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I have a single simple raster in EPSG:7532 that I am trying to project to EPSG:4326 but is failing
The source data is a Lidar point clould that I am able to process using the lidR package. The data source is in the link below
https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/WI_BrownRusk_2020_B20/WI_Brown_2_2020/LAZ/USGS_LPC_WI_BrownRusk_2020_B20_02531702.laz
l1 = readLAS("USGS_LPC_WI_BrownRusk_2020_B20_02531702.laz")
> l1
class : LAS (v1.4 format 6)
memory : 2.5 Gb
extent : 25349, 26849, 170258, 171758 (xmin, xmax, ymin, ymax)
coord. ref. : NAD83(2011) / WISCRS Brown (m) + NAVD88 height - Geoid18 (m)
area : 2.25 km²
points : 35.57 million points
density : 15.79 points/m²
density : 12.89 pulses/m²
convert to a spatRaster:
dsm <- rasterize_canopy(l1, res = 1.0, pitfree(c(0,2,5,10,15), c(0, 1.5)))
> dsm
class : SpatRaster
dimensions : 1500, 1501, 1 (nrow, ncol, nlyr)
resolution : 1, 1 (x, y)
extent : 25349, 26850, 170258, 171758 (xmin, xmax, ymin, ymax)
coord. ref. : NAD83(2011) / WISCRS Brown (m) (EPSG:7532)
source : memory
name : Z
min value : 185.836
max value : 333.709
The point of failure is the attempt to project to geographic format:
dsm_test <- terra::project(dsm, "EPSG:4326", method="bilinear")
> dsm_test <- terra::project(dsm, "EPSG:4326", method="bilinear")
Error: [project] cannot get output boundaries
In addition: Warning messages:
1: In x#ptr$warp(SpatRaster$new(), y, method, mask, FALSE, opt) :
GDAL Error 1: PROJ: vgridshift: could not find required grid(s).
2: In x#ptr$warp(SpatRaster$new(), y, method, mask, FALSE, opt) :
GDAL Error 1: PROJ: pipeline: Pipeline: Bad step definition: proj=vgridshift (File not found or invalid)
3: In x#ptr$warp(SpatRaster$new(), y, method, mask, FALSE, opt) :
GDAL Error 1: Too many points (961 out of 961) failed to transform, unable to compute output bounds.
A similar topic here, but seems different.
https://stackoverflow.com/questions/72404897/what-is-causing-this-raster-reprojection-error
This issue is not resulting from reprojection from EPSG:7532 to EPSG:4326 per se, but seems rather connected to the fact, that your SpatRaster object created via rasterize_canopy() comes with a vertical datum, apparently causing problems downstream:
VERTCRS["NAVD88 height - Geoid18 (m)",
VDATUM["North American Vertical Datum 1988"],
CS[vertical,1],
AXIS["up",up,
LENGTHUNIT["meter",1]],
GEOIDMODEL["GEOID18"],
ID["EPSG",5703]]]
The quick & dirty solution would be to simply override crs definition by EPSG:7532 and dropping references in Z dimension, although this does not feel 100 % right. On the other hand, I'm not sure how terra handles vertical crs information and if it is possible to keep this information at all.
library(lidR)
library(terra)
#> terra 1.6.49
l1 = readLAS("USGS_LPC_WI_BrownRusk_2020_B20_02531702.laz")
#> Warning: There are 53206 points flagged 'withheld'.
dsm <- rasterize_canopy(l1, res = 1.0, pitfree(c(0,2,5,10,15), c(0, 1.5)))
crs(dsm) <- "epsg:7532"
dsm_4326 <- project(dsm, "epsg:4326", method="bilinear")
dsm_4326
#> class : SpatRaster
#> dimensions : 1235, 1727, 1 (nrow, ncol, nlyr)
#> resolution : 1.093727e-05, 1.093727e-05 (x, y)
#> extent : -88.0786, -88.05972, 44.49092, 44.50443 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> source(s) : memory
#> name : Z
#> min value : 185.8360
#> max value : 295.1357
From my personal point of view, it would be better to have both crs (xy, z) listed in the SpatRaster summary with reproject() being able to address z-transformations separately (or have e.g. terra::transform()), e.g. when you wanted to just transform your heights but still keep EPSG:7532 as your xy reference system.
