fitoprincipe/geebap

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

Contact

Rodrigo E. Principe: fitoprincipe82@gmail.com

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:

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:

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}

fitoprincipe / geebap