sample_srs

We have demonstrated a simple example of using thesample_srs() function invignette("sgsR"). Wewill demonstrate additional examples below.

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

#> Simple feature collection with 200 features and 0 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431250 ymin: 5337710 xmax: 438510 ymax: 5343230#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>                  geometry#> 1  POINT (431770 5340890)#> 2  POINT (434330 5341750)#> 3  POINT (432750 5339510)#> 4  POINT (431990 5339850)#> 5  POINT (435050 5343030)#> 6  POINT (433510 5341850)#> 7  POINT (434630 5337830)#> 8  POINT (435470 5343210)#> 9  POINT (435390 5340170)#> 10 POINT (433170 5340970)

sample_srs(raster = mraster,# input mrasternSamp =200,# number of desired sample unitsaccess = access,# define access road networkmindist =200,# minimum distance sample units must be apart from one anotherbuff_inner =50,# inner buffer - no sample units within this distance from roadbuff_outer =200,# outer buffer - no sample units further than this distance from roadplot =TRUE)# plot

#> Simple feature collection with 200 features and 0 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431130 ymin: 5337710 xmax: 438550 ymax: 5343170#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>                  geometry#> 1  POINT (431450 5342850)#> 2  POINT (433370 5340870)#> 3  POINT (438550 5338070)#> 4  POINT (438350 5338590)#> 5  POINT (434410 5339990)#> 6  POINT (437530 5342030)#> 7  POINT (438330 5339470)#> 8  POINT (436850 5338450)#> 9  POINT (431450 5342490)#> 10 POINT (434890 5342530)

sample_systematic

Thesample_systematic() function applies systematicsampling across an area with thecellsize parameterdefining the resolution of the tessellation. The tessellation shape canbe modified using thesquare parameter. AssigningTRUE (default) to thesquare parameter resultsin a regular grid and assigningFALSE results in ahexagonal grid.

The location of sample units can also be adjusted using thelocations parameter, wherecenters takes thecenter,corners takes all corners, andrandomtakes a random location within each tessellation. Random start pointsand translations are applied when the function is called.

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

#> Simple feature collection with 38 features and 0 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431122.5 ymin: 5337766 xmax: 438445.6 ymax: 5343227#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>                    geometry#> 1  POINT (436297.3 5343227)#> 2  POINT (435313.9 5343046)#> 3  POINT (434330.5 5342864)#> 4  POINT (433347.1 5342683)#> 5  POINT (432363.7 5342501)#> 6  POINT (431380.3 5342320)#> 7  POINT (438445.6 5342606)#> 8  POINT (437462.2 5342425)#> 9  POINT (436478.7 5342244)#> 10 POINT (435495.3 5342062)

#--- perform grid sampling ---#sample_systematic(raster = sraster,# input srastercellsize =500,# grid distancesquare =FALSE,# hexagonal tessellationlocation ="random",# randomly sample within tessellationplot =TRUE)# plot

#> Simple feature collection with 176 features and 0 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431132.4 ymin: 5337742 xmax: 438535.9 ymax: 5343231#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>                    geometry#> 1  POINT (431379.6 5342970)#> 2  POINT (431235.5 5342688)#> 3  POINT (431575.5 5343041)#> 4  POINT (431831.4 5342717)#> 5    POINT (431331 5341504)#> 6  POINT (431791.2 5342100)#> 7  POINT (432072.2 5342952)#> 8  POINT (431253.6 5340850)#> 9  POINT (431865.8 5341872)#> 10 POINT (432084.9 5342740)

sample_systematic(raster = sraster,# input srastercellsize =500,# grid distanceaccess = access,# define access road networkbuff_outer =200,# outer buffer - no sample units further than this distance from roadsquare =FALSE,# hexagonal tessellationlocation ="corners",# take corners instead of centersplot =TRUE)

