Question

我正在尝试从栅格砖中提取值并获取均值，但是出现一个错误，我认为这与栅格砖的尺寸有关。

数据已从NOAA

下载

我要做的是以下事情：

library(raster)

ERSST <- rotate(brick('sst.mnmean.nc'))
ERSST
class       : RasterBrick 
dimensions  : 89, 180, 16020, 1976  (nrow, ncol, ncell, nlayers)
resolution  : 2, 2  (x, y)
extent      : -179, 181, -89, 89  (xmin, xmax, ymin, ymax) # ignore extent, needs fixing but not relevant for the question
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 
data source : in memory
names       : X1854.01.01, X1854.02.01, X1854.03.01, X1854.04.01, X1854.05.01, X1854.06.01, X1854.07.01, X1854.08.01, X1854.09.01, X1854.10.01, X1854.11.01, X1854.12.01, X1855.01.01, X1855.02.01, X1855.03.01, ... 
min values  :        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8, ... 
max values  :    32.09937,    31.44189,    31.72137,    31.47466,    33.23633,    31.90788,    35.22922,    33.66898,    32.26702,    32.15502,    31.68270,    31.74512,    31.32458,    31.23049,    29.88974, ... 
Date        : 1854-01-01, 2018-08-01 (min, max)

任意点

xy <- data.frame(x = -49, y = 45)

当我提取时，我得到：

extract(ERSST, xy, buffer = 1e+05, small = TRUE, fun = mean)
Error in apply(x, 2, fun2) : dim(X) must have a positive length

让我认为这是尺寸问题的原因是我尝试指定要使用的图层时遇到的错误

extract(ERSST, xy, buffer = 1e+05, small = TRUE, layer = 10, nl = 10)
Error in x[, lyrs] : incorrect number of dimensions

如果我先求平均值似乎很好用（但这不是我想要的，我需要此时的时间序列）

mERSST <- mean(ERSST)
extract(mERSST, xy, buffer = 1e+05, small = TRUE, fun = mean)
[1] 5.649212

也许是栅格图块中的“日期”属性。有任何变通办法或解决方案来防止此错误？

@RobertHijmans的回答使我意识到，即使该点位于多个网格单元的交界处，也总是从提取中得到一个值，如上例所示。

plot(MSST, xlim = c(-60, -40), ylim = c(40, 50))
points(xy)

使用：

extract(mERSST, xy, buffer = 1e+05, small = TRUE, cellnumbers = TRUE)
[[1]]
       cell       value 
3845.000000    5.649212

我只得到一个值，而无论缓冲区有多小，我都希望有4。我在提取物中缺少某些东西吗？所以我尝试将我的点转换为圆并使用它来提取数据

coordinates(xy) <- ~ x + y
proj4string(xy) <- '+init=epsg:4326'

xy_utm <- spTransform(xy, CRS('+init=epsg:32621'))
gbf_utm <- rgeos::gBuffer(xy_utm, width = 1e5, quadsegs = 250L)
gbf <- spTransform(gbf_utm, CRS(proj4string(xy)))


plot(ERSST[[1]], xlim = c(-60, -40), ylim = c(40, 50))
points(xy, pch = 19)
plot(gbf, add = TRUE)

extract(ERSST[[1]], gbf, small = TRUE, weights = TRUE)

这给了我

[[1]]
        value weight
[1,] 1.722664   0.25
[2,] 3.683457   0.25
[3,] 5.985203   0.25
[4,] 8.442450   0.25

