = .data$variable)) + 
-    geom_line() + 
-    facet_wrap(~Pattern) + 
-    theme(legend.position = "bottom") + 
-    guides(colour = guide_legend(title = "Bands")) + 
-    ylab("Value")
-  
-  return(gp)
-}
\ No newline at end of file
diff --git a/R/predict.R b/R/predict.R
new file mode 100644
index 0000000..3e25220
--- /dev/null
+++ b/R/predict.R
@@ -0,0 +1,42 @@
+#' Predict using the twdtw_knn1 model
+#'
+#' This function predicts the classes of new data using the Time Warped Dynamic Time Warping (TWDTW) 
+#' method with a 1-nearest neighbor approach. The prediction is based on the minimum TWDTW distance 
+#' to the known patterns stored in the `twdtw_knn1` model.
+#'
+#' @param object A `twdtw_knn1` model object generated by the `twdtw_knn1` function.
+#' @param newdata A data frame or similar object containing the new observations 
+#' (time series data) to be predicted.
+#' @param ... Additional arguments passed to the \link[twdtw]{twdtw} function.
+#' If provided, they will overwrite twdtw arguments previously passed to \link[dtwSat]{twdtw_knn1}.
+#'
+#' @return A vector of predicted classes for the `newdata`.
+#'
+#' @seealso twdtw_knn1
+#'
+#' @inherit twdtw_knn1 examples
+#'
+#' @export
+predict.twdtw_knn1 <- function(object, newdata, ...){
+
+  # Update twdtw_args with new arguments passed via ...
+  new_twdtw_args <- list(...)
+  matching_twdtw_args <- intersect(names(new_twdtw_args), names(object$twdtw_args))
+  object$twdtw_args[matching_twdtw_args] <- new_twdtw_args[matching_twdtw_args]
+
+  # Convert newdata to time series
+  newdata_ts <- prepare_time_series(newdata)
+
+  # Compute TWDTW distances
+  distances <- sapply(object$data$observations, function(pattern){
+    sapply(newdata_ts$observations, function(ts) {
+      do.call(proxy::dist, c(list(x = as.data.frame(ts), y = as.data.frame(pattern), method = 'twdtw'), object$twdtw_args))
+    })
+  })
+
+  # Find the nearest neighbor for each observation in newdata
+  nearest_neighbor <- apply(distances, 1, which.min)
+
+  # Return the predicted label for each observation based on the nearest neighbor
+  return(factor(object$data$label[nearest_neighbor]))
+}
diff --git a/R/prepare_time_series.R b/R/prepare_time_series.R
new file mode 100644
index 0000000..ed76a56
--- /dev/null
+++ b/R/prepare_time_series.R
@@ -0,0 +1,83 @@
+#' Prepare a Time Series Tibble from a 2D stars Object with Bands and Time Attributes
+#'
+#' This function reshapes a data frame, which has been converted from a stars object, into a nested wide tibble format.
+#' The stars object conversion often results in columns named in formats like "band.YYYY.MM.DD", "XYYYY.MM.DD.band", or "YYYY.MM.DD.band".
+#'
+#' @param x A data frame derived from a stars object containing time series data in wide format.
+#' The column names should adhere to one of the following formats: "band.YYYY.MM.DD", "XYYYY.MM.DD.band", or "YYYY.MM.DD.band".
+#'
+#' @return A nested tibble in wide format. Each row of the tibble corresponds to a unique 'ts_id' that maintains the order from the original stars object.
+#' The nested structure contains observations (time series) for each 'ts_id', including the 'time' of each observation, and individual bands are presented as separate columns.
+#'
+#'
+prepare_time_series <- function(x) {
+
+  # Remove the 'geom' column if it exists
+  x$geom <- NULL
+  x$x <- NULL
+  x$y <- NULL
+  var_names <- names(x)
+  var_names <- var_names[!var_names %in% 'label']
+
+  # Extract date and band information from the column names
+  date_band <- do.call(rbind, lapply(var_names, function(name) {
+
+    # Replace any hyphen with periods for consistent processing
+    name <- gsub("-", "\\.", name)
+
+    # Extract date and band info based on different name patterns
+    if (grepl("^.+\\.\\d{4}\\.\\d{2}\\.\\d{2}$", name)) {
+      date_str <- gsub("^.*?(\\d{4}\\.\\d{2}\\.\\d{2})$", "\\1", name)
+      band_str <- gsub("^(.+)\\.\\d{4}\\.\\d{2}\\.\\d{2}$", "\\1", name)
+    }
+    else if (grepl("^X\\d{4}\\.\\d{2}\\.\\d{2}\\..+$", name)) {
+      date_str <- gsub("^X(\\d{4}\\.\\d{2}\\.\\d{2})\\..+$", "\\1", name)
+      band_str <- gsub("^X\\d{4}\\.\\d{2}\\.\\d{2}\\.(.+)$", "\\1", name)
+    } 
+    else if (grepl("^\\d{4}\\.\\d{2}\\.\\d{2}\\..+$", name)) {
+      date_str <- gsub("^(\\d{4}\\.\\d{2}\\.\\d{2})\\..+$", "\\1", name)
+      band_str <- gsub("^\\d{4}\\.\\d{2}\\.\\d{2}\\.(.+)$", "\\1", name)
+    } 
+    else {
+      stop(paste("Unrecognized format in:", name))
+    }
+
+    # Convert the date string to Date format
+    date <- to_date_time(gsub("\\.", "-", date_str))
+
+    return(data.frame(time = date, band = band_str))
+  }))
+
+  # Construct tiem sereis
+  ns <- nrow(x)
+  x$ts_id <- 1:ns
+  if (!'label' %in% names(x)) {
+    x$label <- NA
+  }
+  x <- pivot_longer(x, !c('ts_id', 'label'), names_to = 'band_date', values_to = 'value')
+  x$band <- rep(date_band$band, ns)
+  x$time <- rep(date_band$time, ns)
+  x$band_date <- NULL
+  result_df <- pivot_wider(x, id_cols = c('ts_id', 'label', 'time'), names_from = 'band', values_from = 'value')
+  result_df <- nest(result_df, .by = c('ts_id', 'label'), .key = 'observations')
+
+  return(result_df)
+
+}
+
+
+#### TO BE REMOVED: twdtw package will export this fucntion
+to_date_time <- function(x){
+  if (!inherits(x, c("Date", "POSIXt"))) {
+    # check if all strings in the vector include hours, minutes, and seconds
+    if (all(grepl("\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}:\\d{2}", x))) {
+      x <- try(as.POSIXct(x), silent = TRUE)
+    } else {
+      x <- try(as.Date(x), silent = TRUE)
+    }
+    if (inherits(x, "try-error")) {
+      stop("Some elements of x could not be converted to a date or datetime format")
+    }
+  }
+  return(x)
+}
diff --git a/R/utils.R b/R/shift_dates.R
similarity index 76%
rename from R/utils.R
rename to R/shift_dates.R
index b9bb556..a37e060 100644
--- a/R/utils.R
+++ b/R/shift_dates.R
@@ -43,22 +43,8 @@ shift_dates <- function(x, origin = "1970-01-01") {
   return(shifted_dates)
 }
 
-#### TO BE REMOVED: twdtw package will export this fucntion 
-to_date_time <- function(x){
-  if (!inherits(x, c("Date", "POSIXt"))) {
-    # check if all strings in the vector include hours, minutes, and seconds
-    if (all(grepl("\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}:\\d{2}", x))) {
-      x <- try(as.POSIXct(x), silent = TRUE)
-    } else {
-      x <- try(as.Date(x), silent = TRUE)
-    }
-    if (inherits(x, "try-error")) {
-      stop("Some elements of x could not be converted to a date or datetime format")
-    }
-  }
+
+shift_ts_dates <- function(x) {
+  x$time <- shift_dates(x$time)
   return(x)
 }
-
-
-
-
diff --git a/R/train.R b/R/train.R
new file mode 100644
index 0000000..e6a8454
--- /dev/null
+++ b/R/train.R
@@ -0,0 +1,274 @@
+#'
+#' Train a KNN-1 TWDTW model with optional GAM resampling
+#'
+#' This function prepares a KNN-1 model with the Time Warp Dynamic Time Warping (TWDTW) algorithm.
+#' If a formula is provided, the training samples are resampled using Generalized Additive Models (GAM).
+#'
+#' @param x A three-dimensional stars object (x, y, time) with bands as attributes.
+#' @param y An sf object with the coordinates of the training points.
+#' @param time_weight A numeric vector with length two (steepness and midpoint of logistic weight) or a function.
+#' See details in \link[twdtw]{twdtw}.
+#' @param cycle_length The length of the cycle, e.g. phenological cycles. Details in \link[twdtw]{twdtw}.
+#' @param time_scale Specifies the time scale for the observations. Details in \link[twdtw]{twdtw}.
+#' @param formula Either NULL or a formula to reduce samples of the same label using Generalized Additive Models (GAM).
+#' Default is \code{band ~ s(time)}. See details.
+#' @param start_column Name of the column in y that indicates the start date. Default is 'start_date'.
+#' @param end_column Name of the column in y that indicates the end date. Default is 'end_date'.
+#' @param label_colum Name of the column in y containing land use labels. Default is 'label'.
+#' @param sampling_freq The time frequency for sampling, including the unit (e.g., '16 day').
+#' If NULL, the function will infer the frequency. This parameter is only used if a formula is provided.
+#' @param ... Additional arguments passed to the \link[mgcv]{gam} function and to \link[twdtw]{twdtw} function.
+#'
+#' @details If \code{formula} is NULL, the KNN-1 model will retain all training samples. If a formula is passed (e.g., \code{band ~ \link[mgcv]{s}(time)}),
+#' then samples of the same label (land cover class) will be resampled using GAM.
+#' Resampling can significantly reduce prediction processing time.
+#'
+#' @return A 'twdtw_knn1' model containing the trained model information and the data used.
+#'
+#' @examples
+#' \dontrun{
+#'
+#' # Read training samples
+#' samples <-
+#'   system.file("mato_grosso_brazil/samples.gpkg", package = "dtwSat") |>
+#'   st_read(quiet = TRUE)
+#'
+#' # Get satellite image time sereis files
+#' tif_files <- system.file("mato_grosso_brazil", package = "dtwSat") |>
+#'   dir(pattern = "\\.tif$", full.names = TRUE)
+#'
+#' # Get acquisition dates
+#' acquisition_date <- regmatches(tif_files, regexpr("[0-9]{8}", tif_files)) |>
+#'   as.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)) |>
+#'       st_set_dimensions(3, c("EVI", "NDVI", "RED", "BLUE", "NIR", "MIR")) |>
+#'       split(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()
+#'
+#' }
+#' @export
+twdtw_knn1 <- function(x, y, time_weight, cycle_length, time_scale,
+                       formula = NULL, start_column = 'start_date',
+                       end_column = 'end_date', label_colum = 'label',
+                       sampling_freq = NULL, ...){
+
+  # Check if x is a stars object with a time dimension
+  if (!inherits(x, "stars") || dim(x)['time'] < 1 || length(dim(x)) != 3) {
+    stop("x must be a three-dimensional stars object with a 'time' dimension")
+  }
+
+  x <- split(x, c("time"))
+
+  # Check if y is an sf object with point geometry
+  if (!inherits(y, "sf") || !all(st_is(y, "POINT"))) {
+    stop("y must be an sf object with point geometry")
+  }
+
+  # check for minimum set of twdtw arguments
+  if (!(is.function(time_weight) || (is.numeric(time_weight) && length(time_weight) == 2))) stop("'time_weight' should be either a function or a numeric vector with length two")
+  if (is.null(cycle_length)) stop("The 'cycle_length' argument is missing.")
+  if (is.null(time_scale)) stop("The 'time_scale' argument is missing for 'cycle_length' type character.")
+
+  # Check for required columns in y
+  required_columns <- c(start_column, end_column, label_colum)
+  missing_columns <- setdiff(required_columns, names(y))
+  if (length(missing_columns) > 0) {
+    stop(paste("Missing required columns in y:", paste(missing_columns, collapse = ", ")))
+  }
+
+  # Convert columns to date-time
+  y[, start_column] <- to_date_time(y[[start_column]])
+  y[, end_column] <- to_date_time(y[[end_column]])
+
+  # Prepare time series samples from stars object
+  ts_data <- st_extract(x, y)
+  ts_data$label <- y[[label_colum]]
+  ts_data <- prepare_time_series(as.data.frame(ts_data))
+  ts_data$ts_id <- NULL
+
+  if(!is.null(formula)) {
+
+    # Check if formula has two
+    if(length(all.vars(formula)) != 2) {
+      stop("The formula should have only one predictor!")
