Learning Objectives

• Create mosaics of more than one multiband image

• Find the intersection of two images

• View the footprint of multiple image tiles

Review

# Handle Multiple Remotely Sensed Images#

Doing analysis over larger areas often requires the use of image mosaics (combining two or more images). Luckily for us geowombat makes this process relatively easy.

## Union (Mosaic) of Remotely Sensed Image#

As an example let’s plot the union with mosaic=True of two images taken on the same day, for the overlapping portions we will use the mean pixel value by setting overlap='mean', but blue band only. Alternatively we could use one of ‘mean’, ‘min’, or ‘max’.

Note we rename the band name with band_names=['blue'].

# Import GeoWombat
import geowombat as gw

# import plotting
import matplotlib.pyplot as plt
import matplotlib.patheffects as pe

from geowombat.data import l8_224077_20200518_B2, l8_224078_20200518_B2

fig, ax = plt.subplots(dpi=200)

with gw.open(
[l8_224077_20200518_B2, l8_224078_20200518_B2],
band_names=['blue'],
mosaic=True,
bounds_by='union'
) as src:
src.where(src != 0).sel(band='blue').gw.imshow(robust=True, ax=ax)



## Intersection of Remotely Sensed Image#

Same idea with the intersection, using bounds_by='intersection', we still need to mosaic the two images mosaic=True.

fig, ax = plt.subplots(dpi=200)
filenames = [l8_224077_20200518_B2, l8_224078_20200518_B2]
with gw.open(filenames,
band_names=['blue'],
mosaic=True,
overlap='max',
bounds_by='intersection') as src:
src.where(src != 0).sel(band='blue').plot.imshow(robust=True, ax=ax)