View source: R/intensityAnalysis.R
intensityAnalysis | R Documentation |
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.
intensityAnalysis(dataset, category_n, category_m, area_km2 = TRUE)
dataset |
list. The result object from |
category_n |
character. The gaining category in the transition of interest (n). |
category_m |
character. The losing category in the transition of interest (m). |
area_km2 |
logical. If TRUE the change is computed in km2, if FALSE in pixel counts. |
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).
The interval level examines how the size and speed of change vary across time intervals.
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.
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:
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.
lv1_tbl: An Interval
object containing the
St and U values.
category_lvlGain: A Category
object
containing the gain of the LUC category in a period (Gtj).
category_lvlLoss: A Category
object
containing the loss of the LUC category in a period (Lti).
transition_lvlGain_n: A Transition
object
containing the annualized rate of gain in category n (Rtin) and
the respective Uniform Intensity (Wtn).
transition_lvlLoss_m: A Transition
object
containing the annualized rate of loss in category m (Qtmj) and
the respective Uniform Intensity (Vtm).
Intensity object
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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/13658816.2013.787618")}.
# 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)
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