+    }
+
+    # Determine sampling frequency
+    if (is.null(sampling_freq)) {
+      sampling_freq <- get_time_series_freq(ts_data)
+    }
+
+    # Shift dates
+    ts_data$observations <- lapply(ts_data$observations, shift_ts_dates)
+
+    # Split data frame by label
+    ts_data <- unnest(ts_data, cols = 'observations')
+    ts_data <- nest(ts_data, .by = 'label', .key = "observations")
+
+    # Define GAM function
+    gam_fun <- function(band, t, pred_t, formula, ...){
+      df <- setNames(list(band, as.numeric(t)), all.vars(formula))
+      pred_t[[all.vars(formula)[2]]] <- as.numeric(pred_t[[all.vars(formula)[2]]])
+      fit <- mgcv::gam(data = df, formula = formula, ...)
+      predict(fit, newdata = pred_t)
+    }
+
+    # Apply GAM function
+    ts_data$observations <- lapply(ts_data$observations, function(ts){
+      y_time <- ts$time
+      ts$time <- NULL
+      pred_time <- setNames(list(seq(min(y_time), max(y_time), by = sampling_freq)), all.vars(formula)[2])
+      cbind(pred_time, as.data.frame(sapply(as.list(ts), function(band) {
+        gam_fun(band, y_time, pred_time, formula, ...)
+      })))
+    })
+
+  }
+
+  model <- list()
+  model$call <- match.call()
+  model$formula <- formula
+  model$data <- ts_data
+  # add twdtw arguments to model
+  model$twdtw_args <- list(time_weight = time_weight,
+                           cycle_length = cycle_length,
+                           time_scale = time_scale,
+                           origin = NULL,
+                           max_elapsed = Inf,
+                           version = "f90")
+  new_twdtw_args <- list(...)
+  matching_twdtw_args <- intersect(names(model$twdtw_args), names(new_twdtw_args))
+  model$twdtw_args[matching_twdtw_args] <- new_twdtw_args[matching_twdtw_args]
+
+  class(model) <- "twdtw_knn1"
+
+  return(model)
+
+}
+
+#' Print method for objects of class twdtw_knn1
+#'
+#' This method provides a structured printout of the important components
+#' of a `twdtw_knn1` object.
+#'
+#' @param x An object of class `twdtw_knn1`.
+#' @param ... ignored
+#' 
+#' @return Invisible `twdtw_knn1` object.
+#'
+#' @export
+print.twdtw_knn1 <- function(x, ...) {
+  cat("\nModel of class 'twdtw_knn1'\n")
+  cat("-----------------------------\n")
+
+  # Printing the call
+  cat("Call:\n")
+  print(x$call)
+
+  # Printing the formula, if available
+  cat("\nFormula:\n")
+  print(x$formula)
+
+  # Printing the data summary
+  cat("\nData:\n")
+  print(x$data)
+
+  # Printing twdtw arguments
+  cat("\nTWDTW Arguments:\n")
+  pretty_arguments(x$twdtw_args)
+
+  invisible(x) # Returns the object invisibly, so it doesn't print twice
+}
+
+#' Print Pretty Arguments
+#'
+#' Display a list of arguments of a given function in a human-readable format.
+#'
+#' @param args A list of named arguments to display.
+#'
+#' @return Invisible NULL. The function is mainly used for its side effect of printing.
+#'
+#' @examples
+#' \dontrun{
+#' pretty_arguments(formals(twdtw_knn1))
+#' }
+#'
+pretty_arguments <- function(args) {
+
+  if (is.null(args)) {
+    cat("Arguments are missing.\n")
+    return(invisible(NULL))
+  }
+
+  for (name in names(args)) {
+    default_value <- args[[name]]
+
+    if (is.symbol(default_value)) {
+      default_value <- as.character(default_value)
+
+    } else if (is.null(default_value)) {
+      default_value <- "NULL"
+
+    } else if (is.vector(default_value) && !is.null(names(default_value))) {
+      # Handle named vectors
+      values <- paste(names(default_value), default_value, sep = "=", collapse = ", ")
+      default_value <- paste0("c(", values, ")")
+    }
+    cat(paste0(" - ", name, ": ", default_value, "\n"))
+  }
+}
+
+
+
+#' Compute the Most Common Sampling Frequency across all observations
+#'
+#' This function calculates the most common difference between consecutive time points.
+#' This can be useful for determining the aproximate sampling frequency of the time series data.
+#'
+#' @param x A data frame including a column called `observations`` with the time series 
+#'
+#' @return A difftime object representing the most common time difference between consecutive samples.
+#'
+get_time_series_freq <- function(x) {
+
+  # Extract the time dimension
+  time_values <- unlist(lapply(x$observations, function(ts) ts$time))
+
+  # Compute the differences between consecutive time points
+  time_diffs <- unlist(lapply(x$observations, function(ts) diff(ts$time)))
+
+  # Convert differences to days (while retaining the difftime class)
+  time_diffs <- as.difftime(time_diffs, units = "days")
+
+  # Identify the mode
+  mode_val_index <- which.max(tabulate(match(time_diffs, unique(time_diffs))))
+  freq <- diff(time_values[mode_val_index:(mode_val_index+1)])
+
+  return(freq)
+
+}
diff --git a/R/zzz.R b/R/zzz.R
index 499fd6c..90fa6ae 100644
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -3,9 +3,8 @@
 #' @import stars
 #' @import ggplot2
 #' @importFrom stats as.formula predict setNames
-#' @importFrom mgcv gam s predict.gam 
-#' @importFrom scales pretty_breaks date_format percent
-#' @importFrom reshape2 melt
-#' @importFrom rlang .data
+#' @importFrom mgcv gam s predict.gam
+#' @importFrom tidyr pivot_longer pivot_wider nest unnest
+#' @importFrom proxy dist
 #' 
 NULL
diff --git a/README.md b/README.md
index 4db524f..031f02c 100644
--- a/README.md
+++ b/README.md
@@ -7,33 +7,42 @@
 [![CRAN](https://www.r-pkg.org/badges/version/dtwSat)](https://cran.r-project.org/package=dtwSat)
 [![Downloads](https://cranlogs.r-pkg.org/badges/dtwSat?color=brightgreen)](https://www.r-pkg.org/pkg/dtwSat)
 [![total](http://cranlogs.r-pkg.org/badges/grand-total/dtwSat)](http://www.r-pkg.org/pkg/dtwSat)
+[![cran checks](https://badges.cranchecks.info/worst/dtwSat.svg)](https://cran.r-project.org/web/checks/check_results_dtwSat.html)
+[![status](https://tinyverse.netlify.com/badge/dtwSat)](https://CRAN.R-project.org/package=dtwSat)
+
 <!-- badges: end -->
   
+Provides a robust approach to land use mapping using multi-dimensional 
+(multi-band) satellite image time series. By leveraging the Time-Weighted Dynamic 
+Time Warping (TWDTW) distance metric in tandem with a 1 Nearest-Neighbor (1-NN) Classifier,
+this package offers functions to produce land use maps based on distinct seasonality patterns, 
+commonly observed in the phenological cycles of vegetation. The approach is described in 
+Maus et al. (2016) and Maus et al. (2019).
+A primary advantage of TWDTW is its capability to handle irregularly sampled and noisy time series, 
+while also requiring minimal training sets. The package includes tools for training the 1-NN-TWDTW model, 
+visualizing temporal patterns, producing land use maps, and visualizing the results.
 
-### Time-Weighted Dynamic Time Warping for satellite image time series analysis
-
-The package *dtwSat* provides an implementation of the Time-Weighted
-Dynamic Time Warping (TWDTW) method for land cover mapping using
-multi-band satellite image time series (Maus et al. 2016, 2019).
-*dtwSat* provides full cycle of land cover classification using image
-time series, ranging from selecting temporal patterns to visualizing,
-and assessing the results.
+## Getting Started
 
-## Installing
-
-Install either from CRAN
+You can install dtwSat from CRAN using the following command:
 
 ``` r
 install.packages("dtwSat")
 ```
 
-or install the development versions from GitHub
+Alternatively, you can install the development version from GitHub:
 
 ``` r
-library(devtools)
 devtools::install_github("vwmaus/dtwSat")
 ```
 
+After installation, you can read the vignette for a quick start guide:
+
+``` r
+vignette("landuse-mapping", "dtwSat")
+```
+
+
 ## References
 
 <div id="refs" class="references csl-bib-body hanging-indent">
diff --git a/inst/REFERENCES.bib b/inst/REFERENCES.bib
index c2dacc5..d1931b3 100644
--- a/inst/REFERENCES.bib
+++ b/inst/REFERENCES.bib
@@ -23,47 +23,6 @@ @article{Maus:2019
   month={Jan}
 }
 
-@article{Olofsson:2014,
- title = {Good Practices for Estimating Area and Assessing Accuracy of Land Change},
- journal = {Remote Sensing of Environment},
- volume = {148},
- pages = {42--57},
- year = {2014},
- issn = {0034--4257},
- doi = {10.1016/j.rse.2014.02.015},
- author = {Pontus Olofsson and Giles M. Foody and Martin Herold and Stephen V. Stehman and Curtis E. Woodcock and Michael A. Wulder}
-}
-
-@article{Olofsson:2013,
-    author = {Pontus Olofsson and Giles M. Foody and Stephen V. Stehman and Curtis E. Woodcock},
-    title = {Making Better Use of Accuracy Aata in Land Change Studies: Estimating Accuracy and Area and Quantifying Uncertainty Using Stratified Estimation},
-    journal = {Remote Sensing of Environment},
-    volume = {129},
-    pages = {122--131},
-    year = {2013},
-    doi = {10.1016/j.rse.2012.10.031}
-}
-
-@article{Giorgino:2009,
-  author = {Toni Giorgino},
-  title = {Computing and Visualizing Dynamic Time Warping Alignments in {R}: The dtw Package},
-  journal = {Journal of Statistical Software},
-  year = {2009},
-  volume = {31},
-  pages = {1--24},
-  number = {7},
-  doi = {10.18637/jss.v031.i07}
-}
-
-@book{Muller:2007,
-  title = {Information Retrieval for Music and Motion},
-  publisher = {Springer-Verlag},
-  year = {2007},
-  author = {Meinard Muller},
-  pages = {64-84},
-  address = {London}
-}
-
 @misc{Didan:2015,
   author = {Kamel Didan},
   title =  {{MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global
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deleted file mode 100644
index 9e9706e..0000000
--- a/inst/lucc_MT/data/samples.csv
+++ /dev/null
@@ -1,604 +0,0 @@
-"longitude","latitude","from","to","label"
--55.9881860661,-12.0364583323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9911845738,-12.0406249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9860559013,-12.0364583323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.985621884,-12.0343749989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9847540916,-12.0302083323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9839257365,-12.0364583323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.976707366,-12.0427083323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9809276839,-12.0322916656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9928805432,-12.0385416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9886201806,-12.0385416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9813615874,-12.0343749989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9869241782,-12.0406249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9877131401,-12.0239583323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9894886521,-12.0427083323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9834917357,-12.0343749989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9420173552,-11.9885416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9817955717,-12.0364583323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9907503619,-12.0385416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9933147716,-12.0406249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.989054376,-12.0406249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9838866222,-12.0260416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9864899993,-12.0385416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.984359818,-12.0385416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9843203165,-12.0281249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9954449694,-12.0406249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9958792951,-12.0427083323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9855830744,-12.0239583323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9916188664,-12.0427083323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9860167045,-12.0260416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9788375803,-12.0427083323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9821902178,-12.0281249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.944147141,-11.9885416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9800601191,-12.0281249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.98175654,-12.0260416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9864504153,-12.0281249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9784033869,-12.0406249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9937490807,-12.0427083323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9809677947,-12.0427083323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9732749126,-12.0364583323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9647542535,-12.0364583323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9711447478,-12.0364583323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9668844182,-12.0364583323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9656222,-12.0406249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9677523978,-12.0406249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9634920021,-12.0406249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9609278238,-12.0385416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9694485489,-12.0385416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9754050774,-12.0364583323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9720127934,-12.0406249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9698825956,-12.0406249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9707108461,-12.0343749989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9728409944,-12.0343749989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9613618043,-12.0406249989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9737089115,-12.0385416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.969014583,-12.0364583323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9651881863,-12.0385416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.963058005,-12.0385416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.970316723,-12.0427083323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9715787302,-12.0385416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9681865086,-12.0427083323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9673183676,-12.0385416656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9617958656,-12.0427083323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9660562943,-12.0427083323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9596656513,-12.0427083323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9745372886,-12.0322916656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9749711426,-12.0343749989,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9766674204,-12.0322916656,"2011-09-01","2012-09-01","Cotton-fallow"
