sankee
Visualize changes in classified time series data with interactive Sankey plots in Google Earth Engine
Contents
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 Conda
sankee can be downloaded through conda-forge within a Conda environment.
conda create -n sankee
conda activate sankee
conda install -c conda-forge sankee
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 that 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)
plot
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)
plot
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 ordered 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. Labels are displayed on-hover on the Sankey nodes.
- An ordered list of labels corresponding to the images. The list must be the same length as
-
dataset (sankee.datasets.Dataset, default: None)
- A premade dataset that defines the band, labels, and palette for all images in
image_list. If a custom dataset is being used, provideband,labels, andpaletteinstead.
- A premade dataset that defines the band, labels, and palette for all images in
-
band (str, default: None)
- The name of the band in all images of
image_listthat contains classified data. If none is provided,datasetmust be provided instead.
- The name of the band in all images of
-
labels (dict, default: None)
- The labels associated with each value of all images in
image_list. Every value in the images must be included as a key in thelabelsdictionary. If none is provided,datasetmust be provided instead.
- The labels associated with each value of all images in
-
palette (dict, default: None)
- The colors associated with each value of all images in
image_list. Every value in the images must be included as a key in thepalettedictionary. If none is provided,datasetmust be provided instead. Colors must be supported byPlotly.
- The colors associated with each value of all images in
-
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. This can be helpful if the region is dominated by one or more unchanging classes and the goal is to visualize changes in smaller classes.
-
max_classes (int, default: None)
- If a value is provided, small classes will be removed until
max_classesremain. Class size is calculated based on total times sampled in the time series.
- If a value is provided, small classes will be removed until
-
n (int, default: 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 depending on the image projection.
-
seed (int, default: 0)
- The seed value used to generate repeatable results during random sampling.
-
dropna (bool, default: True)
- If the
regionextends into areas that contain no data in any image, some samples may have null values. Ifdropnais True, those samples will be dropped. This may lead to fewer samples being returned than were requested byn.
- If the
Returns
- A
PlotlySankey plot object.
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.
Example
sankee.datasets.names()
>> ['NLCD2016', 'MODIS_LC_TYPE1', 'MODIS_LC_TYPE2', 'MODIS_LC_TYPE3', 'CGLS_LC100']
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.Datasetobjects. Ifiis provided, only one object is returned.
Example
# Get the first Dataset object
sankee.datasets.get(0)
>> <sankee.datasets.Dataset> NLCD: USGS National Land Cover Database
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.Imageobjects.
Example
sankee.datasets.NLCD2016.get_images(3)
>> ['USGS/NLCD/NLCD1992', 'USGS/NLCD/NLCD2001', 'USGS/NLCD/NLCD2001_AK', '...']
Dataset properties and attributes
sankee.datasets.Dataset.collection
- Return the image collection associated with the dataset as an Earth Engine object.
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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