I am trying to convert my h5 daily files into a raster format. I converted into raster format. When I extracted my area of interest. I could not extract my area of interest from the raster image Kindly anyone guides me on how to solve this issue. The R code and hf5 file and after conversion raster image are present in link (attached). Thanks
library(rhdf5)
library(sp)
library(raster)
h5ls("reconstruction_indus_CY2001.h5")
h5readAttributes(file = "reconstruction_indus_CY2001.h5", name = "Grid")
h5f = H5Fopen("reconstruction_indus_CY2001.h5")
# h5f
# h5f&'Grid'
#system.time( swe <- h5f$Grid$swe )
system.time( melt <- h5f$Grid$melt )
locations <- data.frame(
lon=c(74.86764,73.48753, 74.87066 , 73.37798 , 78.82102 ,75.85160 ,75.78263 , 78.46446 ),
lat = c(35.16700, 36.25674, 36.49362, 35.21188, 34.20916, 34.48459, 35.76965, 33.23380)
)
coordinates(locations) <- ~lon+lat
proj4string(locations) <- CRS("+proj=longlat")
swe180 <- melt[,,180]
b <- swe180 == 65535
# table(b)
swe180[b] <- -1
b <- swe180 > 200
# table(b)
swe180[b] <- 200
b <- swe180 < 0
# table(b)
swe180[b] <- 20
# image(swe180)
# image(swe180)
# str(swe180)
# h5readAttributes(file = "reconstruction_Sierra_2016.h5", name = "Grid")$ReferencingMatrix
RM <- h5readAttributes(file = "reconstruction_indus_CY2001.h5", name = "Grid")$ReferencingMatrix
#GT <- GridTopology(c(RM[3,1], RM[3,2]+RM[1,2]*dim(swe)[1]), c(RM[2,1], -RM[1,2]), c(dim(swe)[2],dim(swe)[1]))
GT <- GridTopology(c(RM[3,1], RM[3,2]+RM[1,2]*dim(melt)[1]), c(RM[2,1], -RM[1,2]), c(dim(melt)[2],dim(melt)[1]))
# GT <- GridTopology(c(-1.088854e+07, 4718608.3619-463.3127*1978), c(463.3127, 463.3127), c(2171,1978))
# GT
SG = SpatialGrid(GT)
# str(SG)
# proj4string(SG) <- CRS("+proj=sinu")
# str(SG)
proj4string(SG) <- CRS("+proj=utm +zone=43 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0")
locations_aea <- spTransform(locations, CRS(proj4string(SG)))
SGDF = SpatialGridDataFrame(SG, data.frame(melt = as.numeric(t(swe180))))
gridded(SGDF)<- TRUE
r = raster(SGDF)
plot(SGDF, axes=T)
writeRaster(r,"test_2001.tif",overwrite=TRUE)
## Open Raster Files and Extract Area of Interest
shp= readOGR("Hunza.shp")
e = extent(shp)
r1 = raster("test_2001.tif")
crs(r1) = "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 "
plot(r1)
r1_mask = raster::mask(r1,shp)
plot(r1_mask,axes = TRUE,ext = extent(shp))
# Extracting Values as Data Frame
r1_extract = raster::extract(r1,shp, df=TRUE,na.rm = TRUE)
# Stroing as Raster
writeRaster(r1_mask,paste0('/shared/MODIS/shastaH5SWEinR/2001_swe/Hunza/','hunza.tif'))
c = cbind(r1_extract,y)
c1=t(c)
write.csv(c1,file = 'Hunza_SWE_2001.csv')
https://drive.google.com/drive/folders/18-hj2LEYWBN-uIDDTdqZ-x-WUxpCJu7H?usp=sharing
You can use the terra package (raster replacement) to shortcut this. terra can read hdf5 files directly.
