I have a tiff file which contains 6 bands Data link, I used all methods to extract [See here][2] [see here 2][3] all 6 bands as individual layers but unable to extract them as individual files.
library(raster)
library(ncdf4)
library(rgdal)
r <- raster("E:/TRY/bands.tif")
writeRaster(r, paste0(names(r),".tif"), bylayer=TRUE, format="GTiff")
I get just one tif file and the file doesn't have any information whatsoever like what it is.
Appreciate any help
thanks
Try the function raster::stack. This will read all the layers.
library(raster)
library(ncdf4)
library(rgdal)
s1 <- stack("~/Downloads/bands.tif")
writeRaster(s1, paste0(names(s1),".tif"), bylayer=TRUE, format="GTiff")
I was trying to convert a dataset from netcdf to csv format using R.
although I have installed 'raster and 'netcdf4' in R.
but still it doesn't find nc.brick.
rm(list=ls())
library(raster)
library(ncdf4)
nc.brick <- brick(file.choose)
dim(nc.brick)
nc.df <- as.data.frame(nc.brick[[1]],xy=T)
head(nc.df)
write.csv(nc.df,file.choose())
test <- read.csv(file.choose())
Can you see the difference between your usage of file.choose in these two lines:
nc.brick <- brick(file.choose)
write.csv(nc.df,file.choose())
there's your problem.
I need to get information on the extent, resolution, and cell number of the my file. I'm working with raster files.
You can do
library(raster)
# r <- raster("filename")
r <- raster()
extent(r)
ncell(r)
res(r)
You can read more about the methods in the raster package here
I'm trying to read MODIS 17 data files into R, manipulate them (cropping etc.) and then save them as geoTIFF's. The data files come in .hdf format and there doesn't seem to be an easy way to read them into R.
Compared to other topics there isn't a lot of advice out there and most of it is several years old. Some of it also advises using additional programmes but I want to stick with just using R.
What package/s do people use for dealing with .hdf files in R?
Ok, so my MODIS hdf files were hdf4 rather than hdf5 format. It was surprisingly difficult to discover this, MODIS don't mention it on their website but there are a few hints in various blogs and stack exchange posts. In the end I had to download HDFView to find out for sure.
R doesn't do hdf4 files and pretty much all the packages (like rgdal) only support hdf5 files. There are a few posts about downloading drivers and compiling rgdal from source but it all seemed rather complicated and the posts were for MAC or Unix and I'm using Windows.
Basically gdal_translate from the gdalUtils package is the saving grace for anyone who wants to use hdf4 files in R. It converts hdf4 files into geoTIFFs without reading them into R. This means that you can't manipulate them at all e.g. by cropping them, so its worth getting the smallest tiles you can (for MODIS data through something like Reverb) to minimise computing time.
