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

#

# SampleDemDiffCols

# 

# R script generates (or reads in) the CDEM / ASTER-SRTM mosaic 'difference image',

# and for randomly-selected one column / two row subimages, computes and saves tbe 

# difference between the pixels in the pair to create a distribution of differences.

# Three distributions created: North (ASTER CDEM), South i(SRTM/CGIAR), and 

# Border (boundary between ASTER and SRTM), Summary statistics are created for each 

# distribution.

#

# Author: Rick Reeves, NCEAS

# April 29, 2011

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

#

SampleDemDiffCols <- function()

{

require(raster)

require(rgdal)

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

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

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

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

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

#

# 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.

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

eTestAreaExtentSRTM <- extent(-135.0,-105.0, 60.00,60.010) # Creates a 12 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.

rEdgeRegionAster <- crop(inputRasterMerge,eTestAreaExtentAster)

vEdgeRegionAsterDelta <- extract(rDeltaWhole,eTestAreaExtentAster)

rEdgeRegionAsterDelta <- crop(rDeltaWhole,eTestAreaExtentAster)

vEdgeRegionBorder <- extract(inputRasterCDEM,eTestAreaExtentBorder)

rEdgeRegionBorder <- crop(inputRasterMerge,eTestAreaExtentBorder)

vEdgeRegionBorderDelta <- extract(rDeltaWhole,eTestAreaExtentBorder)

rEdgeRegionBorderDelta <- crop(rDeltaWhole,eTestAreaExtentBorder)

vEdgeRegionSrtm <- extract(inputRasterMerge,eTestAreaExtentSRTM)

rEdgeRegionSrtm <- crop(inputRasterMerge,eTestAreaExtentSRTM)

vEdgeRegionSRTMDelta <- extract(rDeltaWhole,eTestAreaExtentSRTM)

rEdgeRegionSRTMDelta <- crop(rDeltaWhole,eTestAreaExtentSRTM)

# 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. 

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

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

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

# 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

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)

#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]

}

#

iCtr = 1

for (iNextCol in colsToGet)

{

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

  neighborCells <- rDeltaEdge@data@values[rColVec]

}

#

iCtr = 1

for (iNextCol in colsToGet)

{

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

  neighborCells <- rDeltaWhole@data@values[rColVec]

}

# 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()

# For now, use 'gdalinfo' to check image statistics

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

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