CODEWITHSUNDEEP
r
Lime
Lime

Reputation: 754

Increase pixel resolution of plot

Not sure as to why my plots are in very low pixels, how do I go about increasing them for my resolution?

I'm using this code:

neighborhood_radius <- 5 * floor(max(res(landcover))) / 2
agg_factor <- round(2 * neighborhood_radius / res(landcover))
r <- raster(landcover) %>% 
  aggregate(agg_factor) 
r <- bcr %>% 
  st_transform(crs = projection(r)) %>% 
  rasterize(r, field = 1) %>% 
  # remove any empty cells at edges
  trim()


forest_cover <- pland_coords %>% 
     # convert to spatial features
     st_as_sf(coords = c("longitude", "latitude"), crs = 4326) %>% 
     st_transform(crs = projection(r)) %>% 
     # rasterize points
     rasterize(r, field = "pland_13_urban") %>% 
     # project to albers equal-area for mapping
     projectRaster(crs = st_crs("EPSG:27700")$proj4string, method = "ngb") %>% 
+     # trim off empty edges of raster
+     trim()

par(mar = c(0.25, 0.25, 2, 0.25))
 t <- str_glue("Proportion of Urban Area\n",
               "{max_lc_year} MODIS Landcover")
 plot(forest_cover, axes = FALSE, box = FALSE, col = viridis(10), main = t)

this is the output: enter image description here

although, I have used the same code for a different dataset, at a large cover and got this: enter image description here

Here is my raster:

r
class      : RasterLayer 
dimensions : 40, 52, 2080  (nrow, ncol, ncell)
resolution : 2316.564, 2316.564  (x, y)
extent     : 463.3127, 120924.6, 5809941, 5902604  (xmin, xmax, ymin, ymax)
crs        : +proj=sinu +lon_0=0 +x_0=0 +y_0=0 +R=6371007.181 +units=m +no_defs 
source     : memory
names      : layer 
values     : 1, 1  (min, max)

If I increase the resolution of r using aggregate such as this:

r <- aggregate(r, fact = 2)

it pixelates the image further and zooms in: enter image description here

Whilst dissaggregate zooms out, and provides slightly less pixels, its also not as great.

Upvotes: 2

Views: 267

Answers (2)

Lime
Lime

Reputation: 754

As Matthew suggested, changing the resolution was the best option, although using aggregate or disaggregate was not the right way for this particular situation.

Instead, I used resample, to transfer the resolution with:

resample(r, landcover, method = 'ngb')

and got this:

enter image description here

Pixels are very similar to the second image, just that the coverage spans closer to 0, otherwise I consider this a success.

Upvotes: 0

Matthew Skiffington
Matthew Skiffington

Reputation: 332

Providing the data would be helpful. It's possible the spatial resolution of your data set is the limiting factor. However, try feeding in a matrix (i.e. a raster object, r, or set of spatial x,y points) with a higher resolution to the rasterize function - my understanding is you should be able to rasterize with arbitrary precision.

c.f. https://gis.stackexchange.com/questions/265064/rasterize-polygons-with-r

Upvotes: 1

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