I want to crop a raster stack using a polygon shapefile i made in ArcGIS, however I get error that extent does not overlap.
First I create the raster stack:
test1 < stack("C:/mydir/test1.tif")
define projection
myCRS <- test1#crs
then read shapefile
myExtent <- readShapePoly("C:/mydir/loc1.shp", verbose=TRUE, proj4string=myCRS)
Crop
myCrop <- crop(test1, myExtent)
Error in .local(x, y, ...) : extents do not overlap
I have searched for a solution, but i only find that projection can be the problem, however they are definetly both in the same CRS:
> test1$test1.1
class : RasterLayer
band : 1 (of 4 bands)
dimensions : 10980, 10980, 120560400 (nrow, ncol, ncell)
resolution : 10, 10 (x, y)
extent : 6e+05, 709800, 5690220, 5800020 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=utm +zone=31 +datum=WGS84 +units=m +no_defs +ellps=WGS84
+towgs84=0,0,0
data source : C:\mydir\test1.tif
names : test1.1
values : 0, 65535 (min, max)
> myExtent
class : SpatialPolygonsDataFrame
features : 1
extent : 499386.6, 517068.2, 6840730, 6857271 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=utm +zone=31 +datum=WGS84 +units=m +no_defs +ellps=WGS84
+towgs84=0,0,0
variables : 2
names : Shape_Leng, Shape_Area
min values : 67444.6461177, 283926851.657
max values : 67444.6461177, 283926851.657
The message is pretty self explanatory. Your extent do not overlap... here how to check it:
library(raster)
ext.ras <- extent(6e+05, 709800, 5690220, 5800020)
ext.pol <- extent(499386.6, 517068.2, 6840730, 6857271)
plot(ext.ras, xlim = c( 499386.6,709800), ylim= c(5690220,6857271), col="red")
plot(ext.pol, add=T, col="blue")
I've created extent object from data in your question. You see one extent in the top left corner and the other in the bottom right. Have you tried reading both files in QGIS, you could probably easily see it.
If they really are suppose to overlap, than I would suspect the way you read your shapefile. Instead of
myExtent <- readShapePoly("C:/mydir/loc1.shp", verbose=TRUE, proj4string=myCRS)
use :
library(rgdal)
myExtent <- readOGR("C:/mydir","loc1.shp")
myExtent <- spTRansform(myExtent, CRS(proj4string(test1)))
Related
I'm merging two MODIS DSR tiles using a R script that I developed, these are the products:
https://drive.google.com/drive/folders/1RG3JkXlbaotBax-h5lEMT7lEn-ObwWsD?usp=sharing
So, I open both products (tile h15v05 and tile h16v05) from same date (2019180), then I open each SDS and merge them together (00h from h15v05 with 00h from h16v05 and so on...)
Visualisation on Panoply (using the merge option) of the two products:
Purple square is the location of the division line that separates the two tiles.
With my code I obtain a plot with pixels with different resolution (and different min/max values) and I don't understand why:
I suspect that the results obtained are due to:
1- Changing from Sinusoidal CRS to longlat WGS84 CRS;
2- Using resample (method ngb) to work with mosaic.
My code is extensive, but here are some parts of it:
# Open scientific dataset as raster
SDSs <- sds(HDFfile)
SDS <- SDSs[SDSnumber]
crs(SDS) <- crs("+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs")
SDSreprojected <- project(SDS, DesiredCRS)
SDSasRaster <- as(SDSreprojected, "Raster")
# Resample SDS based on a reference SDS (SDS GMT_1200_DSR of a first product), I need to do this to be able to use mosaic
SDSresampled <- resample(SDSasRaster,ResampleReference_Raster,method='ngb')
# Create mosaic of same SDS, but first convert stack to list to use mosaic
ListWith_SameSDS_OfGroupFiles <- as.list(StackWith_SameSDS_OfGroupFiles)
ListWith_SameSDS_OfGroupFiles.mosaicargs <- ListWith_SameSDS_OfGroupFiles
ListWith_SameSDS_OfGroupFiles.mosaicargs$fun <- mean
SDSmosaic <- do.call(mosaic, ListWith_SameSDS_OfGroupFiles.mosaicargs)
# Save SDSs mosaic stack to netCDF
writeRaster(StackWith_AllMosaicSDSs_OfGroupFiles, NetCDFpath, overwrite=TRUE, format="CDF", varname= "DSR", varunit="w/m2", longname="Downward Shortwave Radiation", xname="Longitude", yname="Latitude", zname="TimeGMT", zunit="GMT")
Does anyone have an idea of what could be the cause of this mismatch between results?
