buildRAT() reads all pixels of an input raster to obtain the set of
unique values and their counts. The result is returned as a data frame
suitable for use with the class method GDALRaster$setDefaultRAT(). The
returned data frame might be further modified before setting as a Raster
Attribute Table in a dataset, for example, by adding columns containing
class names, color values, or other information (see Details).
An optional input data frame containing such attributes may be given, in
which case buildRAT() will attempt to join the additional columns and
automatically assign the appropriate metadata on the output data frame
(i.e., assign R attributes on the data frame and its columns that define
usage in a GDAL Raster Attribute Table).
Usage
buildRAT(
raster,
band = 1L,
col_names = c("VALUE", "COUNT"),
table_type = "athematic",
na_value = NULL,
join_df = NULL,
quiet = FALSE
)Arguments
- raster
Either a
GDALRasterobject, or a character string containing the file name of a raster dataset to open.- band
Integer scalar, band number to read (default
1L).- col_names
Character vector of length two containing names to use for column 1 (pixel values) and column 2 (pixel counts) in the output data frame (defaults are
c("VALUE", "COUNT")).- table_type
Character string describing the type of the attribute table. One of either
"thematic", or"athematic"for continuous data (the default).- na_value
Numeric scalar. If the set of unique pixel values has an
NA, it will be recoded tona_valuein the returned data frame. IfNULL(the default),NAwill not be recoded.- join_df
Optional data frame for joining additional attributes. Must have a column of unique values with the same name as
col_names[1]("VALUE"by default).- quiet
Logical scalar. If
TRUE``, a progress bar will not be displayed. Defaults toFALSE``.
Value
A data frame with at least two columns containing the set of unique
pixel values and their counts. These columns have attribute "GFU" set to
"MinMax" for the values, and "PixelCount" for the counts. If join_df is
given, the returned data frame will have additional columns that result from
merge(). The "GFU" attribute of the additional columns will be assigned
automatically based on the column names (case-insensitive matching, see
Details). The returned data frame has attribute "GDALRATTableType" set to
table_type.
Details
A GDAL Raster Attribute Table (or RAT) provides attribute information about pixel values. Raster attribute tables can be used to represent histograms, color tables, and classification information. Each row in the table applies to either a single pixel value or a range of values, and might have attributes such as the histogram count for that value (or range), the color that pixels of that value (or range) should be displayed, names of classes, or various other information.
Each column in a raster attribute table has a name, a type (integer,
double, or string), and a GDALRATFieldUsage. The usage
distinguishes columns with particular understood purposes (such as color,
histogram count, class name), and columns that have other purposes not
understood by the library (long labels, ancillary attributes, etc).
In the general case, each row has a field indicating the minimum pixel
value falling into that category, and a field indicating the maximum pixel
value. In the GDAL API, these are indicated with usage values of GFU_Min
and GFU_Max. In the common case where each row is a discrete pixel value,
a single column with usage GFU_MinMax would be used instead.
In R, the table is represented as a data frame with column attribute "GFU"
containing the field usage as a string, e.g., "Max", "Min" or "MinMax"
(case-sensitive).
The full set of possible field usage descriptors is:
| GFU attr | GDAL enum | Description |
"Generic" | GFU_Generic | General purpose field |
"PixelCount" | GFU_PixelCount | Histogram pixel count |
"Name" | GFU_Name | Class name |
"Min" | GFU_Min | Class range minimum |
"Max" | GFU_Max | Class range maximum |
"MinMax" | GFU_MinMax | Class value (min=max) |
"Red" | GFU_Red | Red class color (0-255) |
"Green" | GFU_Green | Green class color (0-255) |
"Blue" | GFU_Blue | Blue class color (0-255) |
"Alpha" | GFU_Alpha | Alpha transparency (0-255) |
"RedMin" | GFU_RedMin | Color range red minimum |
"GreenMin" | GFU_GreenMin | Color range green minimum |
"BlueMin" | GFU_BlueMin | Color range blue minimum |
"AlphaMin" | GFU_AlphaMin | Color range alpha minimum |
"RedMax" | GFU_RedMax | Color range red maximum |
"GreenMax" | GFU_GreenMax | Color range green maximum |
"BlueMax" | GFU_BlueMax | Color range blue maximum |
"AlphaMax" | GFU_AlphaMax | Color range alpha maximum |
buildRAT() assigns GFU "MinMax" on the column of pixel values (named
"VALUE" by default) and GFU "PixelCount" on the column of counts (named
"COUNT" by default).
