##############################################################################################

#

# extractDemSubimages

# 

# R script extracts and saves subimages from various inputs to the 'image boundary analysis';

# also produces 'difference images' by subtracting CDEM components from other DEM components.

# Output files written as GeoTiff files. 

#

# Author: Rick Reeves, NCEAS

# May 9, 2011

##############################################################################################

#

extractDemSubimages <- function()

{

require(raster)

require(rgdal)

#inputRasterMerge <- raster(sFirstImageName)

#inputSecondRaster <- raster(sSecondImageName)

inputRasterAster <- raster("/data/project/organisms/rcr/AsterCgiarMerge/mergeCgiarAsterBdyASTER_BLEven.tif")

inputRasterSRTM <- raster("/data/project/organisms/rcr/AsterCgiarMerge/mergeCgiarAsterBdySRTM_BLEven.tif")

inputRasterMerge <- raster("/data/project/organisms/rcr/ValidateBoundary/mergeCgiarAsterBdyTuesdayClip.tif")

inputRasterCDEM <- raster("/data/project/organisms/rcr/ValidateBoundary/CDEMMosCgiarAsterBdy_BLEven.tif")

inputCdemAster24Row <- raster("/data/project/organisms/rcr/ValidateBoundary/EdgeAsterCDEM24row.tif")

inputCdemSrtm24Row <- raster("/data/project/organisms/rcr/ValidateBoundary/EdgeSrtmCDEM24row.tif")

#

# Difference image for entire merged image takes a while to create, 

# so we created it once, now read it back in.

#

rDeltaWhole <- raster("/data/project/organisms/rcr/ValidateBoundary/DeltaEntireImage.tif")

rDeltaWhole@data@values <-getValues(rDeltaWhole)

# Create extent objects used to extract raster subimges. 

# The object will be centered along the 60 degree North latitude line,

# and have varying depths (number of rows).

# The Western Canada study area runs from -135 (west) to -100 (west) longitude,

# and 55.0 to 64.00 degrees (north) latitude. 

# the ASTER and SRTM/CGIAR image components are merged at the 60 Deg N Latitude line.

eTestAreaExtentAster <- extent(-135.0,-105.0, 60.00009,60.010) # Creates 12 row subimage North of border (All ASTER)

eTestAreaExtentBorder <- extent(-135.0,-105.0, 59.995,60.005) # Creates a 12 row subimage centered on border

eTestAreaExtentSRTM <- extent(-135.0,-105.0, 59.990,60.00) # Creates a 12 row subimage South of border (All SRTM)

eTestAreaExtentAster <- extent(-135.0,-105.0, 60.00009,60.020) # Creates 24 row subimage North of border (All ASTER)

eTestAreaExtentBorder <- extent(-135.0,-105.0, 59.99,60.010) # Creates a 24 row subimage centered on border

eTestAreaExtentSRTM <- extent(-135.0,-105.0, 59.98,60.00) # Creates a 24 row subimage South of border (All SRTM)

# Extract a sub image corresponding to the selected extent.

# Two different alternatives:

# The extract() function returns a vector of cell values, 

# the crop() function returns a complete raster* object.

message("extracts")

browser()

vEdgeRegionAster <- extract(inputRasterAster,eTestAreaExtentAster)

rEdgeRegionAster <- crop(inputRasterAster,eTestAreaExtentAster)

vEdgeRegionAsterDelta <- extract(rDeltaWhole,eTestAreaExtentAster)

rEdgeRegionAsterDelta <- crop(rDeltaWhole,eTestAreaExtentAster)

#vEdgeRegionAsterCDEM <- extract(inputRasterCDEM,eTestAreaExtentAster)

