I have a problem getting raster data in the right orientation. The original raster data when imported into R looks this .
I tried using the transpose function in raster but it didn't work. The transposed data looks like this .
I used the code below. Any help or advice would be greatly appreciated. Also, is there a way to apply the plausible solution to the entire stack (all rasters are of the same extent)? Thank you.
f_PM <- list.files(path=".",
pattern='tif$', full.names=TRUE)
s_PM <- stack(f_PM) ## create a stack of the rasters
plot(s_PM[[1]]) ##check the orientation
PM <- s_PM[[1]] ## pick one raster and try to change the orientation
PM2 <- t(PM)
plot(PM2)
You need to flip then transpose:
> plot(m)
> r = t(flip(m))
> plot(r)
Note there's https://gis.stackexchange.com where spatial questions like this get asked. (Too much noise here to help with most spatial stuff).
Related
I have a dataframe with coordinates for which I want to create polygons, the most normal is a polygon like the one I put in the first image:
But I'm looking for something different, more like this:
As you can see, if the points are enough far, another polygon is created, is this possible in R? Thanks!
Here are the data
csv
I think you would find the concept of concave/alpha hulls relevant. There's an R package alphahull that may cover your need.
install.packages("alphahull")
library(alphahull)
fff <- readr::read_csv("data.csv")
dddd <- ahull(fff[,2:3],alpha = 0.01)
plot(dddd)
And in case you need to convert this output into a spatial data format, please see the following:
https://babichmorrowc.github.io/post/2019-03-18-alpha-hull/
I am trying to download high-resolution climate data for a bunch of lat/long coordinates, and combine them into a single dataframe. I've come up with a solution (below), but it will take forever with the large list of coordinates I have. I asked a related question on the GIS StackExchange to see if anyone knew of a better approach for downloading and merging the data, but I'm wondering if I could somehow just speed up the operation of the loop? Does anyone have any suggestions on how I might do that? Here is a reproducible example:
# Download and merge 0.5 minute MAT/MAP data from WorldClim for a list of lon/lat coordinates
# This is based on https://emilypiche.github.io/BIO381/raster.html
# Make a dataframe with coordinates
coords <- data.frame(Lon = c(-83.63, 149.12), Lat=c(10.39,-35.31))
# Load package
library(raster)
# Make an empty dataframe for dumping data into
coords3 <- data.frame(Lon=integer(), Lat=integer(), MAT_10=integer(), MAP_MM=integer())
# Get WorldClim data for all the coordinates, and dump into coords 3
for(i in seq_along(coords$Lon)) {
r <- getData("worldclim", var="bio", res=0.5, lon=coords[i,1], lat=coords[i,2]) # Download the tile containing the lat/lon
r <- r[[c(1,12)]] # Reduce the layers in the RasterStack to just the variables we want to look at (MAT*10 and MAP_mm)
names(r) <- c("MAT_10", "MAP_mm") # Rename the columns to something intelligible
points <- SpatialPoints(na.omit(coords[i,1:2]), proj4string = r#crs) #give lon,lat to SpatialPoints
values <- extract(r,points)
coords2 <- cbind.data.frame(coords[i,1:2],values)
coords3 <- rbind(coords3, coords2)
}
# Convert MAT*10 from WorldClim into MAT in Celcius
coords3$MAT_C <- coords3$MAT_10/10
Edit: Thanks to advice from Dave2e, I've first made a list, then put intermediate results in the list, and rbind it at the end. I haven't timed this yet to see how much faster it is than my original solution. If anyone has further suggestions on how to improve the speed, I'm all ears! Here is the new version:
coordsList <- list()
for(i in seq_along(coordinates$lon_stm)) {
r <- getData("worldclim", var="bio", res=0.5, lon=coordinates[i,7], lat=coordinates[i,6]) # Download the tile containing the lat/lon
r <- r[[c(1,12)]] # Reduce the layers in the RasterStack to just the variables we want to look at (MAT*10 and MAP_mm)
names(r) <- c("MAT_10", "MAP_mm") # Rename the columns to something intelligible
points <- SpatialPoints(na.omit(coordinates[i,7:6]), proj4string = r#crs) #give lon,lat to SpatialPoints
values <- extract(r,points)
coordsList[[i]] <- cbind.data.frame(coordinates[i,7:6],values)
}
coords_new <- bind_rows(coordsList)
Edit2: I used system.time() to time the execution of both of the above approaches. When I did the timing, I had already downloaded all of the data, so the download time isn't included in my time estimates. My first approach took 45.01 minutes, and the revised approach took 44.15 minutes, so I'm not really seeing a substantial time savings by doing it the latter way. Still open to advice on how to revise the code so I can improve the speed of the operations!
