Title:	Quantitative Analysis and Visualization of LUCC
Version:	1.0.3
Description:	Tools for the analysis of land use and cover (LUC) time series. It includes support for loading spatiotemporal raster data and synthesized spatial plotting. Several LUC change (LUCC) metrics in regular or irregular time intervals can be extracted and visualized through one- and multistep sankey and chord diagrams. A complete intensity analysis according to Aldwaik and Pontius (2012) <doi:10.1016/j.landurbplan.2012.02.010> is implemented, including tools for the generation of standardized multilevel output graphics.
License:	GPL-3
URL:	https://reginalexavier.github.io/OpenLand/, https://github.com/reginalexavier/OpenLand
BugReports:	https://github.com/reginalexavier/OpenLand/issues
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.1
Depends:	R (≥ 3.4.0)
Imports:	dplyr (≥ 0.8.3), tidyr (≥ 1.0.0), ggplot2 (≥ 3.2.1), gridExtra (≥ 2.3), grid, circlize (≥ 0.4.8), networkD3 (≥ 0.4), raster (≥ 3.0.7), methods
Collate:	'OpenLand-package.R' 'rasters_input.R' 'demolandscape.R' 'contingencyTable.R' 'data.R' 'generalfunctions.R' 'intensityClass.R' 'generic_method.R' 'intensityAnalysis.R' 'plotMethods.R' 'otherplots.R'
Suggests:	tmap, knitr, rmarkdown, covr, testthat
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2024-05-03 13:06:15 UTC; tredgi
Author:	Reginal Exavier [aut, cre], Peter Zeilhofer [aut]
Maintainer:	Reginal Exavier <reginalexavier@rocketmail.com>
Repository:	CRAN
Date/Publication:	2024-05-03 13:40:02 UTC

OpenLand: land use and cover (LUC) time series analysis in R.

Description

OpenLand is an open-source R package for the analysis of land use and cover (LUC) time series. It includes support for consistency check and loading spatiotemporal raster data and synthesized spatial plotting. Several LUC change (LUCC) metrics in regular or irregular time intervals can be extracted and visualized through one- and multistep sankey and chord diagrams. A complete intensity analysis according to (Aldwaik and Pontius, 2012) is implemented, including tools for the generation of standardized multilevel output graphics.

Author(s)

Reginal Exavier reginalexavier@rocketmail.com, Peter Zeilhofer zeilhoferpeter@gmail.com

References

Aldwaik, S. Z. and Pontius, R. G. (2012) ‘Intensity analysis to unify measurements of size and stationarity of land changes by interval, category, and transition, Landscape and Urban Planning. Elsevier B.V., 106(1), pp. 103–114. doi:10.1016/j.landurbplan.2012.02.010.

See Also

The core functions in this package: intensityAnalysis, contingencyTable,

Accessor method for objects from Intensity Analysis

Description

Accessor method for objects from Intensity Analysis

Usage

## S4 method for signature 'Interval'
x$name

## S4 method for signature 'Category'
x$name

## S4 method for signature 'Transition'
x$name

Arguments

Create Raster with Random pixel Value

Description

This function creates a raster series with some setup like the layer name and the sample value for the lulc

Usage

.demo_landscape(
  year,
  nrows = 100,
  ncols = 100,
  res = 1,
  xmn = 0,
  xmx = 100,
  ymn = 0,
  ymx = 100,
  crs = NA,
  category = 1:5,
  prob = NULL
)

Arguments

Value

list

See Also

raster

Examples

.demo_landscape(year =  2000:2005,
                res = 1,
                crs = "+proj=utm +zone=21 +south +ellps=GRS80 +units=m +no_defs")

input_rasters

Description

A methods for loading the raster into OpenLand

Usage

.input_rasters(x, ...)

## S4 method for signature 'character'
.input_rasters(x, ...)

## S4 method for signature 'list'
.input_rasters(x, ...)

## S4 method for signature 'RasterLayer'
.input_rasters(x, ...)

## S4 method for signature 'RasterBrick'
.input_rasters(x, ...)

## S4 method for signature 'RasterStack'
.input_rasters(x, ...)

Arguments

Value

A RasterStack

See Also

raster::raster, raster::stack

Class Category

Description

A S4 class for the Category level result of an Intensity analysis. Can be plotted with the plot method plot.