If join_df is given, the additional columns that result from joining will
have GFU assigned automatically based on the column names (ignoring case).
First, the additional column names are checked for containing
the string "name" (e.g., "classname", "TypeName", "EVT_NAME", etc).
The first matching column (if any) will be assigned a GFU of "Name"
(=GFU_Name, the field usage descriptor for class names). Next, columns
named "R" or "Red" will be assigned GFU "Red", columns named "G" or
"Green" will be assigned GFU "Green", columns named "B" or "Blue"
will be assigned GFU "Blue", and columns named "A" or "Alpha" will be
assigned GFU "Alpha". Finally, any remaining columns that have not been
assigned a GFU will be assigned "Generic".
In a variation of RAT, all the categories are of equal size and regularly
spaced, and the categorization can be determined by knowing the value at
which the categories start and the size of a category. This is called
"Linear Binning" and the information is kept specially on the raster
attribute table as a whole. In R, a RAT that uses linear binning would
have the following attributes set on the data frame:
attribute "Row0Min" = the numeric lower bound (pixel value) of the first
category, and attribute "BinSize" = the numeric width of each category (in
pixel value units). buildRAT() does not create tables with linear binning,
but one could be created manually based on the specifications above, and
applied to a raster with the class method GDALRaster$setDefaultRAT().
A raster attribute table is thematic or athematic (continuous). In R, this
is defined by an attribute on the data frame named "GDALRATTableType" with
value of either "thematic" or "athematic".
Note
The full raster will be scanned.
If na_value is not specified, then an NA pixel value (if present)
will not be recoded in the output data frame. This may have implications
if joining to other data (NA will not match), or when using the returned
data frame to set a default RAT on a dataset (NA will be interpreted
as the value that R uses internally to represent it for the type, e.g.,
-2147483648 for NA_integer_). In some cases, removing the row in the output
data frame with value NA, rather than recoding, may be desirable (i.e., by
removing manually or by side effect of joining via merge(), for example).
Users should consider what is appropriate for a particular case.
Examples
evt_file <- system.file("extdata/storml_evt.tif", package="gdalraster")
# make a copy to modify
f <- paste0(tempdir(), "/", "storml_evt_tmp.tif")
file.copy(evt_file, f)
#> [1] TRUE
ds <- new(GDALRaster, f, read_only=FALSE)
ds$getDefaultRAT(band=1) # NULL
#> NULL
# get the full attribute table for LANDFIRE EVT from the CSV file
evt_csv <- system.file("extdata/LF20_EVT_220.csv", package="gdalraster")
evt_df <- read.csv(evt_csv)
nrow(evt_df)
#> [1] 860
head(evt_df)
#> VALUE EVT_NAME EVT_LF EVT_PHYS R
#> 1 -9999 Fill-NoData <NA> <NA> 255
#> 2 7008 North Pacific Oak Woodland Tree Hardwood 203
#> 3 7009 Northwestern Great Plains Aspen Forest and Parkland Tree Hardwood 192
#> 4 7010 Northern Rocky Mountain Western Larch Savanna Tree Conifer 180
#> 5 7011 Rocky Mountain Aspen Forest and Woodland Tree Hardwood 192
#> 6 7012 Rocky Mountain Bigtooth Maple Ravine Woodland Tree Hardwood 171
#> G B RED GREEN BLUE
#> 1 255 255 1.000000 1 1.000000
#> 2 255 171 0.796078 1 0.670588
#> 3 255 138 0.752941 1 0.541176
#> 4 255 148 0.705882 1 0.580392
#> 5 255 138 0.752941 1 0.541176
#> 6 255 138 0.670588 1 0.541176
evt_df <- evt_df[,1:7]
tbl <- buildRAT(ds,
table_type = "thematic",
na_value = -9999,
join_df = evt_df)
#> scanning raster...
