Sampling

sample_srs

We have demonstrated a simple example of using the sample_srs() function in vignette("sgsR"). We will demonstrate additional examples below.

#--- perform simple random sampling ---#
sample_srs(
  raster = sraster, # input sraster
  nSamp = 200, # number of desired sample units
  plot = TRUE
) # plot

#> Simple feature collection with 200 features and 0 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431170 ymin: 5337730 xmax: 438530 ymax: 5343230
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>                  geometry
#> 1  POINT (436990 5342370)
#> 2  POINT (434590 5339850)
#> 3  POINT (432070 5343010)
#> 4  POINT (434150 5341650)
#> 5  POINT (435050 5341950)
#> 6  POINT (433130 5338650)
#> 7  POINT (433570 5340150)
#> 8  POINT (434550 5341550)
#> 9  POINT (432870 5340870)
#> 10 POINT (433410 5342130)

sample_srs(
  raster = mraster, # input mraster
  nSamp = 200, # number of desired sample units
  access = access, # define access road network
  mindist = 200, # minimum distance sample units must be apart from one another
  buff_inner = 50, # inner buffer - no sample units within this distance from road
  buff_outer = 200, # outer buffer - no sample units further than this distance from road
  plot = TRUE
) # plot

#> Simple feature collection with 200 features and 0 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431150 ymin: 5337710 xmax: 438470 ymax: 5343230
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>                  geometry
#> 1  POINT (438030 5339370)
#> 2  POINT (433270 5342850)
#> 3  POINT (438130 5342090)
#> 4  POINT (436190 5341130)
#> 5  POINT (438010 5339050)
#> 6  POINT (437090 5341490)
#> 7  POINT (436790 5338150)
#> 8  POINT (436250 5343210)
#> 9  POINT (438210 5339590)
#> 10 POINT (434030 5341190)

sample_systematic

The sample_systematic() function applies systematic sampling across an area with the cellsize parameter defining the resolution of the tessellation. The tessellation shape can be modified using the square parameter. Assigning TRUE (default) to the square parameter results in a regular grid and assigning FALSE results in a hexagonal grid.

The location of sample units can also be adjusted using the locations parameter, where centers takes the center, corners takes all corners, and random takes a random location within each tessellation. Random start points and translations are applied when the function is called.

#--- perform grid sampling ---#
sample_systematic(
  raster = sraster, # input sraster
  cellsize = 1000, # grid distance
  plot = TRUE
) # plot

#> Simple feature collection with 34 features and 0 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431577.7 ymin: 5337718 xmax: 438506.8 ymax: 5343169
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>                    geometry
#> 1  POINT (436766.3 5337948)
#> 2  POINT (438506.8 5338933)
#> 3  POINT (434533.2 5337833)
#> 4  POINT (435403.4 5338326)
#> 5  POINT (438014.2 5339803)
#> 6    POINT (432300 5337718)
#> 7  POINT (433170.3 5338211)
#> 8  POINT (434040.6 5338703)
#> 9  POINT (434910.9 5339196)
#> 10 POINT (435781.1 5339688)

#--- perform grid sampling ---#
sample_systematic(
  raster = sraster, # input sraster
  cellsize = 500, # grid distance
  square = FALSE, # hexagonal tessellation
  location = "random", # randomly sample within tessellation
  plot = TRUE
) # plot

#> Simple feature collection with 167 features and 0 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431170.1 ymin: 5337734 xmax: 438546.1 ymax: 5343212
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>                    geometry
#> 1  POINT (438431.7 5342706)
#> 2  POINT (437897.2 5343212)
#> 3  POINT (438236.3 5342350)
#> 4  POINT (438546.1 5341509)
#> 5  POINT (437750.4 5342586)
#> 6  POINT (438220.4 5340999)
#> 7  POINT (438294.3 5340087)
#> 8  POINT (437563.4 5343107)
#> 9  POINT (437738.3 5342336)
#> 10 POINT (438039.1 5341457)

sample_systematic(
  raster = sraster, # input sraster
  cellsize = 500, # grid distance
  access = access, # define access road network
  buff_outer = 200, # outer buffer - no sample units further than this distance from road
  square = FALSE, # hexagonal tessellation
  location = "corners", # take corners instead of centers
  plot = TRUE
)

#> Simple feature collection with 650 features and 0 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431222.2 ymin: 5337734 xmax: 438531.6 ymax: 5343226
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>                    geometry
#> 1  POINT (438449.9 5337998)
#> 2  POINT (438477.2 5338497)
#> 3  POINT (438449.9 5337998)
#> 4  POINT (438003.9 5337772)
#> 5  POINT (438161.7 5338013)
#> 6  POINT (438449.9 5337998)
#> 7  POINT (438504.4 5338996)
#> 8  POINT (438477.2 5338497)
#> 9  POINT (438449.9 5337998)
#> 10 POINT (438161.7 5338013)

sample_strat

The sample_strat() contains two methods to perform sampling:

• "Queinnec" - Hierarchical sampling using a focal window to isolate contiguous groups of stratum pixels, which was originally developed by Martin Queinnec.