Here's and example of the code:
library(gdalUtils)
# Provides detailed data on hdf4 files but takes ages
gdalinfo("MOD17A3H.A2000001.h21v09.006.2015141183401.hdf")
# Tells me what subdatasets are within my hdf4 MODIS files and makes them into a list
sds <- get_subdatasets("MOD17A3H.A2000001.h21v09.006.2015141183401.hdf")
sds
[1] "HDF4_EOS:EOS_GRID:MOD17A3H.A2000001.h21v09.006.2015141183401.hdf:MOD_Grid_MOD17A3H:Npp_500m"
[2] "HDF4_EOS:EOS_GRID:MOD17A3H.A2000001.h21v09.006.2015141183401.hdf:MOD_Grid_MOD17A3H:Npp_QC_500m"
# I'm only interested in the first subdataset and I can use gdal_translate to convert it to a .tif
gdal_translate(sds[1], dst_dataset = "NPP2000.tif")
# Load and plot the new .tif
rast <- raster("NPP2000.tif")
plot(rast)
# If you have lots of files then you can make a loop to do all this for you
files <- dir(pattern = ".hdf")
files
[1] "MOD17A3H.A2000001.h21v09.006.2015141183401.hdf" "MOD17A3H.A2001001.h21v09.006.2015148124025.hdf"
[3] "MOD17A3H.A2002001.h21v09.006.2015153182349.hdf" "MOD17A3H.A2003001.h21v09.006.2015166203852.hdf"
[5] "MOD17A3H.A2004001.h21v09.006.2015099031743.hdf" "MOD17A3H.A2005001.h21v09.006.2015113012334.hdf"
[7] "MOD17A3H.A2006001.h21v09.006.2015125163852.hdf" "MOD17A3H.A2007001.h21v09.006.2015169164508.hdf"
[9] "MOD17A3H.A2008001.h21v09.006.2015186104744.hdf" "MOD17A3H.A2009001.h21v09.006.2015198113503.hdf"
[11] "MOD17A3H.A2010001.h21v09.006.2015216071137.hdf" "MOD17A3H.A2011001.h21v09.006.2015230092603.hdf"
[13] "MOD17A3H.A2012001.h21v09.006.2015254070417.hdf" "MOD17A3H.A2013001.h21v09.006.2015272075433.hdf"
[15] "MOD17A3H.A2014001.h21v09.006.2015295062210.hdf"
filename <- substr(files,11,14)
filename <- paste0("NPP", filename, ".tif")
filename
[1] "NPP2000.tif" "NPP2001.tif" "NPP2002.tif" "NPP2003.tif" "NPP2004.tif" "NPP2005.tif" "NPP2006.tif" "NPP2007.tif" "NPP2008.tif"
[10] "NPP2009.tif" "NPP2010.tif" "NPP2011.tif" "NPP2012.tif" "NPP2013.tif" "NPP2014.tif"
i <- 1
for (i in 1:15){
sds <- get_subdatasets(files[i])
gdal_translate(sds[1], dst_dataset = filename[i])
}
Now you can read your .tif files into R using, for example, raster from the raster package and work as normal. I've checked the resulting files against a few I converted manually using QGIS and they match so I'm confident the code is doing what I think it is. Thanks to Loïc Dutrieux and this for the help!
These days you can use the terra package with HDF files
Either get sub-datasets
library(terra)
s <- sds("file.hdf")
s
That can be extracted as SpatRasters like this
s[1]
Or create a SpatRaster of all subdatasets like this
r <- rast("file.hdf")
The following worked for me. It's a short program and just takes in the input folder name. Make sure you know which sub data you want. I was interested in sub data 1.
library(raster)
library(gdalUtils)
inpath <- "E:/aster200102/ast_200102"
setwd(inpath)
filenames <- list.files(,pattern=".hdf$",full.names = FALSE)
for (filename in filenames)
{
sds <- get_subdatasets(filename)
gdal_translate(sds[1], dst_dataset=paste0(substr(filename, 1, nchar(filename)-4) ,".tif"))
}
Use the HEG toolkit provided by NASA to convert your hdf file to geotiff and then use any package ("raster" for example) to read the file. I do the same for both old and new hdf files.
Heres the link: https://newsroom.gsfc.nasa.gov/sdptoolkit/HEG/HEGHome.html
Take a look at the NASA products supported here: https://newsroom.gsfc.nasa.gov/sdptoolkit/HEG/HEGProductList.html
Hope this helps.
