有没有办法将多层栅格（Terra）转换为 R 中的 rasterStack 对象（Raster）？
Is there a way to convert a multi-layer raster (Terra) to a rasterStack object (Raster) in R?
如问题所述，我正在尝试将多层 terra 栅格转换为 rasterStack 对象，以便我可以将它与另一个只接受的包 (biomod2) 一起使用较旧的 raster 个对象。
有什么有效的方法吗？我唯一的其他选择似乎是将其保存为 .tif 然后使用 raster.
将其重新导入到 R
当我使用stack(terraRaster)时，它复制了一些图层。例如，我有一个堆栈，其中包含 19 个 WorldClim 生物气候变量以及地质层。这是它的样子：
> names(current.clim) # Terra rast object
[1] "bio1" "bio2" "bio3" "bio4" "bio5" "bio6" "bio7" "bio8" "bio9" "bio10" "bio11" "bio12" "bio13"
[14] "bio14" "bio15" "bio16" "bio17" "bio18" "bio19"
> names(stack(current.clim)) # Converted to rasterStack
[1] "bio1" "bio2" "bio3" "bio4" "bio5" "bio6" "bio7" "bio8" "bio9" "bio10" "bio11" "bio12" "bio13"
[14] "bio14" "bio15" "bio16" "bio17" "bio18" "bio19"
> names(stack(c(current.clim, geo))) # Converted to rasterStack with added geology variable
Error in `names<-`(`*tmp*`, value = names(from)) :
incorrect number of layer names
[1] "currentclim_30sec.1.1" "currentclim_30sec.2.1" "currentclim_30sec.3.1" "currentclim_30sec.4.1"
[5] "currentclim_30sec.5.1" "currentclim_30sec.6.1" "currentclim_30sec.7.1" "currentclim_30sec.8.1"
[9] "currentclim_30sec.9.1" "currentclim_30sec.10.1" "currentclim_30sec.11.1" "currentclim_30sec.12.1"
[13] "currentclim_30sec.13.1" "currentclim_30sec.14.1" "currentclim_30sec.15.1" "currentclim_30sec.16.1"
[17] "currentclim_30sec.17.1" "currentclim_30sec.18.1" "currentclim_30sec.19.1" "currentclim_30sec.1.2"
[21] "currentclim_30sec.1.3" "currentclim_30sec.2.2" "currentclim_30sec.3.2" "currentclim_30sec.4.2"
[25] "currentclim_30sec.5.2" "currentclim_30sec.6.2" "currentclim_30sec.7.2" "currentclim_30sec.8.2"
[29] "currentclim_30sec.9.2" "currentclim_30sec.10.2" "currentclim_30sec.11.2" "currentclim_30sec.12.2"
[33] "currentclim_30sec.13.2" "currentclim_30sec.14.2" "currentclim_30sec.15.2" "currentclim_30sec.16.2"
[37] "currentclim_30sec.17.2" "currentclim_30sec.18.2" "currentclim_30sec.19.2" "currentclim_30sec.1.4"
[41] "currentclim_30sec.2.3" "currentclim_30sec.3.3" "currentclim_30sec.4.3" "currentclim_30sec.5.3"
[45] "currentclim_30sec.6.3" "currentclim_30sec.7.3" "currentclim_30sec.8.3" "currentclim_30sec.9.3"
[49] "currentclim_30sec.10.3" "currentclim_30sec.11.3" "currentclim_30sec.12.3" "currentclim_30sec.13.3"
[53] "currentclim_30sec.14.3" "currentclim_30sec.15.3" "currentclim_30sec.16.3" "currentclim_30sec.17.3"
[57] "currentclim_30sec.18.3" "currentclim_30sec.19.3" "currentclim_30sec.1.5" "currentclim_30sec.2.4"
[61] "currentclim_30sec.3.4" "currentclim_30sec.4.4" "currentclim_30sec.5.4" "currentclim_30sec.6.4"
[65] "currentclim_30sec.7.4" "currentclim_30sec.8.4" "currentclim_30sec.9.4" "currentclim_30sec.10.4"
[69] "currentclim_30sec.11.4" "currentclim_30sec.12.4" "currentclim_30sec.13.4" "currentclim_30sec.14.4"
[73] "currentclim_30sec.15.4" "currentclim_30sec.16.4" "currentclim_30sec.17.4" "currentclim_30sec.18.4"
[77] "currentclim_30sec.19.4" "currentclim_30sec.1.6" "currentclim_30sec.2.5" "currentclim_30sec.3.5"
[81] "currentclim_30sec.4.5" "currentclim_30sec.5.5" "currentclim_30sec.6.5" "currentclim_30sec.7.5"
