Responding directly to your question: applying the same signature file / spectral response to each image is somewhat risky, due to the potential for variation in how the clear-cut area looks, and how the health forest looks, due to phenological variation. As such, I'd classify each image on its own, and then go on to do change analysis on the output.
Note that using a sub-pixel analysis will most likely provide better results than a hard classification. This approach is very operator dependent, as each image / classification will require a set of endmembers.
If you feel more adventurous, then I'd suggest that you look into LandTrendr. It is a tool developed to work with temporal stacks of Landsat images, and is designed to evaluate disturbances. Here is an example view taken from the landtrendr website:
See more about LandTrendr at http://landtrendr.forestry.oregonstate.edu/
And in the article:
Kennedy, Yang, Cohen; Detecting trends in forest disturbance and recovery using yearly Landsat time series 1. LandTrendr — Temporal segmentation algorithms; Remote Sensing of Environment 114 (2010) 2897–2910
Another option is BFAST in R: http://cran.r-project.org/web/packages/bfast/index.html.
BFAST splits the timeseries into two components, a linear trend and a harmonic seasonal. Below is an illustration of a BFAST analysis.
On this illustration, I have pointed out the two test parameters that the script outlined below will extract. The 'Magnitude of Change' is implemented in BFAST, while the 'st_diff' is something that I have added. Another possible addition that could be implemented is looking at the change in slope between the two components.
Below is an example of how BFAST could be implemented for a regular timeseries of a single index (NDVI, EVI, NDSI, TC-Greeness etc.), which is good for MODIS. To use it with Landsat it may be necessary to modify the timeseries-structure.
library(bfast)
library(raster)
library(gtools)
setwd('D:/RTests/bfast/')
#bfast works on 1 parameter only. In this case I use NDVI, but EVI and Tasseled Cap Greeness are also good for vegetation studies.
#the code can is useful for any type of timeseries of indices.
vegndvi<-brick("ndvi_subset_brick.grd") #loading a raster brick of data. Each timestep is a layer in the brick.
#Establishing a helper-function - the function applies BFAST to 1 pixel at a time
xbfast <- function(data) {
ndvi <- ts(data, frequency=46, start=1) #Here a regular timeseries is established. This works for MODIS, due to the high revisit time. Landsat may need an irregularly spaced time series.
result <- bfast(ndvi, season="harmonic", max.iter=2, breaks=1) #bfast is applied to the data series
niter <- length(result$output) #extracting results
out <- result$output[[niter]] #more extraction
bp <- out$Wt.bp #breakpoint of the seasonality component
st <- out$St #the seasonality component
st_a <- st[1:bp] #seasonality untill the breakpoint
st_b <- st[bp:NROW(ndvi)] #seasonality after the breakpoint
st_amin <- min(st_a) #determines the smallest value in the seasonality-component before the break
st_amax <- max(st_a) #determines the largest value in the seasonality-component before the break
st_bmin <- min(st_b) #determines the smallest value in the seasonality-component after the break
st_bmax <- max(st_b) #determines the largest value in the seasonality-component after the break
st_adif <- st_amax - st_amin #determines the range of the seasonality-component before the break
st_bdif <- st_bmax - st_bmin #determines the range of the seasonality-component after the break
st_dif <- st_bdif - st_adif #determines the change in the seasonality across the break
Magni<-result$Magnitude #magnitude of the biggest change detected in the trend component
Timing<-result$Time #timing of the biggest change detected in the trend component
return(c(st_dif,bp,Magni,Timing))
}
#Establishing a function to apply BFAST to a raster-stack
bfastfun <- function(y) {
percNA <- apply(y, 1, FUN=function(x) (sum(is.na(x))/length(x)) ) #determines the % of NA-values in the given pixel
i <- (percNA<0.2) #tests the percentage of NA against a stop/go barrier
res <- matrix(NA, length(i), 4) #creating an empty result matrix
if (sum(i) > 0) { #if go, then apply.
res <- t(apply(y[i,], 1, xbfast)) #applying the BFAST helper function to the data
}
return(res) #providing the result matrix.
}
processed <- calc(dec_subset, fun=bfastfun)
diff=subset(processed, 1)
time=subset(processed, 2)
magn=subset(processed, 3)
magn_time=subset(processed, 4)
writeRaster(diff,filename="ndvi_diff.tif",overwrite=T,format="GTiff")
writeRaster(time,filename="ndvi_diff_time.tif",overwrite=T,format="GTiff")
writeRaster(magn,filename="ndvi_magni.tif",overwrite=T,format="GTiff")
writeRaster(magn_time,filename="ndvi_magni_timing.tif",overwrite=T,format="GTiff")
If a more detailed analysis of BFAST processing and results is desired, I wrote my masters thesis on the topic and can provide that for additional reading.