print(ResampleReference_Raster)
class : RasterLayer
dimensions : 1441, 897, 1292577 (nrow, ncol, ncell)
resolution : 0.01791556, 0.006942043 (x, y)
extent : -39.16222, -23.09196, 29.99652, 40 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
source : memory
names : MCD18A1.A2019180.h15v05.061.2020343034815
values : 227.5543, 970.2346 (min, max)
print(SDSasRaster)
class : RasterLayer
dimensions : 1399, 961, 1344439 (nrow, ncol, ncell)
resolution : 0.01515284, 0.007149989 (x, y)
extent : -26.10815, -11.54627, 29.99717, 40 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
source : memory
names : MCD18A1.A2019180.h16v05.061.2020343040755
values : 0, 0 (min, max)
print(SDSmosaic)
class : RasterLayer
dimensions : 1441, 897, 1292577 (nrow, ncol, ncell)
resolution : 0.01791556, 0.006942043 (x, y)
extent : -39.16222, -23.09196, 29.99652, 40 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
source : memory
names : layer
values : 0, 62.7663 (min, max)
Also, some of the islands were ignored by the script (bottom right)...
sorry I didn't reply earlier. So I think you're right that this issue is extent to which you are resampling. I think you might be able to get around this by creating a dummy raster that has the extent of the raster you want to resample, but has the resolution of the raster you want to mosaic to.Try:
dummy<-raster(ext = SDSasRaster#extent, resolution=ResampledReference_Raster#res, crs=SDSasRaster#crs)
SDS2<-resample(SDSasRaster, dummy, method="ngb")
Final<-moasic(SDS2, ResampledReference_Raster, fun=mean)
I have two spatial objects that I'm working with - a raster layer and a shapefile of my study region. I'm trying to crop my raster to my shapefile, but I'm having trouble getting the coordinate systems to match.
Here is a summary of my spatial objects:
Raster Layer
> testxx
class : RasterLayer
dimensions : 450, 3245, 1460250 (nrow, ncol, ncell)
resolution : 0.1109257, 0.3826826 (x, y)
extent : -179.9964, 179.9576, -89.98608, 82.22108 (xmin, xmax, ymin, ymax)
crs : NA
source : memory
names : layer
values : 5.389717e-06, 0.0002052335 (min, max)
Shapefile
class : SpatialPolygonsDataFrame
features : 1
extent : 5951759, 7591708, 649338.9, 2306575 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
variables : 8
names : PRUID, PRNAME, PRENAME, PRFNAME, PREABBR, PRFABBR, BUFF_DIST, ORIG_FID
value : 35, Ontario, Ontario, Ontario, Ont., Ont., 10000, 5
This is the code I tried:
# Add a coordinate system to the raster
crs(testxx) <- "+proj=longlat +datum=WGS84 +no_defs"
# change shapefile coordinate system
aoi <- spTransform(on_shp, crs(testxx))
But the coordinates themselves still don't match - the shapefile is still in meters, while the raster is in lat and long.
Thanks!
The coordinate reference system for the SpatialPolygonsDataFrame is clearly wrong. Given this extent:
extent : 5951759, 7591708, 649338.9, 2306575 (xmin, xmax, ymin, ymax)
It cannot be
crs : +proj=longlat +datum=WGS84 +no_defs
So you need to find out what it actually is. To test whether it is correct you could set it to what you think it is and then transform to lon/lat and see if ends up in the right place.
I am trying to transform a raster layer to polygons based on its values. My raster looks like this:
> labels_rast
class : RasterLayer
dimensions : 26, 64, 1664 (nrow, ncol, ncell)
resolution : 0.03000146, 0.02999809 (x, y)
extent : 352032, 352033.9, 8551454, 8551455 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=utm +zone=18 +south +datum=WGS84 +units=m +no_defs+ellps=WGS84 +towgs84=0,0,0
data source : in memory
names : layer
values : 1, 3 (min, max)
When I apply the rasterToPolygons function (dissolve = TRUE), I get extra polygons (defined by horizontal lines):
How can I avoid the creation of the polygons defined by the horizontal lines?
It works for this very similar case:
library(raster)
r <- raster(nrow=26, ncol=64, xmn=352032, xmx=352033.9, ymn=8551454, ymx=8551455, crs="+proj=utm +zone=18 +south +datum=WGS84 +units=m", vals=3)
r[, 20:40] <- 2
r[1:3, 1:60] <- 1
r[24:26, 5:64] <- 1
x <- rasterToPolygons(r, dissolve=TRUE)
plot(r)
lines(x)
I am guessing that it does not work for you because of floating point imprecision. Would have to see your file to be sure. But, if so, perhaps you can round the extent (or resolution) a little bit.
For example
res(labels_rast) <- 0.03
y <- rasterToPolygons(labels_rast, dissolve=TRUE)
I am in trouble making raster stack which have slightly different extent. The answer (1st one) given here is useful but did not help in my case. For example, I want to make a raster stack using bio2 raster for Australia and this Australian raster. The second raster comes for Australia only and the first one is global. So I cropped the global bio2 raster to the same extent of Australian raster using crop() function, but the resultant raster extent (i.e., bio2.au) is slightly different (therefore, I cannot make raster using the cropped raster and the Australian raster, awc). Sample code is below:
library(raster)
awc <- raster("path to Australian raster")
bio2.g <- raster("path to Bio2 global raster")
# crop bio2.g to the same extent of awc
bio2.au <- crop(bio2.g, extent(awc))
# make a raster stack
st <- stack(awc, bio2.au)
Error in compareRaster(x) : different extent
I have also tried using quick=TRUE within the stack() function. But in this case the cell values in awc is lost. Note: the size of awc raster is 4gb.
