Is there a slow method to extract raster?
Slow Method: The alternative method of doing an extract directly from the raster function takes a lot lot longer, and I’m not sure about the data management to get it into the form I want: I have finally gotten around to improving this function.
How to increase the speed of crop, mask and extract raster?
Instead I: Stack the 300mx300m land cover map and the 300mx300m grid from step 2, using the raster package: stack_file <- stack (lc, grid). Use dplyr to calculate the share of land cover class cells in the 1kmx1km cells.
How to extract data from a raster object?
The function take a Raster*object and an sf object as input and return the same output of raster::extract () plus the fraction of pixels covered by polygons. As raster::extract (), exact_extract () can take a fun argument to summarize data within polygons.
How does parallel processing improve the speed of raster?
Parallel processing cut the user time by half, but negated the benefit by doubling the system time. Raster uses this for the extract function, but unfortunately not for the crop or mask function. Unfortunately, this leaves a slighly larger amount of total elapsed time due to “waiting around” by the “IO.”
Which is faster crop mask or extract raster?
The rasterizing isn’t much faster than the original code for tabulating raster values in small polygons, but it shines when it came to large polygon areas that had large rasters to be cropped and the values extracted. I also found getValues () was much faster than the extract () function.
How to increase the speed of crop and extract?
I’m extracting the area and percent cover of different land use types from a raster based on several thousand polygon boundaries. I’ve found that the extract function works much faster if I iterate through each individual polygon and crop then mask the raster down to the size of the particular polygon.