• "random" - Traditional stratified random sampling. This method ignores much of the functionality of the algorithm to allow users the capability to use standard stratified random sampling approaches without the use of a focal window to locate contiguous stratum cells.

#--- perform stratified sampling random sampling ---#
sample_strat(
  sraster = sraster, # input sraster
  nSamp = 200
) # desired sample size # plot
#> Simple feature collection with 200 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431210 ymin: 5337730 xmax: 438530 ymax: 5343190
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>    strata type  rule               geometry
#> x       1  new rule1 POINT (433650 5340910)
#> x1      1  new rule1 POINT (433650 5341090)
#> x2      1  new rule1 POINT (432950 5341650)
#> x3      1  new rule1 POINT (438210 5339230)
#> x4      1  new rule1 POINT (437910 5339630)
#> x5      1  new rule1 POINT (437910 5342430)
#> x6      1  new rule1 POINT (437950 5338650)
#> x7      1  new rule1 POINT (433710 5341390)
#> x8      1  new rule1 POINT (433950 5341350)
#> x9      1  new rule1 POINT (434490 5342170)

#--- extract strata values to existing samples ---#
e.sr <- extract_strata(
  sraster = sraster, # input sraster
  existing = existing
) # existing samples to add strata value to

sample_strat(
  sraster = sraster, # input sraster
  nSamp = 200, # desired sample size
  access = access, # define access road network
  existing = e.sr, # existing sample with strata values
  mindist = 200, # minimum distance sample units must be apart from one another
  buff_inner = 50, # inner buffer - no sample units within this distance from road
  buff_outer = 200, # outer buffer - no sample units further than this distance from road
  plot = TRUE
) # plot

#> Simple feature collection with 400 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431130 ymin: 5337730 xmax: 438530 ymax: 5343230
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>    strata     type     rule               geometry
#> 1       1 existing existing POINT (435990 5339770)
#> 2       1 existing existing POINT (434310 5341470)
#> 3       1 existing existing POINT (433430 5341270)
#> 4       1 existing existing POINT (436990 5338010)
#> 5       1 existing existing POINT (437530 5338050)
#> 6       1 existing existing POINT (436070 5342310)
#> 7       1 existing existing POINT (434370 5340890)
#> 8       1 existing existing POINT (435250 5342070)
#> 9       1 existing existing POINT (431190 5340870)
#> 10      1 existing existing POINT (437890 5341210)

sample_strat(
  sraster = sraster, # input
  nSamp = 200, # desired sample size
  access = access, # define access road network
  existing = e.sr, # existing samples with strata values
  include = TRUE, # include existing sample in nSamp total
  buff_outer = 200, # outer buffer - no samples further than this distance from road
  plot = TRUE
) # plot

#> Simple feature collection with 200 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431130 ymin: 5337790 xmax: 438530 ymax: 5343030
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>    strata     type     rule               geometry
#> 1       1 existing existing POINT (435990 5339770)
#> 2       1 existing existing POINT (434310 5341470)
#> 3       1 existing existing POINT (433430 5341270)
#> 4       1 existing existing POINT (436990 5338010)
#> 5       1 existing existing POINT (437530 5338050)
#> 6       1 existing existing POINT (436070 5342310)
#> 7       1 existing existing POINT (434370 5340890)
#> 8       1 existing existing POINT (435250 5342070)
#> 9       1 existing existing POINT (431190 5340870)
#> 10      1 existing existing POINT (437890 5341210)

#--- perform stratified sampling random sampling ---#
sample_strat(
  sraster = sraster, # input sraster
  method = "random", # stratified random sampling
  nSamp = 200, # desired sample size
  plot = TRUE
) # plot

#> Simple feature collection with 200 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431110 ymin: 5337710 xmax: 438470 ymax: 5343230
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>    strata type               geometry
#> x       1  new POINT (436350 5338410)
#> x1      1  new POINT (434010 5340990)
#> x2      1  new POINT (434630 5338870)
#> x3      1  new POINT (435970 5339490)
#> x4      1  new POINT (437270 5338030)
#> x5      1  new POINT (434150 5339090)
#> x6      1  new POINT (434850 5341650)
#> x7      1  new POINT (433270 5341550)
#> x8      1  new POINT (438210 5338670)
#> x9      1  new POINT (433770 5340570)

sample_sys_strat

sample_sys_strat() function implements systematic stratified sampling on an sraster. This function uses the same functionality as sample_systematic() but takes an sraster as input and performs sampling on each stratum iteratively.

