了解raster::extract和terra:extract

Question

我在完全理解terra时遇到了问题:提取。我希望提取管理GADM多边形的平均栅格值。我的栅格在每个国家/地区都有一个值。我期望特定国家内的每个行政多边形具有相同的值，并且包括一些国家边界的一些多边形被分配面积加权平均值。不幸的是，我当前的脚本不是这样的。raster::extract似乎给出了合理的结果，但terra:extract不是(参见下面的示例代码-提供不同值的输出)。根据我下面的代码，有人能解释一下为什么吗？非常感谢。

## libraries
library(terra)
library(raster)

#===============================================    
## sample example - provides results as expected (1.333, that is (2*0.5+1*1)/1.5)

# sample raster and SpatialPolygons
r <- raster(ncol=2, nrow=3, xmn= 0, ymn= 0, xmx = 30,ymx = 30)
r[] <- c(2, 2, 2, 1, NA, NA)
cds <- rbind(c(7.5,0), c(7.5,20), c(30, 20),c(30,10))
library(sp)
p = Polygon(cds)
ps = Polygons(list(p),1)
sps = SpatialPolygons(list(ps))
plot(r)
plot(sps, add=T)

​

​

# test raster package
test1 <- raster::extract(r , sps, fun=mean, na.rm=T,  weights=TRUE) 
test1 # I get 1.333333 which is what I would expect

# test terra package
sps.spatv <- vect(sps)
r.spatR <-  rast(r) #conversion to SpatRaster class

test2 <- terra::extract(r.spatR, sps.spatv, fun=mean, na.rm=T,  weights=TRUE, exact=TRUE, touches=TRUE) 
test2 # I get 1.333333 which is what I would expect  

#===============================================    
## sample code that leads to different results between raster and terra packages - I wish to understand why such difference.
# sample SpatialPolygonsDataFrame 
ETH <- getData("GADM", country = 'ETH', level = 2)
SOM <- getData("GADM", country = 'SOM', level = 2)
sps <- bind(ETH, SOM)

# sample raster stack
ra <- raster(ncol=31, nrow=24, xmn= 33.3, ymn=  3.67, xmx = 47.5, ymx = 14.65, crs=crs(sps) )
ra[] <- rep(10, 24*31)
ra2 <- raster(ncol=31, nrow=24, xmn= 33.3, ymn= -7.31 , xmx = 47.5, ymx = 3.67, crs=crs(sps) )
ra2[] <- rep(20, 24*31)
ra3 <- merge(ra, ra2)

rb <- raster(ncol=31, nrow=24, xmn= 33.3, ymn=  3.67, xmx = 47.5, ymx = 14.65, crs=crs(sps) )
rb[] <- rep(35, 24*31)
rb2 <- raster(ncol=31, nrow=24, xmn= 33.3, ymn= -7.31 , xmx = 47.5, ymx = 3.67, crs=crs(sps) )
rb2[] <- rep(45, 24*31)
rb3 <- merge(rb, rb2)

stack.r <- stack(ra3, rb3)
names(stack.r) <- c("ra3", "rb3")

plot(stack.r[[1]])
plot(sps, add=T)

# raster::extract
rastR <- raster::extract(stack.r, sps, fun=mean, na.rm=T,  weights=TRUE)

# > head(rastR)
# [,1] [,2]
# [1,]   10   35
# [2,]   10   35
# [3,]   10   35
# [4,]   10   35
# [5,]   10   35
# [6,]   10   35

rastR2 <- rastR %>%
  cbind(sps@data["GID_2"]) # add ID

# terra::extract
sps.spatv <- vect(sps)
stack.r.spatR <-  rast(stack.r) 
rastT <- terra::extract(stack.r.spatR, sps.spatv, fun=mean, na.rm=T,  exact=TRUE)
# > head(rastT)
# ID ra3 rb3
# [1,]  1  10  10
# [2,]  2  10  10
# [3,]  3  10  10
# [4,]  4  10  10
# [5,]  5  10  10
# [6,]  6  10  10
rastT2 <- rastT %>%
  cbind(sps@data["GID_2"]) # add ID

Answer 1

更新的答案

感谢您的扩展问题，以及您的坚持，并为花了这么长时间回复您表示歉意。这是terra中的一个错误，我没有立即发现。加权平均结果是乱码(矩阵没有按正确的顺序填充)。现已修复：

您的简化示例数据

library(raster)
library(terra)
#terra version 1.2.17

sp <- getData("GADM", country = 'ETH', level = 2)[1:3,]
sv <- vect(sp)

ra <- raster(ncols=31, nrows=24, xmn= 33.3, ymn=  3.67, xmx = 47.5, ymx = 14.65, crs=crs(sp), vals=rep(10, 24*31))
rb <- raster(ncols=31, nrows=24, xmn= 33.3, ymn=  3.67, xmx = 47.5, ymx = 14.65, crs=crs(sv), vals=rep(35, 24*31))

r_raster <- stack(ra, rb)
names(r_raster) <- c("ra", "rb")
r_terra <-  rast(r_raster) 

extract(r_raster, sp, fun=mean, na.rm=T, small=FALSE)
#     [,1] [,2]
#[1,]   NA   NA
#[2,]   10   35
#[3,]   10   35

extract(r_terra, sv, fun=mean, na.rm=T)
#  ID  ra  rb
#1  1 NaN NaN
#2  2  10  35
#3  3  10  35

测试不带权重的针对raster的和small=TRUE (默认)以及针对terra的touches=TRUE

extract(r_raster, sp, fun=mean, na.rm=T)
#      ra rb
# [1,] 10 35
#[2,] 10 35
#[3,] 10 35

extract(r_terra, sv, fun=mean, na.rm=T, touches=TRUE)
#  ID ra rb
#1  1 10 35
#2  2 10 35
#3  3 10 35

使用权重测试

extract(r_raster, sp, fun=mean, na.rm=T,  weights=TRUE)
#     ra rb
#[1,] 10 35
#[2,] 10 35
#[3,] 10 35

extract(r_terra, sv, fun=mean, na.rm=T,  weights=TRUE)
#     ID ra rb
#[1,]  1 10 35
#[2,]  2 10 35
#[3,]  3 10 35

此问题已在版本1.2.17中修复。您应该能够在一个小时内安装该版本，如下所示

install.packages('terra', repos='https://rspatial.r-universe.dev')

我将在接下来的几天里进一步测试它；希望下周能将它提交给CRAN。