10 Bridges to GIS software

library(sf)
library(terra)

E1. Compute global solar irradiation for an area of system.file("raster/dem.tif", package = "spDataLarge") for March 21 at 11:00 AM using the r.sun GRASS GIS through qgisprocess.

library(qgisprocess)
# enable grass
qgis_enable_plugins("grassprovider")
dem = rast(system.file("raster/dem.tif", package = "spDataLarge"))
slope = terrain(dem, "slope", unit = "degrees")
aspect = terrain(dem, "aspect", unit = "degrees")
qgis_algo = qgis_algorithms()
grep("r.sun", qgis_algo$algorithm, value = TRUE)
alg = "grass7:r.sun.incidout"
qgis_show_help(alg)
dem_sun = qgis_run_algorithm(alg,
                             elevation = dem, aspect = aspect, slope = slope,
                             day = 80, time = 11)
dem_sun

# output global (total) irradiance/irradiation [W.m-2] for given time
gsi_dem = qgis_as_terra(dem_sun$glob_rad)
plot(dem)
plot(gsi_dem)

E2. Compute catchment area and catchment slope of system.file("raster/dem.tif", package = "spDataLarge") using Rsagacmd.

library(Rsagacmd)
dem = rast(system.file("raster/dem.tif", package = "spDataLarge"))
saga = saga_gis(raster_backend = "terra", vector_backend = "sf")
swi = saga$ta_hydrology$saga_wetness_index
tidy(swi)
swi_results = swi(dem, area_type = 0, slope_type = 1)
swi_results_all = rast(swi_results)
plot(swi_results_all[["area"]])
plot(swi_results_all[["slope"]])

E3. Continue working on the ndvi_segments object created in the SAGA section. Extract average NDVI values from the ndvi raster and group them into six clusters using kmeans(). Visualize the results.

library(Rsagacmd)
saga = saga_gis(raster_backend = "terra", vector_backend = "sf")
ndvi = rast(system.file("raster/ndvi.tif", package = "spDataLarge"))
sg = saga$imagery_segmentation$seed_generation

ndvi_seeds = sg(ndvi, band_width = 2)
plot(ndvi_seeds$seed_grid)

srg = saga$imagery_segmentation$seeded_region_growing
ndvi_srg = srg(ndvi_seeds$seed_grid, ndvi, method = 1)
plot(ndvi_srg$segments)

ndvi_segments = as.polygons(ndvi_srg$segments) |> 
  st_as_sf()

# extract values
ndvi_segments_vals = extract(ndvi, ndvi_segments, fun = "mean")
ndvi_segments = cbind(ndvi_segments, ndvi_segments_vals)

# k-means
ks = kmeans(ndvi_segments[["ndvi"]], centers = 6)
ndvi_segments$k = ks$cluster

# merge polygons
library(dplyr)
ndvi_segments2 = ndvi_segments |> 
  group_by(k) |> 
  summarise()

# visualize results
library(tmap)
tm1 = tm_shape(ndvi) +
  tm_raster(style = "cont", palette = "PRGn", title = "NDVI", n = 7) + 
  tm_shape(ndvi_segments2) +
  tm_borders(col = "red") +
  tm_layout(legend.outside = TRUE)

tm2 = tm_shape(ndvi_segments2) +
  tm_polygons(col = "k", style = "cat", palette = "Set1") +
  tm_layout(legend.outside = TRUE)

tmap_arrange(tm1, tm2)

E4. Attach data(random_points, package = "spDataLarge") and read system.file("raster/dem.tif", package = "spDataLarge") into R. Select a point randomly from random_points and find all dem pixels that can be seen from this point (hint: viewshed can be calculated using GRASS GIS). Visualize your result. For example, plot a hillshade, the digital elevation model, your viewshed output, and the point. Additionally, give mapview a try.

library(rgrass)
dem = rast(system.file("raster/dem.tif", package = "spDataLarge"))
data(random_points, package = "spDataLarge")
random_point = random_points[sample(1:nrow(random_points), 1), ]

link2GI::linkGRASS(dem)
write_RAST(dem, vname = "dem")

execGRASS("r.viewshed",
          input = "dem", 
          coordinates = sf::st_coordinates(random_point),
          output = "view",
          flags = "overwrite")
out = read_RAST("view")

