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: 431110 ymin: 5337730 xmax: 438550 ymax: 5343210
#> CRS: NA
#> First 10 features:
#> strata type rule geometry
#> x 1 new rule1 POINT (437910 5339750)
#> x1 1 new rule2 POINT (434350 5342330)
#> x2 1 new rule2 POINT (437890 5341070)
#> x3 1 new rule2 POINT (435010 5339750)
#> x4 1 new rule2 POINT (436130 5337790)
#> x5 1 new rule2 POINT (434170 5339230)
#> x6 1 new rule2 POINT (437870 5343110)
#> x7 1 new rule2 POINT (438470 5340630)
#> x8 1 new rule2 POINT (437950 5342430)
#> x9 1 new rule2 POINT (434130 5341330)
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: 431130 ymin: 5337730 xmax: 438550 ymax: 5343230
#> CRS: NA
#> First 10 features:
#> strata type rule geometry
#> 1 1 existing existing POINT (434350 5342270)
#> 2 1 existing existing POINT (434330 5340910)
#> 3 1 existing existing POINT (433470 5342990)
#> 4 1 existing existing POINT (434810 5337750)
#> 5 1 existing existing POINT (435870 5342990)
#> 6 1 existing existing POINT (434290 5339190)
#> 7 1 existing existing POINT (437910 5343150)
#> 8 1 existing existing POINT (435170 5342410)
#> 9 1 existing existing POINT (433910 5341250)
#> 10 1 existing existing POINT (435910 5338870)
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: 431130 ymin: 5337730 xmax: 438550 ymax: 5343230
#> CRS: NA
#> First 10 features:
#> strata type rule geometry
#> 1 1 existing existing POINT (434350 5342270)
#> 2 1 existing existing POINT (434330 5340910)
#> 3 1 existing existing POINT (433470 5342990)
#> 4 1 existing existing POINT (434810 5337750)
#> 5 1 existing existing POINT (435870 5342990)
#> 6 1 existing existing POINT (434290 5339190)
#> 7 1 existing existing POINT (437910 5343150)
#> 8 1 existing existing POINT (435170 5342410)
#> 9 1 existing existing POINT (433910 5341250)
#> 10 1 existing existing POINT (435910 5338870)
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: 431110 ymin: 5337710 xmax: 438550 ymax: 5343210
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#> strata geometry
#> 1 1 POINT (435130 5338750)
#> 2 1 POINT (432290 5340230)
#> 3 1 POINT (434170 5340930)
#> 4 1 POINT (436290 5337950)
#> 5 1 POINT (435010 5339750)
#> 6 1 POINT (436850 5339190)
#> 7 1 POINT (436270 5343010)
#> 8 1 POINT (436930 5338130)
#> 9 1 POINT (431790 5342530)
#> 10 1 POINT (434750 5342090)
Now lets sub-sample.