#> Simple feature collection with 618 features and 0 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431316.9 ymin: 5337742 xmax: 438393.9 ymax: 5343239#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>                    geometry#> 1  POINT (431380.7 5342697)#> 2  POINT (431380.7 5342697)#> 3    POINT (431517 5342443)#> 4  POINT (431973.7 5343178)#> 5  POINT (431821.5 5342933)#> 6    POINT (431517 5342443)#> 7  POINT (431380.7 5342697)#> 8  POINT (431821.5 5342933)#> 9  POINT (431957.8 5342679)#> 10 POINT (431805.5 5342433)

sample_strat

Thesample_strat() contains twomethods toperform sampling:

• "Queinnec" - Hierarchical sampling using a focalwindow to isolate contiguous groups of stratum pixels, which wasoriginally developed by Martin Queinnec.

• "random" - Traditional stratified random sampling.Thismethod ignores much of the functionality of thealgorithm to allow users the capability to use standard stratifiedrandom sampling approaches without the use of a focal window to locatecontiguous stratum cells.

#--- perform stratified sampling random sampling ---#sample_strat(sraster = sraster,# input srasternSamp =200)# desired sample size # plot#> Simple feature collection with 200 features and 3 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431150 ymin: 5337750 xmax: 438530 ymax: 5343170#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>    strata type  rule               geometry#> x       1  new rule1 POINT (436050 5342330)#> x1      1  new rule1 POINT (434670 5341170)#> x2      1  new rule1 POINT (434390 5342610)#> x3      1  new rule1 POINT (436950 5338070)#> x4      1  new rule1 POINT (433910 5342950)#> x5      1  new rule1 POINT (437790 5338290)#> x6      1  new rule1 POINT (434690 5341670)#> x7      1  new rule1 POINT (435690 5342470)#> x8      1  new rule1 POINT (436990 5338230)#> x9      1  new rule1 POINT (433790 5342990)

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

sample_strat(sraster = sraster,# input srasternSamp =200,# desired sample sizeaccess = access,# define access road networkexisting = e.sr,# existing sample with strata valuesmindist =200,# minimum distance sample units must be apart from one anotherbuff_inner =50,# inner buffer - no sample units within this distance from roadbuff_outer =200,# outer buffer - no sample units further than this distance from roadplot =TRUE)# plot

#> Simple feature collection with 400 features and 3 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431190 ymin: 5337730 xmax: 438530 ymax: 5343210#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>    strata     type     rule               geometry#> 1       1 existing existing POINT (437770 5337930)#> 2       1 existing existing POINT (437830 5341230)#> 3       1 existing existing POINT (435710 5341910)#> 4       1 existing existing POINT (432990 5341830)#> 5       1 existing existing POINT (435050 5339450)#> 6       1 existing existing POINT (438070 5341070)#> 7       1 existing existing POINT (434350 5341950)#> 8       1 existing existing POINT (433010 5340570)#> 9       1 existing existing POINT (436970 5338190)#> 10      1 existing existing POINT (434730 5342250)

sample_strat(sraster = sraster,# inputnSamp =200,# desired sample sizeaccess = access,# define access road networkexisting = e.sr,# existing samples with strata valuesinclude =TRUE,# include existing sample in nSamp totalbuff_outer =200,# outer buffer - no samples further than this distance from roadplot =TRUE)# plot

#> Simple feature collection with 200 features and 3 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431230 ymin: 5337750 xmax: 438530 ymax: 5343170#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>    strata     type     rule               geometry#> 1       1 existing existing POINT (437770 5337930)#> 2       1 existing existing POINT (437830 5341230)#> 3       1 existing existing POINT (435710 5341910)#> 4       1 existing existing POINT (432990 5341830)#> 5       1 existing existing POINT (435050 5339450)#> 6       1 existing existing POINT (438070 5341070)#> 7       1 existing existing POINT (434350 5341950)#> 8       1 existing existing POINT (433010 5340570)#> 9       1 existing existing POINT (436970 5338190)#> 10      1 existing existing POINT (434730 5342250)

#--- perform stratified sampling random sampling ---#sample_strat(sraster = sraster,# input srastermethod ="random",# stratified random samplingnSamp =200,# desired sample sizeplot =TRUE)# plot

