anchor.seed: Identify seed cells

In scapesClassification: User-Defined Classification of Raster Surfaces

Identify seed cells

Description

Returns a vector of cell numbers at the locations of seed cells and growth buffers.

Usage

anchor.seed(
  attTbl,
  ngbList,
  rNumb = FALSE,
  class = NULL,
  cond.filter = NULL,
  cond.seed,
  cond.growth = NULL,
  lag.growth = Inf,
  cond.isol = NULL,
  lag.isol = 1,
  sort.col = NULL,
  sort.seed = "max",
  saveRDS = NULL,
  overWrite = FALSE,
  isol.buff = FALSE,
  silent = FALSE
)

Arguments

attTbl	data.frame, the attribute table returned by the function attTbl.
ngbList	list, the list of neighborhoods returned by the function ngbList.
rNumb	logic, the neighborhoods of the argument ngbList are identified by cell numbers (rNumb=FALSE) or by row numbers (rNumb=TRUE) (see ngbList). It is advised to use row numbers for large rasters.
class	numeric, the classification number to assign to all cells that meet the function conditions. If NULL, a new class number is assigned every time a new seed cell is identified. Growth buffers have the same classification number as the seed cell to which they refer.
cond.filter	character string, defines for what cells the arguments cond.seed, cond.growth and cond.isol have to be evaluated. It can be NULL. Absolute conditions can be used (see conditions).
cond.seed	character string, the conditions to identify seed cells. Absolute conditions can be used (see conditions). It cannot be NULL.
cond.growth	character string, the conditions to define a growth buffer around seed cells. It can be NULL. Absolute and focal cell conditions can be used (see conditions).
lag.growth	0 or Inf, defines the evaluation lag of focal cell conditions in cond.growth.
cond.isol	character string, the conditions to define an isolation buffer around seed cells and growth buffers. It can be NULL. Absolute and focal cell conditions can be used (see conditions).
lag.isol	0 or Inf, defines the evaluation lag of focal cell conditions in cond.isol.
sort.col	character, the column name in the attTbl on which the sort.seed is based on. It determines in what order seed buffers are computed.
sort.seed	character, the order seed buffers are computed is based on the value seed cells have in the column of attribute table column named sort.col. If sort.seed="max", buffers are computed from the seed cell having the maximum value to the seed cell having the minimum value. If sort.seed="min", buffers are computed in the opposite order.
saveRDS	filename, if a file name is provided save the class vector as an RDS file.
overWrite	logic, if the RDS names already exist, existing files are overwritten.
isol.buff	logic, return the isolation buffer (class = -999).
silent	logic, progress is not printed on the console.

Details

This function implements an algorithm to identify seed cells, growth buffers and isolation buffers.

Condition arguments

The function takes as inputs four sets of conditions with cond.growth and cond.isol taking into account class contiguity and continuity (see conditions):

1. cond.filter, the conditions to define what cells have to be evaluated by the function.

2. cond.seed, the conditions to identify, at each iteration, the seed cell. The seed cell is the cell around which growth and isolation conditions are applied.

3. cond.growth, the conditions to define a buffer around the seed cell.

4. cond.isol, the conditions to isolate one seed cell (and its growth buffer) from another.
   

Iterations

The argument cond.filter defines the set of cells to be considered by the function.

1. A seed cell is identified based on cond.seed and receives a classification number as specified by the argument class. If class=NULL, then a new class is assigned to every new seed cell.

2. Cells connected with the seed cell meeting the conditions of cond.growth are assigned to the same class of the seed cell (growth buffer). The rule evaluation take into account class continuity (see conditions).

3. Cells connected with the seed cell (or with its growth buffer) meeting the conditions of cond.isol are assigned to the isolation buffer (class = -999). The rule evaluation take into account class continuity (see conditions).

4. A new seed cell is identified based on cond.seed which is now only evaluated for cells that were not identified as seed, growth or isolation cells in previous iterations.

5. A new iteration starts. Seed, growth and isolation cells identified in previous iteration are ignored in successive iterations.

6. The function stops when it cannot identify any new seed cell.
   

Relative focal cell conditions and evaluation lag

• The arguments lag.growth and lag.isol control the evaluation lag of relative focal cell conditions (see conditions).

• When lag.* are set to 0, relative focal cell conditions have a standard behavior and compare the values of the test cells against the value of the focal cell.