在2.6.7版中（这似乎很有意义）。

但是

[[1]]
          value      weight
  [1,] 1.722664 0.001236928
  [2,] 1.722664 0.003935680
  [3,] 1.722664 0.005285056
  [4,] 3.683457 0.005285056
  [5,] 3.683457 0.003935680
  [6,] 3.683457 0.001236928
  [7,] 1.722664 0.002136512
  [8,] 1.722664 0.008321151
  [9,] 1.722664 0.011244799
 [10,] 1.722664 0.011244799
 [11,] 1.722664 0.011244799
 [12,] 3.683457 0.011244799
 [13,] 3.683457 0.011244799
 [14,] 3.683457 0.011244799
 [15,] 3.683457 0.008208703
 [16,] 3.683457 0.001911616
 [17,] 1.722664 0.003036096
 [18,] 1.722664 0.010907455
 [19,] 1.722664 0.011244799
 [20,] 1.722664 0.011244799
 [21,] 1.722664 0.011244799
 [22,] 1.722664 0.011244799
 [23,] 3.683457 0.011244799
 [24,] 3.683457 0.011244799
 [25,] 3.683457 0.011244799
 [26,] 3.683457 0.011244799
 [27,] 3.683457 0.010907455
 [28,] 3.683457 0.003036096
 [29,] 1.722664 0.000449792
 [30,] 1.722664 0.010232767
 [31,] 1.722664 0.011244799
 [32,] 1.722664 0.011244799
 [33,] 1.722664 0.011244799
 [34,] 1.722664 0.011244799
 [35,] 1.722664 0.011244799
 [36,] 3.683457 0.011244799
 [37,] 3.683457 0.011244799
 [38,] 3.683457 0.011244799
 [39,] 3.683457 0.011244799
 [40,] 3.683457 0.011244799
 [41,] 3.683457 0.010232767
 [42,] 3.683457 0.000337344
 [43,] 1.722664 0.003036096
 [44,] 1.722664 0.011244799
 [45,] 1.722664 0.011244799
 [46,] 1.722664 0.011244799
 [47,] 1.722664 0.011244799
 [48,] 1.722664 0.011244799
 [49,] 1.722664 0.011244799
 [50,] 3.683457 0.011244799
 [51,] 3.683457 0.011244799
 [52,] 3.683457 0.011244799
 [53,] 3.683457 0.011244799
 [54,] 3.683457 0.011244799
 [55,] 3.683457 0.011244799
 [56,] 3.683457 0.002923648
 [57,] 5.985203 0.002923648
 [58,] 5.985203 0.011244799
 [59,] 5.985203 0.011244799
 [60,] 5.985203 0.011244799
 [61,] 5.985203 0.011244799
 [62,] 5.985203 0.011244799
 [63,] 5.985203 0.011244799
 [64,] 8.442450 0.011244799
 [65,] 8.442450 0.011244799
 [66,] 8.442450 0.011244799
 [67,] 8.442450 0.011244799
 [68,] 8.442450 0.011244799
 [69,] 8.442450 0.011244799
 [70,] 8.442450 0.002923648
 [71,] 5.985203 0.000337344
 [72,] 5.985203 0.010120319
 [73,] 5.985203 0.011244799
 [74,] 5.985203 0.011244799
 [75,] 5.985203 0.011244799
 [76,] 5.985203 0.011244799
 [77,] 5.985203 0.011244799
 [78,] 8.442450 0.011244799
 [79,] 8.442450 0.011244799
 [80,] 8.442450 0.011244799
 [81,] 8.442450 0.011244799
 [82,] 8.442450 0.011244799
 [83,] 8.442450 0.010007871
 [84,] 8.442450 0.000224896
 [85,] 5.985203 0.002811200
 [86,] 5.985203 0.010795007
 [87,] 5.985203 0.011244799
 [88,] 5.985203 0.011244799
 [89,] 5.985203 0.011244799
 [90,] 5.985203 0.011244799
 [91,] 8.442450 0.011244799
 [92,] 8.442450 0.011244799
 [93,] 8.442450 0.011244799
 [94,] 8.442450 0.011244799
 [95,] 8.442450 0.010682559
 [96,] 8.442450 0.002698752
 [97,] 5.985203 0.001799168
 [98,] 5.985203 0.007871359
 [99,] 5.985203 0.011244799
[100,] 5.985203 0.011244799
[101,] 5.985203 0.011244799
[102,] 8.442450 0.011244799
[103,] 8.442450 0.011244799
[104,] 8.442450 0.011244799
[105,] 8.442450 0.007871359
[106,] 8.442450 0.001799168
[107,] 5.985203 0.001236928
[108,] 5.985203 0.003935680
[109,] 5.985203 0.005285056
[110,] 8.442450 0.005285056
[111,] 8.442450 0.003935680
[112,] 8.442450 0.001236928

在版本2.7-13中是不正确的。

Answer 1

为了简化上述讨论（并修复了带有多边形的提取问题之后），我得到了

library(raster)

r <- raster(xmn=-59, xmx=-39, ymn=41, ymx=49, res=2, vals=1:40)

xy <- SpatialPoints(data.frame(x = -49, y = 45), proj4string = CRS('+init=epsg:4326'))
p <- buffer(xy, width = 1e5, quadsegs = 250L)
plot(r)
plot(p, add=T)

extract(r, xy)       
#26 
extract(r, p)
#[[1]]
#[1] 16 26 25 15

extract(r, p, weights=T)
#[[1]]
#     value weight
#[1,]    15   0.25
#[2,]    16   0.25
#[3,]    25   0.25
#[4,]    26   0.25

extract(r, xy, buffer=100000)
#[[1]]
#value 
#   15 

extract(r, xy, buffer=1000000)
#[[1]]
# [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#[37] 37 38 39 40

r为什么栅格提取会产生暗淡错误？

1 个答案: