Stratification

Fundamental to many structurally guided sampling approaches is the use of stratification methods that allow for more effective and representative sampling protocols. It is important to note that the data sets being used as inputs are considered to be populations.

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

Algorithm Description Approach
strat_kmeans() kmeans Unsupervised
strat_quantiles() Quantiles Unsupervised
strat_breaks() User-defined breaks Supervised
strat_poly() Polygons Supervised
strat_map() Maps (combines) srasters Unsupervised

strat_kmeans

strat_kmeans() uses kmeans clustering to produce an sraster output.

#--- 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(s)   : memory
#> name        : strata
#> min value   :      1
#> max value   :      5

TIP!

plot = FALSE is the default for all functions. plot = TRUE will visualize raster and vector ouputs.

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(s)   : memory
#> name        : strata
#> min value   :      1
#> max value   :     10

strat_quantiles

The strat_quantiles() algorithm divides data into equally sized strata (nStrata). Similar to strat_breaks() users can perform stratification on a single mraster or also input a secondary mraster (mraster2) and specify the desired number of strata (nStrata2).

Note that the dual stratification output will result in a product of $$nStrata * nStrata2$$.

#--- perform quantiles stratification ---#
strat_quantiles(mraster = mraster$zq90, nStrata = 6, plot = TRUE) #> class : SpatRaster #> dimensions : 277, 373, 1 (nrow, ncol, nlyr) #> resolution : 20, 20 (x, y) #> extent : 431100, 438560, 5337700, 5343240 (xmin, xmax, ymin, ymax) #> coord. ref. : UTM Zone 17, Northern Hemisphere #> source(s) : memory #> name : strata #> min value : 1 #> max value : 6 #--- dual stratification - will produce 12 output strata ---# strat_quantiles(mraster = mraster$zq90,
mraster2 = mraster$zsd, nStrata = 3, nStrata2 = 4) #> class : SpatRaster #> dimensions : 277, 373, 1 (nrow, ncol, nlyr) #> resolution : 20, 20 (x, y) #> extent : 431100, 438560, 5337700, 5343240 (xmin, xmax, ymin, ymax) #> coord. ref. : UTM Zone 17, Northern Hemisphere #> source(s) : memory #> name : strata #> min value : 1 #> max value : 12 strat_breaks strat_breaks() stratifies data based on user-defined breaks in mraster. A single mraster can be used, or mraster2 can also be defined. breaks and breaks2 coincide with the user defined breaks for mraster and mraster2 respectively. #--- perform stratification using user-defined breaks ---# #--- define breaks for metric ---# breaks <- c(seq(0,100,20)) breaks #> [1] 0 20 40 60 80 100 #--- perform stratification using user-defined breaks ---# values <- terra::values(mraster$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$pzabove2, breaks = breaks, plot = TRUE) #> class : SpatRaster #> dimensions : 277, 373, 1 (nrow, ncol, nlyr) #> resolution : 20, 20 (x, y) #> extent : 431100, 438560, 5337700, 5343240 (xmin, xmax, ymin, ymax) #> coord. ref. : UTM Zone 17, Northern Hemisphere #> source(s) : memory #> name : strata #> min value : 1 #> max value : 6 #--- stratify on 1 metric only ---# strat_breaks(mraster = mraster$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(s)   : memory
#> name        : strata
#> min value   :      1
#> max value   :      6

strat_poly

Forest inventories with polygon coverages summarizing forest attributes such as species, management type, or photo-interpreted estimates of volume can be stratified using strat_poly().

TIP!

Users may wish to stratify based on categorical or empirical variables that are not available through raster data (e.g. species from forest inventory polygons).

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")

#> Reading layer inventory_polygons' from data source
#>   C:\Users\tgood\AppData\Local\Temp\RtmpCYyraS\Rinst5710142d19b\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

#--- 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")

In our example, attribute = "NUTRIENTS" and features within, c("poor", "rich", "medium"), define the 3 desired strata.

#--- stratify polygon coverage ---#

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
#> Assigning a new crs. Use 'project' to transform a SpatRaster to a new crs

features can be grouped. In our example below, rich and medium features are combined into a single strata, while low is left in isolation. The 2 vectors are specified into a list, which will result in the output of 2 strata (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)
#> Assigning a new crs. Use 'project' to transform a SpatRaster to a new crs

#> $raster #> class : SpatRaster #> dimensions : 277, 373, 1 (nrow, ncol, nlyr) #> resolution : 20, 20 (x, y) #> extent : 431100, 438560, 5337700, 5343240 (xmin, xmax, ymin, ymax) #> coord. ref. : UTM Zone 17, Northern Hemisphere #> source(s) : memory #> name : strata #> min value : 1 #> max value : 2 #> #>$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 details details returns the output outRaster, the stratification $lookUp table, and the polygon ($poly) used to drive the stratification based on attributes and features specified by the users. strat_map Users may wish to pair stratifications. strat_map() faciliates two stratifications of matching extent and resolution to be mapped against one another to generate unique strata based on stratum pairings. This facilitates the user to generate stratifications detailing quantitative and qualitative measures such as structure by species, or multiple qualitative measures such as species by management type. The total number of classes of the output sraster is multiplicative of the number of input strata. For example, if the input sraster has 3 strata and sraster2 has 4 strata, then the output of strat_map() will be 12 strata. 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(s) : 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).

#> $raster #> class : SpatRaster #> dimensions : 277, 373, 3 (nrow, ncol, nlyr) #> resolution : 20, 20 (x, y) #> extent : 431100, 438560, 5337700, 5343240 (xmin, xmax, ymin, ymax) #> coord. ref. : UTM Zone 17, Northern Hemisphere #> source(s) : memory #> names : strata, strata2, stratamapped #> min values : 1, 1, 11 #> max values : 3, 4, 34 #> #>$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`