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Function Signature

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

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Description

This function implements 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. Intensity Analysis examines patterns and processes of land change across three analysis levels, with each consecutive level providing more detailed information than the previous one.

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Analysis Levels

Intensity Analysis includes three levels of analysis:
  1. Interval Level - Examines how the size and speed of change vary across time intervals
  2. 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. 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.

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Parameters

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dataset
list
required
The result object from contingencyTable(). This should be the complete list output containing all 5 objects (lulc_Multistep, lulc_Onestep, tb_legend, totalArea, and totalInterval).
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category_n
character
required
The gaining category in the transition of interest (n). This specifies which land category to analyze for gains.
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category_m
character
required
The losing category in the transition of interest (m). This specifies which land category to analyze for losses.
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area_km2
logical
default:"TRUE"
If TRUE, the change is computed in km². If FALSE, the change is computed in pixel counts.

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Return Value

Returns a list containing 6 objects:
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lulc_table
tibble
Contingency table of LUC transitions at all analysed time steps, containing 6 columns:
  • Period (<fct>): Evaluated period of transition in the format “year t - year t+1”
  • From (<fct>): The category in year t
  • To (<fct>): The category in year t+1
  • km2 (<dbl>): Area in square kilometers that transited from the category From to the category To in the period
  • QtPixel (<int>): Number of pixels that transited from the category From to the category To in the period
  • Interval (<int>): Interval in years of the evaluated period
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interval_lvl
Interval object
An Interval S4 class object containing the St (annual intensity of change for each time interval) and U (uniform intensity of change) values for the interval level analysis.This object represents the first level of intensity analysis and can be visualized using the plot() method.
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category_lvlGain
Category object
A Category S4 class object containing the gain of the LUC category in a period (Gtj).This represents the category level analysis for gains and includes:
  • Gross gain values for each category
  • Annual intensity of gain for each category
  • Stationarity test results
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category_lvlLoss
Category object
A Category S4 class object containing the loss of the LUC category in a period (Lti).This represents the category level analysis for losses and includes:
  • Gross loss values for each category
  • Annual intensity of loss for each category
  • Stationarity test results
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transition_lvlGain_n
Transition object
A Transition S4 class object containing the annualized rate of gain in category n (Rtin) and the respective Uniform Intensity (Wtn).This represents the transition level analysis for the specified gaining category and shows which categories are targeted or avoided by the gaining category.
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transition_lvlLoss_m
Transition object
A Transition S4 class object containing the annualized rate of loss in category m (Qtmj) and the respective Uniform Intensity (Vtm).This represents the transition level analysis for the specified losing category and shows which categories target or avoid the losing category.

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Examples

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Basic Usage

# First, create a contingency table and customize the legend
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")
)

# Add colors in the same order as the category values
SL_2002_2014$tb_legend$color <- c(
  "#FFE4B5", "#228B22", "#00FF00", "#CAFF70",
  "#EE6363", "#00CD00", "#436EEE", "#FFAEB9",
  "#FFA54F", "#68228B", "#636363"
)

# Perform intensity analysis
result <- intensityAnalysis(
  dataset = SL_2002_2014,
  category_n = "Ap",  # Gaining category (Agriculture)
  category_m = "SG",  # Losing category (Savanna)
  area_km2 = TRUE
)

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Using Pixel Counts Instead of Area

# Perform analysis with pixel counts instead of km²
result <- intensityAnalysis(
  dataset = SL_2002_2014,
  category_n = "Urban",
  category_m = "Forest",
  area_km2 = FALSE
)

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Accessing Results

# View the interval level data
result$interval_lvl

# View category level gains
result$category_lvlGain

# View transition level data for gains
result$transition_lvlGain_n

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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

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See Also

  • contingencyTable - Create the input dataset for intensity analysis
  • plot - Visualize intensity analysis results

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