coord. ref. xy: NAD83(2011) / WISCRS Brown (m) (EPSG:7532)
coord. ref. z: NAVD88 height - Geoid18 (m) (EPSG:5703)
I am struggling to open a NetCDF file and convert it into a raster using R. The
data is supposed to be on a regular grid of 25 km by 25 km. It contains sea
ice concentration in the Arctic.
library(terra)
#> terra 1.5.21
library(ncdf4)
file <- "~/Downloads/data_sat_Phil_changt_grid/SIC_SMMR_month_2015.nc"
I am getting a warning about the extent not found.
r <- rast(file)
#> Error in R_nc4_open: No such file or directory
#> Warning: [rast] GDAL did not find an extent. Cells not equally spaced?
We can see that there is a problem with the coordinates/extent.
r
#> class : SpatRaster
#> dimensions : 448, 304, 12 (nrow, ncol, nlyr)
#> resolution : 0.003289474, 0.002232143 (x, y)
#> extent : 0, 1, 0, 1 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84
#> source : SIC_SMMR_month_2015.nc:sic
#> varname : sic
#> names : sic_1, sic_2, sic_3, sic_4, sic_5, sic_6, ...
I can open the nc file with nc_open() and I see that the coordinates are present.
nc <- nc_open(file)
names(nc$var)
#> [1] "lat" "lon" "sic"
lat <- ncvar_get(nc, "lat")
lon <- ncvar_get(nc, "lon")
dim(lat)
#> [1] 304 448
dim(lon)
#> [1] 304 448
dim(r)
#> [1] 448 304 12
Is it possible to assemble this data (the SIC values and the coordinates) to create a SpatRaster?
The nc file can be downloaded here: https://easyupload.io/pfth0s
Created on 2022-05-20 by the reprex package (v2.0.1)
The data are gridded, but the file does not specify the coordinates, nor the coordinate reference system. The file specifies the lon/lat values associated with the cells, but does not help us much, as these are clearly not on a regular grid. That is easy to see from plot(r)
NAflag(r) = -9999
plot(r,1)
And also from
p = cbind(as.vector(lon), as.vector(lat))
plot(p, cex=.1, xlab="lon", ylab="lat")
So what you need to find out, is which coordinate reference system (crs) is used, clearly some kind of polar crs. And what the extent of the data set is.
From the website you point to, I take it we can use:
crs(r) = "+proj=stere +lat_0=90 +lat_ts=70 +lon_0=-45 +k=1 +x_0=0 +y_0=0 +a=6378273 +b=6356889.449 +units=m +no_defs"
ext(r) = c(-3850000, 3750000, -5350000, 5850000)
r
#class : SpatRaster
#dimensions : 448, 304, 12 (nrow, ncol, nlyr)
#resolution : 25000, 25000 (x, y)
#extent : -3850000, 3750000, -5350000, 5850000 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=stere +lat_0=90 +lat_ts=70 +lon_0=-45 +x_0=0 +y_0=0 +a=6378273 +b=6356889.449 +units=m +no_defs
#source : SIC_SMMR_month_2015.nc:sic
#varname : sic
#names : sic_1, sic_2, sic_3, sic_4, sic_5, sic_6, ...
The results look good:
g = geodata::gadm("Greenland", level=0, path=".")
gg = project(g, crs(r))
plot(r,1)
lines(gg)
But this is of course not a good way to do such things; the ncdf file should have contained all the metadata required.
I would like to write a RasterStack object and preserve names and metadata of the individual layers. How to preserve names is explained here. Is there a way to preserve metadata of individual layers when writing a RasterStack object?
