R/polyNDVI.R

In tavimalara/rndvi: Download extract and visualize long-term AHVRR NDVI with R

#' Gather weekly NDVI for polygon areas 
#' 
#' The Data from AVHRR is gathered from the FTP server 
#' and NDVI values are extracted for polygon areas in 
#' a SpatialPolygons object
#' @param yrs 
#' vector c() or sequence seq() of years do be extracted
#' @param wks 
#' vector c() or sequence seq() of weeks do be extracted
#' @param polyfolder 
#' path to the folder where shape files of polygons are saved
#' @return A object of the class SpatialPolygonsDataFrame 
#' will be created. The extracted values are stored in the 
#' slot data and the column
#' @export
polyNDVI <- function(yrs,wks,polyfolder) {
  dir.create(paste(tempdir(),"hdfTEMP//",sep="//"))
  hdfFolder=paste(tempdir(),"hdfTEMP//",sep="//")
  dir.create(paste(tempdir(),"tiffTEMP//",sep="//"))
  tiffFolder=paste(tempdir(),"tiffTEMP//",sep="//")
  FTPurl <- "ftp://ftp.star.nesdis.noaa.gov/pub/corp/scsb/wguo/data/VHP_16km/VH/"
  filenames1 <- getURI(FTPurl)
  filenames2 <- substr(filenames1,11,(nchar(filenames1))-2)
  filevector1 <- strsplit(filenames2,"\r\n-rw-rw-r--")[[1]]
  filevector2 <- substr(filevector1,46,75)
  filevector3 <- filevector2[grep("*.SM.hdf",filevector2)]
  filevector4 <- unique(grep(paste(paste("P",yrs,sep=""),collapse="|"),filevector3, value=TRUE))
  filevector5 <-unique(grep(paste(paste(intWeeks[wks],".SM",sep=""),collapse="|"),filevector4, value=TRUE))
  satTest<-data.frame(sat=substr(filevector5,13,14), 
                      year=substr(filevector5,17,20), 
                      week=substr(filevector5,22,23),
                      filter=rep(NA,times=length(filevector5)),stringsAsFactors = FALSE)
  for (i in 1:length(filevector5)) {
    vect <- ((as.numeric(paste0(satData$year2,satData$week2))-as.numeric(paste0(satTest$year,satTest$week)[i]))+(as.numeric(paste0(satData$year1,satData$week1))-as.numeric(paste0(satTest$year,satTest$week)[i])))
    nr<-which(abs(vect-0)==min(abs(vect-0)))
    if (as.numeric(paste0(satData$year2,satData$week2))[nr] < as.numeric(paste0(satTest$year,satTest$week)[i])) {
      nr = nr+1
    }
    satTest$filter[i] <- as.character(satTest$sat[i]) == satData$satID[nr]
  }
  filevector <- filevector5[satTest$filter]
  polyvector <- list.files(polyfolder, pattern=".shp$")
  polylist <- list()
  for (j in 1:length(polyvector)){
    polylist[[j]] <- readOGR(dsn=polyfolder,layer=substr(polyvector[j],1,nchar(polyvector[j])-4))
  }
  temp <- data.frame(centroid.x=rep(NA,length(polyvector)),
                     centroid.y=rep(NA,length(polyvector)))
  values <- matrix(NA,ncol=length(filevector),nrow=length(polyvector),
                   dimnames=list(substr(polyvector,1,nchar(polyvector)-4),paste0("NDVI.",substr(filevector,17,23))))
  sds <- matrix(NA,ncol=length(filevector),nrow=length(polyvector),
                dimnames=list(substr(polyvector,1,nchar(polyvector)-4),paste0("SD.",substr(filevector,17,23))))
  out<-cbind(temp,values,sds)
  for (i in 1:length(filevector)) {
    download.file(url=paste(FTPurl,filevector[i],sep=""),
                  destfile=paste(hdfFolder,substr(filevector[i],13,nchar(filevector[i])),sep=""), 
                  mode='wb',method='auto',quiet=T,cacheOK=FALSE)
    hdf <- paste(hdfFolder,substr(filevector[i],13,nchar(filevector[i])),sep="")
    tiff <- paste(tiffFolder,paste(substr(filevector[i],13,nchar(filevector[i])-4),"tif",sep="."),sep="")
    try(gdal_translate(hdf,tiff,sds=F,verbose=TRUE,of = "GTiff",sd_index=2),silent=T)
    r <- raster(tiff, values=TRUE)
    mat <- as.matrix(r)
    mat[mat == min(mat)] <- NA
    r <- raster(mat)
    if (sum(grepl("START_LONGITUDE_RANGE=",gdalinfo(hdf))) == 0){
      extent(r) <- extent(as.numeric(strsplit(gdalinfo(hdf)[grep("geospatial_lon_min=",gdalinfo(hdf))],
                                              "geospatial_lon_min=")[[1]][2]),
                          as.numeric(strsplit(gdalinfo(hdf)[grep("geospatial_lon_max=",gdalinfo(hdf))],
                                              "geospatial_lon_max=")[[1]][2]),
                          as.numeric(strsplit(gdalinfo(hdf)[grep("geospatial_lat_min=",gdalinfo(hdf))],
                                              "geospatial_lat_min=")[[1]][2]),
                          as.numeric(strsplit(gdalinfo(hdf)[grep("geospatial_lat_max=",gdalinfo(hdf))],
                                              "geospatial_lat_max=")[[1]][2]))
    } else {
      extent(r) <- extent(as.numeric(strsplit(gdalinfo(hdf)[grep("START_LONGITUDE_RANGE=",gdalinfo(hdf))],
                                              "START_LONGITUDE_RANGE=")[[1]][2]),
                          as.numeric(strsplit(gdalinfo(hdf)[grep("END_LONGITUDE_RANGE=",gdalinfo(hdf))],
                                              "END_LONGITUDE_RANGE=")[[1]][2]),
                          as.numeric(strsplit(gdalinfo(hdf)[grep("END_LATITUDE_RANGE=",gdalinfo(hdf))],
                                              "END_LATITUDE_RANGE=")[[1]][2]),
                          as.numeric(strsplit(gdalinfo(hdf)[grep("START_LATITUDE_RANGE=",gdalinfo(hdf))],
                                              "START_LATITUDE_RANGE=")[[1]][2]))
    }
    projection(r) <- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
    for (j in 1:length(polyvector)){
      out$centroid.x[j] <- mean(polylist[[j]]@polygons[[1]]@Polygons[[1]]@coords[,1])
      out$centroid.y[j] <- mean(polylist[[j]]@polygons[[1]]@Polygons[[1]]@coords[,2])
      ndvis <- raster::extract(r,polylist[[j]]@polygons[[1]]@Polygons[[1]]@coords)
      out[j,2+i] <- mean(ndvis,na.rm=T)
      out[j,6+i] <- sd(ndvis,na.rm=T)
    }
    lst <- c("Eureka!","Neat-o!","Huzzah!","Yaus!")
    print(paste0("The week ", substr(filevector[i],21,23)," of the year ",substr(filevector[i],17,20)," is done!", " ",lst[sample(1:4, 1)]))
  }
  return(out)
  unlink(tempdir(),recursive=F,force=T)
}