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 locations 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 more samples exist to satisfy
desired sampling count, individual stratum pixels are sampled.
The rule applied to a select a particular sample 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 number
plot = TRUE) # plot
#> Simple feature collection with 200 features and 3 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 431170 ymin: 5337710 xmax: 438510 ymax: 5343230
#> CRS: NA
#> First 10 features:
#> strata type rule geometry
#> x 1 new rule1 POINT (434090 5341390)
#> x1 1 new rule2 POINT (436610 5339150)
#> x2 1 new rule2 POINT (436750 5337990)
#> x3 1 new rule2 POINT (438490 5338990)
#> x4 1 new rule2 POINT (437530 5342790)
#> x5 1 new rule2 POINT (435570 5342490)
#> x6 1 new rule2 POINT (433970 5342750)
#> x7 1 new rule2 POINT (434410 5342090)
#> x8 1 new rule2 POINT (433110 5341950)
#> x9 1 new rule2 POINT (433810 5343010)
In some cases, users might want to include existing
samples within the algorithm. In order to adjust the total number of
samples 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
samples and extracts the stratum for each. These samples can be included
within sample_strat(), which adjusts total samples 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
Notice that e.sr now has an attribute named strata. If
that parameter is not there, sample_strat() will give an
error.
sample_strat(sraster = sraster, # input sraster
nSamp = 200, # desired sample number
access = access, # define access road network
existing = e.sr, # existing samples with strata values
mindist = 200, # minimum distance samples must be apart from one another
buff_inner = 50, # inner buffer - no samples within this distance from road
buff_outer = 200, # outer buffer - no samples 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: 438550 ymax: 5343230
#> CRS: NA
#> First 10 features:
#> strata type rule geometry
#> 1 1 existing existing POINT (438010 5341070)
#> 2 1 existing existing POINT (435850 5341950)
#> 3 1 existing existing POINT (434070 5340610)
#> 4 1 existing existing POINT (435210 5340550)
#> 5 1 existing existing POINT (436910 5337970)
#> 6 1 existing existing POINT (434330 5341130)
#> 7 1 existing existing POINT (434930 5341670)
#> 8 1 existing existing POINT (437970 5340290)
#> 9 1 existing existing POINT (433470 5340930)
#> 10 1 existing existing POINT (434750 5342750)
As seen on the code in the example above, the defined
mindist parameter specifies the minimum euclidean distance
that samples must be apart from one another.
Notice that the sample outputs have type and
rule attributes which outline whether the samples are
existing or new and whether rule1
or rule2 were used to select the individual samples. 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 number
access = access, # define access road network
existing = e.sr, # existing samples with strata values
include = TRUE, # include existing plots 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: 431170 ymin: 5337710 xmax: 438550 ymax: 5343170
#> CRS: NA
#> First 10 features:
#> strata type rule geometry
#> 1 1 existing existing POINT (438010 5341070)
#> 2 1 existing existing POINT (435850 5341950)
#> 3 1 existing existing POINT (434070 5340610)
#> 4 1 existing existing POINT (435210 5340550)
#> 5 1 existing existing POINT (436910 5337970)
#> 6 1 existing existing POINT (434330 5341130)
#> 7 1 existing existing POINT (434930 5341670)
#> 8 1 existing existing POINT (437970 5340290)
#> 9 1 existing existing POINT (433470 5340930)
#> 10 1 existing existing POINT (434750 5342750)
The include parameter determines whether
existing samples should be included in the total count of
samples 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 allocation.
#--- perform stratified sampling random sampling ---#
sample_strat(sraster = sraster, # input sraster
method = "random", #stratified random sampling
nSamp = 200, # desired sample number
plot = TRUE) # plot
#> Simple feature collection with 200 features and 1 field
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 431110 ymin: 5337710 xmax: 438530 ymax: 5343230
#> Projected CRS: UTM Zone 17, Northern Hemisphere
#> First 10 features:
#> strata geometry
#> 1 1 POINT (434510 5343010)
#> 2 1 POINT (434510 5343010)
#> 3 1 POINT (433530 5341070)
#> 4 1 POINT (434750 5341290)
#> 5 1 POINT (433030 5340530)
#> 6 1 POINT (435850 5339430)
#> 7 1 POINT (437810 5342610)
#> 8 1 POINT (434910 5342150)
#> 9 1 POINT (434110 5339770)
#> 10 1 POINT (434710 5342050)
