{ "cells": [ { "cell_type": "markdown", "id": "6486328f", "metadata": {}, "source": [ "# Premade Datasets\n", "\n", "In this notebook, we'll go over some of the basics of premade datasets and take a look at the different datasets available in `sankee`." ] }, { "cell_type": "code", "execution_count": 1, "id": "a12c9da6", "metadata": {}, "outputs": [], "source": [ "import sankee\n", "import ee\n", "\n", "ee.Authenticate()\n", "ee.Initialize()" ] }, { "cell_type": "markdown", "id": "494941b0", "metadata": {}, "source": [ "## Dataset Introduction\n", "\n", "`sankee` uses `sankee.Dataset` objects to define premade Land Use/Land Cover datasets. Most of the LULC datasets in Earth Engine are available, but feel free to [request new ones](https://github.com/aazuspan/sankee/issues/17) if there's something missing. You can see the currently available datasets below." ] }, { "cell_type": "code", "execution_count": 2, "id": "61b24f84", "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": "38bf35dd", "metadata": {}, "source": [ "Each dataset defines the labels and colors assigned to different pixel values, which you can summarize using `Dataset.df`. For example, a value of `123` in the CORINE dataset would be assigned a label of `Ports` and a color of `#E6CCCC`." ] }, { "cell_type": "code", "execution_count": 3, "id": "cd94b839", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idlabelcolor
0111Continuous urban#E6004D
1112Discontinuous urban#FF0000
2121Industrial/Commercial#CC4DF2
3122Road/Rail#CC0000
4123Ports#E6CCCC
\n", "
" ], "text/plain": [ " id label color\n", "0 111 Continuous urban #E6004D\n", "1 112 Discontinuous urban #FF0000\n", "2 121 Industrial/Commercial #CC4DF2\n", "3 122 Road/Rail #CC0000\n", "4 123 Ports #E6CCCC" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sankee.datasets.CORINE.df.head()" ] }, { "cell_type": "markdown", "id": "0699058d", "metadata": {}, "source": [ "Some datasets are generated annually, but others are available on an irregular schedule. Run `Dataset.years` to see the available years." ] }, { "cell_type": "code", "execution_count": 4, "id": "42e480ef", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1986, 1999, 2005, 2011, 2017)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sankee.datasets.CORINE.years" ] }, { "cell_type": "markdown", "id": "642473d7", "metadata": {}, "source": [ "You can retrieve an image from one year using `Dataset.get_year`. This can be useful if you want to view the data on a map before trying to generate a Sankey diagram." ] }, { "cell_type": "code", "execution_count": 5, "id": "88d63ea2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image = sankee.datasets.CORINE.get_year(1986)\n", "image" ] }, { "cell_type": "markdown", "id": "bb55c397", "metadata": {}, "source": [ "Before we go any further, let's get [geemap](https://github.com/giswqs/geemap) up and running so we can take a look at some images." ] }, { "cell_type": "markdown", "id": "058f4979", "metadata": {}, "source": [ "!pip install geemap" ] }, { "cell_type": "code", "execution_count": 6, "id": "214c7928", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1309fc7c57d042ee8f2720e0809a22d1", "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": "7b9979b3", "metadata": {}, "source": [ "Now let's add the 1986 CORINE data we retrieved earlier to the map." ] }, { "cell_type": "code", "execution_count": 7, "id": "c6ef494e", "metadata": {}, "outputs": [], "source": [ "Map.addLayer(image, {}, \"CORINE - 1986\")\n", "Map.centerObject(image)" ] }, { "cell_type": "markdown", "id": "9c6f02cc", "metadata": {}, "source": [ "To look for areas that experienced land cover change, you might want to visually compare against another year's data. Let's add 2017 CORINE data and zoom in on Ankara, Turkey." ] }, { "cell_type": "code", "execution_count": 8, "id": "fdfcb46e", "metadata": {}, "outputs": [], "source": [ "Map.addLayer(sankee.datasets.CORINE.get_year(2017), {}, \"CORINE - 2017\")\n", "\n", "aoi = ee.Geometry.Point([32.806481, 39.92385]).buffer(10_000)\n", "Map.centerObject(aoi)" ] }, { "cell_type": "markdown", "id": "48932c85", "metadata": {}, "source": [ "Once the layers load, try toggling them on and off in the map to see how land cover changed. Finally, let's create a Sankey diagram showing that change. Just run `Dataset.sankify` with a list of available years and a region." ] }, { "cell_type": "code", "execution_count": 9, "id": "371e8658", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5d9ff32796a54177aaa6060fa038ba8a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SankeyPlot()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sankee.datasets.CORINE.sankify(years=[1986, 2017], region=aoi)" ] }, { "cell_type": "markdown", "id": "0400bf18", "metadata": {}, "source": [ "After turning some classes off to simplify the diagram, we can see how some agricultural lands were converted to urban and industral between the two dates.