# simple viz
plot(out)

# hillshade viz
hs = shade(slope = terrain(dem, "slope", unit = "radians"), 
           aspect = terrain(dem, "aspect", unit = "radians"))

library(tmap)
tm_shape(hs) +
    tm_raster(palette = gray(0:100 / 100), n = 100, legend.show = FALSE) +
    tm_shape(dem) +
    tm_raster(alpha = 0.6, palette = hcl.colors(25, "Geyser"), legend.show = FALSE) +
  tm_shape(out) +
  tm_raster(style = "cont", legend.show = FALSE) +
    tm_shape(random_point) +
    tm_symbols(col = "black") +
    tm_layout(frame = FALSE)

# mapview viz
library(mapview)
mapview(out, col = "white", map.type = "Esri.WorldImagery") +
  mapview(point)

E5. Use gdalinfo via a system call for a raster file stored on disk of your choice. What kind of information you can find there?

link2GI::linkGDAL()
our_filepath = system.file("raster/elev.tif", package = "spData")
cmd = paste("gdalinfo", our_filepath)
system(cmd)
# Driver, file path, dimensions, CRS, resolution, bounding box, summary statistics

E6. Use gdalwarp to decrease the resolution of your raster file (for example, if the resolution is 0.5, change it into 1). Note: -tr and -r flags will be used in this exercise.

our_filepath = system.file("raster/elev.tif", package = "spData")
cmd2 = paste("gdalwarp", our_filepath, "new_elev.tif", "-tr 1 1", "-r bilinear")
system(cmd2)

E7. Query all Californian highways from the PostgreSQL/PostGIS database living in the QGIS Cloud introduced in this chapter.

library(RPostgreSQL)
conn = dbConnect(drv = PostgreSQL(), 
                 dbname = "rtafdf_zljbqm", host = "db.qgiscloud.com",
                 port = "5432", user = "rtafdf_zljbqm", password = "d3290ead")
query = paste(
  "SELECT *",
  "FROM highways",
  "WHERE state = 'CA';")
ca_highways = read_sf(conn, query = query, geom = "wkb_geometry")
plot(st_geometry(ca_highways))

E8. The ndvi.tif raster (system.file("raster/ndvi.tif", package = "spDataLarge")) contains NDVI calculated for the Mongón study area based on Landsat data from September 22nd, 2000. Use rstac, gdalcubes, and terra to download Sentinel-2 images for the same area from 2020-08-01 to 2020-10-31, calculate its NDVI, and then compare it with the results from ndvi.tif.

library(rstac)
library(gdalcubes)
?spDataLarge::ndvi.tif
ndvi1 = rast(system.file("raster/ndvi.tif", package = "spDataLarge"))
bbox1 = as.numeric(st_bbox(project(ndvi1, "EPSG:4326")))

# get data
s = stac("https://earth-search.aws.element84.com/v0")
items = s |>
  stac_search(collections = "sentinel-s2-l2a-cogs",
              bbox = bbox1, 
              datetime = "2020-08-01/2020-10-31") |>
  post_request() |> items_fetch()
collection = stac_image_collection(items$features, 
                  property_filter = function(x) {x[["eo:cloud_cover"]] < 10})
v = cube_view(srs = "EPSG:32717", extent = collection,
              dx = xres(ndvi1), dy = yres(ndvi1),
              dt = "P1D")

# calculate ndvi
ndvi2 = raster_cube(collection, v) |>
  select_bands(c("B04", "B08")) |>
  apply_pixel("(B08-B04)/(B08+B04)", "NDVI")

# write results to file
gdalcubes_options(parallel = 2)
gdalcubes::write_tif(ndvi2, dir = ".", prefix = "ndvi2")

# unify two datasets
ndvi2 = rast("ndvi22020-10-10.tif")
plot(ndvi2)
ndvi2 = resample(ndvi2, ndvi1, method = "bilinear")
plot(ndvi2)

# vizualize the final results
ndvi_all = c(ndvi1, ndvi2)
names(ndvi_all) = c("y2000", "y2020")
library(tmap)
tm_shape(ndvi_all) +
  tm_raster(style = "cont")

9 Making maps with R

11 Scripts, algorithms and functions

Geocomputation with R: Solutions

10 Bridges to GIS software

Note: Second Edition is under construction 🏗