Here is replicable code:
# load library
library(raster)
# create example rasters
ras_1 <- raster(nrows=180, ncols=360, xmn=-180, xmx=180, ymn=-90, ymx=90, resolution=, vals=1)
ras_2 <- raster(nrows=180, ncols=360, xmn=-180, xmx=180, ymn=-90, ymx=90, resolution=, vals=2)
ras_3 <- raster(nrows=180, ncols=360, xmn=-180, xmx=180, ymn=-90, ymx=90, resolution=, vals=3)
# assign names
names(ras_1) <- "raster_A"
names(ras_2) <- "raster_B"
names(ras_3) <- "raster_C"
# assign metadata
metadata(ras_1) <- list("metadata_raster_A")
metadata(ras_2) <- list("metadata_raster_B")
metadata(ras_3) <- list("metadata_raster_C")
# check
ras_1
ras_2
ras_3
metadata(ras_1)
metadata(ras_2)
metadata(ras_3)
# create and check stack
raster_stack <- stack(ras_1,
ras_2,
ras_3)
raster_stack
raster_stack[[1]]
metadata(raster_stack[[1]])
# write raster stack to disk
setwd("~")
# load library
library(terra)
# create rast object
raster_stack_terr <- rast(raster_stack)
# write raster stack
terra::writeRaster(raster_stack_terr, "raster_stack_terr_test.tif")
# load and check raster stack
raster_stack_check <- stack("raster_stack_terr_test.tif")
raster_stack_check
raster_stack_check[[1]]
names(raster_stack_check[[1]])
metadata(raster_stack_check[[1]])
Use terra to preseve names according to the 3rd answer from here.
When opening the RasterStack from disk, the metadata is not preserved. See console output:
> metadata(raster_stack_check[[1]])
list()
How to preserve metadata of individual layers when writing and re-loading a RasterStack object? Thanks!
It does not seem like {terra} offers an equivalent to raster::metadata(). However, from my perspective, the use cases would be limited here, because you would only be able to store structured information in corresponding format-specific tags (at least, this is my understanding) when writing to disk.
TIFF files (c.f. here) seem to offer the following tags:
TIFFTAG_DOCUMENTNAME
TIFFTAG_IMAGEDESCRIPTION
TIFFTAG_SOFTWARE
TIFFTAG_DATETIME
TIFFTAG_ARTIST
TIFFTAG_HOSTCOMPUTER
TIFFTAG_COPYRIGHT
TIFFTAG_XRESOLUTION
TIFFTAG_YRESOLUTION
TIFFTAG_RESOLUTIONUNIT
ESRI-Grids, on the other hand, do not offer any possibilities to store metadata except for the known header and maybe the filename as far as I know.
If you only wanted to store certain metadata with your raster object, you might as well make use of attr(r, "meta") <- "foobar". However, I don't see how this (random) information can be stored in specific formats and restored afterwards.
You already noticed names() when using {terra}, but there is also time() to be mentioned. Maybe this already suits your needs, since you did not specify what exactly you intend to store.
# set up a raster stack with three layers
library(terra)
#> terra 1.6.17
# create raster
r <- rast(nrows = 10, ncols = 10)
values(r) <- rnorm(100)
# set metadata
names(r) <- "foo"
time(r) <- as.Date("2000-01-01")
attr(r, "meta") <- "bar"
# inspect
r
#> class : SpatRaster
#> dimensions : 10, 10, 1 (nrow, ncol, nlyr)
#> resolution : 36, 18 (x, y)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84
#> source : memory
#> name : foo
#> min value : -2.503790
#> max value : 1.998731
#> time (days) : 2000-01-01
# write to disk
writeRaster(r, "sample.tif", overwrite = TRUE)
# read from disk
r2 <- rast("sample.tif")
r2
#> class : SpatRaster
#> dimensions : 10, 10, 1 (nrow, ncol, nlyr)
#> resolution : 36, 18 (x, y)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> source : sample.tif
#> name : foo
#> min value : -2.503790
#> max value : 1.998731
#> time (days) : 2000-01-01
# try to access attributes
attr(r2, "meta")
#> NULL
As expected, data stored as attribute has been lost whereas information provided via names() and time() was sustained.
I need to project longitude/latitude coordinates in the terra package, but I don't believe it is working correctly, as I am trying to extract data from a raster with this projection, but the data is not being extracted correctly.
