sankee 0.0.2

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

sankee

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

Contents

• Description
• Installation
  • Using Pip
  • Using Pipenv
• Requirements
• Quick Start
  • Using a Premade Dataset
  • Using a Custom Dataset
• Features
  • Modular Datasets
  • Flexible Time Series
  • Integration with geemap
• API
• Contributing

Description

sankee provides a dead-simple API that combines the power of GEE and Plotly to visualize changes in land cover, plant health, burn severity, or any other classified imagery over a time series in a region of interst using interactive Sankey plots. Use a library of built-in datasets like NLCD, MODIS Land Cover, or CGLS for convenience or define your own custom datasets for flexibility.

sankee works by randomly sampling points in a time series of classified imagery to visualize how cover types changed over time.

Installation

Using Pip

pip install sankee

Using Pipenv

Pipenv can be used to create an isolated environment for running sankee. The following commands set up the environment and create a kernel so that you can run a notebook from it.

pip install pipenv
pipenv install sankee
pipenv shell
python -m ipykernel install --user --name=my-virtualenv-name

Requirements

• An authenticated GEE Python environment (offical guide)

Quick Start

Using a Premade Dataset

Datasets in sankee are used to apply labels and colors to classified imagery (eg. a value of 42 in an NLCD 2016 image should be labeled "Evergeen forest" and colored green). sankee includes premade Dataset objects for common classified datasets in GEE like NLCD, MODIS land cover, and CGLS. See datasets for a detailed explanation.

import ee
import sankee

ee.Initialize()

# Choose a premade dataset object contains band, label, and palette information for NLCD
dataset = sankee.datasets.NLCD2016

# Build a list of images
img_list = [ee.Image(f"USGS/NLCD/NLCD2001"), ee.Image(f"USGS/NLCD/NLCD2016")]
# Build a matching list of labels for the images (optional)
label_list = ["2001", "2016"]

# Define an area of interest
vegas = ee.Geometry.Polygon(
        [[[-115.4127152226893, 36.29589873319828],
          [-115.4127152226893, 36.12082334399102],
          [-115.3248245976893, 36.12082334399102],
          [-115.3248245976893, 36.29589873319828]]])

# Choose a title to display over your plot (optional)
title = "Las Vegas Urban Sprawl, 2001 - 2016"

# Generate your Sankey plot
plot = sankee.sankify(img_list, vegas, label_list, dataset, max_classes=4, title=title)

Using a Custom Dataset

Datasets can also be manually defined for custom images. In this example, we'll classify 1-year and 5-year post-fire Landsat imagery using NDVI and visualize plant recovery using sankee.

import ee
import sankee

ee.Initialize()

# Load fire perimeters from MTBS data
fires = ee.FeatureCollection("users/aazuspan/fires/mtbs_1984_2018")
# Select the 2014 Happy Camp Complex fire perimeter in California
fire = fires.filterMetadata("Fire_ID", "equals", "CA4179612337420140814")

# Load imagery 1 year after fire and 5 years after fire
immediate = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_045031_20150718")
recovery = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_046031_20200807")

# Calculate NDVI
immediate_NDVI = immediate.normalizedDifference(["B5", "B4"])
recovery_NDVI = recovery.normalizedDifference(["B5", "B4"])

# Reclassify continuous NDVI values into classes of plant health
immediate_class = ee.Image(1) \
  .where(immediate_NDVI.lt(0.3), 0) \
  .where(immediate_NDVI.gt(0.5), 2) \
  .rename("health")

recovery_class = ee.Image(1) \
  .where(recovery_NDVI.lt(0.3), 0) \
  .where(recovery_NDVI.gt(0.5), 2) \
  .rename("health")

# Specify the band name for the image
band = "health"

# Assign labels to the pixel values defined above
labels = {
    0: "Unhealthy",
    1: "Moderate",
    2: "Healthy"
}
# Assign colors to the pixel values defined above
palette = {
    0: "#e5f5f9",
    1: "#99d8c9",
    2: "#2ca25f"
}

# Define the images to use and create labels to describe them
img_list = [immediate_class, recovery_class]
label_list = ["Immediate", "Recovery"]

# Generate your Sankey plot
plot = sankee.sankify(img_list, fire, label_list, band=band, labels=labels, palette=palette, scale=20)

Features

Modular Datasets

Datasets in sankee define how classified image values are labeled and colored when plotting. label and palette arguments for sankee functions can be manually provided as dictionaries where pixel values are keys and labels and colors are values. Every value in the image must have a corresponding color and label. Datasets also define the band name in the image in which classified values are found.

Any classified image can be visualized by manually defining a band, palette, and label. However, premade datasets are included for convenience in the sankee.datasets module. To access a dataset, use its name, such as sankee.datasets.NLCD2016. To get a list of all dataset names, run sankee.datasets.names(). Datasets can also be accessed using sankee.datasets.get() which returns a list of Dataset objects that can be selecting by indexing.

