This function adds depth interval ("segment") labels to soil horizon data associated with SoilProfileCollection and data.frame objects. Additional horizon records are inserted when a segment label does not overlap with a horizon boundary. See examples.

segment(object, intervals, trim = TRUE, hzdepcols = NULL)

Arguments

object

either a SoilProfileCollection or data.frame

intervals

a vector of integers over which to slice the horizon data (e.g. c(25, 100) or 25:100)

trim

logical, when TRUE horizons in object are truncated to the min/max specified in intervals. When FALSE, those horizons overlapping an interval are marked as such. Care should be taken when specifying more than one depth interval and trim = FALSE.

hzdepcols

a character vector of length 2 specifying the names of the horizon depths (e.g. c("hzdept", "hzdepb")), only necessary if object is a data.frame.

Value

Either a SoilProfileCollection or data.frame with the original horizon data segmented by depth intervals. There are usually more records in the resulting object, one for each time a segment interval partially overlaps with a horizon. A new column called segment_id identifying the depth interval is added.

Details

This function adds segment labels to soil horizon data according to intgervals (e.g. c(25, 100) or 25:100). Compared to slice, slab, and glom, segment performs no aggregation or resampling of the source data, rather, labels are added to horizon records for subsequent aggregation. This makes it possible to process a very large number of records outside of the constraints associated with e.g. slice or slab.

Author

Stephen Roecker

Examples


# example data
data(sp1)

# upgrade to SPC
depths(sp1) <- id ~ top + bottom

# segment and trim
z <- segment(sp1, intervals = c(0, 10, 20, 30), trim = TRUE)

# display segment labels
# note that there are new horizon boundaries at segments
par(mar = c(0, 0, 3, 1))
plotSPC(z, color = 'segment_id', width = 0.3)


# highlight new horizon records
par(mar = c(0, 0, 2, 1))
plotSPC(z, color = NA, default.color = NA, width = 0.3, lwd = 1)
plotSPC(sp1, color = NA, default.color = NA, 
width = 0.3, lwd = 3, add = TRUE, name = NA, print.id = FALSE)
legend('top', horiz = TRUE, 
legend = c('original', 'segmented'), 
lwd = c(1, 3), cex = 0.85, bty = 'n')


# \donttest{
# same results as slab()
# 10 random profiles
s <- lapply(1:10, random_profile, n_prop = 1, SPC = TRUE, method = 'random_walk')
s <- combine(s)

a.slab <- slab(s, fm = ~ p1, slab.structure = c(0, 10, 20, 30), slab.fun = mean, na.rm = TRUE)

z <- segment(s, intervals = c(0, 10, 20, 30), trim = TRUE)
z <- horizons(z)
z$thick <- z$bottom - z$top

a.segment <- sapply(split(z, z$segment_id), function(i) {
  weighted.mean(i$p1, i$thick)
})


res <- data.frame(
  slab = a.slab$value,
  segment = a.segment,
  diff = a.slab$value - a.segment
)

print(res)
#>           slab  segment          diff
#> 0-10  1.038760 1.038760  0.000000e+00
#> 10-20 2.517833 2.517833 -4.440892e-16
#> 20-30 2.632743 2.632743  0.000000e+00
res$diff < 0.001
#> [1] TRUE TRUE TRUE
# }


data(sp5)

# segment by upper 25-cm
test1 <- segment(sp5, intervals = c(0, 100))
print(test1)
#> SoilProfileCollection with 296 profiles and 1254 horizons
#> profile ID: soil  |  horizon ID: hzID 
#> Depth range: 70 - 100 cm
#> 
#> ----- Horizons (6 / 1254 rows  |  10 / 19 columns) -----
#>    soil hzID top bottom sand silt clay  R25  G25  B25
#>   soil1    1   0      8 32.3 10.9 52.8 0.41 0.38 0.34
#>   soil1    2   8     25 29.0 11.2 58.2 0.31 0.28 0.25
#>   soil1    3  25     55 34.9 11.6 51.9 0.31 0.28 0.25
#>   soil1    4  55    100 38.2 10.9 49.7 0.31 0.28 0.25
#>  soil10    5   0     10 25.2 14.4 58.4 0.43 0.37 0.30
#>  soil10    6  10     25 24.4 14.9 59.0 0.44 0.37 0.31
#> [... more horizons ...]
#> 
#> ----- Sites (6 / 296 rows  |  1 / 1 columns) -----
#>     soil
#>    soil1
#>   soil10
#>  soil100
#>  soil101
#>  soil102
#>  soil103
#> [... more sites ...]
#> 
#> Spatial Data:
#> [EMPTY]
nrow(test1)
#> [1] 1254
print(object.size(test1), units = "Mb")
#> 0.3 Mb

# segment by 1-cm increments
test2 <- segment(sp5, intervals = 0:100)
print(test2)
#> SoilProfileCollection with 296 profiles and 29523 horizons
#> profile ID: soil  |  horizon ID: hzID 
#> Depth range: 70 - 100 cm
#> 
#> ----- Horizons (6 / 29523 rows  |  10 / 19 columns) -----
#>   soil hzID top bottom sand silt clay  R25  G25  B25
#>  soil1    1   0      1 32.3 10.9 52.8 0.41 0.38 0.34
#>  soil1    2   1      2 32.3 10.9 52.8 0.41 0.38 0.34
#>  soil1    3   2      3 32.3 10.9 52.8 0.41 0.38 0.34
#>  soil1    4   3      4 32.3 10.9 52.8 0.41 0.38 0.34
#>  soil1    5   4      5 32.3 10.9 52.8 0.41 0.38 0.34
#>  soil1    6   5      6 32.3 10.9 52.8 0.41 0.38 0.34
#> [... more horizons ...]
#> 
#> ----- Sites (6 / 296 rows  |  1 / 1 columns) -----
#>     soil
#>    soil1
#>   soil10
#>  soil100
#>  soil101
#>  soil102
#>  soil103
#> [... more sites ...]
#> 
#> Spatial Data:
#> [EMPTY]
nrow(test2)
#> [1] 29523
print(object.size(test2), units = "Mb")
#> 5.8 Mb


# segment and aggregate
test3 <- segment(horizons(sp5), 
                 intervals = c(0, 5, 15, 30, 60, 100, 200), 
                 hzdepcols = c("top", "bottom")
)
test3$hzthk <- test3$bottom - test3$top
test3_agg <- by(test3, test3$segment_id, function(x) {
  data.frame(
    hzID = x$hzID[1],
    segment_id = x$segment_id[1],
    average = weighted.mean(x$clay, w = x$hzthk)
  )
})
test3_agg <- do.call("rbind", test3_agg)

head(test3_agg)
#>         hzID segment_id  average
#> 0-5        1        0-5 40.31517
#> 100-200    5    100-200 48.13381
#> 15-30      2      15-30 43.88592
#> 30-60      3      30-60 46.01368
#> 5-15       1       5-15 41.89718
#> 60-100     4     60-100 47.65180