raster

问题 I'm using the doSNOW package and more specifically the parLapply function to perform reclassification (and subsequently other operations) on a list of big raster datasets (OS: Windows x64). The code looks a little like this minimalistic example: library(raster) library(doSNOW) #create list containing test rasters x <- raster(ncol=10980,nrow=10980) x <- setValues(x,1:ncell(x)) list.x <- replicate( 9 , x ) #setting up cluster NumberOfCluster <- 8 cl <- makeCluster(NumberOfCluster)

问题 I'm wondering if I have maximized the speed at which a mean of an area buffered around a point in a raster can be extracted. Can performance be improved any further on these LOCALLY? I use parallel mclapply already, and I know I could get further gains by setting up and running this on a cluster (use a cluster or get more cpu's is not the answer I'm looking for). Replicate some data: library(raster) library(parallel) library(truncnorm) library(gdalUtils) library(velox) library(sf) ras <-

问题 I would like to make a plot using R studio similar to the one below (created in Arc Map) I have tried the following code: # data processing library(ggplot2) # spatial library(raster) library(rasterVis) library(rgdal) # test <- raster(paste(datafold,'oregon_masked_tmean_2013_12.tif',sep="")) # read the temperature raster OR<-readOGR(dsn=ORpath, layer="Oregon_10N") # read the Oregon state boundary shapefile gplot(test) + geom_tile(aes(fill=factor(value),alpha=0.8)) + geom_polygon(data=OR, aes(x

问题 Im working with two rasters each with a different resolution. Im wondering if there is a more efficient way of matching the coarser raster resolution to the finer raster resolution. Right now I am using the mask function to save some time, clip to the correct extent and change the resolution: library(raster) #the raster template with the desired resolution r <- raster(extent(-180, 180, -64, 84), res=0.04166667) # set some pixels to values, others to NA r <- setValues(r, sample(c(1:3, NA),

问题 Let's say I have 4 raster layers with the same extend with data of 4 different years: 2006,2008,2010 and 2012: library(raster) r2006<-raster(ncol=3, nrow=3) values(r2006)<-1:9 r2008<-raster(ncol=3, nrow=3) values(r2008)<-3:11 r2010<-raster(ncol=3, nrow=3) values(r2010)<-5:13 r2012<-raster(ncol=3, nrow=3) values(r2012)<-7:15 Now I want to create raster layers for every year between 2006 and 2013 (or even longer) by inter-/extrapolating (a linear method should be a good start) the values of the

问题 I need to replace the NA 's in the raster object ( r ) from the example below. library(raster) filename <- system.file("external/test.grd", package="raster") r <- raster(filename) I also tried to remove these these (and place the result in a data.frame ), but to no avail. dfr <- as.data.frame(r, na.rm=T) summary(dfr) # test # Min. : 128.4 # 1st Qu.: 293.2 # Median : 371.4 # Mean : 423.2 # 3rd Qu.: 499.8 # Max. :1805.8 # NA's :6097 回答1: I'm not sure it makes sense to remove NA values from a

问题 I am trying to plot a map with positive and negative values. All positive values should have red color while negative should have blue color and zero should have white just like in this sample plot with discrete colors Below is the code I'm using: library (rasterVis) ras1 <- raster(nrow=10,ncol=10) set.seed(1) ras1[] <- rchisq(df=10,n=10*10) ras2=ras1*(-1)/2 s <- stack(ras1,ras2) levelplot(s,par.settings=RdBuTheme()) Thanks very much for providing a general solution which can be applied in

问题 I am trying to plot a map with positive and negative values. All positive values should have red color while negative should have blue color and zero should have white just like in this sample plot with discrete colors Below is the code I'm using: library (rasterVis) ras1 <- raster(nrow=10,ncol=10) set.seed(1) ras1[] <- rchisq(df=10,n=10*10) ras2=ras1*(-1)/2 s <- stack(ras1,ras2) levelplot(s,par.settings=RdBuTheme()) Thanks very much for providing a general solution which can be applied in

问题 I was hoping someone could help me with a loop function I'm working with. I have tried searching Google and Stack Overflow extensively but, as I don't know the exact search terminology, I fear I am missing some results. With that in mind, I apologise in advance if this question has already been asked but I hope that someone can point me in the right direction for a solution. About My Data I have downloaded 1000s of files from NASAs MODIS satellite. As my study area covers a large area, I've

问题 I am trying to calculate evapotranspiration (ET) for running SPEI on a raster dataset by using the Thornthwaite ET formula included in the SPEI package this is my code library(SPEI) library(raster) library(zoo) tm = array(1:(3*4*12*64),c(3,4,12*64)) tm = brick(tm) dates=seq(as.Date("1950-01-01"), as.Date("2013-12-31"), by="month") tm<- setZ(tm,dates) names(tm) <- as.yearmon(getZ(tm)) thornthwaite ET th <- function(Tave, lat) { SPEI::thornthwaite(Tave, lat) } lat <- setValues(a, coordinates(tm