R cran：光柵化多邊形和采樣點坐標

Question

我們正在嘗試創建 Vermeille Coast 的地圖，以便在兩點之間的路徑不穿過陸地的情況下計算采樣點之間的距離。

1/ 我們將兩個 shapefile 綁定在一起（ R cran: sf Sew two MULTILINESTRING/LINESTRING ）

2/我們創建了一個盒子來繪制一個多邊形（ Sf包：關閉一個復雜形狀的多邊形）

3/ 我們確實柵格化了多邊形（ R cran rasterize sfc_polygon ）

（此處提供數據： https ://www.dropbox.com/sh/hzsdklnmvjg4hsz/AAATHLV0pkJXDvSqyRIBlVl_a?dl=0）

library(sf)
library(fasterize)
library(raster)
library(dplyr)
library(tidyverse)

frenchCoast_CoteBanyuls <- st_read("coasts_subnational_France/coasts_subnational.shp")
spainCoast_CoteBanyuls <- st_read("coasts_subnational_Spain/coasts_subnational.shp")

spainurl <- "https://geo.vliz.be/geoserver/wfs?request=getfeature&service=wfs&version=1.0.0&typename=MarineRegions:coasts_subnational&outputformat=SHAPE-ZIP&filter=%3CPropertyIsEqualTo%3E%3CPropertyName%3Emrgid_1%3C%2FPropertyName%3E%3CLiteral%3E3417%3C%2FLiteral%3E%3C%2FPropertyIsEqualTo%3E"
download.file(spainurl, "spain.zip", mode = "wb")
unzip("spain.zip", exdir = "spain", junkpaths = TRUE)

franceurl <- "https://geo.vliz.be/geoserver/wfs?request=getfeature&service=wfs&version=1.0.0&typename=MarineRegions:coasts_subnational&outputformat=SHAPE-ZIP&filter=%3CPropertyIsEqualTo%3E%3CPropertyName%3Emrgid_1%3C%2FPropertyName%3E%3CLiteral%3E19888%3C%2FLiteral%3E%3C%2FPropertyIsEqualTo%3E"
download.file(franceurl, "france.zip", mode = "wb")
unzip("france.zip", exdir = "france", junkpaths = TRUE)

spainCoast_CoteBanyuls <- list.files("spain",
                                     pattern = "shp$",
                                     full.names = TRUE) %>% st_read()
frenchCoast_CoteBanyuls <- list.files("france",
                                      pattern = "shp$",
                                      full.names = TRUE) %>% st_read()


lines_spain <- st_geometry(spainCoast_CoteBanyuls) %>% st_cast("LINESTRING")
spainCoast_l <- st_sf(n = as.character(seq_len(length(lines_spain))), lines_spain)

lines_france <- st_geometry(frenchCoast_CoteBanyuls) %>% st_cast("LINESTRING")
franceCoast_l <- st_sf(n = as.character(seq_len(length(lines_france))), lines_france)

spainmax <- spainCoast_l[which.max(st_length(spainCoast_l)), ]
spainrest <- spainCoast_l[-which.max(st_length(spainCoast_l)), ]

joined <- c(st_geometry(spainmax), st_geometry(franceCoast_l)) %>%
  st_union()

join_end <- st_union(joined, st_geometry(spainrest))

bbox_all <- st_bbox(joined) %>%
  st_as_sfc()

polygon_joined <- bbox_all %>%
  lwgeom::st_split(join_end) %>%
  st_collection_extract("POLYGON")

#Polygons on position 2 and 3 need to be removed (visual inspection)
polygon_end <- polygon_joined[2] # define land as polygone and not sea

polyCombin_df <- st_sf(var = 1, polygon_end)
class(polyCombin_df)
st_crs(25831)$units
polyCombin_df_t <- polyCombin_df %>% st_transform(25831)

我們得到了：

我們確實柵格化了多邊形：

r <- raster(polyCombin_df_t, res = 100)
r <- fasterize(polyCombin_df_t, r, fun = "max")
par(mar=c(1,1,1,1))
plot(r)

到目前為止，它似乎工作：

4/ 我們現在要使用功能points添加沿海岸的3個采樣點坐標，以便應用以下方法計算采樣點之間的距離：（ https://www.r-bloggers.com/2020/02 / 計算距離的三種方法 / ）