# first make a list of rasters saved in the computer
li <- list.files("path to file", pattern = ".tif$", full.names = TRUE)
st <- stack(li, quick=TRUE)
st[[1]] # no cell values for awc
Your suggestions will be highly appreciated. My ultimate goal is to crop several bioclim rasters to the same extent of Australian raster awc and stack them together so that raster cell values are not lost.
Edit (after comment of #Cobin):
Below is the attribute of each raster
# global raster (bigger raster)
> r
class : RasterLayer
dimensions : 21600, 43200, 933120000 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (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:\Worldclim2_Bioclim\wc2.0_bio_30s_02.tif
names : wc2.0_bio_30s_02
values : 0, 37.06667 (min, max)
# Australian raster (smaller raster)
> r1
class : RasterLayer
dimensions : 43201, 49359, 2132358159 (nrow, ncol, ncell)
resolution : 0.0008333333, 0.0008333333 (x, y)
extent : 112.8921, 154.0246, -44.00042, -7.999583 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : D:\SoilAWC5cm.EV1.tif
names : SoilAWC5cm.EV1
values : 2.997789, 27.86114 (min, max)
# new raster, after crop() function is applied
> r2 <- crop(r,extent(r1))
> r2
class : RasterLayer
dimensions : 4320, 4936, 21323520 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : 112.8917, 154.025, -44, -8 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : C:\Users\Anwar\AppData\Local\Temp\Rtmpmg9fyF\raster\r_tmp_2018-11-23_164300_11308_65747.grd
names : wc2.0_bio_30s_02
values : 1.933333, 18.15833 (min, max)
# rebuild r2 to match r1
> r22 <- raster(vals=values(r2),ext=extent(r1), nrows=dim(r1)[1],ncols=dim(r1)[2])
Error in setValues(r, vals) :
length(values) is not equal to ncell(x), or to 1
I suppose that the extent of two raster are differet though the raster masked by crop function.You
should check the both of awc and bio.au extent base on same reolution, rows and columns. Because I couldn't download data from
hyperlink, I give an example of my own data.
r <- raster('/big_raster')
r1 <- raster('/small_raster')
r2 <- crop(r,extent(r1))
r1
class : RasterLayer
dimensions : 74, 157, 11618 (nrow, ncol, ncell)
resolution : 0.0833333, 0.0833333 (x, y)
extent : 89.2185, 102.3018, 30.96238, 37.12905 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : D:\D\temp\Rtest\modis8km.tif
names : modis8km
values : -32768, 32767 (min, max)
r2
class : RasterLayer
dimensions : 74, 157, 11618 (nrow, ncol, ncell)
resolution : 0.08333333, 0.08333333 (x, y)
extent : 89.25, 102.3333, 31, 37.16667 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : in memory
names : g201401a
values : -32768, 7789 (min, max)
Though r1 and r1 with same resolution and dimension, the extent have tiny offset. It cause stack error.
stack(r1,r2)
Error in compareRaster(x) : different extent
So, you should rebuid the r2 to match r1:
r22 <- raster(vals=values(r2),ext=extent(r1),crs=crs(r1),
nrows=dim(r1)[1],ncols=dim(r1)[2])
Now stack(r22,r1) will be successful.
I am having issues converting this spatialpolygondataframe to a raster. When I do ther conversion, the raster has NA as its values. As shown below:
DL3
[1]
class : SpatialPolygonsDataFrame
features : 126
extent : -15.04001, 46.1036, 3.759985, 31.71804 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +towgs84=0,0,0 +ellps=WGS84
variables : 1
names : LFRP
min values : 14
max values : 335.2
This is how I rasterize it:
##TO CONVERT TO RASTER
FunR<-function(r){
ext<-raster(extent(r))
crs(ext)<-crs(r)
D<-rasterize(r,ext,field=1,update=T)
D}
DL4<-lapply(DL3,FunR)
DL4
[1]
class : RasterLayer
dimensions : 45, 40, 1800 (nrow, ncol, ncell)
resolution : 1.52859, 0.6212901 (x, y)
extent : -15.04001, 46.1036, 3.759985, 31.71804 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +towgs84=0,0,0 +ellps=WGS84
data source : in memory
names : layer
values : NA, NA (min, max)
What can I be doing wrongly? I need help with a method to ensure the values in the dataframe reflect in the raster, please.
The principle works, as illustrated below.
library(raster)
SPP <- shapefile(system.file("external/lux.shp", package="raster"))
r <- raster(SPP, ncol=40, nrow=45)
SPP2 <- rasterize(SPP, r, "ID_1")
As this does not work for you, I assume that your polygons are very small relative to the raster cell size such that none of the cells is covered. Can you try with much smaller grid cells? If indeed the polygons are that small, it might make more sense to use their centroids (coordinates(SPP)) and rasterize these (as points).