#> Simple feature collection with 200 features and 2 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431110 ymin: 5337710 xmax: 438550 ymax: 5343210#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>    strata type               geometry#> x       1  new POINT (432750 5343210)#> x1      1  new POINT (437850 5338650)#> x2      1  new POINT (437690 5339490)#> x3      1  new POINT (431190 5340570)#> x4      1  new POINT (438210 5342410)#> x5      1  new POINT (434510 5341570)#> x6      1  new POINT (438270 5340950)#> x7      1  new POINT (433150 5341710)#> x8      1  new POINT (437530 5339230)#> x9      1  new POINT (436590 5339570)

sample_sys_strat

sample_sys_strat() function implements systematicstratified sampling on ansraster. This function uses thesame functionality assample_systematic() but takes ansraster as input and performs sampling on each stratumiteratively.

#--- perform grid sampling on each stratum separately ---#sample_sys_strat(sraster = sraster,# input sraster with 4 stratacellsize =1000,# grid sizeplot =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 40 features and 1 field#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431270.7 ymin: 5337911 xmax: 438370.5 ymax: 5343035#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>    strata                 geometry#> 1       1 POINT (437747.6 5342834)#> 2       1 POINT (438370.5 5338406)#> 3       1 POINT (437803.1 5339229)#> 4       1 POINT (436668.2 5340876)#> 5       1 POINT (435533.3 5342523)#> 6       1 POINT (434709.9 5341955)#> 7       1 POINT (435588.8 5338918)#> 8       1 POINT (435021.4 5339741)#> 9       1 POINT (433886.5 5341388)#> 10      1 POINT (432751.6 5343035)

Just like withsample_systematic() we can specify wherewe want our samples to fall within our tessellations. We specifylocation = "corners" below. Note that the tesselations areall saved to a list file whendetails = TRUE should theuser want to save them.

sample_sys_strat(sraster = sraster,# input sraster with 4 stratacellsize =500,# grid sizesquare =FALSE,# hexagon tessellationlocation ="corners",# samples on tessellation cornersplot =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 1187 features and 1 field#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431109.3 ymin: 5337702 xmax: 438510.8 ymax: 5343231#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>    strata                 geometry#> 1       1 POINT (438138.6 5337796)#> 2       1   POINT (438222 5338072)#> 3       1 POINT (438274.3 5339112)#> 4       1 POINT (438357.7 5339388)#> 5       1   POINT (438222 5338072)#> 6       1 POINT (438024.3 5338283)#> 7       1 POINT (438107.6 5338559)#> 8       1 POINT (438138.6 5337796)#> 9       1 POINT (438138.6 5337796)#> 10      1 POINT (438357.7 5339388)

This sampling approach could be especially useful incombination withstrat_poly() to ensure consistency of sampling accrossspecific 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#>   `C:\Users\goodb\AppData\Local\Temp\RtmpKwkxG7\Rinst2ec85f71703f\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 stratacellsize =500,# grid sizesquare =FALSE,# hexagon tessellationlocation ="random",# randomize plot locationplot =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 179 features and 1 field#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431115.5 ymin: 5337705 xmax: 438554.6 ymax: 5343240#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>    strata                 geometry#> 1       1 POINT (432177.2 5343114)#> 2       1 POINT (432546.4 5343207)#> 3       1 POINT (432304.6 5342684)#> 4       1 POINT (433067.1 5342912)#> 5       1 POINT (432821.6 5342656)#> 6       1 POINT (433560.7 5342583)#> 7       1 POINT (435971.9 5343195)#> 8       1   POINT (434004 5342693)#> 9       1 POINT (434772.7 5342938)#> 10      1 POINT (435483.1 5342823)

sample_nc

sample_nc() function implements the Nearest Centroidsampling algorithm described inMelville &Stone (2016). The algorithm uses kmeans clustering where the numberof clusters (centroids) is equal to the desired sample size(nSamp).

Cluster centers are located, which then prompts the nearest neighbourmraster pixel for each cluster to be selected (assumingdefaultk parameter). These nearest neighbours are theoutput sample units.