Details

The slots categoryData and categoryStationarity can receive tables for "Gain" or "Loss" in the following format:

1. Gain

   • categoryData: <tibble>. A table containing 6 columns:

     1. Period: <fct>. The period [Yt, Yt+1].

     2. To: <fct>. A LUC category j.

     3. Interval: <int>. Duration of the period [Yt, Yt+1] in years.

     4. GG_km2/GG_pixel: <dbl>/<int>. Area of gross gain of category j during [Yt, Yt+1].

     5. Gtj: <dbl>. Annual intensity of gross gain of category j for time interval [Yt, Yt+1].

     6. St: <dbl>. Annual intensity of change for time interval [Yt, Yt+1].

   • categoryStationarity: <tibble>. A table with the results of a stationarity test of the gain of the categories on the Category level, containing 5 columns:

     1. To: <fct>. A category of interest j.

     2. gain: <int>. Number of times a category had gains during all time intervals [Y1, YT].

     3. N: <int>. Total number of evaluated time points (T).

     4. Stationarity: <chr>. Active Gain or Dormant Gain.

     5. Test: <chr>. Y if stationarity was detected and N if not.

2. Loss

   • categoryData: <tibble>. A table containing 6 columns:

     1. Period: <fct>. The period [Yt, Yt+1].

     2. From: <fct>. A LUC category i.

     3. Interval: <int>. Duration of the period [Yt, Yt+1] in years.

     4. GG_km2/GG_pixel: <dbl>/<int>. Area of gross loss of category i during [Yt, Yt+1].

     5. Lti: <dbl>. Annual intensity of gross loss of category i for time interval [Yt, Yt+1].

     6. STt: <dbl>. Annual intensity of change for time interval [Yt, Yt+1].

   • categoryStationarity: <tibble>. A table of stationarity test over the loss of the categories in the Category level, containing 5 columns:

     1. From: <fct>. A category of interest i.

     2. loss: <int>. Number of times a category had losses during all time intervals [Y1, YT].

     3. N: <int>. Total number of evaluated time points (T).

     4. Stationarity: <chr>. Active Loss or Dormant Loss.

     5. Test: <chr>. Y if stationarity was detected and N if not.

Slots

lookupcolor

The colors (character vector) associated with the LUC legend items.

categoryData

tibble. A table of Category level's results (gain (Gtj) or loss (Lti) values).

categoryStationarity

tibble. A table containing results of a stationarity test. A change is considered stationary only if the intensities for all time intervals reside on one side of the uniform intensity, i.e are smaller or bigger than the uniform rate over the whole period.

Class Interval

Description

A S4 class for the Interval level result of an Intensity analysis. Can be plotted with the plot method plot.

Details

The slot intervalData receives a table containing 4 columns in the following format:

1. Period: <fct>. The period of interest [Yt, Yt+1].

2. PercentChange: <dbl>. Changed area on the Interval level (%).

3. St: <dbl>. Annual intensity of change for a time period [Yt, Yt+1].

4. U: <dbl>. Uniform intensity for a LUC category change in a time period of interest.

Slots

intervalData

tibble. A table with the results of an Intensity analysis at the Interval level (St and U values).

Tables of land use and cover (LUC) in the São Lourenço River Basin (2002 - 2014)

Description

A list containing five objects created by the contingencyTable function with SaoLourencoBasin as input (SL_2002_2014 <- contingenceTable(input_raster = SaoLourencoBasin, pixelresolution = 30)).

Usage

data(SL_2002_2014)

Format

A data list with 5 objects:

lulc_Multistep

<tibble> Contingency table for all analysed time steps, containing 8 columns:

1. Period: <chr> The period [Yt, Yt+1].

2. From: <int> numerical code of a LUC category i.

3. To: <int> numerical code of a LUC category j.

4. km2: <dbl> Area in square kilometers that transited from the category i to category j in the period from Yt to Yt+1.

5. QtPixel: <int> Pixel count that transited from the categories i to category j in the period from Yt to Yt+1.

6. Interval: <int> Interval of years between the first and the last year of the period [Yt, Yt+1].

7. yearFrom: <int> The year that the change comes from [Yt]

8. yearTo: <int> The year that the change goes for [Yt+1]

lulc_Onstep

<tibble> Contingency table for the entire analysed period [Yt1, YT], containing 8 columns identical with lulc_Mulstistep

.

tb_legend

<tibble> A table of the pixel value, his name and color containing 3 columns:

1. categoryValue: <int> the pixel value of the LUC category.

2. categoryName: <fct> randomly created string associated with a given pixel value of a LUC category.