[85] "currentclim_30sec.8.5" "currentclim_30sec.9.5" "currentclim_30sec.10.5" "currentclim_30sec.11.5"
[89] "currentclim_30sec.12.5" "currentclim_30sec.13.5" "currentclim_30sec.14.5" "currentclim_30sec.15.5"
[93] "currentclim_30sec.16.5" "currentclim_30sec.17.5" "currentclim_30sec.18.5" "currentclim_30sec.19.5"
[97] "currentclim_30sec.1.7" "currentclim_30sec.2.6" "currentclim_30sec.3.6" "currentclim_30sec.4.6"
[101] "currentclim_30sec.5.6" "currentclim_30sec.6.6" "currentclim_30sec.7.6" "currentclim_30sec.8.6"
[105] "currentclim_30sec.9.6" "currentclim_30sec.10.6" "currentclim_30sec.11.6" "currentclim_30sec.12.6"
[109] "currentclim_30sec.13.6" "currentclim_30sec.14.6" "currentclim_30sec.15.6" "currentclim_30sec.16.6"
[113] "currentclim_30sec.17.6" "currentclim_30sec.18.6" "currentclim_30sec.19.6" "currentclim_30sec.1.8"
[117] "currentclim_30sec.2.7" "currentclim_30sec.3.7" "currentclim_30sec.4.7" "currentclim_30sec.5.7"
[121] "currentclim_30sec.6.7" "currentclim_30sec.7.7" "currentclim_30sec.8.7" "currentclim_30sec.9.7"
[125] "currentclim_30sec.10.7" "currentclim_30sec.11.7" "currentclim_30sec.12.7" "currentclim_30sec.13.7"
[129] "currentclim_30sec.14.7" "currentclim_30sec.15.7" "currentclim_30sec.16.7" "currentclim_30sec.17.7"
[133] "currentclim_30sec.18.7" "currentclim_30sec.19.7" "currentclim_30sec.1.9" "currentclim_30sec.2.8"
[137] "currentclim_30sec.3.8" "currentclim_30sec.4.8" "currentclim_30sec.5.8" "currentclim_30sec.6.8"
[141] "currentclim_30sec.7.8" "currentclim_30sec.8.8" "currentclim_30sec.9.8" "currentclim_30sec.10.8"
[145] "currentclim_30sec.11.8" "currentclim_30sec.12.8" "currentclim_30sec.13.8" "currentclim_30sec.14.8"
[149] "currentclim_30sec.15.8" "currentclim_30sec.16.8" "currentclim_30sec.17.8" "currentclim_30sec.18.8"
[153] "currentclim_30sec.19.8" "currentclim_30sec.1.10" "currentclim_30sec.2.9" "currentclim_30sec.3.9"
[157] "currentclim_30sec.4.9" "currentclim_30sec.5.9" "currentclim_30sec.6.9" "currentclim_30sec.7.9"
[161] "currentclim_30sec.8.9" "currentclim_30sec.9.9" "currentclim_30sec.10.9" "currentclim_30sec.11.9"
[165] "currentclim_30sec.12.9" "currentclim_30sec.13.9" "currentclim_30sec.14.9" "currentclim_30sec.15.9"
[169] "currentclim_30sec.16.9" "currentclim_30sec.17.9" "currentclim_30sec.18.9" "currentclim_30sec.19.9"
[173] "currentclim_30sec.1.11" "currentclim_30sec.2.10" "currentclim_30sec.3.10" "currentclim_30sec.4.10"
[177] "currentclim_30sec.5.10" "currentclim_30sec.6.10" "currentclim_30sec.7.10" "currentclim_30sec.8.10"
[181] "currentclim_30sec.9.10" "currentclim_30sec.10.10" "currentclim_30sec.11.10" "currentclim_30sec.12.10"
[185] "currentclim_30sec.13.10" "currentclim_30sec.14.10" "currentclim_30sec.15.10" "currentclim_30sec.16.10"
[189] "currentclim_30sec.17.10" "currentclim_30sec.18.10" "currentclim_30sec.19.10" "currentclim_30sec.1.12"
[193] "currentclim_30sec.2.11" "currentclim_30sec.3.11" "currentclim_30sec.4.11" "currentclim_30sec.5.11"
[197] "currentclim_30sec.6.11" "currentclim_30sec.7.11" "currentclim_30sec.8.11" "currentclim_30sec.9.11"
