Hfrhyu

I am a bit new to the spatial packages of R. I have a hdf file from which I can get a data.frame with the latitude and longitude of the corners of the pixels, and the latitude and longitude of the centre of the pixel.

I have a specific point, and I would like to figure out which pixel covers that point.

Is there some function for this? what would be the best approach?

More information:

The data.frame is something like this:

> tibble(h)
# A tibble: 15,271 x 1
 h$LatA $LonA $LatB $LonB $LatC $LonC $LatD $LonD $LatCentre $LonCentre
 <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 47.5 167. 47.0 167. 49.7 175. 49.3 175. 48.4 171.
 2 47.8 166. 47.3 167. 50.0 174. 49.6 175. 48.7 170.
 3 48.1 166. 47.6 166. 50.3 174. 50.0 174. 49.0 170.
 4 48.4 166. 47.9 166. 50.7 174. 50.3 174. 49.4 170.
 5 48.8 166. 48.3 166. 51.0 174. 50.7 174. 49.7 170.
 6 49.1 165. 48.6 166. 51.4 174. 51.0 174. 50.0 170.
 7 49.4 165. 48.9 166. 51.7 173. 51.3 174. 50.4 169.
 8 49.7 165. 49.2 165. 52.0 173. 51.7 173. 50.7 169.
 9 52.0 173. 51.7 173. 53.8 182. 53.4 182. 52.8 178.
10 50.0 165. 49.5 165. 52.4 173. 52.0 173. 51.0 169.
# … with 15,261 more rows

As Spacedman sujested, you should share with us more than that, almost a piece of your tibble data. In any case, and without knowledge about your data sources, projection, etc, you could find those "cells" where your points are just by searching the closest cell center. This is just valid (and logic) if you tibble represents a regular raster grid. As I said, the "cell" would be that one with the closest cell center. So, Here is the code with some imaginary points and your small data:

library(dplyr)
library(stringr)
library(tibble)
library(rgdal)
library(RANN)

#THIS REPLICATES YOUR h tibble

l <- c("47.5 167. 47.0 167. 49.7 175. 49.3 175. 48.4 171.",
 "47.8 166. 47.3 167. 50.0 174. 49.6 175. 48.7 170.",
 "48.1 166. 47.6 166. 50.3 174. 50.0 174. 49.0 170.",
 "48.4 166. 47.9 166. 50.7 174. 50.3 174. 49.4 170.",
 "48.8 166. 48.3 166. 51.0 174. 50.7 174. 49.7 170.",
 "49.1 165. 48.6 166. 51.4 174. 51.0 174. 50.0 170.",
 "49.4 165. 48.9 166. 51.7 173. 51.3 174. 50.4 169.",
 "49.7 165. 49.2 165. 52.0 173. 51.7 173. 50.7 169.",
 "52.0 173. 51.7 173. 53.8 182. 53.4 182. 52.8 178.",
 "50.0 165. 49.5 165. 52.4 173. 52.0 173. 51.0 169.")

lt <- strsplit(str_replace_all(str_replace_all(l," ", ","),",,, ", ","), ",")

h <- tibble(LatA = double(), LonA = double(),
 LatB = double(), LonB = double(),
 LatC = double(), LonC = double(),
 LatD = double(), LonD = double(),
 LatCentre = double(), LonCentre = double())

for (l in 1:length(lt))
 h[l,] <- as.numeric(lt[[l]])


#THIS MAKES THE THING
#
# Create points from centers and try to find the nearest with traning points
centers <- data.frame(X = h$LonCentre, Y = h$LatCentre)
trypoints <- data.frame(X = c(169.9, 171.1), Y = c(49.2, 48.9))

# for each point in trypoints, find the nearest neighbor from centers
closest <- RANN::nn2(centers, trypoints, k = 1)

# Get coordinates of nearest neighbor
trypoints$X_snap <- centers[closest$nn.idx, "X"]
trypoints$Y_snap <- centers[closest$nn.idx, "Y"]

In the end, what you have is the point of sampling and the X and Y (X_snap and Y_snap) of the belonging "cell".

> trypoints
 X Y X_snap Y_snap
1 169.9 49.2 170 49.4
2 171.1 48.9 171 48.4