--55.96392608,-12.0427083323,"2011-09-01","2012-09-01","Cotton-fallow"
--55.9415804282,-12.0382604187,"2008-09-01","2009-09-01","Forest"
--55.9254548352,-12.0411572318,"2007-09-01","2008-09-01","Forest"
--55.9254548352,-12.0411572318,"2009-09-01","2010-09-01","Forest"
--55.9254548352,-12.0411572318,"2010-09-01","2011-09-01","Forest"
--55.9254548352,-12.0411572318,"2011-09-01","2012-09-01","Forest"
--55.9192749672,-12.0337220781,"2007-09-01","2008-09-01","Forest"
--55.9192749672,-12.0337220781,"2008-09-01","2009-09-01","Forest"
--55.9192749672,-12.0337220781,"2009-09-01","2010-09-01","Forest"
--55.9192749672,-12.0337220781,"2010-09-01","2011-09-01","Forest"
--55.9192749672,-12.0337220781,"2011-09-01","2012-09-01","Forest"
--55.9192749672,-12.0337220781,"2012-09-01","2013-09-01","Forest"
--55.9169575167,-12.0332392759,"2007-09-01","2008-09-01","Forest"
--55.9169575167,-12.0332392759,"2008-09-01","2009-09-01","Forest"
--55.9169575167,-12.0332392759,"2009-09-01","2010-09-01","Forest"
--55.9169575167,-12.0332392759,"2010-09-01","2011-09-01","Forest"
--55.9169575167,-12.0332392759,"2012-09-01","2013-09-01","Forest"
--55.9168609563,-12.035460166,"2007-09-01","2008-09-01","Forest"
--55.9168609563,-12.035460166,"2008-09-01","2009-09-01","Forest"
--55.9168609563,-12.035460166,"2009-09-01","2010-09-01","Forest"
--55.9400354612,-12.0368120121,"2007-09-01","2008-09-01","Forest"
--55.9400354612,-12.0368120121,"2008-09-01","2009-09-01","Forest"
--55.9400354612,-12.0368120121,"2009-09-01","2010-09-01","Forest"
--55.9400354612,-12.0368120121,"2010-09-01","2011-09-01","Forest"
--55.9400354612,-12.0368120121,"2011-09-01","2012-09-01","Forest"
--55.9169575167,-12.0332392759,"2011-09-01","2012-09-01","Forest"
--55.9400354612,-12.0368120121,"2012-09-01","2013-09-01","Forest"
--55.9254548352,-12.0411572318,"2008-09-01","2009-09-01","Forest"
--55.9181162419,-12.0384535395,"2007-09-01","2008-09-01","Forest"
--55.9181162419,-12.0384535395,"2009-09-01","2010-09-01","Forest"
--55.9181162419,-12.0384535395,"2010-09-01","2011-09-01","Forest"
--55.9181162419,-12.0384535395,"2011-09-01","2012-09-01","Forest"
--55.9181162419,-12.0384535395,"2012-09-01","2013-09-01","Forest"
--55.9393595381,-12.0387432209,"2007-09-01","2008-09-01","Forest"
--55.9393595381,-12.0387432209,"2008-09-01","2009-09-01","Forest"
--55.9393595381,-12.0387432209,"2009-09-01","2010-09-01","Forest"
--55.9393595381,-12.0387432209,"2010-09-01","2011-09-01","Forest"
--55.9393595381,-12.0387432209,"2011-09-01","2012-09-01","Forest"
--55.9393595381,-12.0387432209,"2012-09-01","2013-09-01","Forest"
--55.9403251425,-12.0421228362,"2007-09-01","2008-09-01","Forest"
--55.9403251425,-12.0421228362,"2008-09-01","2009-09-01","Forest"
--55.9403251425,-12.0421228362,"2009-09-01","2010-09-01","Forest"
--55.9403251425,-12.0421228362,"2010-09-01","2011-09-01","Forest"
--55.9403251425,-12.0421228362,"2011-09-01","2012-09-01","Forest"
--55.9412907469,-12.0319839902,"2008-09-01","2009-09-01","Forest"
--55.9435116369,-12.0408675505,"2007-09-01","2008-09-01","Forest"
--55.9435116369,-12.0408675505,"2008-09-01","2009-09-01","Forest"
--55.9435116369,-12.0408675505,"2009-09-01","2010-09-01","Forest"
--55.9435116369,-12.0408675505,"2010-09-01","2011-09-01","Forest"
--55.9435116369,-12.0408675505,"2011-09-01","2012-09-01","Forest"
--55.9435116369,-12.0408675505,"2012-09-01","2013-09-01","Forest"
--55.9422563513,-12.0409641109,"2007-09-01","2008-09-01","Forest"
--55.9422563513,-12.0409641109,"2008-09-01","2009-09-01","Forest"
--55.9422563513,-12.0409641109,"2009-09-01","2010-09-01","Forest"
--55.9422563513,-12.0409641109,"2010-09-01","2011-09-01","Forest"
--55.9199508902,-12.0360395286,"2007-09-01","2008-09-01","Forest"
--55.9181162419,-12.0384535395,"2008-09-01","2009-09-01","Forest"
--55.9415804282,-12.0382604187,"2007-09-01","2008-09-01","Forest"
--55.9415804282,-12.0382604187,"2009-09-01","2010-09-01","Forest"
--55.9415804282,-12.0382604187,"2010-09-01","2011-09-01","Forest"
--55.9415804282,-12.0382604187,"2011-09-01","2012-09-01","Forest"
--55.9415804282,-12.0382604187,"2012-09-01","2013-09-01","Forest"
--55.9168609563,-12.035460166,"2010-09-01","2011-09-01","Forest"
--55.9397457799,-12.0403847483,"2007-09-01","2008-09-01","Forest"
--55.9397457799,-12.0403847483,"2008-09-01","2009-09-01","Forest"
--55.9397457799,-12.0403847483,"2009-09-01","2010-09-01","Forest"
--55.9397457799,-12.0403847483,"2010-09-01","2011-09-01","Forest"
--55.9397457799,-12.0403847483,"2011-09-01","2012-09-01","Forest"
--55.9397457799,-12.0403847483,"2012-09-01","2013-09-01","Forest"
--55.9179231211,-12.0401916274,"2009-09-01","2010-09-01","Forest"
--55.9220752199,-12.0413503527,"2007-09-01","2008-09-01","Forest"
--55.9220752199,-12.0413503527,"2008-09-01","2009-09-01","Forest"
--55.9220752199,-12.0413503527,"2009-09-01","2010-09-01","Forest"
--55.9220752199,-12.0413503527,"2010-09-01","2011-09-01","Forest"
--55.9220752199,-12.0413503527,"2011-09-01","2012-09-01","Forest"
--55.9220752199,-12.0413503527,"2012-09-01","2013-09-01","Forest"
--55.9412907469,-12.0319839902,"2007-09-01","2008-09-01","Forest"
--55.9412907469,-12.0319839902,"2009-09-01","2010-09-01","Forest"
--55.9254548352,-12.0411572318,"2012-09-01","2013-09-01","Forest"
--55.9179231211,-12.0401916274,"2012-09-01","2013-09-01","Forest"
--55.9168609563,-12.035460166,"2011-09-01","2012-09-01","Forest"
--55.9168609563,-12.035460166,"2012-09-01","2013-09-01","Forest"
--55.9179231211,-12.0401916274,"2007-09-01","2008-09-01","Forest"
--55.9179231211,-12.0401916274,"2008-09-01","2009-09-01","Forest"
--55.9403251425,-12.0421228362,"2012-09-01","2013-09-01","Forest"
--55.9179231211,-12.0401916274,"2010-09-01","2011-09-01","Forest"
--55.9179231211,-12.0401916274,"2011-09-01","2012-09-01","Forest"
--55.9382973733,-12.0419297153,"2007-09-01","2008-09-01","Forest"
--55.9382973733,-12.0419297153,"2008-09-01","2009-09-01","Forest"
--55.9382973733,-12.0419297153,"2009-09-01","2010-09-01","Forest"
--55.9382973733,-12.0419297153,"2010-09-01","2011-09-01","Forest"
--55.9382973733,-12.0419297153,"2011-09-01","2012-09-01","Forest"
--55.9382973733,-12.0419297153,"2012-09-01","2013-09-01","Forest"
--55.9412907469,-12.0319839902,"2010-09-01","2011-09-01","Forest"
--55.9412907469,-12.0319839902,"2011-09-01","2012-09-01","Forest"
--55.9412907469,-12.0319839902,"2012-09-01","2013-09-01","Forest"
--55.9381042525,-12.0403847483,"2007-09-01","2008-09-01","Forest"
--55.9381042525,-12.0403847483,"2008-09-01","2009-09-01","Forest"
--55.9381042525,-12.0403847483,"2009-09-01","2010-09-01","Forest"
--55.9381042525,-12.0403847483,"2010-09-01","2011-09-01","Forest"
--55.9381042525,-12.0403847483,"2011-09-01","2012-09-01","Forest"
--55.9381042525,-12.0403847483,"2012-09-01","2013-09-01","Forest"
--55.9423529117,-12.0352670451,"2008-09-01","2009-09-01","Forest"
--55.9423529117,-12.0352670451,"2009-09-01","2010-09-01","Forest"
--55.9409045051,-12.0352670451,"2007-09-01","2008-09-01","Forest"
--55.9409045051,-12.0352670451,"2008-09-01","2009-09-01","Forest"
--55.9409045051,-12.0352670451,"2009-09-01","2010-09-01","Forest"
--55.9409045051,-12.0352670451,"2010-09-01","2011-09-01","Forest"
--55.9409045051,-12.0352670451,"2011-09-01","2012-09-01","Forest"
--55.9409045051,-12.0352670451,"2012-09-01","2013-09-01","Forest"
--55.9199508902,-12.0360395286,"2010-09-01","2011-09-01","Forest"
--55.9199508902,-12.0360395286,"2011-09-01","2012-09-01","Forest"
--55.9199508902,-12.0360395286,"2012-09-01","2013-09-01","Forest"
--55.9212061759,-12.0384535395,"2008-09-01","2009-09-01","Forest"
--55.9423529117,-12.0352670451,"2007-09-01","2008-09-01","Forest"
--55.9422563513,-12.0409641109,"2012-09-01","2013-09-01","Forest"
--55.9212061759,-12.0384535395,"2007-09-01","2008-09-01","Forest"
--55.9212061759,-12.0384535395,"2009-09-01","2010-09-01","Forest"
--55.939552659,-12.0349773638,"2007-09-01","2008-09-01","Forest"
--55.939552659,-12.0349773638,"2008-09-01","2009-09-01","Forest"
--55.939552659,-12.0349773638,"2009-09-01","2010-09-01","Forest"
--55.939552659,-12.0349773638,"2010-09-01","2011-09-01","Forest"
--55.939552659,-12.0349773638,"2011-09-01","2012-09-01","Forest"
--55.939552659,-12.0349773638,"2012-09-01","2013-09-01","Forest"
--55.9365592855,-12.0406744296,"2007-09-01","2008-09-01","Forest"
--55.9365592855,-12.0406744296,"2008-09-01","2009-09-01","Forest"
--55.9365592855,-12.0406744296,"2010-09-01","2011-09-01","Forest"
--55.9365592855,-12.0406744296,"2011-09-01","2012-09-01","Forest"
--55.9365592855,-12.0406744296,"2012-09-01","2013-09-01","Forest"
--55.9212061759,-12.0384535395,"2010-09-01","2011-09-01","Forest"
--55.9212061759,-12.0384535395,"2012-09-01","2013-09-01","Forest"
--55.9422563513,-12.0409641109,"2011-09-01","2012-09-01","Forest"
--55.9423529117,-12.0352670451,"2010-09-01","2011-09-01","Forest"
--55.9423529117,-12.0352670451,"2011-09-01","2012-09-01","Forest"
--55.9423529117,-12.0352670451,"2012-09-01","2013-09-01","Forest"
--55.9199508902,-12.0360395286,"2008-09-01","2009-09-01","Forest"
--55.9212061759,-12.0384535395,"2011-09-01","2012-09-01","Forest"
--55.9365592855,-12.0406744296,"2009-09-01","2010-09-01","Forest"
--55.9199508902,-12.0360395286,"2009-09-01","2010-09-01","Forest"
--55.9646793633,-12.0156249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.976629328,-12.0218749989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9685425484,-12.0239583323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9715397241,-12.0281249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9693725546,-12.0177083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9625493635,-12.0156249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9711061287,-12.0260416656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9689760465,-12.0260416656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9630193291,-12.0281249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.977459362,-12.0156249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9728026799,-12.0239583323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9706726141,-12.0239583323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9672795266,-12.0281249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9613228239,-12.0302083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.963380371,-12.0093749989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9595533541,-12.0114583323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9574233873,-12.0114583323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9492708486,-11.9927083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9770628114,-12.0239583323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.973236211,-12.0260416656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9570625934,-12.0302083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.966809363,-12.0156249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.962947535,-12.0072916656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9795893618,-12.0156249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9736325871,-12.0177083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9740658926,-12.0197916656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9668459643,-12.0260416656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.964715882,-12.0260416656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9694096253,-12.0281249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9651494278,-12.0281249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9608892303,-12.0281249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9672425383,-12.0177083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9651125221,-12.0177083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9655457616,-12.0197916656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9548955977,-12.0197916656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9574588847,-12.0218749989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9646446971,-12.0052083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9616833209,-12.0114583323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.955726352,-12.0135416656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9518993646,-12.0156249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9488387402,-11.9906249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9787593773,-12.0218749989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9570256305,-12.0197916656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9378553357,-12.0197916656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9590871297,-11.9989583323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9459423756,-12.0177083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9399853684,-12.0197916656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9361583922,-12.0218749989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9591927086,-12.0302083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9629825058,-12.0177083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9608524895,-12.0177083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9634157288,-12.0197916