• When lag.* are set to Inf, relative focal cell conditions compare the values of the test cells against the value of the seed cell identified at the start of the iteration.

Value

Class vector. See conditions for more details about class vectors.

See Also

conditions(), attTbl(), ngbList()

Examples

# DUMMY DATA
############################################################################
# LOAD LIBRARIES
library(scapesClassification)
library(terra)

# LOAD THE DUMMY RASTER
r <- list.files(system.file("extdata", package = "scapesClassification"),
                pattern = "dummy_raster\\.tif", full.names = TRUE)
r <- terra::rast(r)

# COMPUTE THE ATTRIBUTE TABLE
at <- attTbl(r, "dummy_var")

# COMPUTE THE LIST OF NEIGBORHOODS
nbs <- ngbList(r)

############################################################################
# EXAMPLE PLOTS
############################################################################
oldpar <- par(mfrow = c(1,2))
m <- c(4.5, 0.5, 2, 3.2)

# 1a. Do not show isol.buff
as <- anchor.seed(attTbl = at, ngbList = nbs, rNumb = FALSE, class = NULL, silent = TRUE,
                  cond.filter = "dummy_var > 1", cond.seed = "dummy_var == max(dummy_var)",
                  cond.growth = "dummy_var<dummy_var[] & dummy_var>2",
                  cond.isol = "dummy_var<dummy_var[]")

plot(cv.2.rast(r,classVector=as), type="classes", mar=m, col=c("#00A600", "#E6E600"),
     axes=FALSE, plg=list(x=1, y=1, cex=.80, title="Classes"))
text(r); lines(r)
mtext(side=3, line=0, cex=1, font=2, adj=0, "1a. Do not show 'isol.buff'")
mtext(side=1, line=0, cex=1, font=2, adj=1, "cond.filter:")
mtext(side=1, line=1, cex=1, font=2, adj=1, "cond.seed:")
mtext(side=1, line=2, cex=1, font=2, adj=1, "cond.growth:")
mtext(side=1, line=3, cex=1, font=2, adj=1, "cond.isol:")
text(xFromCell(r,c(20,43)),yFromCell(r,c(20,43))-0.05,"SEED",col="red",cex=0.80)

# 1b. Show isol.buff
as <- anchor.seed(attTbl = at, ngbList = nbs, rNumb = FALSE, class = NULL, silent = TRUE,
                  cond.filter = "dummy_var > 1", cond.seed = "dummy_var == max(dummy_var)",
                  cond.growth = "dummy_var<dummy_var[] & dummy_var>2",
                  cond.isol = "dummy_var<dummy_var[]", isol.buff = TRUE)

plot(cv.2.rast(r,classVector=as), type="classes", col=c("#00000040", "#00A600", "#E6E600"),
     mar=m, axes=FALSE, plg=list(x=1, y=1, cex=.80, title="Classes"))
text(r); lines(r)
mtext(side=3, line=0, cex=1, font=2, adj=0, "1b. Show 'isol.buff' (class=-999)")
mtext(side=1, line=0, cex=1, adj=0, "dummy_var > 1")
mtext(side=1, line=1, cex=1, adj=0, "dummy_var == max(dummy_var)")
mtext(side=1, line=2, cex=1, adj=0, "dummy_var<dummy_var[] & dummy_var>2")
mtext(side=1, line=3, cex=1, adj=0, "dummy_var<dummy_var[]")
text(xFromCell(r,c(20,43)),yFromCell(r,c(20,43))-0.05,"SEED",col="red",cex=0.80)

# 2a. Lag.growth = Inf
as <- anchor.seed(attTbl = at, ngbList = nbs, rNumb = FALSE, class = NULL, silent = TRUE,
                 cond.filter = "dummy_var > 1", cond.seed = "dummy_var == max(dummy_var)",
                  cond.growth = "dummy_var<dummy_var[]", lag.growth = Inf)

plot(cv.2.rast(r,classVector=as), type="classes", mar=m, col=c("#00A600"),
     axes=FALSE, plg=list(x=1, y=1, cex=.80, title="Classes"))
text(r); lines(r)
mtext(side=3, line=0, cex=1, font=2, adj=0, "2a. Lag.growth* = Inf")
mtext(side=1, line=0, cex=1, font=2, adj=1, "cond.filter:")
mtext(side=1, line=1, cex=1, font=2, adj=1, "cond.seed:")
mtext(side=1, line=2, cex=1, font=2, adj=1, "cond.growth*:")
mtext(side=1, line=3, cex=1, font=2, adj=1, "cond.isol:")
text(xFromCell(r,c(20)),yFromCell(r,c(20))-0.05,"SEED",col="red",cex=0.80)