#rEdgeRegionAsterCDEM <- crop(inputRasterCDEM,eTestAreaExtentAster)

vEdgeRegionBorder <- extract(inputRasterMerge,eTestAreaExtentBorder)

rEdgeRegionBorder <- crop(inputRasterMerge,eTestAreaExtentBorder)

vEdgeRegionBorderDelta <- extract(rDeltaWhole,eTestAreaExtentBorder)

rEdgeRegionBorderDelta <- crop(rDeltaWhole,eTestAreaExtentBorder)

vEdgeRegionSRTM <- extract(inputRasterSRTM,eTestAreaExtentSRTM)

rEdgeRegionSRTM <- crop(inputRasterSRTM,eTestAreaExtentSRTM)

vEdgeRegionSRTMDelta <- extract(rDeltaWhole,eTestAreaExtentSRTM)

rEdgeRegionSRTMDelta <- crop(rDeltaWhole,eTestAreaExtentSRTM)

#vEdgeRegionSrtmCDEM <- extract(inputRasterCDEM,eTestAreaExtentSRTM)

#rEdgeRegionSrtmCDEM <- crop(inputRasterCDEM,eTestAreaExtentSRTM)

message("differences")

browser()

# 

# write these files to disk, get stats.

#

rDeltaEdgeAster <- rEdgeRegionAster - inputCdemAster24Row

rDeltaEdgeBorder <- rEdgeRegionBorder - rEdgeRegionBorderDelta

rDeltaEdgeSRTM <- rEdgeRegionSRTM - rEdgeRegionSrtmCDEM

# Important: In order for the image subtraction to work, the extents

#            of the two images must be IDENTICAL. I used ArcMap GIS Raster Crop By Mask

#            to create subimages with identical extents. 

# Computeexter the difference image  for the entire study area, and for the region along

# the boundary (narrow, maybe 10 pixels either side)

# rDeltaEdge <- rEdgeRegionFirst - rEdgeRegionSecond

# May 10 2011 note: One of these statements generates the 'different origin' fatal error. 

# my workaround was to create ALL of the subimages used here with gdalwarp, and then 

# read them in and subtract them. 

# see other R script: OnlyImage Differences.r

# I will create a reproducable example, send to Raster authors. 

rDeltaEdgeAster <- rEdgeRegionAster - inputCdemAster24Row

rDeltaEdgeSRTM <- rEdgeRegionSRTM - inputCdemSrtm24Row

#rDeltaWhole <- inputRasterMerge - inputRasterCDEM

#writeRaster(rDeltaWhole,filename="DeltaEntireImage.tif",format="GTiff",datatype="INT2S",overwrite=TRUE)

# Using the large difference image, compute subimagee statistics for areas

# North (ASTER) and South (SRTM) of the boundary. These give us an idea 

# re: differences between ASTER and CDEM and CGIAR/SRT and CDEM

# what is raster package way of using subscripts to extract? 

# Now, the interesting part: using the boundary difference image, randomly select 

# one-degree N-S strips throughout the image, and compare adjacent pixel pairs 

# above and below the boundary with pixel pairs straddling the boundary. Subtract  

# the pairs, save the collection of (absolute value) of the differences in a vector,

# so that we have a population of differences above, below, and straddling the boundary 

# line. Compare the populations.

# get a vector of random column index numbers, constrained by column dimension of image

# Loop three times, sampling pixel pairs from above, below, across the border

nColsToGet <-20000

iDiffVecNorth <- vector(mode="integer",length=nColsToGet)

iDiffVecBorder <- vector(mode="integer",length=nColsToGet)

iDiffVecSouth <- vector(mode="integer",length=nColsToGet)

#colsToGet <-sample(1:50,nColsToGet)

# Note: initially, sample the same columns in all regions to get a profile.

#       other 'sample()' calls can be commented out to sample differenct

#       coluns in each 'region'.

# iDiffVecxxxx is a population of differences between adjacent cell pairs. 

# Compute iDiffVecNorth/Border/South on either side of border, and across it. 

# note that North and South samples taken from larger difference image for 

# entire mosaic (sub) image; iDiffBorder taken from the edge region extracted

# from the center of the lerger image.

# Remember, we are sampling a PAIR of pixels (same column from two adjacent rows)

# NOTR 5/9: need to rethink these sampling loops for this new version. Comment for now!

#colsToGet <-sample(1:inputRasterMerge@ncols,nColsToGet)

#message("North Sample")

#browser()

# debug

#nColsToGet <- 2

#colsToGet <- c(20,100)