656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9578922197,-12.0239583323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9612504207,-12.0093749989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9578563353,-12.0135416656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9438123593,-12.0177083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9442454339,-12.0197916656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.950600669,-12.0093749989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9530983446,-11.9906249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.94714103,-11.9927083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9421154012,-12.0197916656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9382884415,-12.0218749989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9404184907,-12.0218749989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9591556633,-12.0197916656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9497693648,-12.0156249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9480723919,-12.0177083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9514006672,-11.9927083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9595196266,-12.0010416656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.959588934,-12.0218749989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.94890352,-12.0114583323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9587224732,-12.0177083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.956592457,-12.0177083323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.961285696,-12.0197916656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9638490325,-12.0218749989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9617189832,-12.0218749989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9509685424,-11.9906249989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9676757943,-12.0197916656,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9553288355,-12.0218749989,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9600222854,-12.0239583323,"2011-09-01","2012-09-01","Soybean-cotton"
--55.9786559709,-11.9906249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9766674204,-12.0322916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9745372886,-12.0322916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9707108461,-12.0343749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9748286723,-11.9927083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9710013886,-11.9947916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9718663378,-11.9989583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9795893618,-12.0156249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9736325871,-12.0177083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9740658926,-12.0197916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9787593773,-12.0218749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9864504153,-12.0281249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9843203165,-12.0281249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9800601191,-12.0281249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9928407115,-12.0281249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9915783427,-12.0322916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9754050774,-12.0364583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9765261687,-11.9906249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9855830744,-12.0239583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9860167045,-12.0260416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9838866222,-12.0260416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.98175654,-12.0260416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9821902178,-12.0281249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9847540916,-12.0302083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9839257365,-12.0364583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9928805432,-12.0385416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.989054376,-12.0406249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9803534987,-11.9885416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9497352753,-12.0052083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9522978657,-12.0072916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9807857731,-11.9906249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9790883096,-11.9927083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.976958491,-11.9927083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9726988537,-11.9927083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9752610588,-11.9947916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9735636744,-11.9968749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9714338228,-11.9968749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9782559418,-11.9989583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9735972044,-12.0072916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9719001724,-12.0093749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9877131401,-12.0239583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9907106128,-12.0281249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9932745526,-12.0302083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9911444374,-12.0302083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9894482109,-12.0322916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9809276839,-12.0322916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.985621884,-12.0343749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9834917357,-12.0343749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9881860661,-12.0364583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9860559013,-12.0364583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9907503619,-12.0385416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9886201806,-12.0385416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.984359818,-12.0385416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9954449694,-12.0406249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9911845738,-12.0406249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9869241782,-12.0406249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9916188664,-12.0427083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9786885868,-12.0010416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9774242015,-12.0052083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.977459362,-12.0156249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9693039713,-11.9968749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9697364698,-11.9989583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9676066018,-11.9989583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9701690489,-12.0010416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9680391644,-12.0010416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.96590928,-12.0010416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9820830806,-11.9968749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9748615106,-12.0031249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9757271383,-12.0072916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9958792951,-12.0427083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9937490807,-12.0427083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9749711426,-12.0343749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9728409944,-12.0343749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.969014583,-12.0364583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9715787302,-12.0385416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9694485489,-12.0385416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.963058005,-12.0385416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9677523978,-12.0406249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9656222,-12.0406249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.970316723,-12.0427083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9681865086,-12.0427083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9731312237,-11.9947916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9842129321,-11.9968749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9744631219,-12.0114583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9898060848,-12.0135416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9889761219,-12.0197916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9404184907,-12.0218749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9813615874,-12.0343749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9817955717,-12.0364583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9864899993,-12.0385416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9933147716,-12.0406249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9894886521,-12.0427083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9784033869,-12.0406249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9809677947,-12.0427083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9788375803,-12.0427083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.976707366,-12.0427083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9920123288,-12.0343749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9518993646,-12.0156249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9769914116,-12.0031249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9731643667,-12.0052083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9714672705,-12.0072916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9740301228,-12.0093749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9497693648,-12.0156249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9438123593,-12.0177083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9378553357,-12.0197916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9399853684,-12.0197916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9514326003,-12.0031249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9480379979,-12.0072916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9480723919,-12.0177083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9459423756,-12.0177083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9442454339,-12.0197916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9684718078,-12.0031249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.979953229,-11.9968749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9765587023,-12.0010416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9752942841,-12.0052083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9668844182,-12.0364583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9382884415,-12.0218749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9837803992,-11.9947916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9421154012,-12.0197916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9803858098,-11.9989583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9720127934,-12.0406249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.976629328,-12.0218749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9518651927,-12.0052083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9476053579,-12.0052083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9501679318,-12.0072916656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9711447478,-12.0364583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9673183676,-12.0385416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9732749126,-12.0364583323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9651881863,-12.0385416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.96392608,-12.0427083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9484707186,-12.0093749989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9698825956,-12.0406249989,"2010-09-01","2011-09-01","Soybean-maize"
--55.9660562943,-12.0427083323,"2010-09-01","2011-09-01","Soybean-maize"
--55.9737089115,-12.0385416656,"2010-09-01","2011-09-01","Soybean-maize"
--55.9250412648,-12.0093749989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9229113145,-12.0093749989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9186514138,-12.0093749989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9165214634,-12.0093749989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9143915131,-12.0093749989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9318637854,-12.0114583323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9297338186,-12.0114583323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9276038518,-12.0114583323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9233439181,-12.0114583323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9212139513,-12.0114583323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9190839845,-12.0114583323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9148240508,-12.0114583323,"2010-09-01","2011-09-01","Soybean-millet"
--55.928036569,-12.0135416656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9237766024,-12.0135416656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9199493678,-12.0156249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.917819368,-12.0156249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9156893682,-12.0156249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9135593684,-12.0156249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9348947306,-12.0260416656,"2012-09-01","2013-09-01","Soybean-millet"
--55.9285044838,-12.0260416656,"2012-09-01","2013-09-01","Soybean-millet"
--55.9310678479,-12.0281249989,"2012-09-01","2013-09-01","Soybean-millet"
--55.9289377491,-12.0281249989,"2012-09-01","2013-09-01","Soybean-millet"
--55.9268076504,-12.0281249989,"2012-09-01","2013-09-01","Soybean-millet"