#> 0...10...20...30...40...50...60...70...80...90...100 - done.
nrow(tbl)
#> [1] 24
head(tbl)
#> VALUE COUNT EVT_NAME
#> 1 -9999 876 Fill-NoData
#> 2 7011 28 Rocky Mountain Aspen Forest and Woodland
#> 3 7046 4564 Northern Rocky Mountain Subalpine Woodland and Parkland
#> 4 7050 570 Rocky Mountain Lodgepole Pine Forest
#> 5 7055 889 Rocky Mountain Subalpine Dry-Mesic Spruce-Fir Forest and Woodland
#> 6 7056 304 Rocky Mountain Subalpine Mesic-Wet Spruce-Fir Forest and Woodland
#> EVT_LF EVT_PHYS R G B
#> 1 <NA> <NA> 255 255 255
#> 2 Tree Hardwood 192 255 138
#> 3 Tree Conifer 191 255 233
#> 4 Tree Conifer 163 240 219
#> 5 Tree Conifer 236 252 204
#> 6 Tree Conifer 236 252 204
# attributes on the data frame and its columns define usage in a GDAL RAT
attributes(tbl)
#> $names
#> [1] "VALUE" "COUNT" "EVT_NAME" "EVT_LF" "EVT_PHYS" "R" "G"
#> [8] "B"
#>
#> $row.names
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
#>
#> $class
#> [1] "data.frame"
#>
#> $GDALRATTableType
#> [1] "thematic"
#>
attributes(tbl$VALUE)
#> $GFU
#> [1] "MinMax"
#>
attributes(tbl$COUNT)
#> $GFU
#> [1] "PixelCount"
#>
attributes(tbl$EVT_NAME)
#> $GFU
#> [1] "Name"
#>
attributes(tbl$EVT_LF)
#> $GFU
#> [1] "Generic"
#>
attributes(tbl$EVT_PHYS)
#> $GFU
#> [1] "Generic"
#>
attributes(tbl$R)
#> $GFU
#> [1] "Red"
#>
attributes(tbl$G)
#> $GFU
#> [1] "Green"
#>
attributes(tbl$B)
#> $GFU
#> [1] "Blue"
#>
ds$setDefaultRAT(band=1, tbl)
#> [1] TRUE
ds$flushCache()
tbl2 <- ds$getDefaultRAT(band=1)
nrow(tbl2)
#> [1] 24
head(tbl2)
#> VALUE COUNT EVT_NAME
#> 1 -9999 876 Fill-NoData
#> 2 7011 28 Rocky Mountain Aspen Forest and Woodland
#> 3 7046 4564 Northern Rocky Mountain Subalpine Woodland and Parkland
#> 4 7050 570 Rocky Mountain Lodgepole Pine Forest
#> 5 7055 889 Rocky Mountain Subalpine Dry-Mesic Spruce-Fir Forest and Woodland
#> 6 7056 304 Rocky Mountain Subalpine Mesic-Wet Spruce-Fir Forest and Woodland
#> EVT_LF EVT_PHYS R G B
#> 1 NA NA 255 255 255
#> 2 Tree Hardwood 192 255 138
#> 3 Tree Conifer 191 255 233
#> 4 Tree Conifer 163 240 219
#> 5 Tree Conifer 236 252 204
#> 6 Tree Conifer 236 252 204
ds$close()
deleteDataset(f)
#> [1] TRUE
# Display
evt_gt <- displayRAT(tbl2, title = "Storm Lake EVT Raster Attribute Table")
class(evt_gt) # an object of class "gt_tbl" from package gt
#> [1] "gt_tbl" "list"
# To show the table:
# evt_gt
# or simply call `displayRAT()` as above but without assignment
# `vignette("raster-attribute-tables")` has example output