The file has multiple sub-datasets, so it is easiest to read it as a SpatDataSet
library(terra)
f <- "reconstruction_indus_CY2001.h5"
s <- sds(f)
s
#class : SpatDataSet
#subdatasets : 3
#dimensions : 3651, 1641 (nrow, ncol)
#nlyr : 1, 365, 365
#resolution : 0.2193784, 0.04930156 (x, y)
#extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=longlat +datum=WGS84 +no_defs
#names : maxswedates, melt, swe
And now get the variable of interest
r <- s$swe
r
#class : SpatRaster
#dimensions : 3651, 1641, 365 (nrow, ncol, nlyr)
#resolution : 0.2193784, 0.04930156 (x, y)
#extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=longlat +datum=WGS84 +no_defs
#data source : swe
#names : swe_1, swe_2, swe_3, swe_4, swe_5, swe_6, ...
A more direct way to get the same result is
r <- rast(f, "//Grid/swe")
And you can discover what is inside of a HDF5 file by running
sds_info(f)
Plot the first layer
plot(r, 1)
Extract area of interest, for example like this
v <- vect("Hunza.shp")
x <- crop(r, v)
y <- mask(x, v)
To save as a raster file you can add a filename to the functions above. Or you can do it later like this
y <- writeRaster(y, "hunza.tif")
To save the values to a csv file:
vy <- values(y)
write.csv(vy, 'Hunza_SWE_2001.csv', row.names=FALSE)
Most function names in terra are the same as in raster. See ?terra for differences. If you want to continue in raster you can do
library(raster)
b <- brick(y)
Related
Hi am trying to extract values for xy point from a sample raster stack ras_dt. The ras_dt is EQUATES data with gridded coordinates within the domain of -115.00,38.00,-110.05,45.00. How can I change the projection and the lat lon coordinates of this raster stack so that I can extract data for point xy as in the code below.
library(raster)
dturl<- "https://www.dropbox.com/s/ztxqpszjfjhpavz/EQUATES_ACONC_O3_SAM.nc?dl=1"
download.file(dturl, "EQUATES_ACONC_O3_SAM.nc")
ras_dt <- raster::stack("EQUATES_ACONC_O3_SAM.nc",varname = "O3")
ras_dt
# the data domain is -115.00,38.00,-110.05,45.00
plot(ras_dt)
xy <- data.frame(lon=-113.0,lat=40.0)
coordinates(xy) <- ~lon + lat
extr_dt <- raster::extract(ras_dt, xy) # how to get O3 values for xy here?
extr_dt
The ras_dt is in "lambertConformalProjection" with following info:
char LambertConformalProjection;
:grid_mapping_name = "lambert_conformal_conic";
:latitude_of_projection_origin = 40.0; // double
:longitude_of_central_meridian = -97.0; // double
:standard_parallel = 33.0, 45.0; // double
:earth_radius = 6370000.0; // double
:_CoordinateTransformType = "Projection";
:_CoordinateAxes = "x y";
You need to set the extent and the coordinate reference system (CRS). Then transform your lon/lat points to that CRS and use extract. From your edited question we now have the CRS.
You specify the "domain", but you need to coordinates in the actual CRS. I do not know what these are, so I will guestimate it, but it will not be correct.
The data
library(terra)
dturl<- "https://www.dropbox.com/s/ztxqpszjfjhpavz/EQUATES_ACONC_O3_SAM.nc?dl=1"
download.file(dturl, "EQUATES_ACONC_O3_SAM.nc", mode="wb")
ras_dt <- rast("EQUATES_ACONC_O3_SAM.nc", "O3")
pcrs <- "+proj=lcc +lon_0=-97 +lat_0=40 +lat_1=33 +lat_2=45 +r =6370000.0"
Rough estimate of the extent:
v <- vect(rbind(c(-115, 38), c(-115, 45), c(-110.05, 38), c(-110.05, 45)), crs="+proj=longlat")
p <- project(v, pcrs)
e <- crds(p) |> apply(2, range) |> as.vector()
e
#[1] -1562368.5 -1025418.3 -139104.3 693662.9
Set the extent and the crs
ext(ras_dt) <- e
crs(ras_dt) <- pcrs
Transform the points to the crs of the raster
xy <- vect(cbind(lon=-113.0,lat=40.0), crs="+proj=longlat")
pxy <- project(xy, pcrs)
And extract
extract(ras_dt, pxy)
# ID O3_LAY=1_TSTEP=1 O3_LAY=1_TSTEP=2
#1 1 41.88482 40.99662
So our raster now looks like this. The spatial resolution should probably be a round number (12,000?)
