dynamic-world 0.1.0

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

dynamic-world

Package that allow for remote monitoring of reforested forests based around Google's Dynamic World App (see attributions bellow).

Install

pip install dynamic-world

Usage

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

Forests

Each forest (or "proyect") is stored inside a directory with a given name. Inside this directory there must be 2 files:

• a forest_config.yml (name is mandatory) which looks like this:

# Name of the forest/proyect
name : Sample

# Locations of the geojson file and dcitionary with carbon factor/metric
geojson : './sample.geojson'
carbon_factor : {
    "trees" : 1,
    "other" : 0, # MUST contain this label
    "factor_pixel" : 1 # MUST contain this label
}

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

• 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 carbon factor [^cf_foot] calculation.

[^cf_foot]: by carbon factor we mean the amount of CO2 (measured in tons) that a forest stores (and therefore is not released into the atmosphere :D)

The forest image is stored inside 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 carbon factor).

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Attributions

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

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Development notes

How to run tests locally

• to test run 'pytest' in the root directory of the proyect
• to run coverage use 'pytest --cov mrv --cov-branch --cov-report term-missing --disable-warnings'

How to run tests in docker

The easiest is to use VScode functionality "Reopen in container" which is quite nicer for development. Alternaitvely:

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

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