#iFirstRow <- 300

#iCtr = 1

#for (iNextCol in colsToGet)

#{

#  rColVec <- cellFromRowCol(rDeltaWhole,iFirstRow:(iFirstRow+1),iNextCol:iNextCol)

#  neighborCells <- rDeltaWhole@data@values[rColVec]

#  iDiffVecNorth[iCtr] <- neighborCells[2] - neighborCells[1]

#  iCtr = iCtr + 1

#}

#

#message("Border Sample - different columns")

#browser()

#colsToGet <-sample(1:inputRasterMerge@ncols,nColsToGet)

#iFirstRow <- 6 # straddle the border of 12 row center section 

#iCtr = 1

#for (iNextCol in colsToGet)

#{

#  rColVec <- cellFromRowCol(rDeltaEdge,iFirstRow:(iFirstRow+1),iNextCol:iNextCol)

#  neighborCells <- rDeltaEdge@data@values[rColVec]

#  iDiffVecBorder[iCtr] <- neighborCells[2] - neighborCells[1]

#  iCtr = iCtr + 1 

#}

##

#message("South Sample - different columns")

##browser()

#colsToGet <-sample(1:inputRasterMerge@ncols,nColsToGet)

#iFirstRow <- 3600

#iCtr = 1

#for (iNextCol in colsToGet)

#{

#  rColVec <- cellFromRowCol(rDeltaWhole,iFirstRow:(iFirstRow+1),iNextCol:iNextCol)

#  neighborCells <- rDeltaWhole@data@values[rColVec]

#  iDiffVecSouth[iCtr] <- neighborCells[2] - neighborCells[1]

#  iCtr = iCtr + 1 

#}

## Compute iDiffVecs on either side of border, and across it. 

#message("Check the cell difference vectors...")

##browser()

# summary stats for each population

#sNorthSum <- sprintf("ASTER sample summary: Min: %f / Median: %d / Mean: %f / Max: %f / Variance: %f sDev: %f",

#                     min(iDiffVecNorth,na.rm=TRUE),median(iDiffVecNorth,na.rm=TRUE),mean(iDiffVecNorth,na.rm=TRUE),

#                     max(iDiffVecNorth,na.rm=TRUE),var(iDiffVecNorth,na.rm=TRUE),sd(iDiffVecNorth,na.rm=TRUE))

#

#sBorderSum <- sprintf("Border sample summary: Min: %f / Median: %d / Mean: %f / Max: %f / Variance: %f sDev: %f",

#                    min(iDiffVecBorder,na.rm=TRUE),median(iDiffVecBorder,na.rm=TRUE),mean(iDiffVecBorder,na.rm=TRUE),

#                    max(iDiffVecBorder,na.rm=TRUE),var(iDiffVecBorder,na.rm=TRUE),sd(iDiffVecBorder,na.rm=TRUE))

#sSouthSum <- sprintf("STRM sample summary: Min: %f / Median: %d / Mean: %f / Max: %f / Variance: %f sDev: %f",

#                     min(iDiffVecSouth,na.rm=TRUE),median(iDiffVecSouth,na.rm=TRUE),mean(iDiffVecSouth,na.rm=TRUE),

#                     max(iDiffVecSouth,na.rm=TRUE),var(iDiffVecSouth,na.rm=TRUE),sd(iDiffVecSouth,na.rm=TRUE))

##

#message(sprintf("statistics for %d N/S adjacent pixel pairs from three mosaic image regions:",nColsToGet))

#message(sNorthSum)

#message(sBorderSum)

#message(sSouthSum)

message("hit key to write output images...")

browser()

writeRaster(rDeltaEdgeSRTM,filename="/data/project/organisms/rcr/ValidateBoundary/EdgeRegionSRTMOnlyDelta24row.tif",format="GTiff",datatype="INT2S",overwrite=TRUE)

writeRaster(rDeltaEdgeAster,filename="/data/project/organisms/rcr/ValidateBoundary/EdgeRegionAsterOnlyDelta24row.tif",format="GTiff",datatype="INT2S",overwrite=TRUE)

# Create this image one time, read it in thereafter.

}