#--- perform simple random sampling ---#sample_nc(mraster = mraster,# inputnSamp =25,# desired sample sizeplot =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: 431990 ymin: 5338010 xmax: 438450 ymax: 5343050#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>        zq90 pzabove2  zsd kcenter               geometry#> 15509  5.86     55.9 1.38       1 POINT (435410 5342410)#> 27274 12.90     75.0 3.30       2 POINT (431990 5341770)#> 66746  9.86     64.4 2.57       3 POINT (438130 5339670)#> 10797 17.50     88.4 4.35       4 POINT (438150 5342670)#> 36036 20.40     92.7 4.62       5 POINT (435650 5341310)#> 3725  20.10     71.7 6.12       6 POINT (438450 5343050)#> 32617  4.58     29.5 1.06       7 POINT (434410 5341490)#> 65471  7.54     13.9 2.02       8 POINT (435010 5339730)#> 47146  3.07      6.7 0.60       9 POINT (434050 5340710)#> 27558 15.90     73.2 4.14      10 POINT (437670 5341770)

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

#--- perform simple random sampling ---#samples<-sample_nc(mraster = mraster,# inputk =2,# number of nearest neighbours to take for each kmeans centernSamp =25,# desired sample sizeplot =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 ispossible when usingdetails = TRUE. The$kplotoutput provides a quick visualization of where the centers are based ona scatter plot of the first 2 layers inmraster. Noticethat the centers are well distributed in covariate space and chosensample units are the closest pixels to each center (nearestneighbours).

#--- perform simple random sampling with details ---#details<-sample_nc(mraster = mraster,# inputnSamp =25,# desired sample numberdetails =TRUE)#> K-means being performed on 3 layers with 25 centers.#--- plot ggplot output ---#details$kplot

sample_clhs

sample_clhs() function implements conditioned Latinhypercube (clhs) sampling methodology from theclhspackage.

The syntax for this function is similar to others shown above,although parameters likeiter, which define the number ofiterations within the Metropolis-Hastings process are important toconsider. In these examples we use a lowiter value forefficiency. Default values foriter within theclhs package are 10,000.

sample_clhs(mraster = mraster,# inputnSamp =200,# desired sample sizeplot =TRUE,# plotiter =100)# number of iterations

Thecost parameter defines themrastercovariate, which is used to constrain the clhs sampling. An examplecould be the distance a pixel is from roadaccess(e.g. fromcalculate_distance() see example below), terrainslope, the output fromcalculate_coobs(), or manyothers.

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

sample_clhs(mraster = mr.c,# inputnSamp =250,# desired sample sizeiter =100,# number of iterationscost ="dist2access",# cost parameter - name defined in calculate_distance()plot =TRUE)# plot

sample_balanced

Thesample_balanced() algorithm performs a balancedsampling methodology from thestratifyR / SamplingBigDatapackages.

sample_balanced(mraster = mraster,# inputnSamp =200,# desired sample sizeplot =TRUE)# plot

#> Simple feature collection with 200 features and 0 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431110 ymin: 5337730 xmax: 438550 ymax: 5343230#> Projected CRS: +proj=utm +zone=17 +ellps=GRS80 +units=m +no_defs#> First 10 features:#>                  geometry#> 1  POINT (433050 5343230)#> 2  POINT (433610 5343210)#> 3  POINT (434450 5343210)#> 4  POINT (435790 5343210)#> 5  POINT (437410 5343150)#> 6  POINT (431210 5343130)#> 7  POINT (436190 5343130)#> 8  POINT (437290 5343110)#> 9  POINT (438310 5343110)#> 10 POINT (438010 5343090)

