method = "Queinnec"
Queinnec, M., White, J. C., & Coops, N. C. (2021). Comparing
airborne and spaceborne photon-counting LiDAR canopy structural
estimates across different boreal forest types. Remote Sensing of
Environment, 262(August 2020), 112510.
This algorithm uses moving window (wrow and
wcol parameters) to filter the input sraster
to prioritize sample unit allocation to where stratum pixels are
spatially grouped, rather than dispersed individuals across the
landscape.
Sampling is performed using 2 rules:
Rule 1 - Sample within spatially grouped stratum
pixels. Moving window defined by wrow and
wcol.
Rule 2 - If no additional sample units exist to
satisfy desired sample size(nSamp), individual stratum
pixels are sampled.
The rule applied to a select each sample unit is defined in the
rule attribute of output samples. We give a few examples
below:
#--- perform stratified sampling random sampling ---#
sample_strat(sraster = sraster, # input sraster
nSamp = 200, # desired sample size
plot = TRUE) # plot
#> Simple feature collection with 200 features and 3 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 431150 ymin: 5337710 xmax: 438550 ymax: 5343230
#> CRS: NA
#> First 10 features:
#> strata type rule geometry
#> x 1 new rule1 POINT (436590 5338130)
#> x1 1 new rule2 POINT (436470 5339570)
#> x2 1 new rule2 POINT (438010 5339930)
#> x3 1 new rule2 POINT (437810 5339270)
#> x4 1 new rule2 POINT (437950 5338970)
#> x5 1 new rule2 POINT (434190 5338970)
#> x6 1 new rule2 POINT (436070 5342870)
#> x7 1 new rule2 POINT (433450 5341470)
#> x8 1 new rule2 POINT (433510 5340430)
#> x9 1 new rule2 POINT (434310 5341610)
In some cases, users might want to include an existing
sample within the algorithm. In order to adjust the total number of
sample units needed per stratum to reflect those already present in
existing, we can use the intermediate function
extract_strata().
This function uses the sraster and existing
sample units and extracts the stratum for each. These sample units can
be included within sample_strat(), which adjusts total
sample units required per class based on representation in
existing.
#--- extract strata values to existing samples ---#
e.sr <- extract_strata(sraster = sraster, # input sraster
existing = existing) # existing samples to add strata value to
TIP!
sample_strat() requires the sraster input
to have an attribute named strata and will give an error if
it doesn’t.
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: 431110 ymin: 5337710 xmax: 438530 ymax: 5343230
#> CRS: NA
#> First 10 features:
#> strata type rule geometry
#> 1 1 existing existing POINT (435970 5339490)
#> 2 1 existing existing POINT (436470 5339550)
#> 3 1 existing existing POINT (435550 5342350)
#> 4 1 existing existing POINT (433810 5341570)
#> 5 1 existing existing POINT (436950 5338530)
#> 6 1 existing existing POINT (436670 5338470)
#> 7 1 existing existing POINT (436750 5339070)
#> 8 1 existing existing POINT (436010 5341590)
#> 9 1 existing existing POINT (431970 5341490)
#> 10 1 existing existing POINT (434350 5340890)
The code in the example above defined the mindist
parameter, which specifies the minimum euclidean distance that new
sample units must be apart from one another.
Notice that the sample units have type and
rule attributes which outline whether they are
existing or new, and whether
rule1 or rule2 were used to select them. If
type is existing (a user provided
existing sample), rule will be
existing as well as seen above.
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: 431110 ymin: 5337710 xmax: 438530 ymax: 5343230
#> CRS: NA
#> First 10 features:
#> strata type rule geometry
#> 1 1 existing existing POINT (435970 5339490)
#> 2 1 existing existing POINT (436470 5339550)
#> 3 1 existing existing POINT (435550 5342350)
#> 4 1 existing existing POINT (433810 5341570)
#> 5 1 existing existing POINT (436950 5338530)
#> 6 1 existing existing POINT (436670 5338470)
#> 7 1 existing existing POINT (436750 5339070)
#> 8 1 existing existing POINT (436010 5341590)
#> 9 1 existing existing POINT (431970 5341490)
#> 10 1 existing existing POINT (434350 5340890)
The include parameter determines whether
existing sample units should be included in the total
sample size defined by nSamp. By default, the
include parameter is set as FALSE.
method = "random
Stratified random sampling with equal probability for all cells
(using default algorithm values for mindist and no use of
access functionality). In essence this method perform the
sample_srs algorithm for each stratum separately to meet
the specified sample size.
#--- 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 1 field
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 431290 ymin: 5337710 xmax: 438530 ymax: 5343230
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#> strata geometry
#> 1 1 POINT (436570 5340230)
#> 2 1 POINT (436570 5340230)
#> 3 1 POINT (438210 5338330)
#> 4 1 POINT (435290 5338710)
#> 5 1 POINT (434930 5342110)
#> 6 1 POINT (433510 5340970)
#> 7 1 POINT (434370 5342170)
#> 8 1 POINT (435770 5338710)
#> 9 1 POINT (435750 5342350)
#> 10 1 POINT (434610 5342010)
Now lets sub-sample.