3. color: <chr> random color associated with the given pixel value of a LUC category.

totalArea

<tibble> A table with the total area of the study area containing 2 columns:

1. area_km2: <dbl> The total area in square kilometers.

2. QtPixel: <int> The total area in pixel counts

.

totalInterval

<int> Total interval of the analysed time series in years

.

Source

https://www.embrapa.br/pantanal/bacia-do-alto-paraguai

Class Transition

Description

A S4 class for the Transition level result of an Intensity analysis. Can be plotted with the plot method plot.

Details

The slots transitionData and transitionStationarity can receive tables for "Gain of category n" or "Loss of category m" in the following format:

1. Gain of category n:

   • transitionData: <tibble>. A table with 7 columns:

     1. Period: <fct>. The period [Yt, Yt+1].

     2. From: <fct>. A category i.

     3. To: <fct>. The gaining category in the transition of interest (n).

     4. Interval: <int>. Duration of the period [Yt, Yt+1].

     5. T_i2n_km2/T_i2n_pixel: <dbl>. Area with transition from category i to category n during time interval [Yt, Yt+1] where i is not equal to n.

     6. Rtin: <dbl>. Annual intensity of transition from category i to category n during time interval [Yt, Yt+1] where i is not equal to n.

     7. Wtn: <dbl>. Value of the uniform intensity of the transition to category n from all non-n categories at time Yt during time interval [Yt, Yt+1].

   • transitionStationarity: <tibble>. A table containing results of a stationarity test over the gain on category n containing 5 columns:

     1. From: <fct>. The losing category in the transition of interest to the category n.

     2. loss: <int>. Number of times the category had losses to the category n.

     3. N: <int>. Total number of transitions to be considered as stationary (T).

     4. Stationarity: <chr>. targeted by or avoided by the category n.

     5. Test: <chr>. Y for stationarity detected and N when not.

2. Loss of category m:

   • transitionData: <tibble>. A table with 7 columns:

     1. Period: <fct>. The period [Yt, Yt+1].

     2. To: <fct>. A category j.

     3. From: <fct>. The losing category in the transition of interest (m).

     4. Interval: <dbl>. Duration of the period [Yt, Yt+1].

     5. T_m2j_km2/T_m2j_pixel: <dbl>. Area with transition from category m to category j during time interval [Yt, Yt+1] where j is not equal to m.

     6. Qtmj: <dbl>. Annual intensity of transition from category m to category j during time interval [Yt, Yt+1] where j is not equal to m.

     7. Vtm: <dbl>. Value of the uniform intensity of the transition from category m to all non-m categories at time Y_t+1 during time interval [Yt, Yt+1].

   • transitionStationarity: <tibble>. A table containing results of a stationarity test over the loss of category m containing 5 columns:

     1. To: <fct>. The gaining category in the transition of interest from the category m.

     2. gain: <int>. Number of times the category had gains from the category m.

     3. N: <int>. Total number of transitions to be considered as stationary (T).

     4. Stationarity: <chr>. targeted or avoided the category m.

     5. Test: <chr>. Y for stationarity detected and N when not.

Slots

lookupcolor

The colors (character vector) associated with the LUC legend items.

transitionData

tibble. A table of Transition level's results (gain n (Rtin & Wtn) or loss m (Qtmj & Vtm) values).

transitionStationarity

tibble. A table containing results of a stationarity test. A change is considered stationary only if the intensities for all time intervals reside on one side of the uniform intensity, i.e are smaller or bigger than the uniform rate over the whole period.

Accumulates changes in a LULC raster time series

Description

This function calculates the number of times a pixel has changed during the analysed period. It returns a raster with the number of changes as pixel value and a table containing the areal percentage of every pixel value (number of changes).

Usage

acc_changes(path)

Arguments

Value

Two objects, a RasterLayer and a table.

Examples

url <- "https://zenodo.org/record/3685230/files/SaoLourencoBasin.rda?download=1"
temp <- tempfile()
download.file(url, temp, mode = "wb") # downloading the SaoLourencoBasin dataset
load(temp)
# the acc_changes() function, with the SaoLourencoBasin dataset
acc_changes(SaoLourencoBasin)

Area of LUC categories at time points

Description

A grouped barplot representing the areas of LUC categories at each time point of the analysed period.