I was assigned the task to clip a raster from .nc file from a .tif file.
edit (from comment):
i want to extract temp. info from the .nc because i need to check the yearly mean temperature of a specific region. to be comparable the comparison has to occur on exactly the same area. The .nc file is larger than the previously checked area so i need to "clip" it to the extent of a .tif I have. The .tif data is in form 0|1 where it is 0 (or the .tif is smaller than the .nc) the .nc data should be "cliped". In the end i want to keep the .nc data but at the extent of the .tif while still retaining its resolution & projection. (.tif and .nc have different projections&pixel sizes)
Now ordinarily that wouldn't be a problem as i could use raster::crop. This doesn't deal with different projections and different pixel size/resolution though. (I still used it to generate an approximation, but it is not precise enough for the final infromation, as can be seen in the code snippet below). The obvious method to generate a more reliable dataset/rasterset would be to first use a method like raster::projectRaster or raster::sp.Transform # adding sp.transform was done in an edit to the original question and homogenize the datasets but this approach takes too much time, as i have to do this for quite a few .nc files.
I was told the best method would be to generate a normalized matrix from the smaller raster "clip_frame" and then just multiply it with the "nc_to_clip" raster. Doing so should prevent any errors through map projections or other factors. This makes a lot of sense to me in theory but I have no idea how to do this in practice. I would be very grateful to any kind of hint/code snippet or any other help.
I have looked at similar problems on StackOverflow (and other sites) like:
convert matrix to raster in R
Convert raster into matrix with R
https://www.researchgate.net/post/Hi_Is_there_a_way_to_multiply_Raster_value_by_Raster_Latitude
As I am not even sure how to frame the question correctly, I might have overlooked an answer to this problem, if so please point me there!
My (working) code so far, just to give you an idea of how I want to approach the topic (here using the crop-function).
#library(ncdf4)
library(raster)
library(rgdal)
library(tidyverse)
nc_list<-list.files(pattern = ".*0.nc$") # list of .nc files containing raster and temperature information
#nc_to_clip <- lapply(nc_list, raster, varname="GST") # read in as raster
nc_to_clip < -raster(ABC.nc, vername="GST)
clip_frame <- raster("XYZ.tif") # read in .tif for further use as frame
mean_temp_from_raster<-function(input_clip_raster, input_clip_frame){ # input_clip_raster= raster to clip, input_clip_frame
r2_coord<-rasterToPoints(input_clip_raster, spatial = TRUE) # step 1 to extract coordinates
map_clip <- crop(input_clip_raster, extent(input_clip_frame)) # use crop to cut the input_clip_raster (this being the function I have to extend on)
temp<-raster::extract(map_clip, r2_coord#coords) # step 2 to extract coordinates
temp_C<-temp*0.01-273.15 # convert kelvin*100 to celsius
temp_C<-na.omit(temp_C)
mean(temp_C)
return_list<-list(map_clip, mean(temp_C))
return(return_list)
}
mean_tempC<-lapply(nc_to_clip, mean_temp_from_raster,clip_frame)
Thanks!