[201] "currentclim_30sec.10.11" "currentclim_30sec.11.11" "currentclim_30sec.12.11" "currentclim_30sec.13.11"
[205] "currentclim_30sec.14.11" "currentclim_30sec.15.11" "currentclim_30sec.16.11" "currentclim_30sec.17.11"
[209] "currentclim_30sec.18.11" "currentclim_30sec.19.11" "currentclim_30sec.1.13" "currentclim_30sec.2.12"
[213] "currentclim_30sec.3.12" "currentclim_30sec.4.12" "currentclim_30sec.5.12" "currentclim_30sec.6.12"
[217] "currentclim_30sec.7.12" "currentclim_30sec.8.12" "currentclim_30sec.9.12" "currentclim_30sec.10.12"
[221] "currentclim_30sec.11.12" "currentclim_30sec.12.12" "currentclim_30sec.13.12" "currentclim_30sec.14.12"
[225] "currentclim_30sec.15.12" "currentclim_30sec.16.12" "currentclim_30sec.17.12" "currentclim_30sec.18.12"
[229] "currentclim_30sec.19.12" "currentclim_30sec.1.14" "currentclim_30sec.2.13" "currentclim_30sec.3.13"
[233] "currentclim_30sec.4.13" "currentclim_30sec.5.13" "currentclim_30sec.6.13" "currentclim_30sec.7.13"
[237] "currentclim_30sec.8.13" "currentclim_30sec.9.13" "currentclim_30sec.10.13" "currentclim_30sec.11.13"
[241] "currentclim_30sec.12.13" "currentclim_30sec.13.13" "currentclim_30sec.14.13" "currentclim_30sec.15.13"
[245] "currentclim_30sec.16.13" "currentclim_30sec.17.13" "currentclim_30sec.18.13" "currentclim_30sec.19.13"
[249] "currentclim_30sec.1.15" "currentclim_30sec.2.14" "currentclim_30sec.3.14" "currentclim_30sec.4.14"
[253] "currentclim_30sec.5.14" "currentclim_30sec.6.14" "currentclim_30sec.7.14" "currentclim_30sec.8.14"
[257] "currentclim_30sec.9.14" "currentclim_30sec.10.14" "currentclim_30sec.11.14" "currentclim_30sec.12.14"
[261] "currentclim_30sec.13.14" "currentclim_30sec.14.14" "currentclim_30sec.15.14" "currentclim_30sec.16.14"
[265] "currentclim_30sec.17.14" "currentclim_30sec.18.14" "currentclim_30sec.19.14" "currentclim_30sec.1.16"
[269] "currentclim_30sec.2.15" "currentclim_30sec.3.15" "currentclim_30sec.4.15" "currentclim_30sec.5.15"
[273] "currentclim_30sec.6.15" "currentclim_30sec.7.15" "currentclim_30sec.8.15" "currentclim_30sec.9.15"
[277] "currentclim_30sec.10.15" "currentclim_30sec.11.15" "currentclim_30sec.12.15" "currentclim_30sec.13.15"
[281] "currentclim_30sec.14.15" "currentclim_30sec.15.15" "currentclim_30sec.16.15" "currentclim_30sec.17.15"
[285] "currentclim_30sec.18.15" "currentclim_30sec.19.15" "currentclim_30sec.1.17" "currentclim_30sec.2.16"
[289] "currentclim_30sec.3.16" "currentclim_30sec.4.16" "currentclim_30sec.5.16" "currentclim_30sec.6.16"
[293] "currentclim_30sec.7.16" "currentclim_30sec.8.16" "currentclim_30sec.9.16" "currentclim_30sec.10.16"
[297] "currentclim_30sec.11.16" "currentclim_30sec.12.16" "currentclim_30sec.13.16" "currentclim_30sec.14.16"
[301] "currentclim_30sec.15.16" "currentclim_30sec.16.16" "currentclim_30sec.17.16" "currentclim_30sec.18.16"
[305] "currentclim_30sec.19.16" "currentclim_30sec.1.18" "currentclim_30sec.2.17" "currentclim_30sec.3.17"
[309] "currentclim_30sec.4.17" "currentclim_30sec.5.17" "currentclim_30sec.6.17" "currentclim_30sec.7.17"
