使发生数据和气候栅格中的 SpatialGridDataFrame 相互匹配
Make SpatialGridDataFrame from occurrence data and climate raster match each other
我有两个 SpatialGridDataFrame，一个是根据物种出现数据创建的，另一个是根据气候数据栅格创建的。我需要使两个网格相互匹配以执行物种多样性模型。我正在努力使两个对象的网格拓扑相同，这是 运行 我将使用的函数所必需的。
以下是我的数据的可重复性子集：
bioclim <- structure(list(x = c(-83.5543059413, -83.545972608, -83.5376392747,
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8.46236071730001, 8.46236071730001, 8.46236071730001, 8.46236071730001,
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8.44569405070001, 8.44569405070001, 8.44569405070001, 8.44569405070001,
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8.44569405070001, 8.44569405070001, 8.44569405070001, 8.44569405070001,
8.44569405070001, 8.44569405070001, 8.43736071740001, 8.43736071740001,
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8.43736071740001, 8.43736071740001, 8.43736071740001, 8.43736071740001,
8.43736071740001), CHELSA_bio1_1981.2010_V.2.1 = c(24.55, 24.55,
24.7500000000001, 24.95, 24.95, 25.15, 25.35, 25.7500000000001,
25.65, 25.65, 25.65, 25.65, 25.7500000000001, 25.85, 25.85, 25.85,
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25.15, 25.35, 25.15, 24.95, 25.05, 25.35, 25.35, 25.15, 25.45,
25.65, 25.7500000000001, 25.85, 25.85, 25.85, 25.85, 25.85, 25.95,
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25.2500000000001, 25.35, 25.05, 24.95, 24.95, 25.2500000000001,
24.95, 24.95, 25.2500000000001, 25.35, 25.45, 25.55, 25.7500000000001,
25.85, 25.85, 25.85, 25.85, 25.85, 25.65, 25.55, 25.45, 25.35,
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25.7500000000001, 25.65, 25.55, 25.55, 25.35, 25.55, 25.95, 26.05,
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25.65, 25.7500000000001, 25.55, 25.7500000000001, 25.95, 25.7500000000001,
25.85, 25.95, 25.7500000000001, 26.15, 26.15, 26.15, 26.15, 26.15,
26.15, 26.15, 26.05, 26.05, 26.05, 26.15, 26.15, 26.15, 26.05,
25.65)), row.names = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
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494L, 495L, 496L, 497L, 498L, 499L, 500L, 501L, 502L, 503L, 504L,
505L, 506L), class = "data.frame")
occ_df <- structure(list(lon = c(-83.41, -83.51, -83.41143, -83.525, -83.46,
-83.46, -83.43, -83.49, -83.41, -83.44, -83.46944, -83.427778,
-83.516667, -83.433609, -83.411431, -83.55, -83.516805), lat = c(8.54,
8.56, 8.540221, 8.540278, 8.51, 8.52, 8.52, 8.45, 8.52, 8.52,
8.51805, 8.525, 8.566667, 8.531329, 8.540221, 8.45, 8.569294),
`Chrysochlamys glauca` = c(1, 1, 1, 1, 0, 0, 0, 1, 0, 0,
0, 1, 1, 1, 1, 1, 0), `Ocotea atirrensis` = c(0, 1, 0, 0,
1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -17L))
这是我目前拥有的
library(dplyr)
clim <- terra::rast(bioclim)
terra::crs(clim) <- "+proj=longlat +datum=WGS84 +no_defs"
clim_sg_df <- raster::raster(clim) %>%
as("SpatialGridDataFrame")
occ <- occ_df %>%
# rounding lon/lat to fix inconstant coordinate intervals when using gridded()
mutate(across(