\n", "\n", "Now let's look at some other datasets." ] }, { "cell_type": "markdown", "id": "c8998b50", "metadata": {}, "source": [ "## Global Datasets" ] }, { "cell_type": "markdown", "id": "91ccc7d0", "metadata": {}, "source": [ "### MODIS Global Land Cover\n", "\n", "The [MODIS Global Land Cover](https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q1) datasets are global, annual datasets that go back to 2001. The MODIS dataset is split into three sub-datasets in `sankee`: `MODIS_LC_TYPE1`, `MODIS_LC_TYPE2`, and `MODIS_LC_TYPE3`. The `TYPE3` sub-dataset has fewer, more generalized classes than `TYPE2`, which has fewer than `TYPE1`.\n", "\n", "Let's look at an example Sankey diagram showing loss of permanent snow and ice in Greenland." ] }, { "cell_type": "code", "execution_count": 10, "id": "de811c49", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e64f4003c5474873a1c80edc6cc906d8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SankeyPlot()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sankee.datasets.MODIS_LC_TYPE1.sankify(\n", " years=[2001, 2020],\n", " region=ee.Geometry.Point([-61.221966, 80.204697]).buffer(10_000),\n", " title=\"Ice Loss in Greenland\"\n", ")" ] }, { "cell_type": "markdown", "id": "87642fc5", "metadata": {}, "source": [ "### Copernicus Global Land Cover\n", "\n", "Like MODIS, [CGLS](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global) is an annual dataset, but it is currently only available between 2015 and 2019. Let's look at the forest loss that resulted from a devastating 2018 wildfire in Kineta, Greece. " ] }, { "cell_type": "code", "execution_count": 11, "id": "15bdb18a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "de38747c92d647fab7e6f22a7b6212b2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SankeyPlot()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aoi = ee.Geometry.Polygon([\n", " [383.142871, 38.023993],\n", " [383.109218, 38.017772],\n", " [383.109218, 37.970153],\n", " [383.185452, 37.959326],\n", " [383.217731, 37.981249],\n", " [383.227346, 38.004518],\n", " [383.142871, 38.023993]\n", "])\n", "\n", "sankee.datasets.CGLS_LC100.sankify(\n", " years=[2017, 2019], \n", " region=aoi,\n", " title=\"Wildfire in Kineta, Greece\",\n", " max_classes=4,\n", ")" ] }, { "cell_type": "markdown", "id": "05619713", "metadata": {}, "source": [ "## United States Datasets" ] }, { "cell_type": "markdown", "id": "6c7fb464", "metadata": {}, "source": [ "### NLCD\n", "\n", "The [National Land Cover Database](https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2019_REL_NLCD) produces land cover maps of the continental United States on an irregular schedule between 2001 and 2019.\n", "\n", "Let's use NLCD to look at Land Cover change at a mountaintop removal coal mine in West Virginia. This time, we'll include 3 years of data." ] }, { "cell_type": "code", "execution_count": 12, "id": "50000bf2", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cac49c1e0a1b4da6bb4b267443dab9d0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SankeyPlot()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aoi = ee.Geometry.Polygon([\n", " [-81.973301, 38.121953],\n", " [-82.000086, 38.105746],\n", " [-82.009701, 38.077104],\n", " [-82.000429, 38.060617],\n", " [-81.965403, 38.043044],\n", " [-81.950637, 38.073321],\n", " [-81.942052, 38.107907],\n", " [-81.973301, 38.121953],\n", "])\n", "\n", "sankee.datasets.NLCD.sankify(\n", " years=[2001, 2011, 2019],\n", " region=aoi,\n", " title=\"Hobet Coal Mine, West Virginia\"\n", ")" ] }, { "cell_type": "markdown", "id": "0f5f65f0", "metadata": {}, "source": [ "### LCMS\n", "\n", "The [Landscape Change Monitoring System](https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_LCMS_v2020-5) project releases land cover and land use products annually since 1985. `sankee` supports both datasets.