ProduceFalseColourGeotiffs

What does this notebook do?

This notebooks demonstrates how you can call an external set of python functions into a Jupyter Notebook, rather than loading them all explicitly within the notebook cells. This streamlines the notebook, and makes it a lot easier to read the code. This notebook uses two python files: DEAPlotting and DEADataHandling, both of which are also available to download. The notebook develops a query and extracts some data using the DEADataHandling.load_nbart function. It then plots up a false colour image using DEAPlotting and saves it as a Geotiff using the DEADataHandling function.

Inputs

This example uses two external functions called three_band_image and load_nbarx. These functions are available in the Scripts folder of the dea-notebooks Github repository. Note that these functions have been developed by DEA users, not the DEA development team, and so are provided without warranty. If you find an error or bug in the functions, please either create an ‘Issue’ in the Github repository, or fix it yourself and create a ‘Pull’ request to contribute the updated function back into the repository (See the repository README for instructions on creating a Pull request).

Date May 2018

Author Claire Krause

Tags: plot, three_band_image, Landsat5, DEAPlotting, Scripts, DEADataHandling, load_nbarx, dataset_to_geotiff, Geotiff, export

[1]:
% pylab notebook

from datacube import Datacube

# Import the custom scripts. These can be found in the dea-notebooks repository.
import sys
import os.path
sys.path.append(os.path.expanduser('~/dea-notebooks/Scripts'))
import DEAPlotting
import DEADataHandling

dc = Datacube(app='Geotiff')
Populating the interactive namespace from numpy and matplotlib
Failed to resolve driver datacube.plugins.index::s3aio_index

Extract some data using our imported function

[2]:
query = {'lat': (-35.25, -35.35),
         'lon': (149.05, 149.17),
         'time': ('2006-01-01', '2006-03-01')
         }

The DEADataHandling.load_nbarx function automatically loads in the NBART product, and filters it for cloud, contiguity and saturated pixels. These options can be changed by adding product='nbar', or by setting filter_pq=False to the function call.

[3]:
data, crs, affine = DEADataHandling.load_nbarx(dc, 'ls5', query,
                                               bands_of_interest=['swir1', 'nir', 'green'])
Failed to resolve driver datacube.plugins.io.read::s3aio
Failed to resolve driver datacube.plugins.io.read::s3aio_test
Loading ls5_nbart_albers
Loaded ls5_nbart_albers
Generating mask ls5_pq_albers
Masked ls5_nbart_albers with ls5_pq_albers and filtered terrain
[4]:
data
[4]:
<xarray.Dataset>
Dimensions:  (time: 5, x: 492, y: 500)
Coordinates:
  * time     (time) datetime64[ns] 2006-01-25T23:40:21.500000 ...
  * y        (y) float64 -3.953e+06 -3.953e+06 -3.953e+06 -3.953e+06 ...
  * x        (x) float64 1.542e+06 1.542e+06 1.542e+06 1.542e+06 1.542e+06 ...
Data variables:
    swir1    (time, y, x) float64 1.64e+03 1.571e+03 1.537e+03 1.79e+03 ...
    nir      (time, y, x) float64 2.331e+03 2.237e+03 2.403e+03 2.739e+03 ...
    green    (time, y, x) float64 651.0 644.0 840.0 845.0 1.023e+03 ...
Attributes:
    crs:      EPSG:3577
    affine:   | 25.00, 0.00, 1542325.00|\n| 0.00,-25.00,-3953075.00|\n| 0.00,...

Draw a false colour image using our imported function

[5]:
DEAPlotting.three_band_image(data, ['swir1', 'nir', 'green'], time=2);