matches("lon|lat"),
~ round(.x, 2)
))
sp::coordinates(occ) <- ~ lon + lat
sp::proj4string(occ) <- "+proj=longlat +datum=WGS84 +no_defs"
sp::gridded(occ) <- TRUE
occ_sg_df <- as(occ_sp, "SpatialGridDataFrame")
# terra::plot(clim_sg_df)
# terra::plot(occ_sg_df, add = TRUE)
我有两个主要问题。首先，尽管有相同的投影，但我无法将两个网格绘制在彼此之上，所以我怀疑我的代码有错误。第二，两个对象之间的网格大小不同，我真的不知道如何将它们均质化。
我需要让 clim_sg_df 和 occ_sg_df 相互匹配，以便满足以下条件 returns TRUE:
identical(as(occ_sg_df, "SpatialGrid"), as(clim_sg_df, "SpatialGrid"))
请注意，我需要保留栅格中的所有像元并使用 sp 包，因为这是我将用于分析的函数所需要的。
#here you have first raster created
clim <- terra::rast(bioclim)
terra::crs(clim) <- "+proj=longlat +datum=WGS84 +no_defs"
#lets create another raster with exactly the same size
#lets clear it
#
occ <- clim
occ[] <- NA
occ_df <- occ_df %>%
# rounding lon/lat to fix inconstant coordinate intervals when using gridded()
mutate(across(
matches("lon|lat"),
~ round(.x, 2)
))
sp::coordinates(occ_df) <- ~ lon + lat
sp::proj4string(occ_df) <- "+proj=longlat +datum=WGS84 +no_defs"
# lets create a vector object which later will be used for rasterization
occ_vec <- vect(occ_df)
# now the important step - rasterizing vector data over raster grid
#
occ <- rasterize(occ_vec, occ)
terra::plot(clim)
terra::plot(occ, add = TRUE )
由 reprex package (v2.0.1)
创建于 2022-01-24
PS。为了更好的可见性，您可以更改颜色，例如
terra::plot(occ, add = TRUE, col = "red" )
您应该使用 rasterize 而不是 gridded。例如这个光栅和这些点：
library(terra)
r <- rast(system.file("ex/elev.tif", package="terra"))
set.seed(123)
pts <- spatSample(r, 1000, xy=TRUE, na.rm=TRUE, replace=TRUE)
xy <- as.matrix(pts[, c("x", "y")])
你可以做到
spp <- rasterize(xy, r)
或者按单元格统计出现次数
spp <- rasterize(xy, r, 1, fun=sum)
除了矩阵，您还可以使用 SpatVector 个点。
在一张地图上绘制两个重叠的栅格绝非易事，但 Grzegorz Sapijaszko 展示了如何做到这一点。您还可以使用透明度。但通常只在栅格顶部添加点更有意义：
plot(r)
points(xy, cex=.5)
或者有一个点的单元格的中心
pspp <- as.points(spp)
plot(r)
points(pspp, cex=.25)
或者添加单元格的轮廓（如果出现的单元格数量不是太多）
polspp <- as.polygons(spp)
plot(r)
lines(polspp)
使用 R 附带的示例数据更方便，但您可以使用您的示例数据
library(terra)
r <- rast(bioclim)
pts <- vect(data.frame(occ_df), geom=c("lon", "lat"))
x <- rasterize(pts, r)
plot(r)
lines(as.polygons(x))
points(pts, col="red")
在您的示例数据中，您有多个物种。
pts
# class : SpatVector
# geometry : points
# dimensions : 17, 2 (geometries, attributes)
# extent : -83.55, -83.41, 8.45, 8.569294 (xmin, xmax, ymin, ymax)
# coord. ref. :
# names : Chrysochlamys.glauca Ocotea.atirrensis
# type : <num> <num>
# values : 1 0
# 1 1
# 1 0
您可以像这样将 pts 子集化为单个物种
glauca <- pts[pts$Chrysochlamys.glauca == 1, ]
如果您愿意，也可以使用 raster::rasterize（带有 RasterLayer 和矩阵或 SpatialPointsDataFrame）。或者像这样将对象从 terra 强制返回到 raster/sp：
library(raster)
xr <- raster(x)
pp <- as(pts, "Spatial")
plot(xr)
points(pp)
我有两个 SpatialGridDataFrame，一个是根据物种出现数据创建的，另一个是根据气候数据栅格创建的。我需要使两个网格相互匹配以执行物种多样性模型。我正在努力使两个对象的网格拓扑相同，这是 运行 我将使用的函数所必需的。
以下是我的数据的可重复性子集：
bioclim <- structure(list(x = c(-83.5543059413, -83.545972608, -83.5376392747,