Here's my lon/lat points and the code I am using to try to project them.
latlon_df <- structure(list(Lon = c(-103.289, -96.6735, -96.9041, -96.76864,
-102.4694, -96.6814, -97.7504, -99.6754, -96.4802, -103.0007,
-96.8897, -101.8539, -103.9717, -101.253, -99.1134, -96.5849,
-98.0301, -99.9537, -99.4601, -99.7122, -103.8278, -98.931, -102.1081,
-101.7162, -100.115, -101.3448, -100.7805, -103.5606, -96.5302,
-99.4156, -103.281, -100.0063, -97.9928, -100.7208, -98.5289,
-96.762, -96.9218, -97.1024, -103.3793, -101.0841, -102.6745,
-96.9188, -97.5154, -100.7435, -98.6938), Lat = c(45.5194, 44.3099,
43.0526, 44.3252, 45.5183, 43.7316, 45.6796, 45.4406, 44.7154,
44.0006, 43.7687, 43.9599, 43.4737, 44.9875, 45.0292, 44.0867,
45.5735, 44.9895, 44.5256, 43.5938, 43.7343, 45.7163, 45.9189,
43.1672, 45.6716, 45.9154, 45.7963, 44.6783, 44.5073, 43.7982,
43.3784, 44.2912, 43.3841, 43.2002, 44.8579, 43.5048, 43.5033,
45.1055, 44.4245, 45.4167, 44.5643, 44.304, 45.2932, 43.5601,
43.7321)), class = "data.frame", row.names = c(NA, -45L))
latlons <- terra::vect(latlon_df,geom=c('Lon','Lat'),crs="+proj=longlat")
lcc <- terra::project(latlons,"+proj=lcc +lat_0=38.5 +lon_0=262.5 +lat_1=38.5 +lat_2=38.5 +x_0=0 +y_0=0 +R=6371229 +units=m +no_defs")
var_df <- terra::extract(grib_data,lcc)[,-1]
The raster data (grib_data) I am using comes from here (it is way too big for me to put on here). https://nomads.ncep.noaa.gov/pub/data/nccf/com/hrrr/prod/hrrr.20210612/conus/hrrr.t00z.wrfsubhf00.grib2
I am not sure what I am doing wrong here, as I have used this method previously, and it seemed to work fine. Any help would be wonderful.
EDIT: The specific problem I am having is that I am not getting any different values for each lon/lat pair. The value for each variable is different, but all the values for the stations (different lon/lats are the same).
Why do you think it has to do with the projection? Either way, it appears to work for me.
url <- "https://nomads.ncep.noaa.gov/pub/data/nccf/com/hrrr/prod/hrrr.20210612/conus/hrrr.t00z.wrfsubhf00.grib2"
if (!file.exist(basename(url))) download.file(url, basename(url), mode="wb")
url <- paste0(url, ".idx")
if (!file.exist(basename(url))) download.file(url, basename(url), mode="wb")
library(terra)
r <- rast("hrrr.t00z.wrfsubhf00.grib2")
r
#class : SpatRaster
#dimensions : 1059, 1799, 49 (nrow, ncol, nlyr)
#resolution : 3000, 3000 (x, y)
#extent : -2699020, 2697980, -1588806, 1588194 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=lcc +lat_0=38.5 +lon_0=262.5 +lat_1=38.5 +lat_2=38.5 +x_0=0 +y_0=0 +R=6371229 +units=m +no_defs
#source : hrrr.t00z.wrfsubhf00.grib2
#names : 0[-] ~here", 0[-] ~tops", 0[-] ~here", 0[-] ~here", 0[-] ~face", 1000[~ound", ...
You can check of the points overlap with the raster data
plot(r, 1)
points(lcc)
And extract. It takes very long with grib files, but it does appear to work
e <- extract(r, lcc)
head(e[,c(1,6,9)])
# ID 0[-] SFC="Ground or water surface" 0[-] SFC="Ground or water surface".1
#1 1 85100 11.775471
#2 2 54400 11.087971
#3 3 79300 9.900471
#4 4 49200 10.712971
#5 5 70800 9.212971
#6 6 56600 11.400471
Make sure you have the current (CRAN) version, or perhaps the development version that you can install like this:
install.packages('terra', repos='https://rspatial.r-universe.dev')
You can speed things up a lot by doing a single read from disk (by adding zero in this example)
e <- extract(r+0, lcc)
That is not always possible and I need to do some optimization behind the scences.
