bio_var is a stack of 6 cropped raster files from worldclim (10m resolution). I am getting the following error. I was trying to extract raster values using an occurrence dataset
raster:extract (bio_var[[2]], Burk_spatialP)
but i got the below error.
raster:extract(bio_var[[2]], Burk_spatialP) : NA/NaN argument
I have used the following code to crop the raster
BIO<-crop(bio,lanka, mask=TRUE)
bio is the rasterstack of climatic variables from worldclim.
I am not sure what is the reason behind it. May I know how can I resolve it?
Thanks Rahul
Related
When I import my DEM (digital elevation model) into R, it shows NA values instead of actual numbers for the elevations.
I can go into QGIS, use the Identify tool, and when I click on pixels, I get elevation values.
The DEM I am attempting to use has been merged, but again, QGIS says there are elevation values present. I have imported other (smaller) DEMs with no issue, but the merged DEM I am using does not show any elevation values, according to R. It is stopping me from doing any spatial analysis on the DEM.
I have also imported a landuse raster and it doesn't seem to have any issues so I am stumped.
Any insight would be very helpful, thank you!
EDIT
I am including a screenshot of the output from the View() function: it is how I know there are NA values instead of real numbers in the DEM in R.
Here is the link to the View() function results screenshot
Apologies if I am unclear - I am very new to R.
Package landscapemetrics can calculate area of each patch for a given raster file, shape of that patch and so on. I want to have not only tibble-frame with patch metrics calculated, but a new raster where each pixel within specific patch will have a value of the area of that patch, shape indicator and so on. We can do it with function spatialize_lsm() (it produces a Large list nested object with probably RasterObject objects within):
library(landscapemetrics)
plot(podlasie_ccilc) # this raster data is provided with package
podlasie.metrics.area <- spatialize_lsm(podlasie_ccilc, what = 'lsm_p_area') # creates a list
plot(podlasie.metrics.area) # produces an error...
How to get a desirable raster file with patch metrics from that list? I guess it is a question of raster package or something else, since landscapemetrics documentation tells nothing about this step.
I not that this data and new raster do not have resolution of the pixel like in meters (30, 30 for Landsat satellite image, for example). So we cannot plot the new raster produced:
podlasie.metrics.area[[1]]
plot(podlasie.metrics.area[[1]])
So I guess landscapemetrics cannot deal with such rasters, we can even use its function to check a suitability of the prior raster for patch discovering:
check_landscape(podlasie_ccilc)
Upd. I did it for the Landsat dataset with resolution 30, 30 and it produced patch area raster, but again I cannot open/show/save as raster it, because of the same error.
Package maintainer helps to solve a problem (yes, it is just related to the structure of list):
plot(podlasie.metrics.area[[1]]$lsm_p_area)
I am running into a problem with the "writeRaster" function in the raster package in R. I am importing a raster (TIF) that I made in ArcGIS (a distance to feature raster).
My goal was to resample the distance raster to the correct resolution and extent, then "mask" it with the appropriate raster to crop it to the shape I require. When I check the results of the mask with the basic plot function, everything looks great and I can see that each pixel in the new masked raster has a distance value.
However, when I write this raster to a file using the writeRaster function, the resulting raster looks like "swiss cheese" and has missing values for any distance over 35km. After much reading, I cannot find any documentation to suggest that there is a way to modify the maximum value set by writeRaster---or that it should even be setting a max value. I have included my code and the basic plots below. A big thank you to anyone who attempts to help me with this!
#Read in distance to fresh water raster
distFW <- raster("D:/Academia/Arc Data/Grackle/NicaCR_90mlayers/dist_FW.tif")
[plot(distFW)][1]
#Resample this layer to the desired resolution and template
NiCR_DistFW<-as.integer(resample(distFW,NiCRrast.tmpl,method="ngb"))
#essentially the same as the first plot
[plot(NiCR_DistFW)][2]
#Mask the resampled raster to the desired shape
NiCR.DistFW.mask.utm <- mask(NiCR_DistFW,NiCR_Mask) #with CA countries cut out.
