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Using GeoPandas with Rasterio to sample point data

This example shows how to use GeoPandas with Rasterio. Rasterio is a package for reading and writing raster data.

In this example a set of vector points is used to sample raster data at those points.

The raster data used is Copernicus Sentinel data 2018 for Sentinel data.

import geopandas
import rasterio
import matplotlib.pyplot as plt
from shapely.geometry import Point

Create example vector data

Generate a geodataframe from a set of points

# Create sampling points
points = [
    Point(625466, 5621289),
    Point(626082, 5621627),
    Point(627116, 5621680),
    Point(625095, 5622358),
]
gdf = geopandas.GeoDataFrame([1, 2, 3, 4], geometry=points, crs=32630)

The GeoDataFrame looks like this:

gdf.head()

	0	geometry
0	1	POINT (625466 5621289)
1	2	POINT (626082 5621627)
2	3	POINT (627116 5621680)
3	4	POINT (625095 5622358)

Open the raster data

Use rasterio to open the raster data to be sampled

src = rasterio.open("s2a_l2a_fishbourne.tif")

Let’s see the raster data with the point data overlaid.

from rasterio.plot import show

fig, ax = plt.subplots()

# transform rasterio plot to real world coords
extent = [src.bounds[0], src.bounds[2], src.bounds[1], src.bounds[3]]
ax = rasterio.plot.show(src, extent=extent, ax=ax, cmap="pink")

gdf.plot(ax=ax)

<Axes: >

Sampling the data

Rasterio requires a list of the coordinates in x,y format rather than as the points that are in the geomentry column.

This can be achieved using the code below

coord_list = [(x, y) for x, y in zip(gdf["geometry"].x, gdf["geometry"].y)]

Carry out the sampling of the data and store the results in a new column called value. Note that if the image has more than one band, a value is returned for each band.

gdf["value"] = [x for x in src.sample(coord_list)]
gdf.head()

	0	geometry	value
0	1	POINT (625466 5621289)	[684.0, 1005.0, 707.0, 265.0]
1	2	POINT (626082 5621627)	[999.0, 1105.0, 1115.0, 1340.0]
2	3	POINT (627116 5621680)	[284.0, 713.0, 310.0, 5405.0]
3	4	POINT (625095 5622358)	[237.0, 564.0, 250.0, 3680.0]