# 2b. Lag.growth = 0
as <- anchor.seed(attTbl = at, ngbList = nbs, rNumb = FALSE, class = NULL, silent = TRUE,
                  cond.filter = "dummy_var > 1", cond.seed = "dummy_var == max(dummy_var)",
                  cond.growth = "dummy_var<dummy_var[]", lag.growth = 0)

plot(cv.2.rast(r,classVector=as), type="classes", mar=m, col=c("#00A600", "#E6E600"),
     axes=FALSE, plg=list(x=1, y=1, cex=.80, title="Classes"))
text(r); lines(r)
mtext(side=3, line=0, cex=1, font=2, adj=0, "2b. Lag.growth* = 0")
mtext(side=1, line=0, cex=1, adj=0, "dummy_var > 1")
mtext(side=1, line=1, cex=1, adj=0, "dummy_var == max(dummy_var)")
mtext(side=1, line=2, cex=1, adj=0, "dummy_var < dummy_var[]")
mtext(side=1, line=3, cex=1, adj=0, "NULL")
text(xFromCell(r,c(20,43)),yFromCell(r,c(20,43))-0.05,"SEED",col="red",cex=0.80)

# 3a. Without sorting
as <- anchor.seed(attTbl = at, ngbList = nbs, rNumb = FALSE, class = NULL, silent = TRUE,
                  cond.filter = "dummy_var > 1", cond.seed = "dummy_var >= 5",
                  cond.isol = "dummy_var<dummy_var[]", isol.buff = TRUE)

seeds <- which(!is.na(as) & as !=-999)
cc    <- c("#00000040", terrain.colors(8)[8:1])
plot(cv.2.rast(r,classVector=as), type="classes", mar=m, col=cc,
     axes=FALSE, plg=list(x=1, y=1, cex=.80, title="Classes"))
text(r); lines(r)
mtext(side=3, line=0, cex=1, font=2, adj=0, "3a. Without sorting")
mtext(side=1, line=0, cex=1, font=2, adj=1, "cond.filter:")
mtext(side=1, line=1, cex=1, font=2, adj=1, "cond.seed:")
mtext(side=1, line=2, cex=1, font=2, adj=1, "cond.growth:")
mtext(side=1, line=3, cex=1, font=2, adj=1, "cond.isol:")
text(xFromCell(r,seeds),yFromCell(r,seeds)-0.05,"SEED",col="red",cex=0.80)

# 3b. Sort buffer evaluation based on 'dummy_var' values
as <- anchor.seed(attTbl = at, ngbList = nbs, rNumb = FALSE, class = NULL, silent = TRUE,
                  cond.filter = "dummy_var > 1", cond.seed = "dummy_var >= 5",
                  cond.isol = "dummy_var<dummy_var[]", isol.buff = TRUE,
                  sort.col = "dummy_var", sort.seed = "max")

seeds <- which(!is.na(as) & as !=-999)
plot(cv.2.rast(r,classVector=as), type="classes",col=c("#00000040", "#00A600", "#E6E600"),
     mar=m, axes=FALSE, plg=list(x=1, y=1, cex=.80, title="Classes"))
text(r); lines(r)
mtext(side=3, line=0, cex=1, font=2, adj=0, "3b. Sort.col='dummy_var'; Sort.seed='max'")
mtext(side=1, line=0, cex=1, adj=0, "dummy_var > 1")
mtext(side=1, line=1, cex=1, adj=0, "dummy_var >= 5")
mtext(side=1, line=2, cex=1, adj=0, "NULL")
mtext(side=1, line=3, cex=1, adj=0, "dummy_var < dummy_var[]; isol.buff = -999")
text(xFromCell(r,seeds),yFromCell(r,seeds)-0.05,"SEED",col="red",cex=0.80)
par(oldpar)

scapesClassification documentation built on March 18, 2022, 6:32 p.m.