--55.9336313256,-12.0302083323,"2012-09-01","2013-09-01","Soybean-millet"
--55.9315012103,-12.0302083323,"2012-09-01","2013-09-01","Soybean-millet"
--55.9293710951,-12.0302083323,"2012-09-01","2013-09-01","Soybean-millet"
--55.9272409798,-12.0302083323,"2012-09-01","2013-09-01","Soybean-millet"
--55.9340647852,-12.0322916656,"2012-09-01","2013-09-01","Soybean-millet"
--55.9319346535,-12.0322916656,"2012-09-01","2013-09-01","Soybean-millet"
--55.9298045217,-12.0322916656,"2012-09-01","2013-09-01","Soybean-millet"
--55.9276743899,-12.0322916656,"2012-09-01","2013-09-01","Soybean-millet"
--55.9255442582,-12.0322916656,"2012-09-01","2013-09-01","Soybean-millet"
--55.9323681773,-12.0343749989,"2012-09-01","2013-09-01","Soybean-millet"
--55.930238029,-12.0343749989,"2012-09-01","2013-09-01","Soybean-millet"
--55.9281078807,-12.0343749989,"2012-09-01","2013-09-01","Soybean-millet"
--55.9259777324,-12.0343749989,"2012-09-01","2013-09-01","Soybean-millet"
--55.9285414522,-12.0364583323,"2012-09-01","2013-09-01","Soybean-millet"
--55.9264112874,-12.0364583323,"2012-09-01","2013-09-01","Soybean-millet"
--55.9212480474,-12.0218749989,"2012-09-01","2013-09-01","Soybean-millet"
--55.9195510362,-12.0239583323,"2012-09-01","2013-09-01","Soybean-millet"
--55.9174209705,-12.0239583323,"2012-09-01","2013-09-01","Soybean-millet"
--55.9199841548,-12.0260416656,"2012-09-01","2013-09-01","Soybean-millet"
--55.9178540726,-12.0260416656,"2012-09-01","2013-09-01","Soybean-millet"
--55.9157239904,-12.0260416656,"2012-09-01","2013-09-01","Soybean-millet"
--55.9182872554,-12.0281249989,"2012-09-01","2013-09-01","Soybean-millet"
--55.9161571566,-12.0281249989,"2012-09-01","2013-09-01","Soybean-millet"
--55.9306345661,-12.0260416656,"2012-09-01","2013-09-01","Soybean-millet"
--55.9203488576,-12.0072916656,"2010-09-01","2011-09-01","Soybean-millet"
--55.913959056,-12.0072916656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9271712152,-12.0093749989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9199493678,-12.0156249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9135593684,-12.0156249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9203821804,-12.0177083323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9182521641,-12.0177083323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9161221478,-12.0177083323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9139921315,-12.0177083323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9211817061,-12.0010416656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9190518216,-12.0010416656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9190518216,-12.0010416656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9169219372,-12.0010416656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9169219372,-12.0010416656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9147920527,-12.0010416656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9126621682,-12.0010416656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9343933929,-12.0031249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9322634919,-12.0031249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9322634919,-12.0031249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.930133591,-12.0031249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9216139873,-12.0031249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9194840863,-12.0031249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9194840863,-12.0031249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9173541854,-12.0031249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9173541854,-12.0031249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9152242845,-12.0031249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9152242845,-12.0031249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9130943835,-12.0031249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9130943835,-12.0031249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9326959361,-12.0052083323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9326959361,-12.0052083323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9305660186,-12.0052083323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9284361012,-12.0052083323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9241762664,-12.0052083323,"2011-09-01","2012-09-01","Soybean-millet"
--55.922046349,-12.0052083323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9199164316,-12.0052083323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9177865142,-12.0052083323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9156565968,-12.0052083323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9135266794,-12.0052083323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9331284608,-12.0072916656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9309985269,-12.0072916656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9288685931,-12.0072916656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9267386592,-12.0072916656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9267386592,-12.0072916656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9246087253,-12.0072916656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9224787914,-12.0072916656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9203488576,-12.0072916656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9182189237,-12.0072916656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9182189237,-12.0072916656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9160889898,-12.0072916656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9335610662,-12.0093749989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9335610662,-12.0093749989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9314311159,-12.0093749989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9314311159,-12.0093749989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9293011655,-12.0093749989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9250412648,-12.0093749989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9229113145,-12.0093749989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9207813641,-12.0093749989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9165214634,-12.0093749989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9143915131,-12.0093749989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9339937522,-12.0114583323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9297338186,-12.0114583323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9276038518,-12.0114583323,"2011-09-01","2012-09-01","Soybean-millet"
--55.925473885,-12.0114583323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9233439181,-12.0114583323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9212139513,-12.0114583323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9190839845,-12.0114583323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9169540177,-12.0114583323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9301665523,-12.0135416656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9301665523,-12.0135416656,"2011-09-01","2012-09-01","Soybean-millet"
--55.928036569,-12.0135416656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9259065857,-12.0135416656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9216466191,-12.0135416656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9216466191,-12.0135416656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9195166358,-12.0135416656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9195166358,-12.0135416656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9173866525,-12.0135416656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9152566692,-12.0135416656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9152566692,-12.0135416656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9263393671,-12.0156249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9263393671,-12.0156249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9242093673,-12.0156249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9242093673,-12.0156249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9220793676,-12.0156249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9220793676,-12.0156249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.917819368,-12.0156249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9156893682,-12.0156249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9203821804,-12.0177083323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9182521641,-12.0177083323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9161221478,-12.0177083323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9139921315,-12.0177083323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9165550081,-12.0197916656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9165550081,-12.0197916656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9144249753,-12.0197916656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9323314307,-12.0239583323,"2012-09-01","2013-09-01","Soybean-millet"
--55.9302013649,-12.0239583323,"2012-09-01","2013-09-01","Soybean-millet"
--55.9327646483,-12.0260416656,"2012-09-01","2013-09-01","Soybean-millet"
--55.9353280454,-12.0281249989,"2012-09-01","2013-09-01","Soybean-millet"
--55.9331979466,-12.0281249989,"2012-09-01","2013-09-01","Soybean-millet"
--55.9144249753,-12.0197916656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9224787914,-12.0072916656,"2011-09-01","2012-09-01","Soybean-millet"
--55.913959056,-12.0072916656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9271712152,-12.0093749989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9160889898,-12.0072916656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9207813641,-12.0093749989,"2010-09-01","2011-09-01","Soybean-millet"
--55.925473885,-12.0114583323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9259065857,-12.0135416656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9237766024,-12.0135416656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9173866525,-12.0135416656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9246087253,-12.0072916656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9211817061,-12.0010416656,"2011-09-01","2012-09-01","Soybean-millet"
--55.9147920527,-12.0010416656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9126621682,-12.0010416656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9309985269,-12.0072916656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9339937522,-12.0114583323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9169540177,-12.0114583323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9148240508,-12.0114583323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9237438882,-12.0031249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9135266794,-12.0052083323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9331284608,-12.0072916656,"2010-09-01","2011-09-01","Soybean-millet"
--55.9186514138,-12.0093749989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9343933929,-12.0031249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9284361012,-12.0052083323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9199164316,-12.0052083323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9293011655,-12.0093749989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9318637854,-12.0114583323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9241762664,-12.0052083323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9288685931,-12.0072916656,"2010-09-01","2011-09-01","Soybean-millet"
--55.922046349,-12.0052083323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9156565968,-12.0052083323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9177865142,-12.0052083323,"2010-09-01","2011-09-01","Soybean-millet"
--55.930133591,-12.0031249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9186196375,-11.9989583323,"2011-09-01","2012-09-01","Soybean-millet"
--55.9237438882,-12.0031249989,"2010-09-01","2011-09-01","Soybean-millet"
--55.9216139873,-12.0031249989,"2011-09-01","2012-09-01","Soybean-millet"
--55.9186196375,-11.9989583323,"2010-09-01","2011-09-01","Soybean-millet"
--55.9305660186,-12.0052083323,"2010-09-01","2011-09-01","Soybean-millet"
diff --git a/inst/lucc_MT/data/samples_projection b/inst/lucc_MT/data/samples_projection
deleted file mode 100644
index 4128610..0000000
--- a/inst/lucc_MT/data/samples_projection
+++ /dev/null
@@ -1 +0,0 @@
-"+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
\ No newline at end of file
diff --git a/inst/lucc_MT/data/timeline b/inst/lucc_MT/data/timeline
deleted file mode 100644
index 43db978..0000000
--- a/inst/lucc_MT/data/timeline
+++ /dev/null
@@ -1,137 +0,0 @@
-2007-09-14
-2007-09-30
-2007-10-16
-2007-11-01
-2007-11-17
-2007-12-03
-2007-12-19
-2008-01-01
-2008-01-17
-2008-02-02
-2008-02-18
-2008-03-05
-2008-03-21
-2008-04-06
-2008-04-22
-2008-05-08
-2008-05-24
-2008-06-09
-2008-06-25
-2008-07-11
-2008-07-27
-2008-08-12
-2008-08-28
-2008-09-13
-2008-09-29
-2008-10-15
-2008-10-31
-2008-11-16
-2008-12-02
-2008-12-18
-2009-01-01
-2009-01-17
-2009-02-02
-2009-02-18
-2009-03-06
-2009-03-22
-2009-04-07
-2009-04-23
-2009-05-09
-2009-05-25
-2009-06-10
-2009-06-26
-2009-07-12
-2009-07-28
-2009-08-13
-2009-08-29
-2009-09-14
-2009-09-30
-2009-10-16
-2009-11-01
-2009-11-17
-2009-12-03
-2009-12-19
-2010-01-01
-2010-01-17
-2010-02-02
-2010-02-18
-2010-03-06
-2010-03-22
-2010-04-07
-2010-04-23
-2010-05-09
-2010-05-25
-2010-06-10
-2010-06-26
-2010-07-12
-2010-07-28
-2010-08-13
-2010-08-29
-2010-09-14
-2010-09-30
-2010-10-16
-2010-11-01
-2010-11-17
-2010-12-03
-2010-12-19
-2011-01-01
-2011-01-17
-2011-02-02
-2011-02-18
-2011-03-06
-2011-03-22
-2011-04-07
-2011-04-23
-2011-05-09
-2011-05-25
-2011-06-10
-2011-06-26
-2011-07-12
-2011-07-28
-2011-08-13
-2011-08-29
-2011-09-14
-2011-09-30
-2011-10-16
-2011-11-01
-2011-11-17
-2011-12-03
