Mapping Snow Loss with Premade Datasets

In this example, we’ll use sankee to visualize the conversion from snow and ice to barren and grassland on Baranof Island in Sitka, Alaska, as identified by the MODIS Land Cover dataset.

Setup

import sankee
import ee

ee.Authenticate()
ee.Initialize()

Premade Datasets

sankee plots are based on changes sampled randomly from a time series of classified images. You can use sankee with your own custom data, but it also includes premade classified datasets to get things up and running quickly. You can see the available datasets below.

sankee.datasets.datasets

[<Dataset: LCMS LC - Land Change Monitoring System Land Cover>,
 <Dataset: LCMS LU - Land Change Monitoring System Land Use>,
 <Dataset: NLCD - National Land Cover Database>,
 <Dataset: MCD12Q1 - MODIS Global Land Cover Type 1>,
 <Dataset: MCD12Q1 - MODIS Global Land Cover Type 2>,
 <Dataset: MCD12Q1 - MODIS Global Land Cover Type 3>,
 <Dataset: CGLS - Copernicus Global Land Cover>,
 <Dataset: C-CAP - NOAA Coastal Change Analysis Program 30m>,
 <Dataset: Canada Forested Ecosystem Land Cover>,
 <Dataset: LCMAP - Landscape Change Monitoring, Assessment, and Projection>,
 <Dataset: CORINE - Coordination of Information on the Environment>]

Each of the datasets above all of the parameters needed to create a Sankey diagram. For example, here are the class labels and colors for LCMS Land Cover.

sankee.datasets.LCMS_LC.df

	id	label	color
0	1	Trees	#005e00
1	2	Tall Shrubs & Trees Mix	#008000
2	3	Shrubs & Trees Mix	#00cc00
3	4	Grass/Forb/Herb & Trees Mix	#b3ff1a
4	5	Barren & Trees Mix	#99ff99
5	6	Tall Shrubs	#b30088
6	7	Shrubs	#e68a00
7	8	Grass/Forb/Herb & Shrubs Mix	#ffad33
8	9	Barren & Shrubs Mix	#ffe0b3
9	10	Grass/Forb/Herb	#ffff00
10	11	Barren & Grass/Forb/Herb Mix	#AA7700
11	12	Barren or Impervious	#d3bf9b
12	13	Snow or Ice	#ffffff
13	14	Water	#4780f3
14	15	No Data	#1B1716

In this example, we’ll use MODIS Type 1 Land Cover.

dataset = sankee.datasets.MODIS_LC_TYPE1

Parameters

In order to build our Sankey diagram, we need to define a few parameters. First of all, what area are we interested in? This could be an image footprint, a fire perimeter, a buffered point, etc. For this example, we’ll use a polygon from Baranof Island in Alaska.

aoi = ee.Geometry.Polygon([
    [-135.197519, 56.888425],
    [-134.774492, 56.888425],
    [-134.774492, 57.240839],
    [-135.197519, 57.240839],
])

The other required parameter is the year range. The available years will depend on the dataset (check them with dataset.years). We’ll pick three, but you can technically pick as many as you want.

years = [2001, 2010, 2019]

Plotting

That’s all we need to generate a Sankey diagram. When we run dataset.sankify, random points will be sampled within our area of interest (adjust the number of points with n), and their land cover classification will be displayed over time. The initial plot looks pretty messy, so try turning classes on and off by clicking the color-coded buttons at the bottom!

plot = dataset.sankify(years=years, region=aoi)
plot

If you turn off most of the smaller classes and the larger classes that didn’t see much change, you can see how permanent snow and ice is being converted to grassland and barren. You can also hover over each link to see what percent of cover was changed in each time period.

plot = dataset.sankify(years=years, region=aoi)
plot

Mapping Images

To get a better look at the underlying data above, we can visualize the images using geemap.

!pip install geemap

import geemap

Map = geemap.Map()
Map

sankee can retrieve a single year’s image from a dataset using get_year. We’ll use that to grab each year used in the Sankey diagram and add it to the map.

for year in years:
    img = dataset.get_year(year)
    Map.addLayer(img, {}, f"MODIS - {year}")

Map.addLayer(ee.Image().paint(aoi, 0, 4), {"palette": ["#fff"]}, "AOI")
Map.centerObject(aoi)

Click the images on and off in the interactive map to get a closer look at how the snow and ice extent shrunk over time.

If you’re curious, you can also access the FeatureCollection of sample points that were used to generate the Sankey diagram using plot.samples.

Map.addLayer(plot.samples, {}, "Samples")