stratification

Fundamental to many structurally guided sampling approaches is the use of stratification methods that allow for more effective and representative sampling protocols. Below, I will identify the stratification methods included in sgsR and demonstrate some worked examples for each functions.

Currently, there are 5 functions associated with the strat verb in the sgsR package:

strat_kmeans

We will demonstrate a preliminary example of using the strat_kmeans() algorithm to prepare our sraster input data and provide additional examples below.

#--- perform stratification using k-means ---#
strat_kmeans(mraster = mraster, # input
             nStrata = 5) # algorithm will produce 4 strata
#> class       : SpatRaster 
#> dimensions  : 277, 373, 1  (nrow, ncol, nlyr)
#> resolution  : 20, 20  (x, y)
#> extent      : 431100, 438560, 5337700, 5343240  (xmin, xmax, ymin, ymax)
#> coord. ref. : UTM Zone 17, Northern Hemisphere 
#> source      : memory 
#> name        : strata 
#> min value   :      1 
#> max value   :      5

Notice that the code above did not plot anything. This is because plot = FALSE is set as the default for all functions in the sgsR package. Below, we will demonstrate examples where the plot = TRUE was set.

strat_kmeans(mraster = mraster, # input
             nStrata = 10, # algorithm will produce 10 strata
             iter = 1000, # set minimum number of iterations to determine kmeans centers
             algorithm = "MacQueen", # use MacQueen algorithm
             plot = TRUE) # plot output

#> class       : SpatRaster 
#> dimensions  : 277, 373, 1  (nrow, ncol, nlyr)
#> resolution  : 20, 20  (x, y)
#> extent      : 431100, 438560, 5337700, 5343240  (xmin, xmax, ymin, ymax)
#> coord. ref. : UTM Zone 17, Northern Hemisphere 
#> source      : memory 
#> name        : strata 
#> min value   :      1 
#> max value   :     10

strat_quantiles

The strat_quantiles() algorithm divides data into equally sized strata (nStrata). Similar to the strat_breaks() function, the (quantile) algorithm have the capability to perform stratification on a single mraster; users can also input a secondary mraster (mraster2) and specify the number associated with the desired strata (nStrata2). Note that the dual stratification output will result in a product of \(nStrata * nStrata2\).

#--- perform quantiles stratification ---#
strat_quantiles(mraster = mraster$zq90,
                nStrata = 6,
                plot = TRUE)

#> class       : SpatRaster 
#> dimensions  : 277, 373, 1  (nrow, ncol, nlyr)
#> resolution  : 20, 20  (x, y)
#> extent      : 431100, 438560, 5337700, 5343240  (xmin, xmax, ymin, ymax)
#> coord. ref. : UTM Zone 17, Northern Hemisphere 
#> source      : memory 
#> name        : strata 
#> min value   :      1 
#> max value   :      6

#--- dual stratification - will produce 12 output strata ---#
strat_quantiles(mraster = mraster$zq90, 
                mraster2 = mraster$zsd,
                nStrata = 3, 
                nStrata2 = 4)
#> class       : SpatRaster 
#> dimensions  : 277, 373, 1  (nrow, ncol, nlyr)
#> resolution  : 20, 20  (x, y)
#> extent      : 431100, 438560, 5337700, 5343240  (xmin, xmax, ymin, ymax)
#> coord. ref. : UTM Zone 17, Northern Hemisphere 
#> source      : memory 
#> name        : strata 
#> min value   :      1 
#> max value   :     12

strat_breaks

strat_breaks() function stratifies the data based on the breaks in covariates that the user defines. Single mraster can be defined or additional mraster2 can also be defined. breaks and breaks2 coincide with the user defined breaks for mraster and mraster2 respectively.

#--- perform stratification using user-defined breaks ---#

#--- define breaks for metric ---#
breaks <- c(seq(0,100,20))

breaks
#> [1]   0  20  40  60  80 100

#--- perform stratification using user-defined breaks ---#

values <- terra::values(mraster$zq90)

#--- define breaks for metric ---#
breaks2 <- c(5,10,15,20,25)

breaks2
#> [1]  5 10 15 20 25

Once the breaks are created, we can use them as input into the strat_breaks function using the breaks and breaks2 parameters.

#--- stratify on 1 metric only ---#
strat_breaks(mraster = mraster$pzabove2,
             breaks = breaks,
             plot = TRUE)

#> class       : SpatRaster 
#> dimensions  : 277, 373, 1  (nrow, ncol, nlyr)
#> resolution  : 20, 20  (x, y)
#> extent      : 431100, 438560, 5337700, 5343240  (xmin, xmax, ymin, ymax)
#> coord. ref. : UTM Zone 17, Northern Hemisphere 
#> source      : memory 
#> name        : strata 
#> min value   :      1 
#> max value   :      6
#--- stratify on 1 metric only ---#
strat_breaks(mraster = mraster$zq90,
             breaks = breaks2,
             plot = TRUE)

#> class       : SpatRaster 
#> dimensions  : 277, 373, 1  (nrow, ncol, nlyr)
#> resolution  : 20, 20  (x, y)
#> extent      : 431100, 438560, 5337700, 5343240  (xmin, xmax, ymin, ymax)
#> coord. ref. : UTM Zone 17, Northern Hemisphere 
#> source      : memory 
#> name        : strata 
#> min value   :      1 
#> max value   :      6

strat_poly

The strat_poly() algorithm stratifies the input based on spatial polygon attributes and features. Users may wish to stratify based on categorical or empirical variables that are not available through ALS data (e.g. species from forest inventory polygons). This function allows the user to define the attribute of interest as well as how the features within the attribute are to be grouped for stratification.