Usage

barplotLand(
  dataset,
  legendtable,
  title = NULL,
  caption = "LUC Categories",
  xlab = "Year",
  ylab = "Area (km2 or pixel)",
  area_km2 = TRUE,
  ...
)

Arguments

Value

a barplot

See Also

ggplot2::theme

Examples

# editing the category names

SL_2002_2014$tb_legend$categoryName <- factor(c("Ap", "FF", "SA", "SG", "aa", "SF",
                                             "Agua", "Iu", "Ac", "R", "Im"),
                                  levels = c("FF", "SF", "SA", "SG", "aa", "Ap",
                                             "Ac", "Im", "Iu", "Agua", "R"))

SL_2002_2014$tb_legend$color <- c("#FFE4B5", "#228B22", "#00FF00", "#CAFF70",
                                  "#EE6363", "#00CD00", "#436EEE", "#FFAEB9",
                                  "#FFA54F", "#68228B", "#636363")
# the plot
barplotLand(dataset = SL_2002_2014$lulc_Multistep,
            legendtable = SL_2002_2014$tb_legend,
            area_km2 = TRUE)

One step transitions (Chord diagram)

Description

A circlize plot representing the one step transitions between two times point of interest.

Usage

chordDiagramLand(
  dataset,
  legendtable,
  legposition = c(x = -1.3, y = 0),
  legtitle = "Categories",
  sectorcol = "gray80",
  area_km2 = TRUE,
  legendsize = 1,
  y.intersp = 1,
  x.margin = c(-1, 1)
)

Arguments

Value

A Chord Diagram

Examples

# editing the category names

SL_2002_2014$tb_legend$categoryName <- factor(c("Ap", "FF", "SA", "SG", "aa", "SF",
                                               "Agua", "Iu", "Ac", "R", "Im"),
                                     levels = c("FF", "SF", "SA", "SG", "aa", "Ap",
                                              "Ac", "Im", "Iu", "Agua", "R"))

SL_2002_2014$tb_legend$color <- c("#FFE4B5", "#228B22", "#00FF00", "#CAFF70",
                                  "#EE6363", "#00CD00", "#436EEE", "#FFAEB9",
                                  "#FFA54F", "#68228B", "#636363")

# the plot
chordDiagramLand(dataset = SL_2002_2014$lulc_Onestep,
                 legendtable = SL_2002_2014$tb_legend)

Contingency table

Description

Extracts LUC transitions for all input grids of the time series.

Usage

contingencyTable(input_raster, pixelresolution = 30)

Arguments

Value

A list that contains 5 objects.

• lulc_Mulstistep: <tibble> Contingency table for all analysed time steps, containing 8 columns:

  1. Period: <chr> The period [Yt, Yt+1].

  2. From: <dbl> numerical code of a LUC category i.

  3. To: <dbl> numerical code of a LUC category j.

  4. km2: <dbl> Area in square kilometers that transited from the category i to category j in the period from Yt to Yt+1.

  5. Interval: <dbl> Interval of years between the first and the last year of the period [Yt, Yt+1].

  6. QtPixel: <int> Pixel count that transited from the categories i to category j in the period from Yt to Yt+1.

  7. yearFrom: <chr> The year that the change comes from [Yt].

  8. yearTo: <chr> The year that the change goes for [Yt+1].

• lulc_Onestep:<tibble> Contingency table for the entire analysed period [Y1, YT], containing 8 columns identical with lulc_Mulstistep.

• tb_legend: <tibble> A table of the pixel value, his name and color containing 3 columns:

  1. categoryValue: <dbl> the pixel value of the LUC category.

  2. categoryName: <factor> randomly created string associated with a given pixel value of a LUC category.

  3. color: <chr> random color associated with the given pixel value of a LUC category. Before further analysis, one would like to change the categoryName and color values.

     • Therefore the category names have to be in the same order as the categoryValue and the levels should be put in the right order for legend plotting. Like:

       
               myobject$tb_legend$categoryName <- factor(c("name1", "name2", "name3", "name4"),
                                                      levels = c("name3", "name2", "name1", "name4"))

     • The colors have to in the same order as the values in the categoryValue column. Colors can be given by the color name (eg. "black") or an HEX value (eg. #FFFFFF). Like:

       
              myobject$tb_legend$color <- c("#CDB79E", "red", "#66CD00", "yellow")

• totalArea: <tibble> A table with the total area of the study area containing 2 columns:

  1. area_km2: <numeric> The total area in square kilometers.

  2. QtPixel: <numeric> The total area in pixel counts.

• totalInterval: <numeric> Total interval of the analysed time series in years.