sample_balanced(mraster = mraster,# inputnSamp =100,# desired sample sizealgorithm ="lcube",# algorithm typeaccess = access,# define access road networkbuff_inner =50,# inner buffer - no sample units within this distance from roadbuff_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: 431190 ymin: 5337750 xmax: 438510 ymax: 5343230#> Projected CRS: +proj=utm +zone=17 +ellps=GRS80 +units=m +no_defs#> First 10 features:#>                  geometry#> 1  POINT (434870 5343230)#> 2  POINT (435650 5343210)#> 3  POINT (435030 5343110)#> 4  POINT (437370 5343030)#> 5  POINT (432950 5343010)#> 6  POINT (437870 5342950)#> 7  POINT (434230 5342890)#> 8  POINT (435950 5342870)#> 9  POINT (433770 5342850)#> 10 POINT (435050 5342850)

sample_ahels

Thesample_ahels() function performs the adaptedHypercube Evaluation of a Legacy Sample (ahels) algorithmusingexisting sample data and anmraster. Newsample units are allocated based on quantile ratios between theexisting sample andmraster covariatedataset.

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

Malone BP, Minansy B, Brungard C. 2019. Some methods toimprove the utility of conditioned Latin hypercube sampling. PeerJ7:e6451 DOI 10.7717/peerj.6451

This algorithm:

1. Determines the quantile distributions ofexistingsample units andmraster covariates.

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

3. Prioritizes sampling within those quantile to improverepresentation.

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

#--- remove `type` variable from existing  - causes plotting issues ---#existing<- existing%>%select(-type)sample_ahels(mraster = mraster,existing = existing,# existing sampleplot =TRUE)# plot

#> Simple feature collection with 339 features and 7 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431150 ymin: 5337750 xmax: 438530 ymax: 5343210#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>      type.x zq90 pzabove2  zsd strata type.y  rule               geometry#> 1  existing 5.34     63.9 1.20      1    new rule1 POINT (437770 5337930)#> 2  existing 7.23     69.0 1.64      1    new rule1 POINT (437830 5341230)#> 3  existing 9.57     78.1 2.51      1    new rule1 POINT (435710 5341910)#> 4  existing 5.07      2.8 1.18      1    new rule1 POINT (432990 5341830)#> 5  existing 3.94      7.6 0.79      1    new rule1 POINT (435050 5339450)#> 6  existing 6.67     77.2 1.41      1    new rule1 POINT (438070 5341070)#> 7  existing 3.22     17.1 0.59      1    new rule1 POINT (434350 5341950)#> 8  existing 7.69     41.1 1.99      1    new rule1 POINT (433010 5340570)#> 9  existing 8.22     89.4 1.68      1    new rule1 POINT (436970 5338190)#> 10 existing 8.61     51.0 2.16      1    new rule1 POINT (434730 5342250)

The first matrix output shows the quantile ratios between the sampleand the covariates. A value of 1.0 indicates that the sample isrepresentative of quantile coverage. Values > 1.0 indicate overrepresentation of sample units, while < 1.0 indicate underrepresentation.

sample_ahels(mraster = mraster,existing = existing,# existing samplenQuant =20,# define 20 quantilesnSamp =300)# desired sample size

#> Simple feature collection with 500 features and 7 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431110 ymin: 5337710 xmax: 438550 ymax: 5343210#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>      type.x zq90 pzabove2  zsd strata type.y  rule               geometry#> 1  existing 5.34     63.9 1.20      1    new rule1 POINT (437770 5337930)#> 2  existing 7.23     69.0 1.64      1    new rule1 POINT (437830 5341230)#> 3  existing 9.57     78.1 2.51      1    new rule1 POINT (435710 5341910)#> 4  existing 5.07      2.8 1.18      1    new rule1 POINT (432990 5341830)#> 5  existing 3.94      7.6 0.79      1    new rule1 POINT (435050 5339450)#> 6  existing 6.67     77.2 1.41      1    new rule1 POINT (438070 5341070)#> 7  existing 3.22     17.1 0.59      1    new rule1 POINT (434350 5341950)#> 8  existing 7.69     41.1 1.99      1    new rule1 POINT (433010 5340570)#> 9  existing 8.22     89.4 1.68      1    new rule1 POINT (436970 5338190)#> 10 existing 8.61     51.0 2.16      1    new rule1 POINT (434730 5342250)