#--- perform grid sampling on each stratum separately ---#
sample_sys_strat(
  sraster = sraster, # input sraster with 4 strata
  cellsize = 1000, # grid size
  plot = TRUE # plot output
)
#> Warning: [readStart] source already open for reading
#> Processing strata : 1
#> Warning: [extract] source already open for reading
#> Processing strata : 2
#> Warning: [extract] source already open for reading
#> Processing strata : 3
#> Warning: [extract] source already open for reading
#> Processing strata : 4
#> Warning: [extract] source already open for reading

#> Simple feature collection with 45 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431367.8 ymin: 5337763 xmax: 438545.4 ymax: 5343233
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>    strata                 geometry
#> 1       1 POINT (437570.5 5343016)
#> 2       1 POINT (434571.1 5342955)
#> 3       1 POINT (434591.5 5341955)
#> 4       1 POINT (433612.2 5340935)
#> 5       1   POINT (436632 5339996)
#> 6       1 POINT (434632.4 5339955)
#> 7       1 POINT (437652.2 5339017)
#> 8       1 POINT (435652.6 5338976)
#> 9       1 POINT (436672.9 5337997)
#> 10      2 POINT (435986.2 5337763)

Just like with sample_systematic() we can specify where we want our samples to fall within our tessellations. We specify location = "corners" below. Note that the tesselations are all saved to a list file when details = TRUE should the user want to save them.

sample_sys_strat(
  sraster = sraster, # input sraster with 4 strata
  cellsize = 500, # grid size
  square = FALSE, # hexagon tessellation
  location = "corners", # samples on tessellation corners
  plot = TRUE # plot output
)
#> Processing strata : 1
#> Warning: [extract] source already open for reading
#> Processing strata : 2
#> Warning: [extract] source already open for reading
#> Processing strata : 3
#> Warning: [extract] source already open for reading
#> Processing strata : 4
#> Warning: [extract] source already open for reading

#> Simple feature collection with 1147 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431141.8 ymin: 5337707 xmax: 438544.1 ymax: 5343218
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>    strata                 geometry
#> 1       1 POINT (438497.4 5337896)
#> 2       1 POINT (438497.4 5337896)
#> 3       1   POINT (438472 5338184)
#> 4       1   POINT (438472 5338184)
#> 5       1 POINT (438497.4 5337896)
#> 6       1 POINT (437973.9 5338140)
#> 7       1 POINT (438210.2 5338305)
#> 8       1   POINT (438472 5338184)
#> 9       1   POINT (438472 5338184)
#> 10      1 POINT (438210.2 5338305)

This sampling approach could be especially useful incombination with strat_poly() to ensure consistency of sampling accross specific management units.

#--- read polygon coverage ---#
poly <- system.file("extdata", "inventory_polygons.shp", package = "sgsR")
fri <- sf::st_read(poly)
#> Reading layer `inventory_polygons' from data source 
#>   `/tmp/Rtmp3vajEL/Rinst12f525c175ea/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

#--- 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")

#--- get polygon stratification ---#
srasterpoly <- strat_poly(
  poly = fri,
  attribute = attribute,
  features = features,
  raster = sraster
)

#--- systematatic stratified sampling for each stratum ---#
sample_sys_strat(
  sraster = srasterpoly, # input sraster from strat_poly() with 3 strata
  cellsize = 500, # grid size
  square = FALSE, # hexagon tessellation
  location = "random", # randomize plot location
  plot = TRUE # plot output
)
#> Processing strata : 1
#> Warning: [extract] source already open for reading
#> Processing strata : 2
#> Warning: [extract] source already open for reading
#> Processing strata : 3
#> Warning: [extract] source already open for reading
#> Simple feature collection with 174 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431130.8 ymin: 5337702 xmax: 438544.7 ymax: 5343225
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>    strata                 geometry
#> 1       1 POINT (431256.8 5343150)
#> 2       1 POINT (431130.8 5342690)
#> 3       1 POINT (431763.6 5342500)
#> 4       1 POINT (432126.7 5343202)
#> 5       1 POINT (432825.1 5343225)
#> 6       1 POINT (431258.5 5340671)
#> 7       1 POINT (432014.1 5342191)
#> 8       1 POINT (432681.2 5342972)
#> 9       1 POINT (431215.6 5340244)
#> 10      1 POINT (432133.4 5341747)

sample_nc

sample_nc() function implements the Nearest Centroid sampling algorithm described in Melville & Stone (2016). The algorithm uses kmeans clustering where the number of clusters (centroids) is equal to the desired sample size (nSamp).