-83.5293059414, -83.5209726081, -83.5126392748, -83.5043059415,
-83.4959726082, -83.4876392749, -83.4793059416, -83.4709726083,
-83.462639275, -83.4543059417, -83.4459726084, -83.4376392751,
-83.4293059418, -83.4209726085, -83.4126392752, -83.5543059413,
-83.545972608, -83.5376392747, -83.5293059414, -83.5209726081,
-83.5126392748, -83.5043059415, -83.4959726082, -83.4876392749,
-83.4793059416, -83.4709726083, -83.462639275, -83.4543059417,
-83.4459726084, -83.4376392751, -83.4293059418, -83.4209726085,
-83.4126392752, -83.4043059419, -83.5543059413, -83.545972608,
-83.5376392747, -83.5293059414, -83.5209726081, -83.5126392748,
-83.5043059415, -83.4959726082, -83.4876392749, -83.4793059416,
-83.4709726083, -83.462639275, -83.4543059417, -83.4459726084,
-83.4376392751, -83.4293059418, -83.4209726085, -83.4126392752,
-83.4043059419, -83.3959726086, -83.3876392753, -83.5543059413,
-83.545972608, -83.5376392747, -83.5293059414, -83.5209726081,
-83.5126392748, -83.5043059415, -83.4959726082, -83.4876392749,
-83.4793059416, -83.4709726083, -83.462639275, -83.4543059417,
-83.4459726084, -83.4376392751, -83.4293059418, -83.4209726085,
-83.4126392752, -83.4043059419, -83.3959726086, -83.3876392753,
-83.379305942, -83.5543059413, -83.545972608, -83.5376392747,
-83.5293059414, -83.5209726081, -83.5126392748, -83.5043059415,
-83.4959726082, -83.4876392749, -83.4793059416, -83.4709726083,
-83.462639275, -83.4543059417, -83.4459726084, -83.4376392751,
-83.4293059418, -83.4209726085, -83.4126392752, -83.4043059419,
-83.3959726086, -83.3876392753, -83.379305942, -83.3709726087,
-83.5543059413, -83.545972608, -83.5376392747, -83.5293059414,
-83.5209726081, -83.5126392748, -83.5043059415, -83.4959726082,
-83.4876392749, -83.4793059416, -83.4709726083, -83.462639275,
-83.4543059417, -83.4459726084, -83.4376392751, -83.4293059418,
-83.4209726085, -83.4126392752, -83.4043059419, -83.3959726086,
-83.3876392753, -83.379305942, -83.3709726087, -83.5543059413,
-83.545972608, -83.5376392747, -83.5293059414, -83.5209726081,
-83.5126392748, -83.5043059415, -83.4959726082, -83.4876392749,
-83.4793059416, -83.4709726083, -83.462639275, -83.4543059417,
-83.4459726084, -83.4376392751, -83.4293059418, -83.4209726085,
-83.4126392752, -83.4043059419, -83.3959726086, -83.3876392753,
-83.379305942, -83.3709726087, -83.5543059413, -83.545972608,
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occ_df <- structure(list(lon = c(-83.41, -83.51, -83.41143, -83.525, -83.46,
-83.46, -83.43, -83.49, -83.41, -83.44, -83.46944, -83.427778,
-83.516667, -83.433609, -83.411431, -83.55, -83.516805), lat = c(8.54,
8.56, 8.540221, 8.540278, 8.51, 8.52, 8.52, 8.45, 8.52, 8.52,
8.51805, 8.525, 8.566667, 8.531329, 8.540221, 8.45, 8.569294),
`Chrysochlamys glauca` = c(1, 1, 1, 1, 0, 0, 0, 1, 0, 0,
0, 1, 1, 1, 1, 1, 0), `Ocotea atirrensis` = c(0, 1, 0, 0,
1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -17L))
这是我目前拥有的
library(dplyr)