Good day everyone..
I have 13 bioclimatic variables (in .tiff format) that I will used to perform sdm by using dismo package.
I followed the tutorial written by Robert J. Hijmans and Jane Elith.
However, when I tried to stack all of the variables, I got the following error
Error in .local(.Object, ...) :
Error in .rasterObjectFromFile(x, band = band, objecttype = "RasterLayer", :
Cannot create a RasterLayer object from this file.
All of my file's coordinate system, extent, and cell size have been adjusted so they are all the same..
when I tried to used the alternative brick function, I got the following error :
Error in .rasterObjectFromFile(x, objecttype = "RasterBrick", ...) :
Cannot create a RasterLayer object from this file.
In addition: There were 12 warnings (use warnings() to see them)
I used the warning() message but it was empty..
do any of you have any hints regarding what may be the cause of such errors?
i've tried to google it, but unfortunately, no answer can solve it.
Thank you in advance..
Here presented is the fraction of the transcript
#setting the workspace
setwd("D:/Riset/MaxentSelaginella/newpaperproject_part2/MakalahVI/Workspace_R")
#Loading Libraries
library("sp")
library("raster")
library("maptools")
library("rgdal")
library("dismo")
library("rJava")
#open the csv file
obs.data <- read.csv(file = "data3/Selaginella_plana.csv", sep = ",")
#open Environmental Data
files <- list.files(path = "data3/tif/", pattern = ".tif", full.names=TRUE)
#stacking all the files
predictors <- brick(files)
I guess you need to use stack instead of brick. As per brick help, in fact:
A RasterBrick is a multi-layer raster object. They are typically created from
a multi-layer (band) file; but they can also exist entirely in memory.
They are similar to a RasterStack (that can be created with stack), but processing
time should be shorter when using a RasterBrick. Yet they are less flexible as they can only point to a single file.
So, if we try to “stack” multiple files:
library(raster)
r <- raster(ncols = 100, nrows = 100, vals = 1:10000)
rfile1 <- tempfile(fileext = ".tif")
writeRaster(r, filename = rfile1)
rfile2 <- tempfile(fileext = ".tif")
writeRaster(r, filename = rfile2)
files_to_stack <- c(rfile1, rfile2)
This fails:
brick(files_to_stack)
#> Warning in if (x == "" | x == ".") {: the condition has length > 1 and only
#> the first element will be used
#> Warning in if (!start %in% c("htt", "ftp")) {: the condition has length > 1
#> and only the first element will be used
#> Warning in if (fileext %in% c(".GRD", ".GRI")) {: the condition has length
#> > 1 and only the first element will be used
#> Warning in if (!file.exists(x)) {: the condition has length > 1 and only
#> the first element will be used
.....
#> Error in .rasterObjectFromFile(x, objecttype = "RasterBrick", ...): Cannot create a RasterLayer object from this file.
While this works:
stack(files_to_stack)
#> class : RasterStack
#> dimensions : 100, 100, 10000, 2 (nrow, ncol, ncell, nlayers)
#> resolution : 3.6, 1.8 (x, y)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
#> names : file46e41bcd78e3, file46e43ea75bad
#> min values : 1, 1
#> max values : 10000, 10000
If you want to have a brick to get some gain in “efficiency” in further
processing, you can save the different "layers" as a multiband tiff, and then open using brick:
rfile_multi <- tempfile(fileext = ".tif")
writeRaster(stack(files_to_stack), filename = rfile_multi)
brick(rfile_multi)
#> class : RasterBrick
#> dimensions : 100, 100, 10000, 2 (nrow, ncol, ncell, nlayers)
#> resolution : 3.6, 1.8 (x, y)
#> extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
#> data source : D:\RTemp\RtmpacXztJ\file4808784f268c.tif
#> names : file4808784f268c.1, file4808784f268c.2
#> min values : 1, 1
#> max values : 10000, 10000
Created on 2018-11-10 by the reprex package (v0.2.1)