Extracts LUC transitions for all input grids of the time series.
contingencyTable(input_raster, pixelresolution = 30)
| input_raster | path (character), Raster* object or list of Raster*
objects. See |
|---|---|
| pixelresolution | numeric. The pixel spatial resolution in meter. |
A list that contains 5 objects.
lulc_Mulstistep: <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 the
category i
to category j in the period from Yt to Yt+1.
Interval: <dbl> Interval of years between the first and
the last year of the period [Yt, Yt+1].
QtPixel: <int> Pixel count that transited from the categories
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 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:
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, 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:
area_km2: <numeric> The total area in square kilometers.
QtPixel: <numeric> The total area in pixel counts.
totalInterval: <numeric> Total interval of the analysed
time series in years.
# \donttest{ 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) #> | | | 0% | |========== | 14% | |==================== | 29% | |============================== | 43% | |======================================== | 57% | |================================================== | 71% | |============================================================ | 86% | |======================================================================| 100% #> #> | | | 0% | |========== | 14% | |==================== | 29% | |============================== | 43% | |======================================== | 57% | |================================================== | 71% | |============================================================ | 86% | |======================================================================| 100% #> #> | | | 0% | |========== | 14% | |==================== | 29% | |============================== | 43% | |======================================== | 57% | |================================================== | 71% | |============================================================ | 86% | |======================================================================| 100% #> #> | | | 0% | |========== | 14% | |==================== | 29% | |============================== | 43% | |======================================== | 57% | |================================================== | 71% | |============================================================ | 86% | |======================================================================| 100% #> #> | | | 0% | |========== | 14% | |==================== | 29% | |============================== | 43% | |======================================== | 57% | |================================================== | 71% | |============================================================ | 86% | |======================================================================| 100% #> #> $lulc_Multistep #> # A tibble: 130 × 8 #> Period From To km2 QtPixel Interval yearFrom yearTo #> <chr> <int> <int> <dbl> <int> <int> <int> <int> #> 1 2002-2008 2 2 6543. 7269961 6 2002 2008 #> 2 2002-2008 2 10 1.56 1736 6 2002 2008 #> 3 2002-2008 2 11 55.2 61320 6 2002 2008 #> 4 2002-2008 2 12 23.9 26609 6 2002 2008 #> 5 2002-2008 3 2 37.5 41649 6 2002 2008 #> 6 2002-2008 3 3 2133. 2370190 6 2002 2008 #> 7 2002-2008 3 7 155. 172718 6 2002 2008 #> 8 2002-2008 3 11 7.48 8307 6 2002 2008 #> 9 2002-2008 3 12 0.356 395 6 2002 2008 #> 10 2002-2008 3 13 0.081 90 6 2002 2008 #> # … with 120 more rows #> #> $lulc_Onestep #> # A tibble: 45 × 8 #> Period From To km2 QtPixel Interval yearFrom yearTo #> <chr> <int> <int> <dbl> <int> <int> <int> <int> #> 1 2002-2014 2 2 6169. 6854816 12 2002 2014 #> 2 2002-2014 2 9 2.39 2651 12 2002 2014 #> 3 2002-2014 2 10 10.4 11513 12 2002 2014 #> 4 2002-2014 2 11 412. 457631 12 2002 2014 #> 5 2002-2014 2 12 29.7 33015 12 2002 2014 #> 6 2002-2014 3 2 110. 121762 12 2002 2014 #> 7 2002-2014 3 3 2091. 2323665 12 2002 2014 #> 8 2002-2014 3 7 116. 129304 12 2002 2014 #> 9 2002-2014 3 9 7.00 7774 12 2002 2014 #> 10 2002-2014 3 11 9.32 10359 12 2002 2014 #> # … with 35 more rows #> #> $tb_legend #> # A tibble: 11 × 3 #> categoryValue categoryName color #> <int> <fct> <chr> #> 1 2 IEY #002F70 #> 2 3 XOM #8EA4DE #> 3 4 BWK #0A468D #> 4 5 KEU #EAACAC #> 5 7 LBF #5F1415 #> 6 8 DPB #DCE2F6 #> 7 9 CYV #A13F3F #> 8 10 KGU #6F8DD2 #> 9 11 FIV #C5CFF0 #> 10 12 JUB #CE7575 #> 11 13 QTX #295EAE #> #> $totalArea #> # A tibble: 1 × 2 #> area_km2 QtPixel #> <dbl> <int> #> 1 22418. 24908860 #> #> $totalInterval #> [1] 12 #> # }