Description Usage Arguments Author(s) Examples
Runs bfastmonitor on a rasterBrick object for a set of locations, determined by an object of class Spatial-class.
1 2 3 4 |
x |
A rasterBrick or rasterStack, ideally with time written to the z dimension. In case time is not written to the z dimension, the |
y |
A SpatialPoints, SpatialPointsDataFrame, SpatialPolygons, SpatialPolygonsDataFrame, SpatialLines, SpatialLinesDataFrame, or extent. bfastmonitor will be ran at these locations. In case each feature of the object covers several pixels (typically SpatialPolygons(DataFrames), SpatialLines(DataFrames) and extent), an aggregation function ( |
start |
See |
formula |
See |
order |
See |
lag |
See |
slag |
See |
history |
See |
type |
See |
h |
See |
level |
See |
mc.cores |
Numeric NUmber of cores to use (for parallel processing) |
... |
Arguments to be passed to zooExtract |
Loic Dutrieux
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | # Load data
data(tura)
# 1- SpatialPoints case
# Generate SpatialPoints
sp <- sampleRegular(x = tura, size = 20, sp=TRUE)
# Run bfmSpOver with monitoring period starting year 2005 and all other default parameters of bfastmonitor
out <- bfmSpOver(tura, y = sp, start=c(2005,1))
# Visualize the results
plot(tura, 166)
# Build color palette
colfunc <- colorRampPalette(c("yellow", "red"))
colList <- colfunc(2013 - 2005)
points(out, col= colList[out$breakpoint - 2005], pch=16, cex = abs(out$magnitude/max(out$magnitude)))
# Color corresponds to timing of break and size to magnitude
# 2 - SpatialPolygons case
data(turaSp)
# Run bfmSpOver with monitoring period starting year 2002 and mean spatial aggregation function
out2 <- bfmSpOver(tura, y = turaSp, fun = mean, start=c(2002,1))
# Visualize
plot(tura, 166)
# Build color palette
colfunc <- colorRampPalette(c("yellow", "red"))
colList <- colfunc(2013 - 2002)
plot(out2, col = colList[out2$breakpoint - 2002], add = TRUE)
# Interpretation: The redder the latter the break was detected. If transparent, no break detected in spatially aggregated polygon time-series.
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