我使用terra中的rast()函数导入了一个多层tiff文件(7"bio"层(，然后使用从其他地方添加其他层
current.raster <- rast("./bio_layers.tif")
# add1, add2, and add3 are SpatRaster objects from tif files that were modified
# in earlier steps and are stored in memory
new.rast <- c(current.raster, add1, add2, add3)
new.rast
class : SpatRaster
dimensions : 848, 3084, 10 (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)
sources : bio_layers.tif (7 layers)
memory
memory
... and 1 more source(s)
当我使用stack()将其转换为rasterStack对象时；CCD_ 5"；层被改变为CCD_ 6的第一层的值。请参阅下面我的光栅摘要。
summary(new.rast)
bio1 bio2 bio3 bio4 bio11 bio15 bio18
Min. :-16.60 Min. :1.00 Min. : 12.29 Min. : 0.00 Min. :-32.15 Min. :34.31 Min. : 9.00
1st Qu.:-14.45 1st Qu.:6.95 1st Qu.: 15.29 1st Qu.:14.41 1st Qu.:-30.52 1st Qu.:55.30 1st Qu.: 53.00
Median :-11.74 Median :7.42 Median : 15.90 Median :14.67 Median :-27.87 Median :59.26 Median : 63.00
Mean :-12.07 Mean :7.53 Mean : 16.19 Mean :14.75 Mean :-28.23 Mean :59.21 Mean : 70.24
3rd Qu.: -9.79 3rd Qu.:8.02 3rd Qu.: 16.79 3rd Qu.:15.27 3rd Qu.:-26.35 3rd Qu.:63.18 3rd Qu.: 83.00
Max. : 0.00 Max. :9.56 Max. :100.00 Max. :16.37 Max. : 0.00 Max. :91.12 Max. :153.00
NA's :78844 NA's :78844 NA's :78844 NA's :78844 NA's :78844 NA's :78844 NA's :78844
add1 add2 add3
Min. : -15.42 Min. : 0.00 Min. :0.00
1st Qu.: 38.41 1st Qu.: 0.71 1st Qu.:0.00
Median : 114.79 Median : 1.74 Median :0.00
Mean : 166.82 Mean : 3.45 Mean :0.00
3rd Qu.: 222.14 3rd Qu.: 3.84 3rd Qu.:0.00
Max. :1584.36 Max. :49.38 Max. :0.02
NA's :79057 NA's :79326 NA's :81712
summary(stack(new.rast))
bio1 bio2 bio3 bio4 bio11 bio15 bio18 add1
Min. -16.60000 1.000000e+00 1.253205e+01 0.000000e+00 -32.15000 3.511098e+01 10 -16.60000
1st Qu. -14.45833 6.950000e+00 1.528698e+01 1.441101e+01 -30.51667 5.537055e+01 53 -14.45833
Median -11.77917 7.425000e+00 1.589191e+01 1.467350e+01 -27.88333 5.927956e+01 63 -11.77917
3rd Qu. -9.78750 8.016666e+00 1.679012e+01 1.526218e+01 -26.35000 6.308383e+01 83 -9.78750
Max. 0.00000 9.575000e+00 1.000000e+02 1.638678e+01 0.00000 8.897108e+01 148 0.00000
NA's 2046158.00000 2.046158e+06 2.046158e+06 2.046158e+06 2046158.00000 2.046158e+06 2046158 2046158.00000
add2 add3
Min. -16.60000 -16.60000
1st Qu. -14.45833 -14.45833
Median -11.77917 -11.77917
3rd Qu. -9.78750 -9.78750
Max. 0.00000 0.00000
NA's 2046158.00000 2046158.00000
注意"；CCD_ 7层数据已经改变；CCD_ 8"；层为什么会发生这种情况？当我将这些层的数据转换为rasterStack对象时，有没有一种方法可以保留存储在内存中的数据？
我已经解决了这个问题，使用开发版本或光栅，我现在可以得到：
library(raster)
library(terra)
s <- rast(system.file("ex/logo.tif", package="terra"))
r1 <- s[[1]] * 2
r2 <- s[[2]] * 3
x <- c(s, r1, r2)
names(x) <- letters[1:5]
rst <- stack(x)
summary(x)
# a b c d e
# Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
# 1st Qu.:131.0 1st Qu.:138.0 1st Qu.:151.0 1st Qu.:262.0 1st Qu.:414.0
# Median :196.0 Median :199.0 Median :215.0 Median :392.0 Median :597.0
# Mean :182.3 Mean :185.4 Mean :192.8 Mean :364.6 Mean :556.1
# 3rd Qu.:254.0 3rd Qu.:255.0 3rd Qu.:254.0 3rd Qu.:508.0 3rd Qu.:765.0
# Max. :255.0 Max. :255.0 Max. :255.0 Max. :510.0 Max. :765.0
summary(rst)
# a b c d e
#Min. 0 0 0 0 0
#1st Qu. 131 138 151 262 414
#Median 196 199 215 392 597
#3rd Qu. 254 255 254 508 765
#Max. 255 255 255 510 765
#NA's 0 0 0 0 0
您可以使用安装开发版本install.packages('raster', repos='https://rspatial.r-universe.dev')