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

#

# makeMarkTable.r

# 

# R script generates table of adjacent ASTER / SRTM / Mosaic Border / CDEM pixel values

# to be used to generate plots of 'delta elevation' vs 'pixel pair proximity' and 'elevation'

# suggested by Mark Schildhauer on May 3. 

#

# Author: Rick Reeves, NCEAS

# May 4, 2011

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

#

makeMarkTable <- function()

{

require(raster)

require(rgdal)

inputCgiarRaster  <- raster("/data/project/organisms/rcr/AsterCgiarMerge/mergeCgiarAsterBdySRTM_BL.tif")

inputAsterRaster  <- raster("/data/project/organisms/rcr/AsterCgiarMerge/mergeCgiarAsterBdyASTER_BL.tif")

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

inputCDEMRaster   <- 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.

#

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.

#eTestAreaExtent <- extent(-135.2,-100.2, 59.997,60.001) # Creates a 5 row subimage

eTestAreaExtent <- extent(-135.2,-100.2, 59.995,60.005) # Creates a 12 row subimage

# 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(inputAsterRaster,eTestAreaExtent)

rEdgeRegionCgiar <- crop(inputCgiarRaster,eTestAreaExtent)

rEdgeRegionMosaic <- crop(inputMosaicRaster,eTestAreaExtent)

rEdgeRegionCDEM <- crop(inputCDEMRaster,eTestAreaExtent)

# 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)

rEdgeRegionDelta <- rEdgeRegionMosaic - rEdgeRegionCDEM  # not used in this version (yet)

# 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)

# Here is the output matrix, (nColSamples * 3) rows, four columns 

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

mOutTable <- matrix(nrow=(nColsToGet * 3), ncol=5)

colnames(mOutTable) <- c("ColumnID","elevNorth","elevSouth","cdemNorth","cdemSouth")

# in this difference image, the border edge occurs at column 6

# NOTE Wed eve: A better start and end for the border: firstRow=5, lastRow = 8....

iFirstRow <- 5 #4 # two rows before the border           

iLastRow  <- 8 #7 # two rows after the border

{

#browser()

# slight misalignement in the Aster and Cgiar edge regions, getting NAs wheen extracting values. 

# For now, get values on both sides of border from the mosaic

#   rColVecAster  <- cellFromRowCol(rEdgeRegionAster,iFirstRow:(iLastRow),iNextCol:iNextCol) # figure out the actual row numbers for mosaic components LATER

#   rColVecCgiar  <- cellFromRowCol(rEdgeRegionCgiar,iFirstRow:(iLastRow),iNextCol:iNextCol)

#   rColVecAster  <- cellFromRowCol(rEdgeRegionMosaic,iFirstRow:(iLastRow),iNextCol:iNextCol) # approximation is the Cgiar/Aster area in mosaic.

#   rColVecCgiar  <- cellFromRowCol(rEdgeRegionMosaic,iFirstRow:(iLastRow),iNextCol:iNextCol)

   rColVecMosaic <- cellFromRowCol(rEdgeRegionMosaic,iFirstRow:(iLastRow),iNextCol:iNextCol)

   rColVecCDEM   <- cellFromRowCol(rEdgeRegionCDEM,iFirstRow:(iLastRow),iNextCol:iNextCol)  

# Split the column vector into the pairs that Mark requested 

# For each column sampled, the output table has three rows:

#

# Row 1: from (input) Aster layer:  Two pixels above border

#

# each with four columns, arranged into two column pairs:

#

#  North Pixel and South Pixel elevation, North Pixel and South Pixel CDEM (baseline)

# 

   mOutTable[iRowCtr,2:3] <- rEdgeRegionMosaic@data@values[rColVecMosaic[1:2]]  # First column pair from extracted vector: 

   mOutTable[iRowCtr,4:5] <- rEdgeRegionCDEM@data@values[rColVecCDEM[1:2]]   # entirely top (ASTER part of mosaic) image 

   iRowCtr = iRowCtr + 1

   mOutTable[iRowCtr,2:3] <- rEdgeRegionMosaic@data@values[rColVecMosaic[2:3]] # Second column pair: 

   mOutTable[iRowCtr,4:5] <- rEdgeRegionCDEM@data@values[rColVecCDEM[2:3]]   # straddles border region 

   iRowCtr = iRowCtr + 1  

#   

   mOutTable[iRowCtr,1]   <- iNextCol           # The (radomly sampled) col ID (surrogate for longitude)

   mOutTable[iRowCtr,2:3] <- rEdgeRegionMosaic@data@values[rColVecMosaic[3:4]]  # third column pair:

   mOutTable[iRowCtr,4:5] <- rEdgeRegionCDEM@data@values[rColVecCDEM[3:4]]   # entirely bottom (SRTM part of mosaic) image 

   iRowCtr = iRowCtr + 1  

browser()

write.csv(mOutTable,file="tableForMark4000_5_8.csv",row.names=FALSE)

}