Cluster centers are located, which then prompts the nearest neighbour mraster pixel for each cluster to be selected (assuming default k parameter). These nearest neighbours are the output sample units.

#--- perform simple random sampling ---#
sample_nc(
  mraster = mraster, # input
  nSamp = 25, # desired sample size
  plot = TRUE
)
#> K-means being performed on 3 layers with 25 centers.

#> Simple feature collection with 25 features and 4 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431150 ymin: 5338110 xmax: 438510 ymax: 5343210
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>        zq90 pzabove2  zsd kcenter               geometry
#> 66397 17.80     93.3 3.44       1 POINT (431150 5339670)
#> 37774 17.70     59.7 5.12       2 POINT (433110 5341210)
#> 9113  10.10     64.9 2.57       3 POINT (434310 5342750)
#> 32927  6.83     13.4 1.80       4 POINT (433150 5341470)
#> 23870 14.60     88.4 3.51       5 POINT (438510 5341970)
#> 44432 16.90     87.5 4.22       6 POINT (431990 5340850)
#> 77781 15.60     22.0 4.68       7 POINT (435030 5339070)
#> 744   23.50     90.5 6.75       8 POINT (438510 5343210)
#> 16935 15.50     94.8 2.71       9 POINT (434090 5342330)
#> 859    6.19     60.5 1.45      10 POINT (433350 5343190)

Altering the k parameter leads to a multiplicative increase in output sample units where total output samples = \(nSamp * k\).

#--- perform simple random sampling ---#
samples <- sample_nc(
  mraster = mraster, # input
  k = 2, # number of nearest neighbours to take for each kmeans center
  nSamp = 25, # desired sample size
  plot = TRUE
)
#> K-means being performed on 3 layers with 25 centers.

#--- total samples = nSamp * k (25 * 2) = 50 ---#
nrow(samples)
#> [1] 50

Visualizing what the kmeans centers and sample units looks like is possible when using details = TRUE. The $kplot output provides a quick visualization of where the centers are based on a scatter plot of the first 2 layers in mraster. Notice that the centers are well distributed in covariate space and chosen sample units are the closest pixels to each center (nearest neighbours).

#--- perform simple random sampling with details ---#
details <- sample_nc(
  mraster = mraster, # input
  nSamp = 25, # desired sample number
  details = TRUE
)
#> K-means being performed on 3 layers with 25 centers.

#--- plot ggplot output ---#

details$kplot

sample_clhs

sample_clhs() function implements conditioned Latin hypercube (clhs) sampling methodology from the clhs package.

The syntax for this function is similar to others shown above, although parameters like iter, which define the number of iterations within the Metropolis-Hastings process are important to consider. In these examples we use a low iter value for efficiency. Default values for iter within the clhs package are 10,000.

sample_clhs(
  mraster = mraster, # input
  nSamp = 200, # desired sample size
  plot = TRUE, # plot
  iter = 100
) # number of iterations

The cost parameter defines the mraster covariate, which is used to constrain the clhs sampling. An example could be the distance a pixel is from road access (e.g. from calculate_distance() see example below), terrain slope, the output from calculate_coobs(), or many others.

#--- cost constrained examples ---#
#--- calculate distance to access layer for each pixel in mr ---#
mr.c <- calculate_distance(
  raster = mraster, # input
  access = access, # define access road network
  plot = TRUE
) # plot

sample_clhs(
  mraster = mr.c, # input
  nSamp = 250, # desired sample size
  iter = 100, # number of iterations
  cost = "dist2access", # cost parameter - name defined in calculate_distance()
  plot = TRUE
) # plot

sample_balanced

The sample_balanced() algorithm performs a balanced sampling methodology from the stratifyR / SamplingBigData packages.

sample_balanced(
  mraster = mraster, # input
  nSamp = 200, # desired sample size
  plot = TRUE
) # plot

#> Simple feature collection with 200 features and 0 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431110 ymin: 5337730 xmax: 438530 ymax: 5343230
#> Projected CRS: +proj=utm +zone=17 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
#> First 10 features:
#>                  geometry
#> 1  POINT (431970 5343230)
#> 2  POINT (434530 5343210)
#> 3  POINT (432210 5343190)
#> 4  POINT (433610 5343190)
#> 5  POINT (433670 5343190)
#> 6  POINT (431410 5343170)
#> 7  POINT (434250 5343090)
#> 8  POINT (438450 5343090)
#> 9  POINT (434110 5343050)
#> 10 POINT (432630 5342970)