[313] "currentclim_30sec.8.17" "currentclim_30sec.9.17" "currentclim_30sec.10.17" "currentclim_30sec.11.17"
[317] "currentclim_30sec.12.17" "currentclim_30sec.13.17" "currentclim_30sec.14.17" "currentclim_30sec.15.17"
[321] "currentclim_30sec.16.17" "currentclim_30sec.17.17" "currentclim_30sec.18.17" "currentclim_30sec.19.17"
[325] "currentclim_30sec.1.19" "currentclim_30sec.2.18" "currentclim_30sec.3.18" "currentclim_30sec.4.18"
[329] "currentclim_30sec.5.18" "currentclim_30sec.6.18" "currentclim_30sec.7.18" "currentclim_30sec.8.18"
[333] "currentclim_30sec.9.18" "currentclim_30sec.10.18" "currentclim_30sec.11.18" "currentclim_30sec.12.18"
[337] "currentclim_30sec.13.18" "currentclim_30sec.14.18" "currentclim_30sec.15.18" "currentclim_30sec.16.18"
[341] "currentclim_30sec.17.18" "currentclim_30sec.18.18" "currentclim_30sec.19.18" "layer"
地质似乎是这里的问题...
class : SpatRaster
dimensions : 848, 3084, 1 (nrow, ncol, nlyr)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -141, -115.3, 67.49167, 74.55833 (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326)
source : memory
name : geology_30sec
min value : 1
max value : 15
或作为 rasterLayer...
class : RasterLayer
dimensions : 848, 3084, 2615232 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -141, -115.3, 67.49167, 74.55833 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
source : memory
names : geology_30sec
values : 1, 15 (min, max)
您可以使用 raster::stack 或 raster::brick
library(terra)
s = rast(system.file("ex/logo.tif", package="terra"))
library(raster)
sr = stack(s)
# or
br = brick(s)
您目前可能需要光栅的开发版本。您可以使用 install.packages('raster', repos='rspatial.r-universe.dev')
安装它
如问题所述，我正在尝试将多层 terra 栅格转换为 rasterStack 对象，以便我可以将它与另一个只接受的包 (biomod2) 一起使用较旧的 raster 个对象。
有什么有效的方法吗？我唯一的其他选择似乎是将其保存为 .tif 然后使用 raster.
当我使用stack(terraRaster)时，它复制了一些图层。例如，我有一个堆栈，其中包含 19 个 WorldClim 生物气候变量以及地质层。这是它的样子：
> names(current.clim) # Terra rast object
[1] "bio1" "bio2" "bio3" "bio4" "bio5" "bio6" "bio7" "bio8" "bio9" "bio10" "bio11" "bio12" "bio13"
[14] "bio14" "bio15" "bio16" "bio17" "bio18" "bio19"
> names(stack(current.clim)) # Converted to rasterStack
[1] "bio1" "bio2" "bio3" "bio4" "bio5" "bio6" "bio7" "bio8" "bio9" "bio10" "bio11" "bio12" "bio13"
[14] "bio14" "bio15" "bio16" "bio17" "bio18" "bio19"
> names(stack(c(current.clim, geo))) # Converted to rasterStack with added geology variable
Error in `names<-`(`*tmp*`, value = names(from)) :
incorrect number of layer names
[1] "currentclim_30sec.1.1" "currentclim_30sec.2.1" "currentclim_30sec.3.1" "currentclim_30sec.4.1"
[5] "currentclim_30sec.5.1" "currentclim_30sec.6.1" "currentclim_30sec.7.1" "currentclim_30sec.8.1"
[9] "currentclim_30sec.9.1" "currentclim_30sec.10.1" "currentclim_30sec.11.1" "currentclim_30sec.12.1"
[13] "currentclim_30sec.13.1" "currentclim_30sec.14.1" "currentclim_30sec.15.1" "currentclim_30sec.16.1"
[17] "currentclim_30sec.17.1" "currentclim_30sec.18.1" "currentclim_30sec.19.1" "currentclim_30sec.1.2"
[21] "currentclim_30sec.1.3" "currentclim_30sec.2.2" "currentclim_30sec.3.2" "currentclim_30sec.4.2"