-2011-12-19
-2012-01-01
-2012-01-17
-2012-02-02
-2012-02-18
-2012-03-05
-2012-03-21
-2012-04-06
-2012-04-22
-2012-05-08
-2012-05-24
-2012-06-09
-2012-06-25
-2012-07-11
-2012-07-27
-2012-08-12
-2012-08-28
-2012-09-13
-2012-09-29
-2012-10-15
-2012-10-31
-2012-11-16
-2012-12-02
-2012-12-18
-2013-01-01
-2013-01-17
-2013-02-02
-2013-02-18
-2013-03-06
-2013-03-22
-2013-04-07
-2013-04-23
-2013-05-09
-2013-05-25
-2013-06-10
-2013-06-26
-2013-07-12
-2013-08-13
-2013-08-29
diff --git a/inst/lucc_MT/patterns/araguaia.csv b/inst/lucc_MT/patterns/araguaia.csv
deleted file mode 100644
index 5963c44..0000000
--- a/inst/lucc_MT/patterns/araguaia.csv
+++ /dev/null
@@ -1,45 +0,0 @@
-date,mir,blue,nir,red,evi,ndvi
-2003-08-29,0.14739560800314064,0.03375004786473038,0.16061986869675754,0.06745353123598899,0.1744863791459053,0.3993501997794543
-2003-09-06,0.157352603470522,0.03283999361940011,0.15760991443727204,0.06761722512983084,0.16909019940948328,0.3908705414590904
-2003-09-14,0.166365304317564,0.03228892645263741,0.15600700391187805,0.06796599367343695,0.165777889375191,0.38518743214636003
-2003-09-22,0.1734582725724762,0.03242836176730144,0.15714924688969617,0.0686589554917648,0.16657126021120217,0.3850443219951707
-2003-09-30,0.17779525989035463,0.03335403459044552,0.16163057855506222,0.06967886328863515,0.1725760599478699,0.3920953908862747
-2003-10-08,0.17870440189667308,0.035026416772141515,0.1695738045221412,0.07091837574100046,0.18426725424949741,0.40719887006264455
-2003-10-16,0.17608685024863752,0.037109866694218584,0.17995824907191404,0.07210093281851691,0.20047225236465455,0.4290373382994531
-2003-10-24,0.17013006507800504,0.03918608169908758,0.19136827279395896,0.07292006549863311,0.21938179028890187,0.4554032562772271
-2003-11-01,0.16154606808416627,0.04087412505004342,0.20217471990296168,0.07317250127345427,0.23855967868985076,0.4830280792289939
-2003-11-09,0.1512338960461685,0.041873144066829654,0.2108630020902044,0.07274613984144263,0.25555856025201784,0.5085005720149143
-2003-11-17,0.14021953314069602,0.042204927574101075,0.21676337555121764,0.07180918689659588,0.2687784419502823,0.5292618793865862
-2003-11-25,0.12950402634263466,0.042033045186983066,0.21965148893933137,0.07063862685959069,0.27715768558976833,0.5433908207714471
-2003-12-03,0.11978880032162414,0.041723439864045066,0.22018250307003648,0.06961945565349638,0.2809486495633471,0.5507410945514427
-2003-12-11,0.1115861441706858,0.04166675366768011,0.2192806618282189,0.06912008870845215,0.28092144850123757,0.5519489713213668
-2003-12-19,0.10494851334437523,0.04207862834271925,0.2178721202425613,0.06927526452532279,0.27839458457848393,0.5487837440398329
-2003-12-27,0.09972902208799964,0.043049174214916615,0.2167413269286631,0.07007865292829155,0.2747628298136268,0.5433268428879617
-2004-01-04,0.09550298845254201,0.044328907077615186,0.2160014402972491,0.07119573052188678,0.27098913190402385,0.5374967692659045
-2004-01-12,0.0917642532782145,0.04548986091984521,0.21530166748080942,0.07213989571118518,0.26756406342199635,0.532740288074907
-2004-01-20,0.08801521799953277,0.04598525039890601,0.21387231158483536,0.07234351303425546,0.26442081066111617,0.5298077556922617
-2004-01-28,0.08391386876877482,0.04540769401237883,0.2110775568888287,0.07139973233629925,0.2613606440408012,0.5290190919525677
-2004-02-05,0.07937434015443924,0.04360095182683821,0.2065748695420763,0.06918180451013763,0.25806176882809717,0.5301001495579897
-2004-02-13,0.07450057344826219,0.04065092921378689,0.20043627467576508,0.06580936955217019,0.254378579397443,0.532648027491538
-2004-02-21,0.06966689375763725,0.03700022154188866,0.19336685892795216,0.06176098048712692,0.25047533797265775,0.5361452241054652
-2004-02-29,0.06531495503354413,0.033171318896810895,0.18625024908997712,0.057588566654946514,0.2466781292493536,0.5401556138668995
-2004-03-08,0.061951413708870334,0.029757206808600157,0.18015518473798128,0.053903459859318714,0.24352563705864286,0.5443950725206345
-2004-03-16,0.05998092421547459,0.027191389840628393,0.17587807624881563,0.0511507298730356,0.2414642349843929,0.5486610116184384
-2004-03-24,0.05962786580424056,0.025631940616367834,0.17373382655777153,0.04949060628896217,0.2407514474109368,0.5528600117231096
-2004-04-01,0.060934981320364,0.02502931407661026,0.17364000799499646,0.04887022143567699,0.2414207282822007,0.5569214944244473
-2004-04-09,0.0636749767468329,0.025038481719891915,0.17494100053886008,0.04893365113014275,0.24316838413993908,0.5607952372878434
-2004-04-17,0.06750530445765626,0.025283589122385597,0.17689735557291725,0.04928881980244361,0.24560086162138384,0.5644413565528826
-2004-04-25,0.07193885743704981,0.025388340767777292,0.1787289661726774,0.04953572252460384,0.2482334205190675,0.5678333279597554
-2004-05-03,0.07652401889659972,0.025123786950867012,0.17990969657064151,0.04942245547914148,0.25067122875850845,0.5709203051114895
-2004-05-11,0.08089285332226885,0.024503818559083825,0.1803383460858965,0.048942656930490394,0.25268186615664284,0.5736069511722656
-2004-05-19,0.0848243279340348,0.023669082108743834,0.18013614753072504,0.04823050089515993,0.25410666903781187,0.5756679601621144
-2004-05-27,0.08832644259444705,0.022927565149191385,0.17971794456725929,0.04760996684017513,0.25487986252944683,0.5766693645222478
-2004-06-04,0.09154106927142404,0.022548973710341917,0.17942378032528716,0.04738633769974419,0.2548682601400938,0.5760070091751255
-2004-06-12,0.09479248965629977,0.022711410984285214,0.17942076954594297,0.0478017787147809,0.2538124568906927,0.5728324226775636
-2004-06-20,0.0984198696396643,0.023462461310021228,0.17966074341709878,0.04897223769212221,0.2513813984720936,0.566315060103165
-2004-06-28,0.1027556609381923,0.024650478844696903,0.1797715878616486,0.05081646083978328,0.24714983138853244,0.5556991999607237
-2004-07-06,0.10802579349893218,0.026074293497807193,0.17935598215516363,0.05318057685840513,0.24081307365893567,0.5405610759148308
-2004-07-14,0.11428102096408499,0.02748518394086073,0.1780312768437244,0.055824490845978,0.23228331147694947,0.5210151521251677
-2004-07-22,0.12144745459042529,0.028710461348427935,0.1756200556110752,0.058551267752860674,0.22171866188868067,0.4975866164133348
-2004-07-30,0.12927541277095625,0.029710329749120672,0.17227961390656615,0.06124927716924343,0.20964499520251292,0.47138565636258273
-2004-08-07,0.13750509358429996,0.03051311873502884,0.16828041463943977,0.06387106940984308,0.19663102641092445,0.4435262266159805
diff --git a/inst/lucc_MT/patterns/campo_cerrado.csv b/inst/lucc_MT/patterns/campo_cerrado.csv
deleted file mode 100644
index c7525fa..0000000
--- a/inst/lucc_MT/patterns/campo_cerrado.csv
+++ /dev/null
@@ -1,45 +0,0 @@
-date,mir,blue,nir,red,evi,ndvi
-2003-09-14,0.21413687134018405,0.048672053929892906,0.23336044543861054,0.1139625352737606,0.19341421281796733,0.3526594875417768
-2003-09-22,0.21362667186245604,0.04973961018982551,0.23980984777402398,0.11416749095994455,0.20381472473003215,0.36598947241844165
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diff --git a/inst/lucc_MT/patterns/cerradao.csv b/inst/lucc_MT/patterns/cerradao.csv
deleted file mode 100644
index 8e172e2..0000000
--- a/inst/lucc_MT/patterns/cerradao.csv
+++ /dev/null
@@ -1,45 +0,0 @@
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diff --git a/inst/lucc_MT/patterns/cerrado.csv b/inst/lucc_MT/patterns/cerrado.csv
deleted file mode 100644
index 6d48938..0000000
--- a/inst/lucc_MT/patterns/cerrado.csv
+++ /dev/null
@@ -1,45 +0,0 @@
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diff --git a/inst/lucc_MT/patterns/cerrado_rupestre.csv b/inst/lucc_MT/patterns/cerrado_rupestre.csv
deleted file mode 100644
index efa3ab2..0000000
--- a/inst/lucc_MT/patterns/cerrado_rupestre.csv
+++ /dev/null
@@ -1,45 +0,0 @@
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diff --git a/inst/lucc_MT/patterns/ciliary_forest.csv b/inst/lucc_MT/patterns/ciliary_forest.csv
deleted file mode 100644
index 4d10167..0000000
--- a/inst/lucc_MT/patterns/ciliary_forest.csv
+++ /dev/null
@@ -1,45 +0,0 @@
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diff --git a/inst/lucc_MT/patterns/dunas.csv b/inst/lucc_MT/patterns/dunas.csv
deleted file mode 100644
index 2f5a409..0000000
--- a/inst/lucc_MT/patterns/dunas.csv
+++ /dev/null
@@ -1,45 +0,0 @@
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diff --git a/inst/lucc_MT/patterns/fallow_cotton.csv b/inst/lucc_MT/patterns/fallow_cotton.csv
deleted file mode 100644
index 661d7f5..0000000
--- a/inst/lucc_MT/patterns/fallow_cotton.csv
+++ /dev/null
@@ -1,45 +0,0 @@
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diff --git a/inst/lucc_MT/patterns/millet_cotton.csv b/inst/lucc_MT/patterns/millet_cotton.csv
deleted file mode 100644
index 480f407..0000000
--- a/inst/lucc_MT/patterns/millet_cotton.csv
+++ /dev/null
@@ -1,45 +0,0 @@
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diff --git a/inst/lucc_MT/patterns/pasture.csv b/inst/lucc_MT/patterns/pasture.csv
deleted file mode 100644
index 81db957..0000000
--- a/inst/lucc_MT/patterns/pasture.csv
+++ /dev/null
@@ -1,45 +0,0 @@
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diff --git a/inst/lucc_MT/patterns/patt.rds b/inst/lucc_MT/patterns/patt.rds
deleted file mode 100644
index 8ac5dd4..0000000
Binary files a/inst/lucc_MT/patterns/patt.rds and /dev/null differ
diff --git a/inst/lucc_MT/patterns/soy_corn.csv b/inst/lucc_MT/patterns/soy_corn.csv
deleted file mode 100644
index 78b4e22..0000000
--- a/inst/lucc_MT/patterns/soy_corn.csv
+++ /dev/null
@@ -1,45 +0,0 @@
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diff --git a/inst/lucc_MT/patterns/soy_cotton.csv b/inst/lucc_MT/patterns/soy_cotton.csv
deleted file mode 100644
index ef26a5e..0000000
--- a/inst/lucc_MT/patterns/soy_cotton.csv
+++ /dev/null
@@ -1,45 +0,0 @@
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diff --git a/inst/lucc_MT/patterns/soy_fallow.csv b/inst/lucc_MT/patterns/soy_fallow.csv
deleted file mode 100644
index 7cb37b7..0000000
--- a/inst/lucc_MT/patterns/soy_fallow.csv
+++ /dev/null
@@ -1,45 +0,0 @@
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diff --git a/inst/lucc_MT/patterns/soy_millet.csv b/inst/lucc_MT/patterns/soy_millet.csv
deleted file mode 100644
index 3a8ef6b..0000000
--- a/inst/lucc_MT/patterns/soy_millet.csv
+++ /dev/null
@@ -1,45 +0,0 @@
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diff --git a/inst/mato_grosso_brazil/samples.gpkg b/inst/mato_grosso_brazil/samples.gpkg
index 2fa2e37..888cbee 100644
Binary files a/inst/mato_grosso_brazil/samples.gpkg and b/inst/mato_grosso_brazil/samples.gpkg differ
diff --git a/inst/reduce_time/patterns/cotton_MODIS13Q1.csv b/inst/reduce_time/patterns/cotton_MODIS13Q1.csv
deleted file mode 100644
index ad8d67e..0000000
--- a/inst/reduce_time/patterns/cotton_MODIS13Q1.csv
+++ /dev/null
@@ -1,24 +0,0 @@
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diff --git a/inst/reduce_time/patterns/maize_MODIS13Q1.csv b/inst/reduce_time/patterns/maize_MODIS13Q1.csv
deleted file mode 100644
index 0194889..0000000
--- a/inst/reduce_time/patterns/maize_MODIS13Q1.csv
+++ /dev/null
@@ -1,24 +0,0 @@
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diff --git a/inst/reduce_time/patterns/soybean_MODIS13Q1.csv b/inst/reduce_time/patterns/soybean_MODIS13Q1.csv
deleted file mode 100644
index ff318b1..0000000
--- a/inst/reduce_time/patterns/soybean_MODIS13Q1.csv
+++ /dev/null
@@ -1,24 +0,0 @@
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diff --git a/inst/reduce_time/ts_MODIS13Q1.csv b/inst/reduce_time/ts_MODIS13Q1.csv
deleted file mode 100644
index d2e8725..0000000
--- a/inst/reduce_time/ts_MODIS13Q1.csv
+++ /dev/null
@@ -1,94 +0,0 @@
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-2012-03-22,0.8541,0.6418,0.0322,0.4094,0.0178,0.0609
-2012-04-07,0.8614,0.6827,0.0335,0.45,0.0168,0.0536
-2012-04-23,0.7968,0.5966,0.0461,0.4078,0.0225,0.0694
-2012-05-09,0.8309,0.6055,0.0361,0.391,0.019,0.0607
-2012-06-01,0.7016,0.3898,0.0469,0.2675,0.0179,0.0842
-2012-06-10,0.528,0.2432,0.0602,0.1949,0.0229,0.127
-2012-07-03,0.3303,0.1096,0.0589,0.117,0.0194,0.1637
-2012-07-19,0.3243,0.1171,0.0675,0.1323,0.0206,0.1701
-2012-08-04,0.322,0.1365,0.08,0.156,0.0326,0.1939
-2012-08-20,0.2579,0.1838,0.2229,0.3779,0.081,0.3034
-2012-09-05,0.2489,0.184,0.2055,0.3417,0.0966,0.2673
-2012-09-14,0.2675,0.2052,0.2092,0.362,0.1008,0.3167
-2012-10-06,0.3047,0.227,0.1476,0.277,0.0777,0.2942
-2012-10-29,0.3206,0.2337,0.1398,0.2718,0.0722,0.2951
-2012-11-01,0.276,0.2122,0.1608,0.2834,0.0726,0.3471
-2012-11-30,0.1941,0.193,0.2661,0.3943,0.2402,0.1671
-2012-12-07,0.5567,0.4362,0.1012,0.3554,0.0831,0.209
-2012-12-24,0.8884,0.7403,0.0287,0.486,0.0152,0.0868
-2013-01-09,0.9436,0.7865,0.0147,0.5075,0.0039,0.0715
-2013-01-17,0.942,0.9185,0.0185,0.6205,0.0124,0.108
-2013-02-15,0.5129,0.2975,0.0736,0.2286,0.0589,0.1131
-2013-02-19,0.3488,0.1924,0.0922,0.191,0.0337,0.2226
-2013-03-12,0.3518,0.1964,0.0932,0.1944,0.0439,0.1962
-2013-04-01,0.8148,0.6866,0.0445,0.4363,0.0369,0.0766
-2013-04-17,0.8964,0.6713,0.0226,0.4137,0.0124,0.0495
-2013-05-08,0.8669,0.5752,0.0244,0.3425,0.0142,0.0599
-2013-05-19,0.8378,0.5342,0.0285,0.3231,0.0154,0.0654
-2013-05-26,0.8295,0.5434,0.0307,0.3296,0.0185,0.0646
-2013-06-13,0.7172,0.4156,0.0457,0.2775,0.021,0.1003
-2013-06-29,0.3458,0.122,0.0627,0.129,0.0196,0.1603
-2013-07-15,0.3208,0.1163,0.0671,0.1305,0.0227,0.1745
-2013-07-31,0.317,0.1309,0.0827,0.1595,0.0253,0.2039
diff --git a/man/create_patterns.Rd b/man/create_patterns.Rd
deleted file mode 100644
index 0ff9bd5..0000000
--- a/man/create_patterns.Rd
+++ /dev/null
@@ -1,41 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/create_patterns.R
-\name{create_patterns}
-\alias{create_patterns}
-\title{Create a Pattern Using GAM}
-\usage{
-create_patterns(
-  x,
-  y,
-  formula = band ~ s(time),
-  start_column = "start_date",
-  end_column = "end_date",
-  label_colum = "label",
-  sampling_freq = NULL,
-  ...