ras_dt
#class : SpatRaster
#dimensions : 70, 45, 2 (nrow, ncol, nlyr)
#resolution : 11932.23, 11896.67 (x, y)
#extent : -1562369, -1025418, -139104.3, 693662.9 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=lcc +lat_0=40 +lon_0=-97 +lat_1=33 +lat_2=45 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs
#source : EQUATES_ACONC_O3_SAM.nc:O3
#varname : O3 (O3 )
#names : O3_LAY=1_TSTEP=1, O3_LAY=1_TSTEP=2
#unit : ppbV , ppbV
Can you find and provide the correct extent somewhere in the documentation? And perhaps ask the data providers to follow the NetCDF conventions such that all the metadata required to use the data is stored in the files.
I wish to extract raster values based on a list of coordinates. I’ve found online some scripts that include coordinates(), SpatialPoints(), crs() and spTransform() and other that don’t. Could someone kindly explain if script 1 or script 2 is correct and why? Thank you very much!
SCRIPT 1
sites <- read.csv("df.csv")
coordinates(sites)= ~ Longitude+ Latitude
mypoints = SpatialPoints(sites,proj4string = CRS("+init=epsg:4326"))
myproj = CRS(myraster)
points.proj = spTransform(mypoints, myproj)
myvalues = extract(myraster, points.proj)
SCRIPT 2
sites <- read.csv("df.csv")
myvalues = extract(myraster, cbind(sites$Longitude, y=sites$Latitude), df=TRUE, method='simple', cellnumbers=T)
Either could be correct. With RasterLayer r and data.frame sites you can do
v <- extract(r, sites[, c("Longitude", "Latitude")])
Under the assumption that "Longitude" and "Latitude" are variables in sites.
However that only works when r also has a ("Longitude", "Latitude") coordinate reference system. That may not be the case. Consider this RasterLayer
f <- system.file("external/test.grd", package="raster")
r <- raster(f)
r
#class : RasterLayer
#dimensions : 115, 80, 9200 (nrow, ncol, ncell)
#resolution : 40, 40 (x, y)
#extent : 178400, 181600, 329400, 334000 (xmin, xmax, ymin, ymax)
#crs : +proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +towgs84=565.237,50.0087,465.658,-0.406857,0.350733,-1.87035,4.0812 +no_defs
#source : C:/soft/R/R-3.6.1/library/raster/external/test.grd
#names : test
#values : 128.434, 1805.78 (min, max)
The crs is "sterea ..." and the extent "178400, 181600, ...) shows that the coordinates are clearly not longitude and latitude (they are expressed in meters away from the origin of the crs.)
In this case, you might have a point in the area covered by r
site <- data.frame(Longitude=5.745039, Latitude=50.96254)
But extract returns NA because the crs do not match
extract(r, site)
# [,1]
#[1,] NA
So we do
pts <- SpatialPoints(site)
crs(pts) <- "+proj=longlat +datum=WGS84"
rcrs <- crs(r)
ptrans <- spTransform(pts, rcrs)
And now it works
extract(r, ptrans)
#1529.66
When I try to run band math the result is always an image of a color and the values min and max very different from the one predicted.
I did not find any question here that showed this problem.
