geebap

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Best Available Pixel (Bap) Composite using the Python API of Google Earth Engine (Gee)

This code is based on Pixel-Based Image Compositing for Large-Area Dense Time Series Applications and Science. (White et al., 2014) http://www.tandfonline.com/doi/full/10.1080/07038992.2014.945827

It uses a series of pixel based scores to generate a composite with the Best Available Pixel, assuming it is the one that has better score.

License and Copyright

2017 Rodrigo E. Principe - geebap - https://github.com/fitoprincipe/geebap

This work thanks to: "Dirección de Bosques - SAyDS" (Argentine Nation) and CIEFAP (Centro de Investigación y Extensión Forestal Andino Patagónico)

Contact

Rodrigo E. Principe: [email protected]

Installation

To use this package you must have installed and running Google Earth Engine Python API: https://developers.google.com/earth-engine/python_install

Once you have that, proceed

::

pip install geebap

this will install also geetools that you could use besides geebap

Installation in DataLab

After following Option 1 or 2 in https://developers.google.com/earth-engine/python_install, open a new notebook and write:

.. code:: python

import sys
!{sys.executable} -m pip install geebap

Available Collections

Collections come from geetools.collection. For examples see: https://github.com/gee-community/gee_tools/tree/master/notebooks/collection

Available Scores

• Satellite
• Distance to clouds and shadows masks
• Atmospheric Opacity
• Day of the year (best_doy)
• Masked pixels percentage
• Outliers
• Absolute value of a vegetation index

Available Indices

• ndvi
• evi
• nbr

Some considerations

• Sites size should not be too big. Works with 300 km2 tiles

Basic Usage

If you are using Jupyter, you can download a notebook from https://github.com/fitoprincipe/geebap/blob/master/Best_Available_Pixel_Composite.ipynb

else, if you are using another approach, like Spyder, create an empty script and paste the following code:

.. code:: python

import ee
ee.Initialize()

import geebap
from geetools import tools

import pprint
pp = pprint.PrettyPrinter(indent=2)

# SEASON
a_season = geebap.Season('11-15', '03-15')

# MASKS
cld_mask = geebap.masks.Mask()

# Combine masks in a tuple
masks = (cld_mask,)

# FILTERS
filt_cld = geebap.filters.CloudCover()
# filt_mask = geebap.filters.MaskCover() # Doesn't work

# Combine filters in a tuple
filters = (filt_cld,)#, filt_mask)

# SCORES
best_doy = geebap.scores.Doy('01-15', a_season)
sat = geebap.scores.Satellite()
out = geebap.scores.Outliers(("ndvi",))
ind = geebap.scores.Index("ndvi")
maskpercent = geebap.scores.MaskPercentKernel()
dist = geebap.scores.CloudDist()

# Combine scores in a tuple
scores = (
    best_doy,
    sat,
    out,
    ind,
    maskpercent,
    dist
)

# BAP OBJECT
BAP = geebap.Bap(range=(0, 0),
                 season=a_season,
                 masks=masks,
                 scores=scores,
                 filters=filters)

# SITE
site = ee.Geometry.Polygon([[-71.5,-42.5],
                            [-71.5,-43],
                            [-72,-43],
                            [-72,-42.5]])

# COMPOSITE
composite = BAP.build_composite_best(2019, site=site, indices=("ndvi",))

# `composite` is a ee.Image object, so you can do anything
# from here..
one_value = tools.image.getValue(composite,
                                 site.centroid(),
                                 30, 'client')
pp.pprint(one_value)

Prints:

::

{ 'blue': 733,
  'col_id': 29,
  'date': 20190201,
  'green': 552,
  'ndvi': 0.7752976417541504,
  'nir': 2524,
  'red': 313,
  'score': 5.351020336151123,
  'swir': 661,
  'swir2': 244,
  'thermal': 2883}