Dear Loic,
Thank you for your reply!
Re. aggregation, I agree - I will use either mean or median.
gcp_grid are points as below
structure(list(longitude = c(-179L, -178L, -177L, -177L, -177L,
-176L), latitude = c(-15L, -15L, -14L, 51L, 52L, -22L)), .Names =
c("longitude",
"latitude"), row.names
On 13/01/2017 10:59, Miluji Sb wrote:
> Thank you for your reply. This is what I did:
>
> ###
> library(data.table)
> library(raster)
> library(rgdal)
> library(foreign)
>
> elevation_world <- getData('worldclim', var='alt', res=2.5)
>
> # Aggregate Elevation to 1 degree
>
Thank you for your reply. This is what I did:
###
library(data.table)
library(raster)
library(rgdal)
library(foreign)
elevation_world <- getData('worldclim', var='alt', res=2.5)
# Aggregate Elevation to 1 degree
elevation_world_1deg <- aggregate(elevation_world, fact = 24, fun = sum)
# Extract
R raster::getData("SRTM", ...) will return elevation rasters at 90m
resolution.
See:
https://www.rdocumentation.org/packages/raster/versions/2.5-8/topics/getData
http://www.cgiar-csi.org/data/srtm-90m-digital-elevation-database-v4-1
--Mel.
On 1/11/2017 6:26 AM, Miluji Sb wrote:
Dear