I worked out this way
r.stack <- stack("path to raster file"))
I use resampling instead of crop to cut out the white edges that were in the original images
prj <- "+proj=utm +zone=23 +south +datum=WGS84 +units=m"
r <- raster(res=11.47, ext=extent(c(301496, 323919, 9888968, 9913982)), crs=prj, vals=NA
r.stack <- resample(r.stack, r)
After that the images have this configuration:
> class : RasterBrick
> dimensions : 2181, 1955, 4263855, 4 (nrow, ncol, ncell, nlayers)
> resolution : 11.47, 11.47 (x, y)
> extent : 301496, 323919.8, 9888966, 9913982 (xmin, xmax, ymin, ymax)
>coord. ref. : +proj=utm +zone=23 +south +datum=WGS84 +units=m +ellps=WGS84 +towgs84=0,0,0
>data source : in memory
>names : l.1, l.2, l.3, l.4
>min values : -36.12217, -45.12768, -46.30455, -35.26328
>max values : 10.567671, 4.050200, 3.878345, 11.613799
and than use the function below for calc
f <- function(x){
(x[[2]])/(x[[1]])
}
s <- r.stack[[c(1,2)]]
r2 <- calc(s, f)
and I also run overlay whit the fun
f <- function(x,y){
y/x
}
r2 <- overlay(r.stack[[1]], r.stack[[2]], fun= f)
Any of the methods result in a image of one value
Am I missing some steps?
Here is your code with some example data (without that it is hard to answer questions). I have simplified one function, a bit, but the results are the same.
library(raster)
b <- brick(system.file("external/rlogo.grd", package="raster"))
b <- b/10 + 1
f <- function(x){ x[2]/ x[1] }
s <- b[[c(1,2)]]
r1 <- calc(s, f)
f <- function(x,y){ y / x }
r2 <- overlay(b[[1]], b[[2]], fun= f)
Or simply
r3 <- b[[2]] / b[[1]]
r3
#class : RasterLayer
#dimensions : 77, 101, 7777 (nrow, ncol, ncell)
#resolution : 1, 1 (x, y)
#extent : 0, 101, 0, 77 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=merc +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
#data source : in memory
#names : layer
#values : 0.7692308, 1.7 (min, max)
r1 and r2 are the same.
The reason that you get a "single color" is because most values are near 1, but there are a few big outliers; probably because of a division by a number between -1 and 1? This might illustrate it:
q <- quantile(r3, c(0.1, 0.9))
d <- clamp(r3, q[1], q[2])
plot(d)
And look at the extremes
i <- which.max(r3)
b[i][,2:1]
I am working with temperature .nc files from NARCCAP. These data have a polar stereographic projection. From temperature minimums and maximums, I've created a matrix of days that qualify as maple syrup production days.
I want to turn this matrix into a raster, and project this raster to a lon/lat projection.
## This is the metadata for the projection from the .nc file:
# float lat[xc,yc]
# long_name: latitude
# standard_name: latitude
# units: degrees_north
# axis: Y
# float lon[xc,yc]
# long_name: longitude
# standard_name: longitude
# units: degrees_east
# axis: X
# float tasmax[xc,yc,time]
# coordinates: lon lat level
# _FillValue: 1.00000002004088e+20
# original_units: K
# long_name: Maximum Daily Surface Air Temperature
# missing_value: 1.00000002004088e+20
# original_name: T_MAX_GDS5_HTGL
# units: K
# standard_name: air_temperature
# cell_methods: time: maximum (interval: 24 hours)
# grid_mapping: polar_stereographic
# grid_mapping_name: polar_stereographic
# latitude_of_projection_origin: 90
# standard_parallel: 60
# false_easting: 4700000
# false_northing: 8400000
# longitude_of_central_meridian: 263
# straight_vertical_longitude_from_pole: 263
# The production days matrix I've created is called from a saved file:
path.ecp2 <- paste0("E:/all_files/production/narccap/GFDL/Production_Days_SkinnerECP2",
year, ".RData")
file.ecp2 <- get(load(path.ecp2))
dim(file.ecp2)
# 147 116
rast.ecp2 <- raster(file.ecp2)
rast.ecp2 <- flip(t(rast.ecp2), 2)
# class : RasterLayer
# dimensions : 116, 147, 17052 (nrow, ncol, ncell)
# resolution : 0.006802721, 0.00862069 (x, y)
# extent : 0, 1, 0, 1 (xmin, xmax, ymin, ymax)
# coord. ref. : NA
# data source : in memory
# names : layer
# values : 0, 671 (min, max)
# I assign the polar stereographic crs to this production days raster:
crs("+init=epsg:3031")
ecp2.proj <- "+proj=stere +lat_0=-90 +lat_ts=-71 +lon_0=0 +k=1 +x_0=4700000 +y_0=8400000 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0"
crs(rast.ecp2) <- crs(ecp2.proj)
rast.ecp2
# class : RasterLayer
# dimensions : 116, 147, 17052 (nrow, ncol, ncell)
# resolution : 0.006802721, 0.00862069 (x, y)
# extent : 0, 1, 0, 1 (xmin, xmax, ymin, ymax)
# coord. ref. : +proj=stere +lat_0=-90 +lat_ts=-71 +lon_0=0 +k=1 +x_0=4700000 +y_0=8400000 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
# data source : in memory
# names : layer
# values : 0, 671 (min, max)
When I use the steps that worked for me previously (see here), the values of rast.ecp2 all go to NA. Where am I going wrong?