Examples

url <- "https://zenodo.org/record/3685230/files/SaoLourencoBasin.rda?download=1"
temp <- tempfile()
download.file(url, temp, mode = "wb") #downloading the online dataset
load(temp)
# the contingencyTable() function, with the SaoLourencoBasin dataset
contingencyTable(input_raster = SaoLourencoBasin, pixelresolution = 30)

Performs the intensity analysis based on cross-tabulation matrices of each time step

Description

This function implements an Intensity Analysis (IA) according to Aldwaik & Pontius (2012), a quantitative method to analyze time series of land use and cover (LUC) maps. For IA, a cross-tabulation matrix is composed for each LUC transition step in time.

Usage

intensityAnalysis(dataset, category_n, category_m, area_km2 = TRUE)

Arguments

Details

IA includes three levels of analysis of LUC changes. Consecutive analysis levels detail hereby information given by the previous analysis level (Aldwaik and Pontius, 2012, 2013).

1. The interval level examines how the size and speed of change vary across time intervals.

2. The category level examines how the size and intensity of gross losses and gross gains in each category vary across categories for each time interval.

3. The transition level examines how the size and intensity of a category’s transitions vary across the other categories that are available for that transition.

At each analysis level, the method tests for stationarity of patterns across time intervals.

The function returns a list with 6 objects:

1. lulc_table: tibble. Contingency table of LUC transitions at all analysed time steps, containing 6 columns:

   1. Period: <fct>. Evaluated period of transition in the format year t - year t+1.

   2. From: <fct>. The category in year t.

   3. To: <fct>. The category in year t+1.

   4. km2: <dbl>. Area in square kilometers that transited from the category From. to the category To in the period.

   5. QtPixel: <int>. Number of pixels that transited from. the category From to the category To in the period.

   6. Interval: <int>. Interval in years of the evaluated period.

2. lv1_tbl: An Interval object containing the St and U values.

3. category_lvlGain: A Category object containing the gain of the LUC category in a period (Gtj).

4. category_lvlLoss: A Category object containing the loss of the LUC category in a period (Lti).

5. transition_lvlGain_n: A Transition object containing the annualized rate of gain in category n (Rtin) and the respective Uniform Intensity (Wtn).

6. transition_lvlLoss_m: A Transition object containing the annualized rate of loss in category m (Qtmj) and the respective Uniform Intensity (Vtm).

Value

Intensity object

References

Aldwaik, S. Z. and Pontius, R. G. (2012) ‘Intensity analysis to unify measurements of size and stationarity of land changes by interval, category, and transition, Landscape and Urban Planning. Elsevier B.V., 106(1), pp. 103–114. doi:10.1016/j.landurbplan.2012.02.010.

Aldwaik, S. Z. and Pontius, R. G. (2013) ‘Map errors that could account for deviations from a uniform intensity of land change, International Journal of Geographical Information Science. Taylor & Francis, 27(9), pp. 1717–1739. doi:10.1080/13658816.2013.787618.

Examples

# editing the category name

SL_2002_2014$tb_legend$categoryName <- factor(c("Ap", "FF", "SA", "SG", "aa", "SF",
                                             "Agua", "Iu", "Ac", "R", "Im"),
                                  levels = c("FF", "SF", "SA", "SG", "aa", "Ap",
                                             "Ac", "Im", "Iu", "Agua", "R"))

SL_2002_2014$tb_legend$color <- c("#FFE4B5", "#228B22", "#00FF00", "#CAFF70",
                                  "#EE6363", "#00CD00", "#436EEE", "#FFAEB9",
                                  "#FFA54F", "#68228B", "#636363")

intensityAnalysis(dataset = SL_2002_2014, category_n = "Ap", category_m = "SG", area_km2 = TRUE)

Net and gross changes of LUC categories

Description

A stacked barplot showing net and gross changes of LUC categories during the entire analysed time period.

Usage

netgrossplot(
  dataset,
  legendtable,
  title = NULL,
  xlab = "LUC category",
  ylab = "Area (Km2)",
  legend_title = "Changes",
  changesLabel = c(GC = "Gross change", NG = "Net gain", NL = "Net loss"),
  color = c(GC = "gray70", NG = "#006400", NL = "#EE2C2C"),
  area_km2 = TRUE
)

Arguments

Value

A bar plot

Examples

# editing the category names

SL_2002_2014$tb_legend$categoryName <- factor(c("Ap", "FF", "SA", "SG", "aa", "SF",
                                             "Agua", "Iu", "Ac", "R", "Im"),
                                     levels = c("FF", "SF", "SA", "SG", "aa", "Ap",
                                              "Ac", "Im", "Iu", "Agua", "R"))

# the plot
netgrossplot(dataset = SL_2002_2014$lulc_Multistep,
             legendtable = SL_2002_2014$tb_legend,
             title = NULL,
             xlab = "LUC Category",
             changes = c(GC = "Gross changes", NG = "Net Gain", NL = "Net Loss"),
             color = c(GC = "gray70", NG = "#006400", NL = "#EE2C2C"))

Methods for function plot in package OpenLand

Description

Plot Intensity objects based on Intensity Analysis output.

