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https://github.com/aazuspan/sankee

Visualize classified time series data with interactive Sankey plots in Google Earth Engine
https://github.com/aazuspan/sankee

earth-engine earth-observation gee interactive lulc sankey time-series visualization

Last synced: 3 days ago
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Visualize classified time series data with interactive Sankey plots in Google Earth Engine

Host: GitHub
URL: https://github.com/aazuspan/sankee
Owner: aazuspan
License: mit
Created: 2021-02-14T08:45:24.000Z (almost 4 years ago)
Default Branch: main
Last Pushed: 2024-03-13T05:07:15.000Z (8 months ago)
Last Synced: 2024-06-11T17:38:59.229Z (5 months ago)
Topics: earth-engine, earth-observation, gee, interactive, lulc, sankey, time-series, visualization
Language: Python
Homepage: https://sankee.readthedocs.io/en/latest/index.html
Size: 14.9 MB
Stars: 96
Watchers: 6
Forks: 15
Open Issues: 2
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # sankee

[![Earth Engine Python](https://img.shields.io/badge/Earth%20Engine%20API-Python-green)](https://developers.google.com/earth-engine/tutorials/community/intro-to-python-api)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/aazuspan/sankee/HEAD?filepath=docs%2Fexamples%2Fmodis_snow_and_ice.ipynb)

[![conda-forge link](https://img.shields.io/conda/vn/conda-forge/sankee)](https://anaconda.org/conda-forge/sankee)

[![conda-forge link](https://img.shields.io/pypi/v/sankee)](https://pypi.org/project/sankee)

[![Testing workflow](https://github.com/aazuspan/sankee/actions/workflows/ci.yml/badge.svg)](https://github.com/aazuspan/sankee/actions/workflows/ci.yml)

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[![Read the Docs](https://readthedocs.org/projects/sankee/badge/?version=latest&style=flat)](https://sankee.readthedocs.io/en/latest/?badge=latest)

[![Downloads](https://pepy.tech/badge/sankee)](https://pepy.tech/project/sankee)

Visualize changes in classified time series data with interactive Sankey plots in Google Earth Engine

![Sankee example showing grassland expansion in the Nile Delta](docs/_static/demo.gif)

## Description

Use [Earth Engine](https://github.com/google/earthengine-api) to visualize changes in land cover, plant health, burn severity, or any other time series of classified imagery with interactive Sankey plots. Use a library of [built-in datasets](https://sankee.readthedocs.io/en/latest/sankee.html#premade-datasets) for convenience or define your own [custom datasets](https://sankee.readthedocs.io/en/latest/sankee.html#custom-datasets) for flexibility.

## Installation

### Pip

```bash

pip install sankee

```

### Conda

```bash

conda install -c conda-forge sankee

```

## Quickstart

### Premade Datasets

Visualize annual changes in land cover using [popular LULC datasets](https://sankee.readthedocs.io/en/latest/sankee.html#supported-datasets) with a couple lines of code. Just choose a dataset, an area of interest, and a list of years to generate a Sankey diagram from a premade dataset. Below, we can look at 30 years of vegetation recovery in the area devastated by the Mt. St. Helens eruption.

```python

import sankee

import ee

ee.Initialize()

sankee.datasets.LCMS_LC.sankify(

  years=[1990, 2000, 2010, 2020],

  region=ee.Geometry.Point([-122.192688, 46.25917]).buffer(2000),

  max_classes=3,

  title="Mt. St. Helens Recovery"

)

```

![Proportions of land cover over 4 periods increase from barren to grass and tree cover in a Sankey diagram](docs/_static/helens_recovery.png)

Check out the [example notebook](https://sankee.readthedocs.io/en/latest/examples/modis_snow_and_ice.html) for an interactive demo.

### Custom Datasets

Any classified images can be used by defining the dataset parameters (which pixel values correspond to which labels and colors). For example, we can look at classified [Dynamic World](https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1) scenes just two weeks apart that show substantial snow loss on Mountain Jefferson during the 2021 Heat Dome in the Pacific Northwest.

```python

import sankee

import ee

ee.Initialize()

# Load a set of classified images

img_list = [

    ee.Image("GOOGLE/DYNAMICWORLD/V1/20210616T185919_20210616T190431_T10TEQ"),

    ee.Image("GOOGLE/DYNAMICWORLD/V1/20210706T185919_20210706T190638_T10TEQ")

]

# Which band contains the classified data?

band = "label"

# What labels correspond to which pixel values?

labels = {

    0: "Water", 1: "Trees", 2: "Grass", 3: "Flooded", 4: "Crops",

    5: "Shrub / Scrub", 6: "Build", 7: "Bare", 8: "Snow / Ice",

}

# What colors should be applied to which pixel values?

palette = {

    0: "#419BDF", 1: "#397D49", 2: "#88B053", 3: "#7A87C6", 4: "#E49635",

    5: "#DFC35A", 6: "#C4281B", 7: "#A59B8F", 8: "#B39FE1"

}

plot = sankee.sankify(

    image_list=img_list, 

    band=band, 

    labels=labels,

    palette=palette,

    region=ee.Geometry.Point([-121.80183, 44.67655]).buffer(3000), 

    max_classes=3,

    title="Mt. Jefferson Snow Loss - June 2021"

)

```

![](docs/_static/jefferson_snow.png)

### Integration with geemap

`sankee` premade datasets are usable through the [geemap](https://github.com/giswqs/geemap) interactive GUI. Check out the [documentation](https://geemap.org/notebooks/75_sankee/) and [video tutorials](https://www.youtube.com/watch?v=IZWpJYX6w8I) by [@giswqs](https://github.com/giswqs).

## Contributing

If you find bugs or have feature requests, please open an issue!

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