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

sample_existing

Acknowledging thatexisting sample networks are commonis important. There is significant investment into these samples, and inorder to keep inventories up-to-date, we often need to collect new datafor sample units. Thesample_existing algorithm providesthe user with methods for sub-sampling anexisting samplenetwork should the financial / logistical resources not be available tocollect data at all sample units. The functions allows users to choosebetween algorithm types using (type = "clhs" - default,type = "balanced",type = "srs",type = "strat"). Differences in type result in callinginternalsample_existing_*() functions(sample_existing_clhs() (default),sample_existing_balanced(),sample_existing_srs(),sample_existing_strat()). These functions are not exportedto be used stand-alone, however they employ the same functionality astheirsample_clhs() etc counterparts.

While usingsample_existing(), should the user wish tospecify algorithm specific parameters(e.g. algorithm = "lcube" insample_balanced()orallocation = "equal" insample_strat()),they can specify withinsample_existing() as if calling thefunction directly.

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

• We have a systematic sample where sample units are 200mapart.

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

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

See ourexisting 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: 431166.7 ymin: 5337700 xmax: 438559.5 ymax: 5343229#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>      zq90 pzabove2  zsd                 geometry#> 858 21.20     94.6 4.15 POINT (432611.9 5338202)#> 733 21.10     95.4 3.72 POINT (432970.8 5339860)#> 62  19.60     94.2 4.73 POINT (437390.7 5342232)#> 354 22.20     92.2 5.65 POINT (435806.4 5340260)#> 703 11.30     84.4 2.73 POINT (433572.2 5339296)#> 463  7.19      6.0 1.94 POINT (434917.3 5340163)#> 490  5.51      1.3 2.62 POINT (435074.1 5339550)#> 646 26.30     95.8 6.59 POINT (433583.5 5340017)#> 810 17.20     50.5 5.78 POINT (432358.1 5339704)#> 9   13.40     90.1 2.96 POINT (438123.1 5342942)

#--- sub sample using ---#sample_existing(existing = existing,# our existing samplenSamp =300,# desired sample sizeraster = mraster,# include mraster metrics to guide sampling of existingplot =TRUE)# plot#> Simple feature collection with 300 features and 3 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431129.5 ymin: 5337700 xmax: 438556.3 ymax: 5343237#> Projected CRS: +proj=utm +zone=17 +ellps=GRS80 +units=m +no_defs#> First 10 features:#>        zq90 pzabove2  zsd                 geometry#> 91950  9.96     85.1 2.42 POINT (433669.2 5338407)#> 91986 21.00     82.6 5.75 POINT (431984.7 5340653)#> 91877 21.70     94.7 3.43 POINT (432765.5 5340918)#> 91779 14.50     95.3 2.99 POINT (433811.5 5340402)#> 91835  2.76      7.5 0.45 POINT (434233.4 5339008)#> 91515  9.92     41.4 2.63 POINT (436732.7 5339191)#> 91686  8.22     16.5 2.20 POINT (434965.7 5339718)#> 91861 12.20     89.7 2.59 POINT (434823.4 5337723)#> 91624 22.80     92.2 5.78 POINT (435843.6 5339094)#> 91477 15.00     51.7 4.21 POINT (435227.7 5342266)

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

#--- sub sample using ---#sample_existing(existing = existing,# our existing samplenSamp =300,# desired sample sizeraster = dist,# include mraster metrics to guide sampling of existingcost =4,# either provide the index (band number) or the name of the cost layerplot =TRUE)# plot#> Simple feature collection with 300 features and 4 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431155.3 ymin: 5337723 xmax: 438488.4 ymax: 5343237#> Projected CRS: +proj=utm +zone=17 +ellps=GRS80 +units=m +no_defs#> First 10 features:#>        zq90 pzabove2  zsd dist2access                 geometry#> 91929 20.20     95.4 3.29    171.4450 POINT (432862.5 5340028)#> 91631 14.80     95.9 3.22    243.9676 POINT (435085.4 5340271)#> 91485 12.20     80.1 3.13    187.7314   POINT (437659 5338122)#> 91255 17.30     92.7 4.24    358.8087 POINT (437607.4 5341896)#> 92002 18.40     91.6 4.38    195.5259   POINT (432683 5339199)#> 91680 17.00     91.7 3.47    198.0760 POINT (435723.9 5338541)#> 91629 22.00     92.6 4.92    101.3995   POINT (435302 5339935)#> 91748 16.90     93.5 3.66    368.9830 POINT (434087.9 5340342)#> 91975 17.00     92.1 3.97    579.6924 POINT (433284.4 5338635)#> 91690  3.74     37.2 0.75    156.6058 POINT (434315.9 5340727)