sample_balanced(
  mraster = mraster, # input
  nSamp = 100, # desired sample size
  algorithm = "lcube", # algorithm type
  access = access, # define access road network
  buff_inner = 50, # inner buffer - no sample units within this distance from road
  buff_outer = 200
) # outer buffer - no sample units further than this distance from road
#> Simple feature collection with 100 features and 0 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431310 ymin: 5337750 xmax: 438370 ymax: 5343110
#> Projected CRS: +proj=utm +zone=17 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
#> First 10 features:
#>                  geometry
#> 1  POINT (431870 5343110)
#> 2  POINT (435450 5343110)
#> 3  POINT (436710 5343030)
#> 4  POINT (434430 5342990)
#> 5  POINT (435430 5342930)
#> 6  POINT (435290 5342910)
#> 7  POINT (436030 5342890)
#> 8  POINT (436110 5342890)
#> 9  POINT (434470 5342810)
#> 10 POINT (433030 5342770)

sample_ahels

The sample_ahels() function performs the adapted Hypercube Evaluation of a Legacy Sample (ahels) algorithm usingexisting sample data and an mraster. New sample units are allocated based on quantile ratios between the existing sample and mraster covariate dataset.

This algorithm was adapted from that presented in the paper below, which we highly recommend.

Malone BP, Minansy B, Brungard C. 2019. Some methods to improve the utility of conditioned Latin hypercube sampling. PeerJ 7:e6451 DOI 10.7717/peerj.6451

This algorithm:

1. Determines the quantile distributions of existing sample units and mraster covariates.

2. Determines quantiles where there is a disparity between sample units and covariates.

3. Prioritizes sampling within those quantile to improve representation.

To use this function, user must first specify the number of quantiles (nQuant) followed by either the nSamp (total number of desired sample units to be added) or the threshold (sampling ratio vs. covariate coverage ratio for quantiles - default is 0.9) parameters.

#--- remove `type` variable from existing  - causes plotting issues ---#

existing <- existing %>% select(-type)

sample_ahels(
  mraster = mraster,
  existing = existing, # existing sample
  plot = TRUE
) # plot

#> Simple feature collection with 316 features and 7 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431130 ymin: 5337790 xmax: 438550 ymax: 5343230
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>      type.x zq90 pzabove2  zsd strata type.y  rule               geometry
#> 1  existing 6.92     35.7 1.65      1    new rule1 POINT (435990 5339770)
#> 2  existing 3.31     25.0 0.61      1    new rule1 POINT (434310 5341470)
#> 3  existing 4.68     18.4 1.32      1    new rule1 POINT (433430 5341270)
#> 4  existing 6.90     75.1 1.53      1    new rule1 POINT (436990 5338010)
#> 5  existing 7.08     67.4 1.61      1    new rule1 POINT (437530 5338050)
#> 6  existing 6.50     58.9 1.56      1    new rule1 POINT (436070 5342310)
#> 7  existing 3.55     25.1 0.67      1    new rule1 POINT (434370 5340890)
#> 8  existing 3.78     34.7 0.76      1    new rule1 POINT (435250 5342070)
#> 9  existing 6.79     92.1 1.32      1    new rule1 POINT (431190 5340870)
#> 10 existing 8.30     93.2 1.54      1    new rule1 POINT (437890 5341210)

The first matrix output shows the quantile ratios between the sample and the covariates. A value of 1.0 indicates that the sample is representative of quantile coverage. Values > 1.0 indicate over representation of sample units, while < 1.0 indicate under representation.

sample_ahels(
  mraster = mraster,
  existing = existing, # existing sample
  nQuant = 20, # define 20 quantiles
  nSamp = 300
) # desired sample size

#> Simple feature collection with 500 features and 7 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431130 ymin: 5337710 xmax: 438530 ymax: 5343230
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>      type.x zq90 pzabove2  zsd strata type.y  rule               geometry
#> 1  existing 6.92     35.7 1.65      1    new rule1 POINT (435990 5339770)
#> 2  existing 3.31     25.0 0.61      1    new rule1 POINT (434310 5341470)
#> 3  existing 4.68     18.4 1.32      1    new rule1 POINT (433430 5341270)
#> 4  existing 6.90     75.1 1.53      1    new rule1 POINT (436990 5338010)
#> 5  existing 7.08     67.4 1.61      1    new rule1 POINT (437530 5338050)
#> 6  existing 6.50     58.9 1.56      1    new rule1 POINT (436070 5342310)
#> 7  existing 3.55     25.1 0.67      1    new rule1 POINT (434370 5340890)
#> 8  existing 3.78     34.7 0.76      1    new rule1 POINT (435250 5342070)
#> 9  existing 6.79     92.1 1.32      1    new rule1 POINT (431190 5340870)
#> 10 existing 8.30     93.2 1.54      1    new rule1 POINT (437890 5341210)

Notice that the total number of samples is 500. This value is the sum of existing units (200) and number of sample units defined by nSamp = 300.

sample_existing

Acknowledging that existing sample networks are common is important. There is significant investment into these samples, and in order to keep inventories up-to-date, we often need to collect new data for sample units. The sample_existing algorithm provides the user with methods for sub-sampling an existing sample network should the financial / logistical resources not be available to collect data at all sample units. The functions allows users to choose between algorithm types using (type = "clhs" - default, type = "balanced", type = "srs", type = "strat"). Differences in type result in calling internal sample_existing_*() functions (sample_existing_clhs() (default), sample_existing_balanced(), sample_existing_srs(), sample_existing_strat()). These functions are not exported to be used stand-alone, however they employ the same functionality as their sample_clhs() etc counterparts.