\n", "\n", "Below, we can see 35 years of vegetation recovery surrounding Mount St. Helens after its 1980 eruption." ] }, { "cell_type": "code", "execution_count": 13, "id": "41d15310", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "77b8896feb774ccb9acaef703aa9012f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SankeyPlot()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aoi = ee.Geometry.Polygon([\n", " [-122.289907, 46.289229],\n", " [-122.22328, 46.329545],\n", " [-122.15528, 46.346138],\n", " [-122.053623, 46.323855],\n", " [-122.033704, 46.252682],\n", " [-122.102391, 46.231786],\n", " [-122.161462, 46.24176],\n", " [-122.260371, 46.233686],\n", " [-122.289907, 46.289229],\n", "])\n", "\n", "sankee.datasets.LCMS_LC.sankify(\n", " years=[1985, 2000, 2020],\n", " region=aoi,\n", " title=\"Mount St. Helens Recovery\"\n", ")" ] }, { "cell_type": "markdown", "id": "e898e503", "metadata": {}, "source": [ "### LCMAP\n", "\n", "The [Land Change Monitoring, Assessment, and Projection](https://samapriya.github.io/awesome-gee-community-datasets/projects/lcmap/#lcmap-products) dataset, hosted in the [Earth Engine Community Datasets](https://samapriya.github.io/awesome-gee-community-datasets/), also provides annual land cover data back to 1985.\n", "\n", "We can use that long time series to look at urban sprawl around the city of Las Vegas." ] }, { "cell_type": "code", "execution_count": 14, "id": "461d3066", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2191440d288e4c53b1cc3ef4f932615e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SankeyPlot()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aoi = ee.Geometry.Polygon([\n", " [-115.01184401606046, 36.24170785506492],\n", " [-114.98849806879484, 36.29928186470082],\n", " [-115.25628981684171, 36.35238941394592],\n", " [-115.34692702387296, 36.310348922031565],\n", " [-115.37988600824796, 36.160811202271944],\n", " [-115.30298171137296, 36.03653336474891],\n", " [-115.25628981684171, 36.05207884201088],\n", " [-115.26590285395109, 36.226199908103695],\n", " [-115.19174513910734, 36.25499793268206],\n", "])\n", "\n", "sankee.datasets.LCMAP.sankify(\n", " years=[1985, 2000, 2020],\n", " region=aoi,\n", " title=\"Las Vegas Expansion\"\n", ")" ] }, { "cell_type": "markdown", "id": "f0737e95", "metadata": {}, "source": [ "## Other Datasets" ] }, { "cell_type": "markdown", "id": "3a4b0781", "metadata": {}, "source": [ "### C-CAP\n", "\n", "The [NOAA Coastal Change Analysis Program](https://samapriya.github.io/awesome-gee-community-datasets/projects/ccap_mlc/) maps land cover across coastal areas of the United states at irregular intervals.\n", "\n", "Below, we can see the effect of clearcuts on a forest in the Oregon Coast Range." ] }, { "cell_type": "code", "execution_count": 15, "id": "fccf37cf", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3303d37b9a8446169b5a9c657f3ade97", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SankeyPlot()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aoi = ee.Geometry.Polygon([\n", " [-123.867847, 43.544301],\n", " [-123.954382, 43.439692],\n", " [-123.877462, 43.329906],\n", " [-123.760709, 43.322913],\n", " [-123.664558, 43.503475],\n", " [-123.867847, 43.544301],\n", "])\n", "\n", "sankee.datasets.CCAP_LC30.sankify(\n", " years=[1996, 2016],\n", " region=aoi,\n", " scale=30,\n", " title=\"Clearcuts, Oregon Coast Range\"\n", ")" ] }, { "cell_type": "markdown", "id": "f2e541d1", "metadata": {}, "source": [ "### Canada Forest Ecosystems\n", "\n", "Finally, the [Canada Forested Ecosystem Land Cover](https://samapriya.github.io/awesome-gee-community-datasets/projects/ca_lc/) dataset maps land cover in Canadian forests annually from 1984 to 2019. Below, we can see the effects of construction of a new natural gas refinery in Alberta." ] }, { "cell_type": "code", "execution_count": 16, "id": "c01dfa14", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bc3a8091407d4f039ef2314d78b94da9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SankeyPlot()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aoi = ee.Geometry.Polygon([\n", " [249.444319, 55.057876],\n", " [249.444319, 55.092961],\n", " [249.476945, 55.092961],\n", " [249.476945, 55.057876],\n", " [249.444319, 55.057876],\n", "])\n", "\n", "sankee.datasets.CA_FOREST_LC.sankify(\n", " years=[1984, 2019],\n", " region=aoi,\n", " title=\"Refinery Construction, Alberta\"\n", ")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "000142bba90542ff80dab87efc9da544": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "padding": "0px 8px 0px 8px" } }, "000280a36a0c4ed5b1644aa876699f1f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "CheckboxModel", "state": { "description": "2015", "disabled": false, "indent": false, "layout": "IPY_MODEL_8a3ccf2f815c4263a79dd262cbe61440", "style": "IPY_MODEL_0395dc5ad71b4a08bb68d94dbeb72512", "value": true } }, "000eae870ffe4cb4a5aed0e92d94ac9a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "height": "25px", "padding": "0px 0px 0px 5px", "width": "25px" } }, "0010101b3d164462bf95f9c33558ca5f": { 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