{ "cells": [ { "cell_type": "markdown", "id": "outside-vertical", "metadata": {}, "source": [ "# Mapping Snow Loss with Premade Datasets\n", "\n", "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." ] }, { "cell_type": "markdown", "id": "becoming-arrow", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 1, "id": "considered-desert", "metadata": { "ExecuteTime": { "end_time": "2021-04-21T00:21:40.979498Z", "start_time": "2021-04-21T00:21:28.091809Z" } }, "outputs": [], "source": [ "import sankee\n", "import ee\n", "\n", "ee.Authenticate()\n", "ee.Initialize()" ] }, { "cell_type": "markdown", "id": "bfc73c6f", "metadata": {}, "source": [ "## Premade Datasets\n", "\n", "`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." ] }, { "cell_type": "code", "execution_count": 2, "id": "105cd210", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sankee.datasets.datasets" ] }, { "cell_type": "markdown", "id": "ce12ceac", "metadata": {}, "source": [ "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](https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_LCMS_v2021-7)." ] }, { "cell_type": "code", "execution_count": 3, "id": "624d5cee", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idlabelcolor
01Trees#005e00
12Tall Shrubs & Trees Mix#008000
23Shrubs & Trees Mix#00cc00
34Grass/Forb/Herb & Trees Mix#b3ff1a
45Barren & Trees Mix#99ff99
56Tall Shrubs#b30088
67Shrubs#e68a00
78Grass/Forb/Herb & Shrubs Mix#ffad33
89Barren & Shrubs Mix#ffe0b3
910Grass/Forb/Herb#ffff00
1011Barren & Grass/Forb/Herb Mix#AA7700
1112Barren or Impervious#d3bf9b
1213Snow or Ice#ffffff
1314Water#4780f3
1415No Data#1B1716
\n", "
" ], "text/plain": [ " id label color\n", "0 1 Trees #005e00\n", "1 2 Tall Shrubs & Trees Mix #008000\n", "2 3 Shrubs & Trees Mix #00cc00\n", "3 4 Grass/Forb/Herb & Trees Mix #b3ff1a\n", "4 5 Barren & Trees Mix #99ff99\n", "5 6 Tall Shrubs #b30088\n", "6 7 Shrubs #e68a00\n", "7 8 Grass/Forb/Herb & Shrubs Mix #ffad33\n", "8 9 Barren & Shrubs Mix #ffe0b3\n", "9 10 Grass/Forb/Herb #ffff00\n", "10 11 Barren & Grass/Forb/Herb Mix #AA7700\n", "11 12 Barren or Impervious #d3bf9b\n", "12 13 Snow or Ice #ffffff\n", "13 14 Water #4780f3\n", "14 15 No Data #1B1716" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sankee.datasets.LCMS_LC.df" ] }, { "cell_type": "markdown", "id": "5d1c6359", "metadata": {}, "source": [ "In this example, we'll use [MODIS Type 1 Land Cover](https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q1)." ] }, { "cell_type": "code", "execution_count": 4, "id": "20110c4d", "metadata": {}, "outputs": [], "source": [ "dataset = sankee.datasets.MODIS_LC_TYPE1" ] }, { "cell_type": "markdown", "id": "7db45b1a", "metadata": {}, "source": [ "## Parameters\n", "\n", "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." ] }, { "cell_type": "code", "execution_count": 5, "id": "advisory-animal", "metadata": { "ExecuteTime": { "end_time": "2021-04-21T00:21:49.750803Z", "start_time": "2021-04-21T00:21:49.739244Z" } }, "outputs": [], "source": [ "aoi = ee.Geometry.Polygon([\n", " [-135.197519, 56.888425],\n", " [-134.774492, 56.888425],\n", " [-134.774492, 57.240839],\n", " [-135.197519, 57.240839],\n", "])" ] }, { "cell_type": "markdown", "id": "b8ef2fde", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": 6, "id": "50edbdb8", "metadata": {}, "outputs": [], "source": [ "years = [2001, 2010, 2019]" ] }, { "cell_type": "markdown", "id": "under-listening", "metadata": {}, "source": [ "### Plotting\n", "\n", "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!" ] }, { "cell_type": "code", "execution_count": 7, "id": "documented-seeking", "metadata": { "ExecuteTime": { "end_time": "2021-04-21T00:21:52.907945Z", "start_time": "2021-04-21T00:21:52.282928Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2337590e708f41c5905dd529107fb122", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SankeyPlot()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot = dataset.sankify(years=years, region=aoi)\n", "plot" ] }, { "cell_type": "markdown", "id": "f0ec5df9", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": 8, "id": "ef12c13a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "28377f1ca118420ab34bcd797dc48d17", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SankeyPlot()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot = dataset.sankify(years=years, region=aoi)\n", "plot" ] }, { "cell_type": "markdown", "id": "77cdb561", "metadata": {}, "source": [ "## Mapping Images\n", "\n", "To get a better look at the underlying data above, we can visualize the images using [geemap](https://github.com/giswqs/geemap)." ] }, { "cell_type": "code", "execution_count": null, "id": "460d0d8c", "metadata": {}, "outputs": [], "source": [ "!pip install geemap" ] }, { "cell_type": "code", "execution_count": 9, "id": "2213c90a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "28175d1039304128ba35a6c7ca8e2119", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map(center=[20, 0], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=HBox(children=(Togg…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import geemap\n", "\n", "Map = geemap.Map()\n", "Map" ] }, { "cell_type": "markdown", "id": "43dbe53e", "metadata": {}, "source": [ "`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." ] }, { "cell_type": "code", "execution_count": 10, "id": "b6d352fc", "metadata": {}, "outputs": [], "source": [ "for year in years:\n", " img = dataset.get_year(year)\n", " Map.addLayer(img, {}, f\"MODIS - {year}\")\n", "\n", "Map.addLayer(ee.Image().paint(aoi, 0, 4), {\"palette\": [\"#fff\"]}, \"AOI\")\n", "Map.centerObject(aoi)" ] }, { "cell_type": "markdown", "id": "b8a1c78b", "metadata": {}, "source": [ "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." ] }, { "cell_type": "markdown", "id": "dec31aa1", "metadata": {}, "source": [ "If you're curious, you can also access the `FeatureCollection` of sample points that were used to generate the Sankey diagram using `plot.samples`." ] }, { "cell_type": "code", "execution_count": 11, "id": "dde580ef", "metadata": {}, "outputs": [], "source": [ "Map.addLayer(plot.samples, {}, 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