[plot(NiCR.DistFW.mask.utm)][3]
#write raster to file (this is where things get weird)
writeRaster(x=NiCR.DistFW.mask.utm, filename='DistFWmask2.tif', format='GTiff', datatype='INT2S') #a way to ensure INT2S
#read the newly written raster file in to R so we can review it
dFW <-raster("DistFWMask2.tif")
[plot(dFW)_writeRaster_result][4]
[1]: https://i.stack.imgur.com/v9RkK.jpg
[2]: https://i.stack.imgur.com/v2DG3.jpg
[3]: https://i.stack.imgur.com/cCwJe.jpg
[4]: https://i.stack.imgur.com/MjWj7.jpg
As you can see from plot 4, an undesirable max value has been set. I was the raster I write to file to look like the one in plot 3, not plot 4.
Thanks in advance for any advice.
Well friends, after taking an hour to detail my question I managed to figure out the answer myself. It had to do with setting the datatype.
INT2S has a maximum value of 32,767
by switching it to INT4S, I capture the full range of values in my raster.
Problem solved!
We have a dataset that contains latitude and longitude coordinates, as well as attribute information, each in its own separate column, stored as numeric. These coordinates have been geocoded based on the geographic coordinate system WGS 1984.
We know that we have significant spatial autocorrelation in our data, which we are hoping to visualize in a bubble plot using the “sp” package. We are modeling our example off of others online, such as here: https://beckmw.wordpress.com/2013/01/07/breaking-the-rules-with-spatial-correlation/ . However, when we try to use the coordinates command within "sp", we keep getting an error message:
Code example:
coords <- data.frame(lead$X, lead$Y)
coordinates(coords) <- c("lead6.X","lead6.Y")
Error in if (nchar(projargs) == 0) projargs <- as.character(NA) missing value where TRUE/FALSE needed
We can't load our direct code because it's sensitive and hosted on a virtual environment without access to the internet. Does anyone have ideas for why this might be happening? We've looked into the proj4 package but can't figure out how to specify a projection system (or is that even the error that we are getting?). If anyone knows of any other packages in R or ways to visualize spatial autocorrelation, those would be much appreciated too.
Your code is a bit "strange": seems you are trying to build a dataset containing only coordinates. AFAIU, you may need something in this line :
data <- data.frame(lead$X, lead$Y, lead$Z)
,with lead$Z corresponding to a generic "variable" you want to inspect, then
coordinates(data) <- c('X','Y')`
proj4string(data) <- "+init=epsg:4326"
, which should give you a proper "SpatialPointsDataframe" with lat-lon WGS84 geographic coordinates (the first line could be also dropped, and you'll keep all variables in the data of the spatialpointsdataframe).
HTH
I am trying to create a species distribution model in R. I have created raster layers in ArcMap and have imported them into R. They cannot be stacked unless the extents are exactly the same and they all have the same number of rows and columns.
However, when I alter these factors to successfully stack them they lose all their values and my stacked data frame is just filled with NAs.
Does anyone know how I can alter the extent and resolution of my raster layers so they can be successfully stacked -- so I can then attach environmental info to presence points.
Cheers
One way to do this is to choose a raster that has the projection and extent that you want and use that as a template for the others
For example, if you have rasterA and rasterB. You can use projectRaster() to make a new version of rasterA with the same extent and resolution as rasterB. You should then be able to stack new.rasterA & rasterB.
new.rasterA <- projectRaster(rasterB, rasterA) # define the projection and extent
r.stack <- stack(new.rasterA, rasterB) # add them to a raster stack object
I had the same issue and I solved this in arcgis by snapping each raster to a mask of my study area.
This can be done by clicking geoprocessing -> environments -> processing extent - then select a layer you want to snap to in the snap raster box. I did this before I extracted (clipped) each layer and it worked perfectly. You can check the extent in properties when you are done for each layer you do to double check before you upload them into R.