While using sample_existing(), should the user wish to specify algorithm specific parameters (e.g. algorithm = "lcube" in sample_balanced() or allocation = "equal" in sample_strat()), they can specify within sample_existing() as if calling the function directly.

I give applied examples for all methods below that are based on the following scenario:

• We have a systematic sample where sample units are 200m apart.

• We know we only have resources to sample 300 of them.

• We have some ALS data available (mraster), which we can use to improve knowledge of the metric populations.

See our existing sample for the scenario below.

#--- generate existing samples and extract metrics ---#
existing <- sample_systematic(raster = mraster, cellsize = 200, plot = TRUE)

#--- sub sample using ---#
e <- existing %>%
  extract_metrics(mraster = mraster, existing = .)

#--- sub sample using ---#
sample_existing(existing = e, nSamp = 300, type = "clhs")
#> Simple feature collection with 300 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431124.5 ymin: 5337700 xmax: 438545.2 ymax: 5343237
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>      zq90 pzabove2  zsd                 geometry
#> 166 17.60     98.5 4.17 POINT (436998.9 5341857)
#> 474  9.17     14.3 2.55 POINT (434075.2 5342736)
#> 655  9.89     33.2 2.76 POINT (432876.3 5341796)
#> 240  8.04     92.8 1.81 POINT (437099.3 5338366)
#> 500 11.90     65.5 3.39 POINT (433881.9 5342684)
#> 720 20.90     85.7 5.13 POINT (432964.4 5339129)
#> 890 16.70     94.4 3.18 POINT (431315.3 5339105)
#> 775 13.40     80.2 3.55 POINT (432680.6 5338640)
#> 512 14.00     30.0 3.72 POINT (434510.7 5339540)
#> 200  9.36     40.8 2.63 POINT (437485.9 5338469)

#--- sub sample using ---#
sample_existing(
  existing = existing, # our existing sample
  nSamp = 300, # desired sample size
  raster = mraster, # include mraster metrics to guide sampling of existing
  plot = TRUE
) # plot
#> Simple feature collection with 300 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431124.5 ymin: 5337700 xmax: 438555.1 ymax: 5343235
#> Projected CRS: +proj=utm +zone=17 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
#> First 10 features:
#>        zq90 pzabove2  zsd                 geometry
#> 91482  8.90     90.1 2.00 POINT (436661.3 5338457)
#> 91955 14.90     90.8 3.35 POINT (432205.9 5341204)
#> 91250  8.60     92.5 1.69 POINT (437977.6 5341290)
#> 91886 15.00     88.8 3.64 POINT (433209.1 5338987)
#> 91590  5.79     48.2 1.37 POINT (434860.5 5342117)
#> 91480  6.91     68.6 1.57 POINT (436764.1 5338070)
#> 91291  2.88     21.3 0.66 POINT (438310.4 5338481)
#> 91879 12.60     56.6 3.09 POINT (432374.7 5342905)
#> 92079  6.58     84.6 1.42   POINT (431149 5340509)
#> 91378 22.60     90.3 6.48 POINT (437216.7 5340260)

#--- create distance from roads metric ---#
dist <- calculate_distance(raster = mraster, access = access)

#--- sub sample using ---#
sample_existing(
  existing = existing, # our existing sample
  nSamp = 300, # desired sample size
  raster = dist, # include mraster metrics to guide sampling of existing
  cost = 4, # either provide the index (band number) or the name of the cost layer
  plot = TRUE
) # plot
#> Simple feature collection with 300 features and 4 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431215 ymin: 5337710 xmax: 438479.2 ymax: 5343240
#> Projected CRS: +proj=utm +zone=17 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
#> First 10 features:
#>        zq90 pzabove2  zsd dist2access                 geometry
#> 91928 18.90     89.9 4.50    70.08395 POINT (432296.4 5341642)
#> 91426 12.50     90.5 3.01   185.62433 POINT (436419.1 5341703)
#> 91920 20.10     93.2 3.88    46.05984 POINT (432707.5 5340096)
#> 91604 21.00     86.4 6.63   235.92496 POINT (435283.8 5339746)
#> 91635  3.37     19.4 0.63    27.47076 POINT (434679.5 5341241)
#> 91929 18.70     69.5 3.82   127.35095 POINT (432245.1 5341835)
#> 91771  8.73     52.5 2.37   122.25643 POINT (433661.7 5341177)
#> 91545 13.80     83.1 3.70   482.14827 POINT (435401.3 5341640)
#> 91630 13.70     47.6 3.44    51.61126 POINT (435039.2 5339888)
#> 92069 19.70     93.9 4.74    22.72837 POINT (431868.3 5337803)