PS:
I don't have much experience working with .nc files and/or RasterLayers in R as I used to work with ArcGIS/Python (arcpy) for problems like this, which is not an option right now.
Perhaps something like this?
library(raster)
nc <- raster(ABC.nc, vername="GST)
clip <- raster("XYZ.tif")
x <- as(extent(clip), "SpatialPolygons")
crs(x) <- crs(clip)
y <- sp::spTransform(x, crs(nc))
clipped <- crop(nc, y)
I'm still new to R and don't know how to create a loop for my workprocess to make it more efficient.
I have a Digital Elevation Model (raster Barrow_5m.tif), a shapefile for lakes and buffer with 10 iDs in a row of the table each.
In the script below I created a new raster file for all values of the lake and the buffer shape file with the data from the DEM raster. This works fine.
setwd("...")
Barrow_5m <- raster("Barrow_5m.tif")
Barrow_DTLB <- st_read("Barrow_DTLB.shp")
Barrow_DTLB_Buffer <- st_read("Barrow_DTLB_BufferOUT.shp")
Barrow_lake <- crop(Barrow_5m, extent(Barrow_DTLB))
raster_lake <- rasterize(Barrow_DTLB, Barrow_lake, mask = TRUE)
Barrow_buffer <- crop(Barrow_2m, extent(Barrow_DTLB_Buffer))
raster_buffer <- rasterize(Barrow_DTLB_Buffer, Barrow_buffer, mask = TRUE)
writeRaster(raster_lake, "raster_lake.tif")
writeRaster(raster_buffer, "raster_buffer.tif")
But now I want to have a raster file for every id of the lake and the buffer shapefile seperately, so 2x10 files.
I thought it's best to write a loop for this, but my skills are not enough so far to do this.
Also other questions didn't bring the solution so far. I tried to help me with this.
Alternatively I could use my end product tif from the script above and undo this in files for every ID.
I want to write the loop and not do it by hand for all the IDs of the shapefiles, because afterwards I am going to do the same with an even bigger shapefile of more values.
I found a solution now, by extracting data by the ID.
It creates a largelist with 11 elements and all values of each id, which is sufficient for my further work. You can also directly creat the mean, max, min, etc values of each element (so each ID).
k <- Barrow_DTLB$ID #k= number of rows
LakesA <- extract(raster_lakeA, Barrow_DTLB[k, ])
LakesA_mean <- extract(raster_lakeA, Barrow_DTLB[k, ], fun=mean)
Maybe this solution is also helpful for a few, who already viewed the question.
I think this should work:
for (i in unique(raster_lake)){
r <- raster_lake
r[!(values(r) == i)] <- NA
r <- trim(r)
writeRaster(r, paste0("raster_lake_", i, ".tif"))
}
I'm programming a script for the calculation of cover around points in R.
I have two inputs: an IMG raster file, and a .csv with all the points.
I've used this script:
library(raster)
library(rgdal)
#load in raster and locality data
map <- raster('map.IMG')
sites <- read.csv('points.csv', header=TRUE)
#convert lat/lon to appropirate projection
coordinates(sites) <- c("X", "Y")
proj4string(sites) <- CRS("+init=epsg:27700")
#extract values to points
Landcover<-extract (map, sites, buffer=2000)
extraction <- lapply(Landcover, function(serial) prop.table(table(serial)))
# Write .csv file
lapply(extraction, function(x) write.table( data.frame(x), 'test2.csv' , append= T, sep=',' ))
I get a .csv file in my map, but the data isn't organised in the way I would like it to be.
There a three columns in the csv file. One with 'x', one with 'Freq' (Which I think is the code for every class in my image) and one with the cover part, somewhere between 0-1. See the image included.Image
I want to have on the rows the serial and classes, and under that the correct serial with it's coverage.
Also every point isn't named, so I can't see which is which. In the points.csv I have for example a 'serial' code for every point, which i would like to use for that.
Can somebody steer me in the right direction?
I hope I have been clear with my questions, thank in advance!