This script has been very useful and I managed to convert a batch of 36 files using it. However, my problem is that the conversion does not seem correct. When I do it using ArcGIS 'Make NetCDF Raster Layer tool', I get different results + I am able to convert the numbers to C from Kelvin using simple formula: RasterValue * 0.02 - 273.15. With the results from R conversion I don't get the right results after conversion which leads me to believe ArcGIS conversion is good, and R conversion returns an error.
library(gdalUtils)
library(raster)
setwd("D:/Data/Climate/MODIS")
# Get a list of sds names
sds <- get_subdatasets('MOD11C3.A2009001.006.2016006051904.hdf')
# Isolate the name of the first sds
name <- sds[1]
filename <- 'Rasterinr.tif'
gdal_translate(sds[1], dst_dataset = filename)
# Load the Geotiff created into R
r <- raster(filename)
# Identify files to read:
rlist=list.files(getwd(), pattern="hdf$", full.names=FALSE)
# Substract last 5 digits from MODIS filename for use in a new .img filename
substrRight <- function(x, n){
substr(x, nchar(x)-n+1, nchar(x))
}
filenames0 <- substrRight(rlist,9)
# Suffixes for MODIS files for identyfication:
filenamessuffix <- substr(filenames0,1,5)
listofnewnames <- c("2009.01.MODIS_","2009.02.MODIS_","2009.03.MODIS_","2009.04.MODIS_","2009.05.MODIS_",
"2009.06.MODIS_","2009.07.MODIS_","2009.08.MODIS_","2009.09.MODIS_","2009.10.MODIS_",
"2009.11.MODIS_","2009.12.MODIS_",
"2010.01.MODIS_","2010.02.MODIS_","2010.03.MODIS_","2010.04.MODIS_","2010.05.MODIS_",
"2010.06.MODIS_","2010.07.MODIS_","2010.08.MODIS_","2010.09.MODIS_","2010.10.MODIS_",
"2010.11.MODIS_","2010.12.MODIS_",
"2011.01.MODIS_","2011.02.MODIS_","2011.03.MODIS_","2011.04.MODIS_","2011.05.MODIS_",
"2011.06.MODIS_","2011.07.MODIS_","2011.08.MODIS_","2011.09.MODIS_","2011.10.MODIS_",
"2011.11.MODIS_","2011.12.MODIS_")
# Final new names for converted files:
newnames <- vector()
for (i in 1:length(listofnewnames)) {
newnames[i] <- paste0(listofnewnames[i],filenamessuffix[i],".img")
}
# Loop converting files to raster from NetCDF
for (i in 1:length(rlist)) {
sds <- get_subdatasets(rlist[i])
gdal_translate(sds[1], dst_dataset = newnames[i])
}
I also posted this question on stack gis 1. From the netcdf4 data that have sub categories, I want to be able to read "Retrieval/fs" variable. I also want to read them and convert to raster girds, but it seems that raster doesn't support netcdf4. I appreciate any suggestions.
library(ncdf4)
library(raster)
file <- "http://140906_B7101Ar_150909171225s.nc4"
names(file$var)
"latitude" ... "longitude"... "Retrieval/fs"
lat <- raster(file, varname="latitude")
lon <- raster(file, varname="longitude")
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘raster’ for signature ‘"ncdf4"’
raster does work with ncdf4 You are now showing actual code. file is a character vector. You cannot do names(file$var) with that (at least you won't get "latitude" ... "longitude"... "Retrieval/fs". So file is probably ncdf4 object (see the error message), while the raster function expects a filename (but not a url).
If you download the file and then do
library(raster)
x <- brick(filename, var="Retrieval/fs")
Things should work if the ncdf file had regular raster data.
However, it does not so you cannot directly import this as a raster. Instead you can get the lat and lon and values from the files, treat these as points and then rasterize (interpolate) these to get a regular raster.
Here is the answer to the question I asked. Since the data is not gridded, I retrieve the lon and lat information along with the variables to create a dataframe.
fs <- ncvar_get(ncfile, "Retrieval/fs")
xlon <- ncvar_get(ncfile, "longitude")
xlat <- ncvar_get(ncfile, "latitude")
d <- data.frame( as.vector(xlon),as.vector(xlat), as.vector(fs))# create a dataframe
coordinates(d) <- c("xlon","xlat")
proj4string(d) <- CRS("+proj=longlat")
spoint <- SpatialPoints(coords = d) #create a spatial point object