approxExtrapDemand-methods: Extrapolate land use area in time

In simonmoulds/lulcc2: Land Use Change Modelling in R

Description

Extrapolate land use area from two or more observed land use maps to provide a valid (although not necessarily realistic) demand scenario.

Usage

approxExtrapDemand(lu, ...)

## S4 method for signature 'LulcRasterStack'
approxExtrapDemand(lu, tout, ...)

## S4 method for signature 'DiscreteLulcRasterStack'
approxExtrapDemand(lu, tout, ...)

Arguments

lu	an LulcRasterStack object containing at least two maps
...	additional arguments to Hmisc::approxExtrap
tout	numeric vector specifying the timesteps where interpolation is to take place. Comparable to the xout argument of Hmisc::approxExtrap

Details

Many allocation routines, including the two included with lulcc2, require non-spatial estimates of land use demand for every timestep in the study period. Some routines are coupled to complex economic models that predict future or past land use demand based on economic considerations; however, linear extrapolation of trends remains a useful technique.

Value

A matrix.

See Also

Hmisc::approxExtrap

Examples

## Plum Island Ecosystems

## load observed land use maps
lu <- DiscreteLulcRasterStack(x=stack(pie[1:3]),
                              categories=c(1,2,3),
                              labels=c("Forest","Built","Other"),
                              t=c(0,6,14))

## obtain demand scenario by interpolating between observed maps
dmd <- approxExtrapDemand(lu=lu, tout=0:14)

## plot
matplot(dmd, type="l", ylab="Demand (no. of cells)", xlab="Time point",
        lty=1, col=c("Green","Red","Blue"))
legend("topleft", legend=lu@labels, col=c("Green","Red","Blue"), lty=1)

## linear extrapolation is also possible
dmd <- approxExtrapDemand(lu=lu, tout=c(0:50))

## plot
matplot(dmd, type="l", ylab="Demand (no. of cells)", xlab="Time point",
        lty=1, col=c("Green","Red","Blue"))
legend("topleft", legend=lu@labels, col=c("Green","Red","Blue"), lty=1)

simonmoulds/lulcc2 documentation built on Dec. 23, 2021, 2:24 a.m.