#--- 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 samplenSamp =300,# desired sample sizeraster = dist,# include mraster metrics to guide sampling of existingcost =4,# either provide the index (band number) or the name of the cost layeraccess = access,# roads layerbuff_inner =50,# inner buffer - no sample units within this distance from roadbuff_outer =300,# outer buffer - no sample units further than this distance from roadplot =TRUE)# plot#> Simple feature collection with 300 features and 4 fields#> Geometry type: POINT#> Dimension:     XY#> Bounding box:  xmin: 431155.3 ymin: 5337712 xmax: 438559.5 ymax: 5343237#> Projected CRS: +proj=utm +zone=17 +ellps=GRS80 +units=m +no_defs#> First 10 features:#>        zq90 pzabove2  zsd dist2access                 geometry#> 91333  7.84     31.4 1.92   143.46549 POINT (435179.2 5342710)#> 91607 13.40     79.9 3.69   282.74638 POINT (433187.4 5339524)#> 91294  6.88     14.2 1.77    71.61624 POINT (438006.6 5339060)#> 91647 12.30     94.9 3.43    63.13478 POINT (432623.2 5338923)#> 91449  8.83     77.4 2.19   110.67972 POINT (434375.7 5341003)#> 91355  4.33     10.1 1.03    67.79961 POINT (435167.9 5341989)#> 91210  2.98      3.6 0.54    97.84857 POINT (437835.3 5342280)#> 91340 17.40     88.7 3.56   115.09550 POINT (436419.1 5340416)#> 91217 14.80     91.6 3.65   140.96187 POINT (438317.1 5341163)#> 91228 10.60     96.0 1.95    80.54679 POINT (438040.6 5341223)

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: 431106.9 ymin: 5337700 xmax: 438559.5 ymax: 5343237#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>     zq90 pzabove2      zsd                 geometry#> 4  17.10     71.3 3.680000 POINT (438182.9 5343218)#> 5  19.50     93.6 5.190000 POINT (438556.3 5342269)#> 14 16.10     83.8 3.190000 POINT (438171.6 5342497)#> 17  5.05      5.3 1.710000 POINT (437846.7 5343001)#> 20 22.10     91.7 4.770000 POINT (438436.7 5341716)#> 27  2.39      2.3 0.350000 POINT (437678.5 5342893)#> 28  4.73     64.5 1.050000 POINT (437570.2 5343061)#> 29 23.40     83.9 8.429999 POINT (437461.9 5343229)#> 34  2.98      3.6 0.540000 POINT (437835.3 5342280)#> 38 17.40     98.8 2.260000 POINT (437402.1 5342953)

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: 431110 ymin: 5337709 xmax: 438556.3 ymax: 5343226#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>     zq90 pzabove2  zsd                 geometry#> 4  17.10     71.3 3.68 POINT (438182.9 5343218)#> 5  19.50     93.6 5.19 POINT (438556.3 5342269)#> 8  19.20     89.8 5.78 POINT (438231.4 5342773)#> 19 21.30     81.5 5.96   POINT (438545 5341548)#> 32 15.80     82.1 4.04 POINT (438051.9 5341944)#> 33 11.00     55.0 2.80 POINT (437943.6 5342112)#> 34  2.98      3.6 0.54 POINT (437835.3 5342280)#> 39 24.40     96.8 7.55 POINT (437293.8 5343121)#> 40 25.70     97.2 6.75 POINT (438533.7 5340827)#> 43 11.30     76.3 3.01 POINT (438208.8 5341331)

