Postprocessing for NDVI and MSAVI2 temporal series of Landsat 5 and 7

This program must be used sequentially after eco.NDVI. The inputs required (tab, correct, method) are the same described and used in that function. The algorithm stacks the images and save the stack into the working directory with the name "time.tif". If the user wishes, the program can also compute images consisting in the max, min, mean and var for each pixel across the temporal sequence. Default is "mean".

eco.NDVI.post(tab, correct = c("COST", "DOS"), method = c("NDVI", "MSAVI2"),
  datatype = c("FLT4S", "FLT8S", "INT4U", "INT4S", "INT2U", "INT2S", "INT1U",
  "INT1S", "LOG1S"), what = c("mean", "max", "min", "var", "none"))

Arguments

tab	Table used with eco.NDVI.
correct	Correction method used in eco.NDVI.
method	The vegetation index used in eco.NDVI.
datatype	Type of data, see dataType. Default "FLT4S".
what	Functions to apply over the created stack. The allowed values are: "none", "max", "min", "mean" and "var". The functions are implemented with calc. For passing more than one function as argument, the following syntax must be used: c("fun_1", "fun_2", "fun_i"), where fun_1...fun_n are the functions that you want to compute.

References

Chander G., B. Markham, and D. Helder. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment, 113: 893-903.

Chavez P. 1989. Radiometric calibration of Landsat Thematic Mapper multispectral images. Photogrammetric Engineering and Remote Sensing, 55: 1285-1294.

Chavez P. 1996. Image-based atmospheric corrections-revisited and improved. Photogrammetric engineering and remote sensing, 62: 1025-1035.

Goslee S. 2011. Analyzing remote sensing data in R: the landsat package. Journal of Statistical Software, 43: 1-25.

Song C., C. Woodcock, K. Seto, M. Lenney and S. Macomber. 2001. Classification and change detection using Landsat TM data: when and how to correct atmospheric effects?. Remote sensing of Environment, 75: 230-244.

Tucker C. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8: 127-150.

See also

eco.NDVI

extract

Examples

# NOT RUN {
require(raster)

data(tab)
data(eco3)
temp <- list()

# we create 4 simulated rasters for the data included in the object tab:

for(i in 1:4) {
temp[[i]] <- runif(19800, 0, 254)
temp[[i]] <- matrix(temp[[i]], 180, 110)
temp[[i]] <- raster(temp[[i]], crs="+proj=utm")
extent(temp[[i]])<-c(3770000, 3950000, 6810000, 6920000)
}

writeRaster(temp[[1]], "20040719b4.tif", overwrite = T)
writeRaster(temp[[2]], "20040719b3.tif", overwrite = T)
writeRaster(temp[[3]], "20091106b4.tif", overwrite = T)
writeRaster(temp[[4]], "20091106b3.tif", overwrite = T)

# Computing NDVI images: 

eco.NDVI(tab, "COST", "NDVI", "LT5")

# Mean NDVI image computed over the NDVI images that we calculated:

eco.NDVI.post(tab, "COST", "NDVI", what = c("mean", "var"))
mean.ndvi <- raster("NDVI.COST.mean.tif")
plot(mean.ndvi)

# Extraction of the mean NDVI for each point in the object eco and plot
# of the data:

ndvi <- extract(mean.ndvi, eco3[["XY"]])
ndvi<- aue.rescale(ndvi)
plot(eco3[["XY"]][, 1], eco3[["XY"]][, 2], col=rgb(ndvi, 0, 0),
pch=15, main = "Mean NDVI", xlab = "X", ylab  = "Y")
# }