Usage

plot(x, y, ...)

## S4 method for signature 'Interval,ANY'
plot(
  x,
  y,
  labels = c(leftlabel = "Interval Change Area (percent of map)", rightlabel =
    "Annual Change Area (percent of map)"),
  title = NA,
  labs = c(type = "Changes", ur = "Uniform Intensity"),
  marginplot = c(lh = -10, rh = 0),
  leg_curv = c(x = 0.1, y = 0.1),
  color_bar = c(fast = "#B22222", slow = "#006400", area = "gray40"),
  fontsize_ui = 10,
  ...
)

## S4 method for signature 'Category,ANY'
plot(
  x,
  y,
  labels = c(leftlabel = "Annual Change Area (km2 or pixels)", rightlabel =
    "Annual Change Intensity (percent of category)"),
  title = NA,
  labs = c(type = "Categories", ur = "Uniform Intensity"),
  marginplot = c(lh = 0.5, rh = 0.5),
  leg_curv = c(x = 0.1, y = 0.1),
  fontsize_ui = 10,
  ...
)

## S4 method for signature 'Transition,ANY'
plot(
  x,
  y,
  labels = c(leftlabel = "Annual Transition Area (km2 or pixels)", rightlabel =
    "Annual Transition Intensity (percent of category)"),
  title = NA,
  labs = c(type = "Categories", ur = "Uniform Intensity"),
  marginplot = c(lh = 0.5, rh = 0.5),
  leg_curv = c(x = 0.1, y = 0.1),
  fontsize_ui = 10,
  ...
)

Arguments

Value

An intensity graph

Sankey diagram of LUC transitions (one or multistep)

Description

A sankey showing the one or multi step LUC transitions during the analysed period.

Usage

sankeyLand(dataset, legendtable, iterations = 0)

Arguments

Value

A sankey diagram

See Also

sankeyNetwork

Examples

# editing the category names

SL_2002_2014$tb_legend$categoryName <- factor(c("Ap", "FF", "SA", "SG", "aa", "SF",
                                             "Agua", "Iu", "Ac", "R", "Im"),
                                     levels = c("FF", "SF", "SA", "SG", "aa", "Ap",
                                              "Ac", "Im", "Iu", "Agua", "R"))

SL_2002_2014$tb_legend$color <- c("#FFE4B5", "#228B22", "#00FF00", "#CAFF70",
                                  "#EE6363", "#00CD00", "#436EEE", "#FFAEB9",
                                  "#FFA54F", "#68228B", "#636363")

# onestep sankey
sankeyLand(dataset = SL_2002_2014$lulc_Onestep,
           legendtable = SL_2002_2014$tb_legend)

# multistep sankey
sankeyLand(dataset = SL_2002_2014$lulc_Multistep,
           legendtable = SL_2002_2014$tb_legend)

Summary of multiple parameters in a raster directory

Description

Listing major characteristics of raster inputs. Those characteristics are the dimensions, the resolution, the extent, the values (min, max) and the coordinate reference system.

Usage

summary_dir(path)

Arguments

Value

Table with the raster parameters in columns

Examples

url <- "https://zenodo.org/record/3685230/files/SaoLourencoBasin.rda?download=1"
temp <- tempfile()
download.file(url, temp, mode = "wb") # downloading the SaoLourencoBasin dataset
load(temp)
# the acc_changes() function, with the SaoLourencoBasin dataset

summary_dir(raster::unstack(SaoLourencoBasin))

Quantitative summary of a unique categorical raster

Description

This function presents a summary with the pixel quantity of each category present in a categorical raster.

Usage

summary_map(path)

Arguments

Value

A table containing in columns the pixel counts for each pixel value

Examples

url <- "https://zenodo.org/record/3685230/files/SaoLourencoBasin.rda?download=1"
temp <- tempfile()
download.file(url, temp, mode = "wb") # downloading the SaoLourencoBasin dataset
load(temp)
summary_map(SaoLourencoBasin[[1]])