Users define the input poly and its associated attribute. A raster layer must be defined to guide the spatial extent and resolution for the output stratification polygon. Based on the vector or list of features, stratification is applied and the polygon is rasterized into its appropriate strata.

#--- load in polygon coverage ---#
poly <- system.file("extdata", "inventory_polygons.shp", package = "sgsR")

fri <- sf::st_read(poly)
#> Reading layer `inventory_polygons' from data source 
#>   `C:\Users\tgood\AppData\Local\Temp\Rtmp2Rc5aV\Rinstc404c2d28f\sgsR\extdata\inventory_polygons.shp' 
#>   using driver `ESRI Shapefile'
#> Simple feature collection with 632 features and 3 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 431100 ymin: 5337700 xmax: 438560 ymax: 5343240
#> Projected CRS: UTM_Zone_17_Northern_Hemisphere

attribute column within the polygon must be defined and the features (within the attribute) must be specified to define the composition of the output strata. In our demonstration below, attribute = "NUTRIENTS" and features within NUTRIENTS (poor, rich, medium) will define the 3 desired output classes.

#--- stratify polygon coverage ---#
#--- specify polygon attribute to stratify ---#

attribute <- "NUTRIENTS"

#--- specify features within attribute & how they should be grouped ---#
#--- as a single vector ---#

features <- c("poor", "rich", "medium")

srasterpoly <- strat_poly(poly = fri, # input polygon
                          attribute = attribute, # attribute to stratify by
                          features = features, # features within attribute
                          raster = sraster, # raster to define extent and resolution for output
                          plot = TRUE) # plot output

features can be converted into amalgamated classes. In our demonstration below, rich and medium features are combined together, while low is left in isolation. The 2 vectors are specified into a list, which will result in the output of 2 classes (low & rich/medium).

#--- or as multiple lists ---#
g1 <- "poor"
g2 <- c("rich", "medium")

features <- list(g1, g2)

strat_poly(poly = fri,
           attribute = attribute,
           features = features,
           raster = sraster,
           plot = TRUE,
           details = TRUE)

#> $raster
#> class       : SpatRaster 
#> dimensions  : 277, 373, 1  (nrow, ncol, nlyr)
#> resolution  : 20, 20  (x, y)
#> extent      : 431100, 438560, 5337700, 5343240  (xmin, xmax, ymin, ymax)
#> coord. ref. : UTM Zone 17, Northern Hemisphere 
#> source      : memory 
#> name        : strata 
#> min value   :      1 
#> max value   :      2 
#> 
#> $lookUp
#>   strata features
#> 1      1     poor
#> 2      2     rich
#> 3      2   medium
#> 
#> $poly
#>  class       : SpatVector 
#>  geometry    : polygons 
#>  dimensions  : 524, 2  (geometries, attributes)
#>  extent      : 431100, 438560, 5337700, 5343240  (xmin, xmax, ymin, ymax)
#>  coord. ref. : UTM_Zone_17_Northern_Hemisphere 
#>  names       : features strata
#>  type        :    <chr>  <int>
#>  values      :     poor      1
#>                    poor      1
#>                    poor      1

You may have noticed that the details parameter was present in the demonstration code above. details parameter returns the output outRaster, the $lookUp table associated with the strata, and the polygon ($poly) is created to drive the stratification based on attributes and features specified by the users.

strat_map

There are situations where multiple levels of stratification may be sought-after. For instance, the user may wish to combine the output from strat_poly() (see the demonstration above) with 3 classes, with the 4 class kmeans stratification generated above in this vignette.

The total number of classes of the output is multiplicative of the number of the strata. For example, if sraster has 3 strata and sraster2 has 4 strata, then the output of strat_map() will be 12 strata in total. There may be occasions where stratum do not interact spatially, this will result in fewer output strata.

#--- map srasters ---#
strat_map(sraster = srasterpoly, # strat_poly 3 class stratification
          sraster2 = sraster, # strat_kmeans 4 class stratification
          plot = TRUE)

#> class       : SpatRaster 
#> dimensions  : 277, 373, 1  (nrow, ncol, nlyr)
#> resolution  : 20, 20  (x, y)
#> extent      : 431100, 438560, 5337700, 5343240  (xmin, xmax, ymin, ymax)
#> coord. ref. : UTM Zone 17, Northern Hemisphere 
#> source      : memory 
#> categories  : label 
#> name        : strata 
#> min value   :     11 
#> max value   :     34

The convention for the numeric value of the output strata is the concatenation (merging) of sraster strata and sraster2 strata. Check $lookUP for a clear depiction of this step.

strat_map(sraster = srasterpoly, # strat_poly 3 class stratification
          sraster2 = sraster, # strat_poly 3 class stratification
          stack = TRUE, # stack input and output strata into multi layer output raster
          details = TRUE, # provide additional details
          plot = TRUE) # plot output
#> Stacking sraster, sraster2, and their combination (stratamapped).

#> $raster
#> class       : SpatRaster 
#> dimensions  : 277, 373, 3  (nrow, ncol, nlyr)
#> resolution  : 20, 20  (x, y)
#> extent      : 431100, 438560, 5337700, 5343240  (xmin, xmax, ymin, ymax)
#> coord. ref. : UTM Zone 17, Northern Hemisphere 
#> sources     : memory  
#>               memory  
#>               memory  
#> names       : strata, strata2, stratamapped 
#> min values  :      1,       1,           11 
#> max values  :      3,       4,           34 
#> 
#> $lookUp
#>    strata strata2 stratamapped
#> 1       3       2           32
#> 2       3       1           31
#> 3       1       3           13
#> 4       1       4           14
#> 5       3       4           34
#> 6       3       3           33
#> 7       1       2           12
#> 8       1       1           11
#> 9       2       2           22
#> 10      2       3           23
#> 11      2       4           24
#> 12      2       1           21