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: 431166.7 ymin: 5337723 xmax: 438522.4 ymax: 5343237#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>    zq90 pzabove2   zsd                 geometry#> 1  20.4     65.6  7.06 POINT (432246.6 5343200)#> 2  10.7     74.7  2.96 POINT (437610.5 5338567)#> 3  13.5     23.1  4.44 POINT (435133.9 5339826)#> 4  19.6     89.9  5.15 POINT (435817.7 5340981)#> 5  19.8     94.7  5.40 POINT (433452.6 5338743)#> 6  20.9     72.3  6.66 POINT (431465.8 5342935)#> 7  12.0     77.0  3.18 POINT (437536.2 5340898)#> 8  10.2     50.5  2.71 POINT (435339.2 5338769)#> 9  17.4     84.3  3.74 POINT (433449.4 5342072)#> 10 29.0     86.9 10.67 POINT (433344.2 5338911)

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: 431110 ymin: 5337709 xmax: 438522.4 ymax: 5343226#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>    strata zq90 pzabove2  zsd                 geometry#> 1       4 17.6     85.0 4.09 POINT (433400.9 5342516)#> 2       4 19.0     93.0 4.09 POINT (436273.6 5341750)#> 3       4 15.0     51.7 4.21 POINT (435227.7 5342266)#> 4       4 18.9     82.6 4.03 POINT (432175.5 5342203)#> 5       4 17.1     96.3 3.60 POINT (434162.2 5338011)#> 6       4 19.2     87.3 4.31 POINT (437368.1 5340790)#> 7       4 14.5     98.6 3.14 POINT (436068.3 5342807)#> 8       4 21.5     93.8 4.03 POINT (435866.2 5340536)#> 9       4 17.0     92.1 3.97 POINT (433284.4 5338635)#> 10      4 21.6     97.3 4.28   POINT (435926 5340812)

#--- 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: 431129.5 ymin: 5337709 xmax: 438548.2 ymax: 5343237#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>    strata zq90 pzabove2  zsd                 geometry#> 1       4 15.9     93.6 4.28 POINT (435407.1 5343095)#> 2       4 15.2     76.7 3.66 POINT (431671.1 5341878)#> 3       4 12.9     91.1 3.19 POINT (433235.9 5339080)#> 4       4 15.7     73.9 3.94 POINT (431443.1 5341493)#> 5       4 14.7     93.5 3.11 POINT (431614.4 5338273)#> 6       4 18.9     74.5 3.87 POINT (436826.5 5341630)#> 7       4 20.3     95.8 4.19 POINT (436490.3 5341414)#> 8       4 17.0     91.7 3.47 POINT (435723.9 5338541)#> 9       4 17.9     89.7 4.04 POINT (431383.3 5341217)#> 10      4 17.1     90.8 3.65 POINT (433680.5 5339128)

#--- 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: 431192.5 ymin: 5337780 xmax: 438559.5 ymax: 5343237#> Projected CRS: UTM Zone 17, Northern Hemisphere#> First 10 features:#>    strata zq90 pzabove2  zsd                 geometry#> 1       4 16.8     91.9 4.61   POINT (437069 5339408)#> 2       4 15.6     70.6 3.92   POINT (435171 5338661)#> 3       4 17.5     89.3 4.35 POINT (436202.5 5340753)#> 4       4 15.3     69.1 3.64 POINT (432634.5 5339644)#> 5       4 15.5     82.8 3.31   POINT (437103 5341571)#> 6       4 19.5     93.7 3.19 POINT (436034.4 5340644)#> 7       4 18.8     95.9 3.20 POINT (431696.9 5339991)#> 8       4 21.3     92.8 3.33 POINT (432443.8 5338093)#> 9       4 16.5     97.2 2.07 POINT (431192.5 5339666)#> 10      4 16.2     85.8 3.54 POINT (433016.1 5342744)