-)
-}
-\arguments{
-\item{x}{A three dimensions stars object (x, y, time) with the satellite image time series.}
-
-\item{y}{An sf object with the coordinates of the training points.}
-
-\item{formula}{A formula for the GAM. Default is \code{band ~ \link[mgcv]{s}(time)}.}
-
-\item{start_column}{Name of the column in y that indicates the start date. Default is 'start_date'.}
-
-\item{end_column}{Name of the column in y that indicates the end date. Default is 'end_date'.}
-
-\item{label_colum}{Name of the column in y that contains land use labels. Default is 'label'.}
-
-\item{sampling_freq}{The time frequency for sampling including unit, e.g '16 day'. If NULL, the function will infer it.}
-
-\item{...}{Additional arguments passed to the GAM function.}
-}
-\value{
-A list containing the predicted values for each label.
-}
-\description{
-This function creates a pattern based on Generalized Additive Models (GAM).
-It uses the specified formula to fit the model and predict values.
-}
diff --git a/man/extract_time_series.Rd b/man/extract_time_series.Rd
deleted file mode 100644
index c4aebe3..0000000
--- a/man/extract_time_series.Rd
+++ /dev/null
@@ -1,21 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/create_patterns.R
-\name{extract_time_series}
-\alias{extract_time_series}
-\title{Extract Time Series from a Stars Object for Specified Points}
-\usage{
-extract_time_series(x, y)
-}
-\arguments{
-\item{x}{A stars object containing the raster time series data.}
-
-\item{y}{An sf object containing the point geometries and their associated labels.}
-}
-\value{
-A data.frame with the extracted time series for each point in the sf object,
-with additional columns for the ID and label of each sample.
-}
-\description{
-This function extracts a time series from a stars object for each specified point in the sf object.
-Each extracted sample is then labeled with an ID and the label from the sf object.
-}
diff --git a/man/get_stars_time_freq.Rd b/man/get_stars_time_freq.Rd
deleted file mode 100644
index 47d90c4..0000000
--- a/man/get_stars_time_freq.Rd
+++ /dev/null
@@ -1,18 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/create_patterns.R
-\name{get_stars_time_freq}
-\alias{get_stars_time_freq}
-\title{Compute the Most Common Sampling Frequency in a Stars Object}
-\usage{
-get_stars_time_freq(x)
-}
-\arguments{
-\item{x}{A stars object containing time series data.}
-}
-\value{
-A difftime object representing the most common time difference between consecutive samples.
-}
-\description{
-This function calculates the most common difference between consecutive time points in a stars object.
-This can be useful for determining the sampling frequency of the time series data.
-}
diff --git a/man/get_time_series_freq.Rd b/man/get_time_series_freq.Rd
new file mode 100644
index 0000000..02dea06
--- /dev/null
+++ b/man/get_time_series_freq.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/train.R
+\name{get_time_series_freq}
+\alias{get_time_series_freq}
+\title{Compute the Most Common Sampling Frequency across all observations}
+\usage{
+get_time_series_freq(x)
+}
+\arguments{
+\item{x}{A data frame including a column called `observations`` with the time series}
+}
+\value{
+A difftime object representing the most common time difference between consecutive samples.
+}
+\description{
+This function calculates the most common difference between consecutive time points.
+This can be useful for determining the aproximate sampling frequency of the time series data.
+}
diff --git a/man/plot.twdtw_knn1.Rd b/man/plot.twdtw_knn1.Rd
new file mode 100644
index 0000000..866d3d7
--- /dev/null
+++ b/man/plot.twdtw_knn1.Rd
@@ -0,0 +1,75 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plot.R
+\name{plot.twdtw_knn1}
+\alias{plot.twdtw_knn1}
+\title{Plot Patterns from twdtw-knn1 model}
+\usage{
+\method{plot}{twdtw_knn1}(x, bands = NULL, ...)
+}
+\arguments{
+\item{x}{A model of class \code{"twdtw_knn1"}.}
+
+\item{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.}
+
+\item{...}{Additional arguments passed to \code{\link[ggplot2]{ggplot}}. Currently not used.}
+}
+\value{
+A \code{\link[ggplot2]{ggplot}} object displaying the time series patterns.
+}
+\description{
+This function visualizes time series patterns from the \code{"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.
+}
+\examples{
+\dontrun{
+
+# Read training samples
+samples <-
+  system.file("mato_grosso_brazil/samples.gpkg", package = "dtwSat") |>
+  st_read(quiet = TRUE)
+
+# Get satellite image time sereis files
+tif_files <- system.file("mato_grosso_brazil", package = "dtwSat") |>
+  dir(pattern = "\\\\.tif$", full.names = TRUE)
+
+# Get acquisition dates
+acquisition_date <- regmatches(tif_files, regexpr("[0-9]{8}", tif_files)) |>
+  as.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)) |>
+      st_set_dimensions(3, c("EVI", "NDVI", "RED", "BLUE", "NIR", "MIR")) |>
+      split(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()
+
+}
+}
+\seealso{
+twdtw_knn1
+}
diff --git a/man/plot_patterns.Rd b/man/plot_patterns.Rd
deleted file mode 100644
index 97e24d6..0000000
--- a/man/plot_patterns.Rd
+++ /dev/null
@@ -1,26 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/plot_patterns.R
-\name{plot_patterns}
-\alias{plot_patterns}
-\title{Plot Patterns from Time Series Data}
-\usage{
-plot_patterns(x, ...)
-}
-\arguments{
-\item{x}{A list where each element is a data.frame representing a time series.
-Each data.frame should have the same number of rows and columns,
-with columns representing different attributes (e.g., bands or indices)
-and rows representing time points.
-The name of each element in the list will be used as the facet title.}
-
-\item{...}{Not used.}
-}
-\value{
-A ggplot object displaying the time series patterns.
-}
-\description{
-This function takes a list of time series data and creates a multi-faceted plot
-where each facet corresponds to a different time series from the list.
-Within each facet, different attributes (columns of the time series) are
-plotted as lines with different colors.
-}
diff --git a/man/predict.twdtw_knn1.Rd b/man/predict.twdtw_knn1.Rd
new file mode 100644
index 0000000..d3d0083
--- /dev/null
+++ b/man/predict.twdtw_knn1.Rd
@@ -0,0 +1,75 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/predict.R
+\name{predict.twdtw_knn1}
+\alias{predict.twdtw_knn1}
+\title{Predict using the twdtw_knn1 model}
+\usage{
+\method{predict}{twdtw_knn1}(object, newdata, ...)
+}
+\arguments{
+\item{object}{A \code{twdtw_knn1} model object generated by the \code{twdtw_knn1} function.}
+
+\item{newdata}{A data frame or similar object containing the new observations
+(time series data) to be predicted.}
+
+\item{...}{Additional arguments passed to the \link[twdtw]{twdtw} function.
+If provided, they will overwrite twdtw arguments previously passed to \link[dtwSat]{twdtw_knn1}.}
+}
+\value{
+A vector of predicted classes for the \code{newdata}.
+}
+\description{
+This function predicts the classes of new data using the Time Warped Dynamic Time Warping (TWDTW)
+method with a 1-nearest neighbor approach. The prediction is based on the minimum TWDTW distance
+to the known patterns stored in the \code{twdtw_knn1} model.
+}
+\examples{
+\dontrun{
+
+# Read training samples
+samples <-
+  system.file("mato_grosso_brazil/samples.gpkg", package = "dtwSat") |>
+  st_read(quiet = TRUE)
+
+# Get satellite image time sereis files
+tif_files <- system.file("mato_grosso_brazil", package = "dtwSat") |>
+  dir(pattern = "\\\\.tif$", full.names = TRUE)
+
+# Get acquisition dates
+acquisition_date <- regmatches(tif_files, regexpr("[0-9]{8}", tif_files)) |>
+  as.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)) |>
+      st_set_dimensions(3, c("EVI", "NDVI", "RED", "BLUE", "NIR", "MIR")) |>
+      split(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()
+
+}
+}
+\seealso{
+twdtw_knn1
+}
diff --git a/man/prepare_time_series.Rd b/man/prepare_time_series.Rd
new file mode 100644
index 0000000..dfce70e
--- /dev/null
+++ b/man/prepare_time_series.Rd
@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/prepare_time_series.R
+\name{prepare_time_series}
+\alias{prepare_time_series}
+\title{Prepare a Time Series Tibble from a 2D stars Object with Bands and Time Attributes}
+\usage{
+prepare_time_series(x)
+}
+\arguments{
+\item{x}{A data frame derived from a stars object containing time series data in wide format.
+The column names should adhere to one of the following formats: "band.YYYY.MM.DD", "XYYYY.MM.DD.band", or "YYYY.MM.DD.band".}
+}
+\value{
+A nested tibble in wide format. Each row of the tibble corresponds to a unique 'ts_id' that maintains the order from the original stars object.
+The nested structure contains observations (time series) for each 'ts_id', including the 'time' of each observation, and individual bands are presented as separate columns.
+}
+\description{
+This function reshapes a data frame, which has been converted from a stars object, into a nested wide tibble format.
+The stars object conversion often results in columns named in formats like "band.YYYY.MM.DD", "XYYYY.MM.DD.band", or "YYYY.MM.DD.band".
+}
diff --git a/man/pretty_arguments.Rd b/man/pretty_arguments.Rd
new file mode 100644
index 0000000..a620918
--- /dev/null
+++ b/man/pretty_arguments.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/train.R
+\name{pretty_arguments}
+\alias{pretty_arguments}
+\title{Print Pretty Arguments}
+\usage{
+pretty_arguments(args)
+}
+\arguments{
+\item{args}{A list of named arguments to display.}
+}
+\value{
+Invisible NULL. The function is mainly used for its side effect of printing.
+}
+\description{
+Display a list of arguments of a given function in a human-readable format.
+}
+\examples{
+\dontrun{
+pretty_arguments(formals(twdtw_knn1))
+}
+
+}
diff --git a/man/print.twdtw_knn1.Rd b/man/print.twdtw_knn1.Rd
new file mode 100644
index 0000000..c909f7a
--- /dev/null
+++ b/man/print.twdtw_knn1.Rd
@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/train.R
+\name{print.twdtw_knn1}
+\alias{print.twdtw_knn1}
+\title{Print method for objects of class twdtw_knn1}
+\usage{
+\method{print}{twdtw_knn1}(x, ...)
+}
+\arguments{
+\item{x}{An object of class \code{twdtw_knn1}.}
+
+\item{...}{ignored}
+}
+\value{
+Invisible \code{twdtw_knn1} object.
+}
+\description{
+This method provides a structured printout of the important components
+of a \code{twdtw_knn1} object.
+}
diff --git a/man/shift_dates.Rd b/man/shift_dates.Rd
index 87d58c1..9af6b7e 100644
--- a/man/shift_dates.Rd
+++ b/man/shift_dates.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/utils.R
+% Please edit documentation in R/shift_dates.R
 \name{shift_dates}
 \alias{shift_dates}
 \title{Shift Dates to Start on a Specified Origin Year}
diff --git a/man/twdtw_knn1.Rd b/man/twdtw_knn1.Rd
new file mode 100644
index 0000000..569963e
--- /dev/null
+++ b/man/twdtw_knn1.Rd
@@ -0,0 +1,105 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/train.R
+\name{twdtw_knn1}
+\alias{twdtw_knn1}
+\title{Train a KNN-1 TWDTW model with optional GAM resampling}
+\usage{
+twdtw_knn1(
+  x,
+  y,
+  time_weight,
+  cycle_length,
+  time_scale,
+  formula = NULL,
+  start_column = "start_date",
+  end_column = "end_date",
+  label_colum = "label",
+  sampling_freq = NULL,
+  ...
+)
+}
+\arguments{
+\item{x}{A three-dimensional stars object (x, y, time) with bands as attributes.}
+
+\item{y}{An sf object with the coordinates of the training points.}
+
+\item{time_weight}{A numeric vector with length two (steepness and midpoint of logistic weight) or a function.
