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Function Signature

contingencyTable(input_raster, pixelresolution = 30)

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Description

Extracts LUC (Land Use and Cover) transitions for all input grids of the time series. This function creates cross-tabulation matrices that show how land categories transition from one time period to another.

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Parameters

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input_raster
character | Raster* object | list
Path (character), Raster* object or list of Raster* objects. See raster::raster() for more information about supported file types.
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pixelresolution
numeric
default:"30"
The pixel spatial resolution in meters. Used to calculate area in square kilometers from pixel counts.

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Return Value

Returns a list containing 5 objects:
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lulc_Multistep
tibble
Contingency table for all analysed time steps, containing 8 columns:
  • Period (<chr>): The period [Yt, Yt+1]
  • From (<dbl>): Numerical code of a LUC category i
  • To (<dbl>): Numerical code of a LUC category j
  • km2 (<dbl>): Area in square kilometers that transited from category i to category j in the period from Yt to Yt+1
  • Interval (<dbl>): Interval of years between the first and last year of the period [Yt, Yt+1]
  • QtPixel (<int>): Pixel count that transited from category i to category j in the period from Yt to Yt+1
  • yearFrom (<chr>): The year that the change comes from [Yt]
  • yearTo (<chr>): The year that the change goes to [Yt+1]
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lulc_Onestep
tibble
Contingency table for the entire analysed period [Y1, YT], containing 8 columns identical to lulc_Multistep.
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tb_legend
tibble
A table of the pixel values, names, and colors containing 3 columns:
  • categoryValue (<dbl>): The pixel value of the LUC category
  • categoryName (<factor>): Randomly created string associated with a given pixel value of a LUC category
  • color (<chr>): Random color associated with the given pixel value of a LUC category
Before further analysis, you should customize the categoryName and color values:Category Names: Names must be in the same order as categoryValue, and levels should be ordered correctly for legend plotting:
myobject$tb_legend$categoryName <- factor(
  c("name1", "name2", "name3", "name4"),
  levels = c("name3", "name2", "name1", "name4")
)
Colors: Colors must be in the same order as values in categoryValue. Use color names (e.g., “black”) or HEX values (e.g., “#FFFFFF”):
myobject$tb_legend$color <- c("#CDB79E", "red", "#66CD00", "yellow")
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totalArea
tibble
A table with the total area of the study area containing 2 columns:
  • area_km2 (<numeric>): The total area in square kilometers
  • QtPixel (<numeric>): The total area in pixel counts
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totalInterval
numeric
Total interval of the analysed time series in years.

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Examples

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Basic Usage

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

# Create contingency table with 30m pixel resolution
result <- contingencyTable(
  input_raster = SaoLourencoBasin, 
  pixelresolution = 30
)

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Customizing Legend

# After creating the contingency table, customize category names and colors
result$tb_legend$categoryName <- factor(
  c("Forest", "Agriculture", "Urban", "Water"),
  levels = c("Forest", "Agriculture", "Urban", "Water")
)

result$tb_legend$color <- c("#228B22", "#FFE4B5", "#636363", "#436EEE")

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See Also

  • intensityAnalysis - Perform intensity analysis on contingency table results
  • plot - Visualize intensity analysis results

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