dynamic-world 0.3.5

Land-use land-cover analysis using Dynamic World App from Earth Engine

dynamic-world 🛰️

Wrapper package around Google's Dynamic World App [^1], to easily monitor forests and calculate their co2 storage on a near-real time.

[^1]: This dataset is produced for the Dynamic World Project by Google in partnership with National Geographic Society and the World Resources Institute.

Install

pip install dynamic-world

An external C library is required as well: GDAL. The Dockerfile already has GDAl installed. If working locally, an easy way to install it is by running conda install -c conda-forge gdal

Google Earth Engine authentication

This package runs computation on Earth Engine and needs to be authenticated beforehand. See the authentication from a Jupyter notebook, or alternatively using a private key creating a service account.

Usage

Given a Forest (defined as a directory with some configuration files, see bellow), this package retrieves statistics and images of it. See Jupyter tutorial for a usage example.

Forests

Each forest (or "proyect") is defined inside a directory with a given name and 2 files:

1. A forest_config.yml (name is mandatory) which looks like this:

# Name of the forest/proyect
name: Sample

# Locations of the geojson file
geojson: './sample.geojson'

# Co2 factor: how many tons of CO2 are stored on average per hectare
co2_factor: {
    'trees': 591.85,
    'grass': 6,
    'bare': 6,
    'crops': 11.5,
    'flooded_vegetation': 6,
    'other': 0,
    'factor_pixel': 100, # Indicates how many pixels are (on average) inside a hectare
  }

# Date in which the reforestation started, in format YYYY-mm-dd
start_date: '2022-01-01'

2. A valid geojson file see (named as defined in forest_config.yml) that defines the area

Internally, forests are stored as a ForestConfig instance (see dynamic_world.configurations for more details).

Available calculations

Given a forest and a pair of dates, we download the forest's landcover image, landcover statistics and total CO2 calculation. In other words, we mean the amount of CO2 (measured in tons) that a forest stores (and therefore is not released into the atmosphere if it was burned :D)

The forest image is stored as a Cloud Optimized Geotiff file. The expression used for the file-name is the following:

f"{forest.name.replace(' ', '_')}_{start_date}_{end_date}.cog.tif"

For reductions we use the Mode (polling). If a very large time interval is specified, recent changes in the forest will be masked by old pixel values. It is encouraged to use the smallest possible time intervals (at least a week is required or there may not be data). However, depending on some factors (such as the amount of clouds), specifying a small time interval may result in many NA (see mrv.calculations documentation for further info on how NA are treated when calculating the co2 factor).

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Development notes

We encourage developers to open the repository using VSCode remote container functionality.

Secrets

To run the tests, you will need only one secret, which is Earth Engine's service account base64-encoded:

SERVICE_ACCOUNT=<very-long-string>

The following snippet can be used to base64-encode the service_account.json file:

python <<HEREDOC
import base64
with open('service_account.json', 'rb') as file:
    file = file.read()
    base64_encoded_data = base64.b64encode(file)
    base64_message = base64_encoded_data.decode('utf-8')
print(base64_message)
HEREDOC

How to run tests locally

# In the root directory of the proyect
pytest

# Run coverage 
pytest --cov mrv --cov-branch --cov-report term-missing --disable-warnings

How to run tests in docker

# Build test docker
docker build --tag dw --file Dockerfile --target dev .

# Run lint and tests
docker run dw /bin/bash -c "flake8 && pytest"