#--- ensure access and existing are in the same CRS ---#

sf::st_crs(existing) <- sf::st_crs(access)

#--- sub sample using ---#
sample_existing(
  existing = existing, # our existing sample
  nSamp = 300, # desired sample size
  raster = dist, # include mraster metrics to guide sampling of existing
  cost = 4, # either provide the index (band number) or the name of the cost layer
  access = access, # roads layer
  buff_inner = 50, # inner buffer - no sample units within this distance from road
  buff_outer = 300, # outer buffer - no sample units further than this distance from road
  plot = TRUE
) # plot
#> Simple feature collection with 300 features and 4 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431239.5 ymin: 5337735 xmax: 438542.8 ymax: 5343240
#> Projected CRS: +proj=utm +zone=17 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
#> First 10 features:
#>        zq90 pzabove2  zsd dist2access                 geometry
#> 91501 12.20     75.3 3.47    84.92854 POINT (434471.6 5338909)
#> 91295 18.10     79.5 4.14    61.87884   POINT (436857 5341613)
#> 91658 15.80     97.0 2.51    83.52699 POINT (431535.5 5340612)
#> 91223  9.43     88.0 1.96   151.02703 POINT (438080.4 5340903)
#> 91659 15.60     67.2 5.30    78.11264 POINT (431484.1 5340805)
#> 91433 20.00     50.6 5.55   213.71056 POINT (434987.8 5340081)
#> 91234 18.50     94.6 3.17   118.09580 POINT (438503.7 5338532)
#> 91369  5.73     62.4 1.34   122.41092 POINT (435286.3 5342851)
#> 91304 13.80     89.0 3.38   116.51806 POINT (436355.5 5342721)
#> 91492 18.30     75.8 5.63    53.15844 POINT (433637.2 5342826)

sample_existing(existing = e, nSamp = 300, type = "balanced")
#> Simple feature collection with 300 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431122 ymin: 5337700 xmax: 438542.8 ymax: 5343228
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>    zq90 pzabove2  zsd                 geometry
#> 4  14.9     87.5 3.53 POINT (438442.5 5342655)
#> 5  19.6     85.0 4.33 POINT (438391.1 5342848)
#> 6  14.4     69.9 4.00 POINT (438339.7 5343042)
#> 9  25.2     85.2 7.38 POINT (438506.1 5341637)
#> 15 17.0     89.9 3.74 POINT (438197.8 5342797)
#> 16 12.4     87.5 2.81 POINT (438146.4 5342990)
#> 17 16.2     89.2 4.28 POINT (438095.1 5343184)
#> 19 17.6     87.6 4.43   POINT (438467 5341006)
#> 26 14.8     81.9 3.54 POINT (438004.5 5342746)
#> 27  6.0     13.9 1.48 POINT (437953.1 5342939)

sample_existing(existing = e, nSamp = 300, type = "balanced", algorithm = "lcube")
#> Simple feature collection with 300 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431149 ymin: 5337710 xmax: 438542.8 ymax: 5343228
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>    zq90 pzabove2  zsd                 geometry
#> 4  14.9     87.5 3.53 POINT (438442.5 5342655)
#> 9  25.2     85.2 7.38 POINT (438506.1 5341637)
#> 10 15.3     97.0 3.29 POINT (438454.7 5341831)
#> 16 12.4     87.5 2.81 POINT (438146.4 5342990)
#> 18 19.8     92.5 5.62 POINT (438518.3 5340813)
#> 22 15.1     93.2 2.93 POINT (438210.1 5341972)
#> 27  6.0     13.9 1.48 POINT (437953.1 5342939)
#> 29 18.2     89.5 4.21 POINT (438530.6 5339988)
#> 31 16.9     60.1 5.09 POINT (438427.8 5340375)
#> 32 17.4     87.3 4.93 POINT (438325.1 5340761)