+See details in \link[twdtw]{twdtw}.}
+
+\item{cycle_length}{The length of the cycle, e.g. phenological cycles. Details in \link[twdtw]{twdtw}.}
+
+\item{time_scale}{Specifies the time scale for the observations. Details in \link[twdtw]{twdtw}.}
+
+\item{formula}{Either NULL or a formula to reduce samples of the same label using Generalized Additive Models (GAM).
+Default is \code{band ~ s(time)}. See details.}
+
+\item{start_column}{Name of the column in y that indicates the start date. Default is 'start_date'.}
+
+\item{end_column}{Name of the column in y that indicates the end date. Default is 'end_date'.}
+
+\item{label_colum}{Name of the column in y containing land use labels. Default is 'label'.}
+
+\item{sampling_freq}{The time frequency for sampling, including the unit (e.g., '16 day').
+If NULL, the function will infer the frequency. This parameter is only used if a formula is provided.}
+
+\item{...}{Additional arguments passed to the \link[mgcv]{gam} function and to \link[twdtw]{twdtw} function.}
+}
+\value{
+A 'twdtw_knn1' model containing the trained model information and the data used.
+}
+\description{
+This function prepares a KNN-1 model with the Time Warp Dynamic Time Warping (TWDTW) algorithm.
+If a formula is provided, the training samples are resampled using Generalized Additive Models (GAM).
+}
+\details{
+If \code{formula} is NULL, the KNN-1 model will retain all training samples. If a formula is passed (e.g., \code{band ~ \link[mgcv]{s}(time)}),
+then samples of the same label (land cover class) will be resampled using GAM.
+Resampling can significantly reduce prediction processing time.
+}
+\examples{
+\dontrun{
+
+# Read training samples
+samples <-
+  system.file("mato_grosso_brazil/samples.gpkg", package = "dtwSat") |>
+  st_read(quiet = TRUE)
+
+# Get satellite image time sereis files
+tif_files <- system.file("mato_grosso_brazil", package = "dtwSat") |>
+  dir(pattern = "\\\\.tif$", full.names = TRUE)
+
+# Get acquisition dates
+acquisition_date <- regmatches(tif_files, regexpr("[0-9]{8}", tif_files)) |>
+  as.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)) |>
+      st_set_dimensions(3, c("EVI", "NDVI", "RED", "BLUE", "NIR", "MIR")) |>
+      split(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()
+
+}
+}
diff --git a/tests/testthat/test-twdtw_classify.R b/tests/testthat/test-twdtw_classify.R
index f5c0c04..8af1da7 100644
--- a/tests/testthat/test-twdtw_classify.R
+++ b/tests/testthat/test-twdtw_classify.R
@@ -1,27 +1,89 @@
-library(stars)
-library(stringr)
-
 # Read training samples
-samples <- st_read(system.file("mato_grosso_brazil/samples.gpkg", package = "dtwSat"))
+samples <- system.file("mato_grosso_brazil/samples.gpkg", package = "dtwSat") |>
+  st_read(quiet = TRUE)
 
 # Satellite image time sereis files
 tif_files <- system.file("mato_grosso_brazil", package = "dtwSat") |>
   dir(pattern = "\\.tif$", full.names = TRUE)
 
-# The acquisition date is in the file name are not the true acquisition date of each pixel
-# MOD13Q1 is a 16-day composite product, so the acquisition date is the first day of the 16-day period
-acquisition_date <- as.Date(str_extract(tif_files, "[0-9]{8}"), format = "%Y%m%d")
+# The acquisition date is in the file name are not the true acquisition date
+# of each pixel. MOD13Q1 is a 16-day composite product, so the acquisition date
+# is the first day of the 16-day period
+acquisition_date <- regmatches(tif_files, regexpr("[0-9]{8}", tif_files)) |>
+  as.Date(format = "%Y%m%d")
+
+# Read the data as a stars object setting the time/date for each observation
+# using along. This will prodcue a 4D array (data-cube) which will then be converted
+# to a 3D array by spliting the 'band' dimension
+dc <- read_stars(tif_files,
+                 proxy = FALSE,
+                 along = list(time = acquisition_date),
+                 RasterIO = list(bands = 1:6)) |>
+  st_set_dimensions(3, c("EVI", "NDVI", "RED", "BLUE", "NIR", "MIR")) |>
+  split(c("band"))
+
+# Create a knn1-twdtw model
+system.time(
+  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 image time series
+system.time(lu <- predict(dc, model = m))
+
+# Visualise land use classification
+ggplot() +
+  geom_stars(data = lu) +
+  theme_minimal()
+
+### OTHER TESTS
+# split time first
+dc <- read_stars(tif_files,
+                 proxy = FALSE,
+                 along = list(time = acquisition_date),
+                 RasterIO = list(bands = 1:6)) |>
+  st_set_dimensions(3, c("EVI", "NDVI", "RED", "BLUE", "NIR", "MIR")) |>
+  split(c("band"))
+
+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),
+                sampling_freq = 60)
+
+plot(m)
+
+system.time(
+  lu <- predict(dc,
+                model = m,
+                cycle_length = 'year',
+                time_scale = 'day',
+                time_weight = c(steepness = 0.1, midpoint = 50))
+)
 
-# Read the data as a stars object, setting time as a dimension and band as attribute
-dc <- read_stars(tif_files, proxy = FALSE, along = list(time = acquisition_date)) |>
-  st_set_dimensions(3, c("EVI", "NDVI", "RED", "BLUE", "NIR", "MIR", "DOY")) |>
-  split("band")
+# Visualise land use classification
+ggplot() +
+  geom_stars(data = lu) +
+  theme_minimal()
 
-# Remove the DOY band - this will be supported int the future
-dc <- dc[c("EVI", "NDVI", "RED", "BLUE", "NIR", "MIR")]
+# Test model without samples reduction
+m <- twdtw_knn1(x = dc,
+                y = samples,
+                cycle_length = 'year',
+                time_scale = 'day',
+                time_weight = c(steepness = 0.1, midpoint = 50))
 
-# Get temporal patters
-ts_patterns <- create_patterns(x = dc, y = samples)
+plot(m)
 
-# Visualize patterns
-plot_patterns(ts_patterns)
+plot(m, bands = c('EVI', 'NDVI'))
diff --git a/vignettes.awk b/vignettes.awk
new file mode 100644
index 0000000..0517265
--- /dev/null
+++ b/vignettes.awk
@@ -0,0 +1,13 @@
+{
+	if (NR == 4) {
+		print("output: rmarkdown::html_vignette")
+	} else if (NR > 4 && NR <= 10) {
+	} else if (NR == 16) {
+		print("\n**For a better version of the dtwSat vignettes see** https://vwmaus.github.io/dtwSat/articles/\n")
+	} else if (NR == 19) {
+		print
+		print("knitr::opts_chunk$set(fig.height = 4.5)")
+		print("knitr::opts_chunk$set(fig.width = 6)")
+	} else
+		print
+}
diff --git a/vignettes/landuse-mapping.Rmd b/vignettes/landuse-mapping.Rmd
new file mode 100644
index 0000000..66d8af0
--- /dev/null
+++ b/vignettes/landuse-mapping.Rmd
@@ -0,0 +1,137 @@
+---
+title: "1. Land use mapping using TWDTW-1NN and stars"
+author: "Victor Maus"
+output:
+  html_document:
+    toc: true
+    toc_float:
+      collapsed: false
+      smooth_scroll: false
+    toc_depth: 2
+vignette: >
+  %\VignetteIndexEntry{1. Land use mapping using TWDTW-1NN and stars}
+  %\VignetteEncoding{UTF-8}
+  %\VignetteEngine{knitr::rmarkdown}
+bibliography: ./../inst/REFERENCES.bib
+---
+
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png")
+```
+
+This vignette offers a concise guide for using version 1.0.0 or higher of the `dtwSat` package to generate a land-use map.
+The package utilizes Time-Weighted Dynamic Time Warping (TWDTW) along with a 1-Nearest Neighbor (1-NN) classifier.
+The subsequent sections will walk you through the process of creating a land-use map based on a set of training samples 
+and a multi-band satellite image time series.
+
+## Reading Training Samples
+
+First, let's read a set of training samples that come with the `dtwSat` package installation.
+
+```{r , echo = TRUE, eval = TRUE, warning = FALSE, message = FALSE}
+library(dtwSat)
+
+samples <- system.file("mato_grosso_brazil/samples.gpkg", package = "dtwSat") |>
+  st_read(quiet = TRUE)
+```
+
+## Preparing the Satellite Image Time Series
+
+The `dtwSat` package supports satellite images read into R using the `stars` package.
+The instalation comes with a set of MOD13Q1 images for a region within the Brazilian Amazon.
+Note that timing is crucial for the TWDTW distance metric. To create a consistent image time series, 
+we start by extracting the date of acquisition from the MODIS file names [@Didan:2015].
+
+```{r , echo = TRUE, eval = TRUE, warning = FALSE, message = FALSE}
+tif_files <- system.file("mato_grosso_brazil", package = "dtwSat") |>
+  dir(pattern = "\\.tif$", full.names = TRUE)
+
+acquisition_date <- regmatches(tif_files, regexpr("[0-9]{8}", tif_files)) |>
+  as.Date(format = "%Y%m%d")
+
+print(acquisition_date)
+```
+
+*Side note:* The date in the file name is not the true acquisition date for each pixel.
+MOD13Q1 is a 16-day composite product, and the date in the file name is the first day of this 16-day period.
+
+With the files and dates in hand, we can construct a stars satellite image time series for `dtwSat`.
+
+```{r , echo = TRUE, eval = TRUE, warning = FALSE, message = FALSE}
+dc <- read_stars(tif_files,
+                 proxy = FALSE,
+                 along = list(time = acquisition_date),
+                 RasterIO = list(bands = 1:6)) |>
+  st_set_dimensions(3, c("EVI", "NDVI", "RED", "BLUE", "NIR", "MIR")) |>
+  split(c("band"))
+
+print(dc)
+```
+
+Note that it's important to set the date for each observation using the parameter `along`.
+This will produce a 4D array (data-cube) that will be collapsed into a 3D array by converting 
+the 'band' dimension into attributes. This prepares the data for training the TWDTW-1NN model.
+
+## Create TWDTW-KNN1 model
+
+```{r , echo = TRUE, eval = TRUE, warning = FALSE, message = FALSE}
+twdtw_model <- 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(twdtw_model)
+```
+
+In addition to the mandatory arguments `x` (satellite data-cube) and `y` (training samples), 
+the TWDTW distance calculation also requires setting `cycle_length`, `time_scale`, and `time_weight`.
+For more details, refer to the documentation using `?twdtw`. The argument `formula = band ~ s(time)` is optional.
+If provided, training samples time sereis are resampled using Generalized Additive Models (GAMs), 
+collapsing all samples with the same land-use label into a single sample. This reduces computational 
+demands. The sample in the model can be visualized as follows:
+
+```{r , echo = TRUE, eval = TRUE, warning = FALSE, message = FALSE}
+plot(twdtw_model)
+```
+
+
+## Land Use Prediction
+
+Finally, we predict the land-use classes for each pixel location in the data-cube:
+
+```{r , echo = TRUE, eval = TRUE, warning = FALSE, message = FALSE}
+lu_map <- predict(dc, model = twdtw_model)
+print(lu_map)
+```
+
+The 'time' dimension was reduced to a single map. We can now visualize it using `ggplot`:
+
+```{r , echo = TRUE, eval = TRUE, warning = FALSE, message = FALSE}
+ggplot() +
+  geom_stars(data = lu_map) +
+  theme_minimal()
+```
+
+Note that some pixels (in a 3x3 box) in the northeast part of the map have `NA` values due 
+to a missing value in the blue band recorded on 2011-11-17. This limitation will be addressed 
+in future versions of the dtwSat package.
+
+Ultimately, we can write the map to a TIFF file we can use:
+
+```{r , echo = TRUE, eval = FALSE, warning = FALSE, message = FALSE}
+write_stars(lu_map, "lu_map.tif")
+```
+
+## Further Reading
+
+This introduction outlined the use of `dtwSat` for land-use mapping. 
+For more in-depth information, refer to the papers by @Maus:2016 and @Maus:2019 and the 
+[`twdtw` R package documentation](https://cran.r-project.org/web/packages/twdtw).
+
+For additional details on how to manage input and output satellite images, 
+[`check` the stars documentation](https://cran.r-project.org/web/packages/stars).
+
+## References