clim <- terra::rast(bioclim)
terra::crs(clim) <- "+proj=longlat +datum=WGS84 +no_defs"
clim_sg_df <- raster::raster(clim) %>%
as("SpatialGridDataFrame")
occ <- occ_df %>%
# rounding lon/lat to fix inconstant coordinate intervals when using gridded()
mutate(across(
matches("lon|lat"),
~ round(.x, 2)
))
sp::coordinates(occ) <- ~ lon + lat
sp::proj4string(occ) <- "+proj=longlat +datum=WGS84 +no_defs"
sp::gridded(occ) <- TRUE
occ_sg_df <- as(occ_sp, "SpatialGridDataFrame")
# terra::plot(clim_sg_df)
# terra::plot(occ_sg_df, add = TRUE)
我有两个主要问题。首先，尽管有相同的投影，但我无法将两个网格绘制在彼此之上，所以我怀疑我的代码有错误。第二，两个对象之间的网格大小不同，我真的不知道如何将它们均质化。
我需要让 clim_sg_df 和 occ_sg_df 相互匹配，以便满足以下条件 returns TRUE:
identical(as(occ_sg_df, "SpatialGrid"), as(clim_sg_df, "SpatialGrid"))
请注意，我需要保留栅格中的所有像元并使用 sp 包，因为这是我将用于分析的函数所需要的。
#here you have first raster created
clim <- terra::rast(bioclim)
terra::crs(clim) <- "+proj=longlat +datum=WGS84 +no_defs"
#lets create another raster with exactly the same size
#lets clear it
#
occ <- clim
occ[] <- NA
occ_df <- occ_df %>%
# rounding lon/lat to fix inconstant coordinate intervals when using gridded()
mutate(across(
matches("lon|lat"),
~ round(.x, 2)
))
sp::coordinates(occ_df) <- ~ lon + lat
sp::proj4string(occ_df) <- "+proj=longlat +datum=WGS84 +no_defs"
# lets create a vector object which later will be used for rasterization
occ_vec <- vect(occ_df)
# now the important step - rasterizing vector data over raster grid
#
occ <- rasterize(occ_vec, occ)
terra::plot(clim)
terra::plot(occ, add = TRUE )
由 reprex package (v2.0.1)
创建于 2022-01-24PS。为了更好的可见性，您可以更改颜色，例如
terra::plot(occ, add = TRUE, col = "red" )
您应该使用 rasterize 而不是 gridded。例如这个光栅和这些点：
library(terra)
r <- rast(system.file("ex/elev.tif", package="terra"))
set.seed(123)
pts <- spatSample(r, 1000, xy=TRUE, na.rm=TRUE, replace=TRUE)
xy <- as.matrix(pts[, c("x", "y")])
你可以做到
spp <- rasterize(xy, r)
或者按单元格统计出现次数
spp <- rasterize(xy, r, 1, fun=sum)
除了矩阵，您还可以使用 SpatVector 个点。
在一张地图上绘制两个重叠的栅格绝非易事，但 Grzegorz Sapijaszko 展示了如何做到这一点。您还可以使用透明度。但通常只在栅格顶部添加点更有意义：
plot(r)
points(xy, cex=.5)
或者有一个点的单元格的中心
pspp <- as.points(spp)
plot(r)
points(pspp, cex=.25)
或者添加单元格的轮廓（如果出现的单元格数量不是太多）
polspp <- as.polygons(spp)
plot(r)
lines(polspp)
使用 R 附带的示例数据更方便，但您可以使用您的示例数据
library(terra)
r <- rast(bioclim)
pts <- vect(data.frame(occ_df), geom=c("lon", "lat"))
x <- rasterize(pts, r)
plot(r)
lines(as.polygons(x))
points(pts, col="red")
在您的示例数据中，您有多个物种。
pts
# class : SpatVector
# geometry : points
# dimensions : 17, 2 (geometries, attributes)
# extent : -83.55, -83.41, 8.45, 8.569294 (xmin, xmax, ymin, ymax)
# coord. ref. :
# names : Chrysochlamys.glauca Ocotea.atirrensis
# type : <num> <num>
# values : 1 0
# 1 1
# 1 0
您可以像这样将 pts 子集化为单个物种
glauca <- pts[pts$Chrysochlamys.glauca == 1, ]
如果您愿意，也可以使用 raster::rasterize（带有 RasterLayer 和矩阵或 SpatialPointsDataFrame）。或者像这样将对象从 terra 强制返回到 raster/sp：
library(raster)
xr <- raster(x)
pp <- as(pts, "Spatial")
plot(xr)
points(pp)