[25] "currentclim_30sec.5.2" "currentclim_30sec.6.2" "currentclim_30sec.7.2" "currentclim_30sec.8.2"
[29] "currentclim_30sec.9.2" "currentclim_30sec.10.2" "currentclim_30sec.11.2" "currentclim_30sec.12.2"
[33] "currentclim_30sec.13.2" "currentclim_30sec.14.2" "currentclim_30sec.15.2" "currentclim_30sec.16.2"
[37] "currentclim_30sec.17.2" "currentclim_30sec.18.2" "currentclim_30sec.19.2" "currentclim_30sec.1.4"
[41] "currentclim_30sec.2.3" "currentclim_30sec.3.3" "currentclim_30sec.4.3" "currentclim_30sec.5.3"
[45] "currentclim_30sec.6.3" "currentclim_30sec.7.3" "currentclim_30sec.8.3" "currentclim_30sec.9.3"
[49] "currentclim_30sec.10.3" "currentclim_30sec.11.3" "currentclim_30sec.12.3" "currentclim_30sec.13.3"
[53] "currentclim_30sec.14.3" "currentclim_30sec.15.3" "currentclim_30sec.16.3" "currentclim_30sec.17.3"
[57] "currentclim_30sec.18.3" "currentclim_30sec.19.3" "currentclim_30sec.1.5" "currentclim_30sec.2.4"
[61] "currentclim_30sec.3.4" "currentclim_30sec.4.4" "currentclim_30sec.5.4" "currentclim_30sec.6.4"
[65] "currentclim_30sec.7.4" "currentclim_30sec.8.4" "currentclim_30sec.9.4" "currentclim_30sec.10.4"
[69] "currentclim_30sec.11.4" "currentclim_30sec.12.4" "currentclim_30sec.13.4" "currentclim_30sec.14.4"
[73] "currentclim_30sec.15.4" "currentclim_30sec.16.4" "currentclim_30sec.17.4" "currentclim_30sec.18.4"
[77] "currentclim_30sec.19.4" "currentclim_30sec.1.6" "currentclim_30sec.2.5" "currentclim_30sec.3.5"
[81] "currentclim_30sec.4.5" "currentclim_30sec.5.5" "currentclim_30sec.6.5" "currentclim_30sec.7.5"
[85] "currentclim_30sec.8.5" "currentclim_30sec.9.5" "currentclim_30sec.10.5" "currentclim_30sec.11.5"
[89] "currentclim_30sec.12.5" "currentclim_30sec.13.5" "currentclim_30sec.14.5" "currentclim_30sec.15.5"
[93] "currentclim_30sec.16.5" "currentclim_30sec.17.5" "currentclim_30sec.18.5" "currentclim_30sec.19.5"
[97] "currentclim_30sec.1.7" "currentclim_30sec.2.6" "currentclim_30sec.3.6" "currentclim_30sec.4.6"
[101] "currentclim_30sec.5.6" "currentclim_30sec.6.6" "currentclim_30sec.7.6" "currentclim_30sec.8.6"
[105] "currentclim_30sec.9.6" "currentclim_30sec.10.6" "currentclim_30sec.11.6" "currentclim_30sec.12.6"
[109] "currentclim_30sec.13.6" "currentclim_30sec.14.6" "currentclim_30sec.15.6" "currentclim_30sec.16.6"
[113] "currentclim_30sec.17.6" "currentclim_30sec.18.6" "currentclim_30sec.19.6" "currentclim_30sec.1.8"
[117] "currentclim_30sec.2.7" "currentclim_30sec.3.7" "currentclim_30sec.4.7" "currentclim_30sec.5.7"
[121] "currentclim_30sec.6.7" "currentclim_30sec.7.7" "currentclim_30sec.8.7" "currentclim_30sec.9.7"
[125] "currentclim_30sec.10.7" "currentclim_30sec.11.7" "currentclim_30sec.12.7" "currentclim_30sec.13.7"
[129] "currentclim_30sec.14.7" "currentclim_30sec.15.7" "currentclim_30sec.16.7" "currentclim_30sec.17.7"
[133] "currentclim_30sec.18.7" "currentclim_30sec.19.7" "currentclim_30sec.1.9" "currentclim_30sec.2.8"
[137] "currentclim_30sec.3.8" "currentclim_30sec.4.8" "currentclim_30sec.5.8" "currentclim_30sec.6.8"
[141] "currentclim_30sec.7.8" "currentclim_30sec.8.8" "currentclim_30sec.9.8" "currentclim_30sec.10.8"
[145] "currentclim_30sec.11.8" "currentclim_30sec.12.8" "currentclim_30sec.13.8" "currentclim_30sec.14.8"
[149] "currentclim_30sec.15.8" "currentclim_30sec.16.8" "currentclim_30sec.17.8" "currentclim_30sec.18.8"
[153] "currentclim_30sec.19.8" "currentclim_30sec.1.10" "currentclim_30sec.2.9" "currentclim_30sec.3.9"
[157] "currentclim_30sec.4.9" "currentclim_30sec.5.9" "currentclim_30sec.6.9" "currentclim_30sec.7.9"
[161] "currentclim_30sec.8.9" "currentclim_30sec.9.9" "currentclim_30sec.10.9" "currentclim_30sec.11.9"
[165] "currentclim_30sec.12.9" "currentclim_30sec.13.9" "currentclim_30sec.14.9" "currentclim_30sec.15.9"
[169] "currentclim_30sec.16.9" "currentclim_30sec.17.9" "currentclim_30sec.18.9" "currentclim_30sec.19.9"
[173] "currentclim_30sec.1.11" "currentclim_30sec.2.10" "currentclim_30sec.3.10" "currentclim_30sec.4.10"
[177] "currentclim_30sec.5.10" "currentclim_30sec.6.10" "currentclim_30sec.7.10" "currentclim_30sec.8.10"
[181] "currentclim_30sec.9.10" "currentclim_30sec.10.10" "currentclim_30sec.11.10" "currentclim_30sec.12.10"
[185] "currentclim_30sec.13.10" "currentclim_30sec.14.10" "currentclim_30sec.15.10" "currentclim_30sec.16.10"
[189] "currentclim_30sec.17.10" "currentclim_30sec.18.10" "currentclim_30sec.19.10" "currentclim_30sec.1.12"
[193] "currentclim_30sec.2.11" "currentclim_30sec.3.11" "currentclim_30sec.4.11" "currentclim_30sec.5.11"
[197] "currentclim_30sec.6.11" "currentclim_30sec.7.11" "currentclim_30sec.8.11" "currentclim_30sec.9.11"
[201] "currentclim_30sec.10.11" "currentclim_30sec.11.11" "currentclim_30sec.12.11" "currentclim_30sec.13.11"
[205] "currentclim_30sec.14.11" "currentclim_30sec.15.11" "currentclim_30sec.16.11" "currentclim_30sec.17.11"
[209] "currentclim_30sec.18.11" "currentclim_30sec.19.11" "currentclim_30sec.1.13" "currentclim_30sec.2.12"
[213] "currentclim_30sec.3.12" "currentclim_30sec.4.12" "currentclim_30sec.5.12" "currentclim_30sec.6.12"
[217] "currentclim_30sec.7.12" "currentclim_30sec.8.12" "currentclim_30sec.9.12" "currentclim_30sec.10.12"
[221] "currentclim_30sec.11.12" "currentclim_30sec.12.12" "currentclim_30sec.13.12" "currentclim_30sec.14.12"
[225] "currentclim_30sec.15.12" "currentclim_30sec.16.12" "currentclim_30sec.17.12" "currentclim_30sec.18.12"
[229] "currentclim_30sec.19.12" "currentclim_30sec.1.14" "currentclim_30sec.2.13" "currentclim_30sec.3.13"
[233] "currentclim_30sec.4.13" "currentclim_30sec.5.13" "currentclim_30sec.6.13" "currentclim_30sec.7.13"
[237] "currentclim_30sec.8.13" "currentclim_30sec.9.13" "currentclim_30sec.10.13" "currentclim_30sec.11.13"
[241] "currentclim_30sec.12.13" "currentclim_30sec.13.13" "currentclim_30sec.14.13" "currentclim_30sec.15.13"
[245] "currentclim_30sec.16.13" "currentclim_30sec.17.13" "currentclim_30sec.18.13" "currentclim_30sec.19.13"
[249] "currentclim_30sec.1.15" "currentclim_30sec.2.14" "currentclim_30sec.3.14" "currentclim_30sec.4.14"
[253] "currentclim_30sec.5.14" "currentclim_30sec.6.14" "currentclim_30sec.7.14" "currentclim_30sec.8.14"
[257] "currentclim_30sec.9.14" "currentclim_30sec.10.14" "currentclim_30sec.11.14" "currentclim_30sec.12.14"