# The projection I want to project TO:
source_rast <- raster(nrow=222, ncol=462, xmn=-124.75, xmx=-67, ymn=25.125, ymx=52.875,
crs="+proj=longlat +datum=WGS84")
rast.ecp2LL <- projectRaster(rast.ecp2, source_rast)
rast.ecp2LL
# class : RasterLayer
# dimensions : 222, 462, 102564 (nrow, ncol, ncell)
# resolution : 0.125, 0.125 (x, y)
# extent : -124.75, -67, 25.125, 52.875 (xmin, xmax, ymin, ymax)
# coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
# data source : in memory
# names : layer
# values : NA, NA (min, max)
I am posting the solution I found to work. It is based on this post and answer. I had to first convert the xc and yc coordinates of the .nc file to longitude and latitude points. Then I could properly reproject the raster. Below is the code that worked.
Note that mycrs is the CRS that "came with" the .nc file. It has to be assigned to the SpatialPoints since converting from xc/yc to SpatialPoints drops the associated CRS.
years <- seq(from=1971, to=2000, by=5)
model <- "CRCM"
convert.lonlat <- function(model, year)
{
max.stem <- "E:/all_files/www.earthsystemgrid.org/CCSM/tasmax_"
inputfile <- paste0(max.stem, model, "_ccsm_", year, "010106.nc")
lat <- raster(inputfile, varname="lat")
lon <- raster(inputfile, varname = "lon")
plat <- rasterToPoints(lat)
plon <- rasterToPoints(lon)
lonlat <- cbind(plon[,3], plat[,3])
lonlat <- SpatialPoints(lonlat, proj4string = crs(base.proj))
mycrs <- crs("+proj=stere +lon_0=263 +x_0=3475000 +y_0=7475000 +lat_0=90 +ellps=WGS84")
plonlat <- spTransform(lonlat, CRSobj = mycrs)
maxs <- brick(inputfile, varname="tasmax")
projection(maxs) <- mycrs
extent(maxs) <- extent(plonlat)
max.lonlat <- projectRaster(maxs, base.proj)
save(max.lonlat, file=paste0("E:/all_files/production/narccap/CCSM/", model, "max_lonlat_", year, ".RData"))
min.stem <- "E:/all_files/www.earthsystemgrid.org/CCSM/tasmin_"
inputfile <- paste0(min.stem, model, "_ccsm_", year, "010106.nc")
lat <- raster(inputfile, varname="lat")
lon <- raster(inputfile, varname = "lon")
plat <- rasterToPoints(lat)
plon <- rasterToPoints(lon)
lonlat <- cbind(plon[,3], plat[,3])
lonlat <- SpatialPoints(lonlat, proj4string = crs(maurer.proj))
mycrs <- crs("+proj=stere +lon_0=263 +x_0=3475000 +y_0=7475000 +lat_0=90 +ellps=WGS84")
plonlat <- spTransform(lonlat, CRSobj = mycrs)
mins <- brick(inputfile, varname="tasmin")
projection(mins) <- mycrs
extent(mins) <- extent(plonlat)
min.lonlat <- projectRaster(mins, maurer.proj)
save(min.lonlat, file=paste0("E:/all_files/production/narccap/CCSM/", model, "min_lonlat_", year, ".RData"))
}
lapply(years, convert.lonlat, model=model)
From here I go on to make the matrix of production days based on the saved files max.lonlat and min.lonlat.
