akima interpolation for irregular grid - r

I am trying to interpolate a irregular raster grid to a regular grid using akima library in R. However, after I define the regular grid and interpolate the values to the new regular grid, I end up in a strange raster position. I'm doing something wrong but I don't see where. If anyone has a solution (or know a different approach), please let me know. Thank you very much.
library(raster)
library(akima)
library(rgdal)
library(sp)
# download the file
url <- 'https://downloads.psl.noaa.gov/Datasets/NARR/Derived/monolevel/air.2m.mon.ltm.nc'
file <- paste0(getwd(), "/airtemp.nc")
download.file(url, file, quiet = TRUE, mode = "wb") # less than 4 mb
# define the grid edges according to https://psl.noaa.gov/data/gridded/data.narr.monolevel.html
y <- c(12.2, 14.3, 57.3, 54.5)
x <- c(-133.5, -65.1, -152.9, -49.4)
xym <- cbind(x, y)
p = Polygon(xym)
ps = Polygons(list(p),1)
sps = SpatialPolygons(list(ps))
# create a spatial grid to 0.3 cell size
xy <- makegrid(sps, cellsize = 0.3)
xy$first <-1
names(xy) <- c('x','y',"first")
coordinates(xy)<-~x+y
gridded(xy)<-T
# read the netcdf file and extract the values
cape <- brick(file)[[1]] #get the first layer only
rp <- rasterToPoints(cape)
rp <- na.exclude(rp)
# interpolate to the crs for Northern America Conformal Conic
r2 <- project(rp[,1:2], paste('+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs'), inv=TRUE, use_ob_tran=TRUE)
# add the transformed coordinates
rp[,1:2] <-r2
rp <- as.data.frame(rp)
# create a spatial points object and plot it
coordinates(rp)<-~x+y
spplot(rp, scales=list(draw = T))
# interpolate the points to the coordinates (takes a while)
akima.sp <- interpp(x = coordinates(rp)[,1], y = coordinates(rp)[,2],
z = rp#data[,names(rp)[1]],
xo = coordinates(xy)[,1],
yo = coordinates(xy)[,2],
linear = F, extrap = F)
# create a raster file
r.a <- rasterFromXYZ(as.matrix(data.frame(akima.sp)))
plot(r.a)

Related

Convert UTM home range to km^2 (gArea function)?

I am calculating a home range using the following data (extract)
x y
437850.3 7220701
465101.3 7210903
489314.6 7159065
513795.7 7114472
532871.0 7075753
I use the following code to calculate home range, with my coordinates converted to UTM:
#Load packages
library(rgdal)
library(ks)
library(rgeos)
library(maptools)
#Project data into UTM
coordinates(data) <- c("x","y")
proj4string(data) <- CRS( "+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs" )
data<-as.data.frame(data)
#Calculate plugin home range
h1 <- Hpi(data[,1:2], pilot = "samse", binned = T)
kernPI1 <- kde(data[,1:2], H = h1)
cont = contourLevels(kernPI1, cont = 95)
line = contourLines(x = kernPI1$eval.points[[1]], y =
kernPI1$eval.points[[2]], z = kernPI1$estimate,
level = cont)
sldf = ContourLines2SLDF(line)
sldf = SpatialLines2PolySet(sldf)
sldf = PolySet2SpatialPolygons(sldf)
gArea(sldf) #result is 2010204962.
I am trying to calculate my area in km^2, and I know for a fact that my areas should be around 1000-10000 km^2. I am guessing this has to do with the UTM coordinates, but am not sure how to proceed beyond that.

Spatial randomnes of observations (point pattern)