# List all sankee built-in datasets
sankee.datasets.names()

>> ['NLCD2016',
    'MODIS_LC_TYPE1',
    'MODIS_LC_TYPE2',
    'MODIS_LC_TYPE3',
    'CGLS_LC100']

# Preview a list of available images belonging to one dataset
sankee.datasets.CGLS_LC100.get_images(3)

>> ['COPERNICUS/Landcover/100m/Proba-V-C3/Global/2015',
    'COPERNICUS/Landcover/100m/Proba-V-C3/Global/2016',
    'COPERNICUS/Landcover/100m/Proba-V-C3/Global/2017',
    '...']

Flexible Time Series

sankee can handle any length of time series. The number of images will determine the number of time steps in the series. The example below shows a three-image time series.

Integration with geemap

geemap is a great tool for exploring changes in GEE imagery before creating plots with sankee. Integration is quick and easy. Just use geemap like you normally would, and pass the images and feature geometries to sankee for plotting. The example at the top of the page shows sankee can be used with geemap.

API

Core function

sankee.sankify(image_list, region, label_list, dataset, band, labels, palette, exclude, max_classes, n, title, scale, seed, dropna)

Generate n random samples points within a region and extract classified pixel values from each image in an image list. Arrange the sample data into a Sankey plot that can be used to visualize changes in image classifications.

Arguments

• image_list (list)
  • An ordered list of images representing a time series of classified data. Each image will be sampled to generate the Sankey plot. Any length of list is allowed, but lists with more than 3 or 4 images may produce unusable plots.
• region (ee.Geometry)
  • A region to generate samples within.
• label_list (list, default: None)
  • An list of labels corresponding to the images. The list must be the same length as image_list. If none is provided, sequential numeric labels will be automatically assigned starting at 0.
• dataset (sankee.datasets.Dataset, default: None)
  • A premade dataset that defines the band, labels, and palette for all images in image_list. If none is provided, band, labels, and palette must be provided instead.
• band (str, default: None)
  • The name of the band in all images of image_list that contains classified data. If none is provided, dataset must be provided instead.
• labels (dict, default: None)
  • The labels associated with each value of all images in image_list. Every value in the images must be included in the labels dictionary. If none is provided, dataset must be provided instead.
• palette (dict, default: None)
  • The colors associated with each value of all images in image_list. Every value in the images must be included in the palette dictionary. If none is provided, dataset must be provided instead. Colors must be supported by Plotly.
• exclude (list, default: None)
  • An optional list of pixel values to exclude from the plot. Excluded values must be raw pixel values rather than class labels.
• max_classes (int, default: None)
  • If a value is provided, small classes will be removed until max_classes remain.
• n (int, defualt: 100)
  • The number of samples points to randomly generate for characterizing all images. More samples will provide more representative data but will take longer to process.
• title (str, default: None)
  • An optional title that will be displayed above the Sankey plot.
• scale (int, default: None)
  • The scale in image units to perform sampling at. If none is provided, GEE will attempt to use the image's nominal scale, which may cause errors.
• seed (int, default: 0)
  • The seed value used to generate repeatable results during random sampling.
• dropna (bool, default: True)
  • If the region extends into areas that contain no data in any image, some samples may have null values. If dropna is True, those samples will be dropped. This may lead to fewer samples being returned than were requested by n.

Returns

• A Plotly Sankey plot object.

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Dataset functions

sankee.datasets.names()

Get a list of supported dataset names. Names can be used to access datasets using sankee.datasets.{dataset_name}.

Arguments

• None

Returns (list)

• A list of strings for supported dataset names.

sankee.datasets.get(i)

Get a list of supported sankee.datasets.Dataset objects.
Arguments

• i (int, default: None)
  • An optional index to retrieve a specific dataset.

Returns (list)

• A list of supported sankee.datasets.Dataset objects. If i is provided, only one object is returned.

sankee.datasets.Dataset.get_images(max_images)

Get a list of image names in the collection of a specific dataset.
Arguments

• max_images (int, default: 20)
  • The max number of images to return.

Returns (list)

• A list of image names that can be used to load ee.Image objects.

Example

sankee.datasets.NLCD2016.get_images(3)

>> ['USGS/NLCD/NLCD1992', 'USGS/NLCD/NLCD2001', 'USGS/NLCD/NLCD2001_AK', '...']

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Dataset properties and attributes

sankee.datasets.Dataset.collection

• Return the image collection associated with the dataset.

sankee.datasets.Dataset.df

• Return a Pandas dataframe describing the classes, labels, and colors associated with the dataset.

sankee.datasets.Dataset.id

• Return the system ID of the image collection.

Contributing

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

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