[261] "currentclim_30sec.13.14" "currentclim_30sec.14.14" "currentclim_30sec.15.14" "currentclim_30sec.16.14"
[265] "currentclim_30sec.17.14" "currentclim_30sec.18.14" "currentclim_30sec.19.14" "currentclim_30sec.1.16"
[269] "currentclim_30sec.2.15" "currentclim_30sec.3.15" "currentclim_30sec.4.15" "currentclim_30sec.5.15"
[273] "currentclim_30sec.6.15" "currentclim_30sec.7.15" "currentclim_30sec.8.15" "currentclim_30sec.9.15"
[277] "currentclim_30sec.10.15" "currentclim_30sec.11.15" "currentclim_30sec.12.15" "currentclim_30sec.13.15"
[281] "currentclim_30sec.14.15" "currentclim_30sec.15.15" "currentclim_30sec.16.15" "currentclim_30sec.17.15"
[285] "currentclim_30sec.18.15" "currentclim_30sec.19.15" "currentclim_30sec.1.17" "currentclim_30sec.2.16"
[289] "currentclim_30sec.3.16" "currentclim_30sec.4.16" "currentclim_30sec.5.16" "currentclim_30sec.6.16"
[293] "currentclim_30sec.7.16" "currentclim_30sec.8.16" "currentclim_30sec.9.16" "currentclim_30sec.10.16"
[297] "currentclim_30sec.11.16" "currentclim_30sec.12.16" "currentclim_30sec.13.16" "currentclim_30sec.14.16"
[301] "currentclim_30sec.15.16" "currentclim_30sec.16.16" "currentclim_30sec.17.16" "currentclim_30sec.18.16"
[305] "currentclim_30sec.19.16" "currentclim_30sec.1.18" "currentclim_30sec.2.17" "currentclim_30sec.3.17"
[309] "currentclim_30sec.4.17" "currentclim_30sec.5.17" "currentclim_30sec.6.17" "currentclim_30sec.7.17"
[313] "currentclim_30sec.8.17" "currentclim_30sec.9.17" "currentclim_30sec.10.17" "currentclim_30sec.11.17"
[317] "currentclim_30sec.12.17" "currentclim_30sec.13.17" "currentclim_30sec.14.17" "currentclim_30sec.15.17"
[321] "currentclim_30sec.16.17" "currentclim_30sec.17.17" "currentclim_30sec.18.17" "currentclim_30sec.19.17"
[325] "currentclim_30sec.1.19" "currentclim_30sec.2.18" "currentclim_30sec.3.18" "currentclim_30sec.4.18"
[329] "currentclim_30sec.5.18" "currentclim_30sec.6.18" "currentclim_30sec.7.18" "currentclim_30sec.8.18"
[333] "currentclim_30sec.9.18" "currentclim_30sec.10.18" "currentclim_30sec.11.18" "currentclim_30sec.12.18"
[337] "currentclim_30sec.13.18" "currentclim_30sec.14.18" "currentclim_30sec.15.18" "currentclim_30sec.16.18"
[341] "currentclim_30sec.17.18" "currentclim_30sec.18.18" "currentclim_30sec.19.18" "layer"
地质似乎是这里的问题...
class : SpatRaster
dimensions : 848, 3084, 1 (nrow, ncol, nlyr)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -141, -115.3, 67.49167, 74.55833 (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326)
source : memory
name : geology_30sec
min value : 1
max value : 15
或作为 rasterLayer...
class : RasterLayer
dimensions : 848, 3084, 2615232 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -141, -115.3, 67.49167, 74.55833 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
source : memory
names : geology_30sec
values : 1, 15 (min, max)
您可以使用 raster::stack 或 raster::brick
library(terra)
s = rast(system.file("ex/logo.tif", package="terra"))
library(raster)
sr = stack(s)
# or
br = brick(s)
您目前可能需要光栅的开发版本。您可以使用 install.packages('raster', repos='rspatial.r-universe.dev')