# sites
site_random <- matrix(data = c(3.164887 , 3.123969 , 3.158125 , 3.160378, 42.402158, 
                42.521957, 42.475956, 42.461188), ncol = 2)

site_random <- data.frame(site_random) 
points(site_random$X1, site_random$X2, pch = 19)

但是，這不起作用，並且不顯示示例站點。 這是由於圖表的比例嗎？

預先感謝您的幫助！

Answer 1

查看如何將點的坐標與柵格的 CRS 相匹配：

library(sf)
library(fasterize)
library(raster)
library(dplyr)
library(tidyverse)

spainurl <- "https://geo.vliz.be/geoserver/wfs?request=getfeature&service=wfs&version=1.0.0&typename=MarineRegions:coasts_subnational&outputformat=SHAPE-ZIP&filter=%3CPropertyIsEqualTo%3E%3CPropertyName%3Emrgid_1%3C%2FPropertyName%3E%3CLiteral%3E3417%3C%2FLiteral%3E%3C%2FPropertyIsEqualTo%3E"
download.file(spainurl, "spain.zip", mode = "wb")
unzip("spain.zip", exdir = "spain", junkpaths = TRUE)

franceurl <- "https://geo.vliz.be/geoserver/wfs?request=getfeature&service=wfs&version=1.0.0&typename=MarineRegions:coasts_subnational&outputformat=SHAPE-ZIP&filter=%3CPropertyIsEqualTo%3E%3CPropertyName%3Emrgid_1%3C%2FPropertyName%3E%3CLiteral%3E19888%3C%2FLiteral%3E%3C%2FPropertyIsEqualTo%3E"
download.file(franceurl, "france.zip", mode = "wb")
unzip("france.zip", exdir = "france", junkpaths = TRUE)

spainCoast_CoteBanyuls <- list.files("spain",
  pattern = "shp$",
  full.names = TRUE
) %>% st_read()

frenchCoast_CoteBanyuls <- list.files("france",
  pattern = "shp$",
  full.names = TRUE
) %>% st_read()
lines_spain <- st_geometry(spainCoast_CoteBanyuls) %>% st_cast("LINESTRING")
spainCoast_l <- st_sf(n = as.character(seq_len(length(lines_spain))), lines_spain)

lines_france <- st_geometry(frenchCoast_CoteBanyuls) %>% st_cast("LINESTRING")
franceCoast_l <- st_sf(n = as.character(seq_len(length(lines_france))), lines_france)

spainmax <- spainCoast_l[which.max(st_length(spainCoast_l)), ]
spainrest <- spainCoast_l[-which.max(st_length(spainCoast_l)), ]

joined <- c(st_geometry(spainmax), st_geometry(franceCoast_l)) %>%
  st_union()

join_end <- st_union(joined, st_geometry(spainrest))

bbox_all <- st_bbox(joined) %>%
  st_as_sfc()

polygon_joined <- bbox_all %>%
  lwgeom::st_split(join_end) %>%
  st_collection_extract("POLYGON")

# Polygons on position 2 and 3 need to be removed (visual inspection)
polygon_end <- polygon_joined[-c(2, 3)] # define land as polygone and not sea

polyCombin_df <- st_sf(var = 1, polygon_end)
class(polyCombin_df)
#> [1] "sf"         "data.frame"
st_crs(25831)$units
#> [1] "m"
polyCombin_df_t <- polyCombin_df %>% st_transform(25831)


ggplot(polyCombin_df_t) +
  geom_sf()

r <- raster(polyCombin_df_t, res = 100)
r <- fasterize(polyCombin_df_t, r, fun = "max")



# sites
site_random <- matrix(data = c(
  3.164887, 3.123969, 3.158125, 3.160378, 42.402158,
  42.521957, 42.475956, 42.461188
), ncol = 2)

site_random <- data.frame(site_random)

site_random_sf <- st_as_sf(site_random, coords = c("X1", "X2"), crs = 4326) %>%
  st_transform(25831)


plot(r)
plot(st_geometry(site_random_sf), add = TRUE)

# If you need as matrix points
site_random_t <- st_coordinates(site_random_sf)[, 1:2] %>% as.data.frame()
names(site_random_t) <- names(site_random)

site_random_t
#>         X1      X2
#> 1 513569.2 4694442
#> 2 510182.5 4707739
#> 3 512997.5 4702635
#> 4 513185.8 4700996

plot(r)
points(site_random_t$X1, site_random_t$X2, pch = 19)

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