I want to assess if the observations in my data are spatially randomly distributed over the sampling area (Sweden). I wanted to reproduce the example given in this answer: Spatial Autocorrelation Analysis (Global Moran's I) in R
Here is a small subset of my data, and the spatial polygon I used. Note that the coordinates are in SWEREF99 (ESPG: 3006)
## spatial polygon of Sweden
library(rworldmap)
library(sp)
worldmap <- getMap(resolution = "high")
sweden <- worldmap[which(worldmap$SOVEREIGNT == "Sweden"),]
plot(sweden)
sweden
## conversion to EPSG: 3006 (SWEREF99 TM) (https://spatialreference.org/ref/epsg/3006/)
crs.laea <- CRS("+proj=utm +zone=33 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs")
sweden_proj <- spTransform(sweden, crs.laea)
## Data subset
x <- c(669894, 669894, 669894, 671088, 671117, 671117, 671117, 670513, 670513, 670513, 669921, 669310, 669310, 669310, 669303, 629720, 630318, 630925, 630925, 630925)
y <- c(7116684, 7116684, 7116684, 7116706, 7114900, 7114900, 7114900, 7114896, 7114896, 7114896, 7114888, 7115473, 7115473, 7115473, 7116075, 7131172, 7131180, 7131190, 7131190, 7131190)
library(spatstat)
coords.ppp_1 <- ppp( x , y , xrange = c(280227, 911417) , yrange = c(6142436, 7605020) )
coords.ppp <- unique(coords.ppp_1)
### plot data and Sweden map for check
plot(coords.ppp_1)
plot(sweden_proj, add=T)
So far it seems ok. Then I convert the spatial polygon to an owin object, simulate random data for comparison, and do the analysis.
library(maptools)
sw <- as.owin.SpatialPolygons(sweden_proj)
# Generate completely spatially random point patterns to compare against the observed
n <- coords.ppp_1$n
ex <- expression(runifpoint( n , sw))
# Compute a simulation envelope using Gest, which estimates the nearest neighbour distance distribution function G(r)
set.seed(1)
res <- envelope( coords.ppp , Gest , nsim = 99, simulate = ex ,verbose = FALSE, savefuns = TRUE )
plot(res)
With the envelope() I get the following error message:
"In envelopeEngine(X = X, fun = fun, simul = simrecipe, nsim = nsim, :
Window containing simulated patterns is not a subset of data window"
I suspect that there is a problem with the conversion between sp and owin, but I couldn't figure out what the issue really is.
Any advice?

raster does not align with shapefile after processing with rgee

I defined a polygon:
library(rgee)
ee_Initialize()
polygon <- ee$Geometry$Polygon(
list(
c(91.17, -13.42),
c(154.10, -13.42),
c(154.10, 21.27),
c(91.17, 21.27),
c(91.17, -13.42)
))
Map$addLayer(polygon)
The polygon covers countries around south-east Asia
For each pixel in the polygon, I want to calculate monthly sum of a given band for a given year as follows:
month_vec <- 1:12
pr_ls <- list()
for(m in seq_along(month_vec)){
month_ref <- month_vec[m]
pr_ls[[m]] <-
ee$ImageCollection("NASA/NEX-GDDP")$
filterBounds(polygon)$ # filter it by polygon
select('pr')$ # select rainfall
filter(ee$Filter$calendarRange(2000, 2000, "year"))$ # filter the year
filter(ee$Filter$calendarRange(month_ref, month_ref, "month"))$ # filter the month
filter(ee$Filter$eq("model","ACCESS1-0"))$ # filter the model
sum() # sum the rainfall
}
Imagecollection_pr <- ee$ImageCollection(pr_ls)
ee_imagecollection_to_local(
ic = Imagecollection_pr,
region = polygon,
dsn = paste0('pr_')
)
Reading a single month's file
my_rast <- raster(list.files(pattern = '.tif', full.names = TRUE)[1])
Since this raster covers southeast asian countries, I downloaded the shapefile
sea_shp <- getData('GADM', country = c('IDN','MYS','SGP','BRN','PHL'), level = 0)
Plotting them on top of each other:
plot(my_rast)
plot(sea_shp, add = T)
There is a misalignment and I am not sure if it is the right raster that has been
processed for the given polygon. I also checked if their projection is same
crs(my_rast)
CRS arguments: +proj=longlat +datum=WGS84 +no_defs
crs(sea_shp)
CRS arguments: +proj=longlat +datum=WGS84 +no_defs
Both of them have the same projection as well. I cannot figure out what went wrong?
EDIT
As suggested in comments, I defined a new polygon covering Australia as follows:
polygon <- ee$Geometry$Polygon(
list(
c(88.75,-45.26),
c(162.58,-45.26),
c(162.58,8.67),
c(88.75,8.67),
c(88.75,-45.26)
)
)
Map$addLayer(polygon)
and repeated the above code. Plotting the raster again for the month of March on polygon gives me this:
Does anyone know if I can check if my raster is reversed w.r.t to polygon boundaries?
This seems to be related to rgdal rather than to the raster package. Some raster downloaded from GEE have data flipped with respect to y. I solved this problem, as follow:
library(rgee)
library(raster)
ee_Initialize()
polygon <- ee$Geometry$Polygon(
list(
c(91.17, -13.42),
c(154.10, -13.42),
c(154.10, 21.27),
c(91.17, 21.27),
c(91.17, -13.42)
))
month_vec <- 1:12
pr_ls <- list()
for(m in seq_along(month_vec)){
month_ref <- month_vec[m]
pr_ls[[m]] <-
ee$ImageCollection("NASA/NEX-GDDP")$
filterBounds(polygon)$ # filter it by polygon
select('pr')$ # select rainfall
filter(ee$Filter$calendarRange(2000, 2000, "year"))$ # filter the year
filter(ee$Filter$calendarRange(month_ref, month_ref, "month"))$ # filter the month
filter(ee$Filter$eq("model","ACCESS1-0"))$ # filter the model
sum() # sum the rainfall
}
Imagecollection_pr <- ee$ImageCollection(pr_ls) %>% ee_get(0)
exp1 <- ee_imagecollection_to_local(
ic = Imagecollection_pr,
region = polygon,
dsn = "pp_via_drive",
via = "drive" # please always use "drive" or "gcs" until rgee 1.0.6 release
)
# One option
gdalinfo <- try (rgdal::GDALinfo(exp1))
if (isTRUE(attr(gdalinfo, "ysign") == 1)) {
exp1_r <- flip(raster(exp1), direction='y')
}
Recent versions of the earthengine Python API causes some inconsistencies when via = "getInfo" is used, please always use via = "drive" until the release of rgee 1.0.6.
There does not seem to be a misalignment. To plot all these countries in one step, you could do
x <- lapply(c('IDN','MYS','SGP','BRN','PHL'), function(i) getData('GADM', country = i, level = 0))
sea_shp <- bind(x)