sample_existing(existing = e, nSamp = 300, type = "srs")
#> Simple feature collection with 300 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431109.8 ymin: 5337708 xmax: 438515.9 ymax: 5343228
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>     zq90  pzabove2  zsd                 geometry
#> 1  14.70 65.599998 4.02 POINT (435347.5 5338728)
#> 2  14.30 88.900002 2.82 POINT (437101.7 5341471)
#> 3  17.30 69.500000 4.58 POINT (432489.7 5341693)
#> 4  20.80  9.900001 5.62 POINT (434652.6 5339785)
#> 5  22.30 88.300003 7.45 POINT (437745.2 5340607)
#> 6  17.10 96.599998 3.17 POINT (435993.4 5340969)
#> 7   9.99 59.000000 2.81 POINT (437047.9 5338559)
#> 8   3.40 16.600000 0.66 POINT (435530.9 5342709)
#> 9  14.40 64.500000 3.82 POINT (437307.2 5340698)
#> 10 20.60 95.700005 4.77 POINT (437140.9 5342102)

sraster <- strat_kmeans(mraster = mraster, nStrata = 4)

e_strata <- extract_strata(sraster = sraster, existing = e)

#--- proportional stratified sampling of existing ---#
sample_existing(existing = e_strata, nSamp = 300, type = "strat", allocation = "prop")
#> Simple feature collection with 299 features and 4 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431109.8 ymin: 5337708 xmax: 438518.3 ymax: 5343225
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>    strata zq90 pzabove2  zsd                 geometry
#> 1       2 18.6     91.2 3.29   POINT (431768 5341295)
#> 2       2 17.9     91.9 3.52 POINT (434177.9 5342349)
#> 3       2 14.3     64.9 4.67 POINT (436159.8 5339565)
#> 4       2 18.1     79.5 4.14   POINT (436857 5341613)
#> 5       2 18.6     87.3 4.54   POINT (435942 5341163)
#> 6       2 17.0     92.9 3.92 POINT (433725.3 5340159)
#> 7       2 21.4     89.6 4.16 POINT (432553.3 5340676)
#> 8       2 16.2     93.6 3.28 POINT (432692.8 5337815)
#> 9       2 17.8     92.9 3.67 POINT (437165.3 5340453)
#> 10      2 18.2     87.4 4.15 POINT (437050.3 5341664)

#--- equal stratified sampling of existing ---#
sample_existing(existing = e_strata, nSamp = 100, type = "strat", allocation = "equal")
#> Simple feature collection with 400 features and 4 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431109.8 ymin: 5337708 xmax: 438555.1 ymax: 5343235
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>    strata zq90 pzabove2  zsd                 geometry
#> 1       2 15.3     96.0 3.33 POINT (433659.3 5338072)
#> 2       2 18.1     79.5 4.14   POINT (436857 5341613)
#> 3       2 17.6     90.7 3.80 POINT (432127.6 5339942)
#> 4       2 16.2     94.2 3.03 POINT (431134.3 5338229)
#> 5       2 18.9     94.6 4.07 POINT (433776.7 5339966)
#> 6       2 16.6     88.9 3.87 POINT (433918.6 5340211)
#> 7       2 15.0     95.7 3.77   POINT (436057 5339951)
#> 8       2 19.4     90.0 4.46 POINT (435349.9 5341833)
#> 9       2 17.0     93.6 2.99 POINT (432462.8 5340238)
#> 10      2 15.7     95.9 2.70 POINT (434549.8 5340172)

#--- manual stratified sampling of existing with user defined weights ---#
s <- sample_existing(existing = e_strata, nSamp = 100, type = "strat", allocation = "manual", weights = c(0.2, 0.6, 0.1, 0.1))

#--- check proportions match weights ---#
table(s$strata) / 100
#> 
#>   1   2   3   4 
#> 0.2 0.6 0.1 0.1

#--- manual stratified sampling of existing with user defined weights ---#
sample_existing(existing = e_strata, nSamp = 100, type = "strat", allocation = "optim", raster = mraster, metric = "zq90")
#> Simple feature collection with 99 features and 4 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431124.5 ymin: 5337803 xmax: 438555.1 ymax: 5343186
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#>    strata zq90 pzabove2  zsd                 geometry
#> 1       2 16.3     97.9 3.54 POINT (437319.5 5339873)
#> 2       2 14.4     94.7 3.34 POINT (435917.5 5342812)
#> 3       2 15.3     95.6 2.47 POINT (435746.3 5338006)
#> 4       2 16.2     86.9 4.01 POINT (433248.2 5339619)
#> 5       2 15.5     93.0 3.76 POINT (431469.5 5338525)
#> 6       2 15.9     74.1 3.63 POINT (437681.6 5341625)
#> 7       2 14.9     83.2 3.57 POINT (431317.8 5342210)
#> 8       2 14.6     82.6 4.00 POINT (435863.7 5339900)
#> 9       2 14.6     95.8 3.04 POINT (434229.3 5342156)
#> 10      2 18.6     95.5 3.10 POINT (434574.3 5338523)