I want writeRaster to write the RAT (raster attribute table) that I've built in R.
I'm running R 3.0.1, raster 2.1-49, and rgdal 0.8-10.
My input raster looks like this:
r <-raster("F:/test.img")
class : RasterLayer
dimensions : 3, 3, 9 (nrow, ncol, ncell)
resolution : 30, 30 (x, y)
extent : 347325, 347415, 4301655, 4301745 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : F:\test.img
names : test
values : 1, 19 (min, max)
I then build my attribute table:
r <- ratify(r)
rat <- levels(r)[[1]]
rat$Pixel_Values <- c(1, 7, 8, 9, 19)
rat$Class_Names <- c("A", "B", "C", "D", "E")
levels(r) <- rat
Which results in a raster with attributes:
r
# class : RasterLayer
# dimensions : 3, 3, 9 (nrow, ncol, ncell)
# resolution : 30, 30 (x, y)
# extent : 347325, 347415, 4301655, 4301745 (xmin, xmax, ymin, ymax)
# coord. ref. : +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
# data source : F:\test.img
# names : test
# values : 1, 19 (min, max)
# attributes :
# ID Pixel_Values Class_Names
# 1 1 A
# 7 7 B
# 8 8 C
# 9 9 D
# 19 19 E
I then attempt to write my raster together with its RAT:
ratRaster <- "F:/testRat.img"
writeRaster(r, filename=ratRaster, datatype="INT1U", RAT=TRUE, progress="window", overwrite=TRUE)
But when I read it back into R, it becomes apparent that the attributes did not persist:
r2 <- raster(ratRaster)
r2
# class : RasterLayer
# dimensions : 3, 3, 9 (nrow, ncol, ncell)
# resolution : 30, 30 (x, y)
# extent : 347325, 347415, 4301655, 4301745 (xmin, xmax, ymin, ymax)
# coord. ref. : +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
# data source : F:\testRat.img
# names : testRat
# values : 1, 19 (min, max)
It would be quick and awesome to build RATs in R. How can I create and export the raster and keep the RAT?
Note that if you use raster's native .grd format (see doc, section 3.3) , the RAT table will be saved:
library(raster)
r <- raster(nrows=5, ncols=5)
r[] <- rep(1:5, 5)
r <- ratify(r)
rat <- levels(r)[[1]]
rat$Pixel_Values <- 1:5
rat$Class_Names <- c("A", "B", "C", "D", "E")
levels(r) <- rat
r
writeRaster(r, filename="raster_rat.grd")
Now re-open:
r2 <- raster("raster_rat.grd")
r2
You could always write the RAT as a csv file, then join that data later.
Write your raster as you specified:
writeRaster(r, filename=ratRaster, datatype="INT1U", RAT=TRUE, progress="window", overwrite=TRUE)
Write the attribute data/table/RAT as a .csv file:
write.csv(rat, file="C:\\merp\\rat.csv", row.names = F)
Then you can join this data in another program later.
For example, if exporting from R to ArcMap, write the raster to disk, write the attribute data as csv file, then join your RAT to the raster using the Add Join tool in ArcMap.
Reading the definition of writeRaster writing the RAT is clearly not implemented, at least for the native and the GTif format. Actually, one of the first things done is to remove the RAT. Not too surprising given the comments in ratify() help:
The functions documented here are
mainly available such that files with a RAT can be read and
processed; currently there is not too much further support.
From and to R you can always use
save(r,file=ratRaster)
and then
load(ratRaster)
It keeps everything.