Converting WRF files to raster stacks! Projection

I have WRF output netCDF files with 149974991 dimensions produced with "Mercator" projection over the Horn Of Africa. I would like to convert netCDF files into raster stack to undertake further analysis. I have been trying different options but it didn't work for me. I am getting values on wrong locations. I require help in this regards and any help is much appreciated.
Here is the code :
ro_rast <- nc_open("wrf_CAM0_daily_pre.nc")
pre <- ncvar_get(ro_rast, "pre") ro_rast$dim$lon$vals -> lon ro_rast$dim$lat$vals -> lat ro_rast$dim$ncl2$vals -> time rm(ro_rast)
r1_brick <- brick(pre, xmn=min(lat), xmx=max(lat), ymn=min(lon), ymx=max(lon), crs=CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
names(r1_brick)<- seq(as.Date('2018-06-01'), as.Date('2018-08-31'), 'days')
# convert names of layer into date par(mar = c(2, 2, 2, 2))
cam1_mean <- t(calc(r1_brick, sum))
# seasonal sum precipitation
cam1 <- flip(cam1_mean, direction = 2)
library(akima)# intepolation
lonlat_reg <- expand.grid(lon = seq(min(lon), max(lon), length.out = 1499),
lat = seq(min(lat), max(lat), length.out = 749))
test <- interp(x = as.vector(lon), y = as.vector(lat), z = as.vector(pre),
xo = unique(lonlat_reg[,"lon"]), yo = unique(lonlat_reg[,"lat"]),
duplicate = "error", linear = FALSE, extrap = FALSE)
test <- interp(x = as.vector(lon), y = as.vector(lat), z = as.vector(pre),
nx = 1499, ny = 749, linear = FALSE, extrap = FALSE)
# turn into a raster
test_ras <- raster(test)
The standard approach would be
library(raster)
b <- brick("wrf_CAM0_daily_pre.nc")
It that does not work, can you point us to the file you are using?
I get this error message (you should have added that to your question).
Error in .rasterObjectFromCDF(x, type = objecttype, band = band, ...) :
cells are not equally spaced; you should extract values as points
I checked the file, and in this case, the raster is not a regular grid. The size of the cells changes with latitude. The file does not provide the x and y values of the coordinate reference system used. So the best you can do is extract these values as points, as you were doing, using the interface of the ncdf4 or another package. You can then not directly make a RasterBrick. But you do so using rasterize or interpolate.

Merging rasterstacks with different dimensions/projections

How can I extract the whole CONUS SSURGO data from FedData and then how can I combine that rasterstack into a single rasterstack with worldclim data?
# FedData Tester
library(FedData)
library(magrittr)
# Extract data for the Village Ecodynamics Project "VEPIIN" study area:
# http://veparchaeology.org
vepPolygon <- polygon_from_extent(raster::extent(672800, 740000, 4102000, 4170000),
proj4string = "+proj=utm +datum=NAD83 +zone=12")
# Get the NRCS SSURGO data (USA ONLY)
SSURGO.VEPIIN <- get_ssurgo(template = vepPolygon,
label = "VEPIIN")
# Plot the NED again
raster::plot(NED)
# Plot the SSURGO mapunit polygons
plot(SSURGO.VEPIIN$spatial,
lwd = 0.1,
add = TRUE)
...
library(raster)
library(sp)
r <- getData("worldclim",var="bio",res=2.5)

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