{
"cells": [
{
"cell_type": "markdown",
"id": "0",
"metadata": {},
"source": [
"[](https://colab.research.google.com/github/gee-community/geemap/blob/master/examples/notebooks/tn_surface_water.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "1",
"metadata": {},
"source": [
"# Automated mapping of surface water in the state of Tennessee using Google Earth Engine cloud computing\n",
"\n",
"Author: Qiusheng Wu ([Website](https://wetlands.io) - [GitHub](https://github.com/giswqs))"
]
},
{
"cell_type": "markdown",
"id": "2",
"metadata": {
"toc": true
},
"source": [
"Contents
\n",
""
]
},
{
"cell_type": "markdown",
"id": "3",
"metadata": {},
"source": [
"## Install geemap"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4",
"metadata": {},
"outputs": [],
"source": [
"# Installs geemap package\n",
"import subprocess\n",
"\n",
"try:\n",
" import geemap\n",
"except ImportError:\n",
" print(\"geemap package not installed. Installing ...\")\n",
" subprocess.check_call([\"python\", \"-m\", \"pip\", \"install\", \"geemap\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5",
"metadata": {},
"outputs": [],
"source": [
"import ee\n",
"import geemap"
]
},
{
"cell_type": "markdown",
"id": "6",
"metadata": {},
"source": [
"## Create an interactive map"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7",
"metadata": {},
"outputs": [],
"source": [
"Map = geemap.Map()\n",
"Map"
]
},
{
"cell_type": "markdown",
"id": "8",
"metadata": {},
"source": [
"## Define region of interest (ROI)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9",
"metadata": {},
"outputs": [],
"source": [
"roi = ee.FeatureCollection(\"TIGER/2018/States\").filter(\n",
" ee.Filter.eq(\"NAME\", \"Tennessee\")\n",
")\n",
"Map.addLayer(roi, {}, \"TN\")\n",
"Map.centerObject(roi, 7)"
]
},
{
"cell_type": "markdown",
"id": "10",
"metadata": {},
"source": [
"## Create Landsat timeseries"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "11",
"metadata": {},
"outputs": [],
"source": [
"images = geemap.landsat_timeseries(\n",
" roi=roi, start_year=1984, end_year=2020, start_date=\"01-01\", end_date=\"12-31\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "12",
"metadata": {},
"outputs": [],
"source": [
"first = images.first()\n",
"\n",
"vis_params = {\"bands\": [\"NIR\", \"Red\", \"Green\"], \"min\": 0, \"max\": 3000}\n",
"\n",
"Map.addLayer(first, vis_params, \"First image\")"
]
},
{
"cell_type": "markdown",
"id": "13",
"metadata": {},
"source": [
"## Calculate Normalized Difference Water Index (NDWI)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "14",
"metadata": {},
"outputs": [],
"source": [
"ndwi_images = images.map(\n",
" lambda img: img.normalizedDifference([\"Green\", \"SWIR1\"]).rename(\"ndwi\")\n",
")\n",
"\n",
"ndwi_palette = [\n",
" \"#ece7f2\",\n",
" \"#d0d1e6\",\n",
" \"#a6bddb\",\n",
" \"#74a9cf\",\n",
" \"#3690c0\",\n",
" \"#0570b0\",\n",
" \"#045a8d\",\n",
" \"#023858\",\n",
"]\n",
"\n",
"first_ndwi = ndwi_images.first()\n",
"\n",
"Map.addLayer(first_ndwi, {\"palette\": ndwi_palette}, \"First NDWI\")"
]
},
{
"cell_type": "markdown",
"id": "15",
"metadata": {},
"source": [
"## Extract surface water extent"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "16",
"metadata": {},
"outputs": [],
"source": [
"water_images = ndwi_images.map(lambda img: img.gt(0).selfMask())\n",
"\n",
"first_water = water_images.first()\n",
"\n",
"Map.addLayer(first_water, {\"palette\": [\"blue\"]}, \"First Water\")"
]
},
{
"cell_type": "markdown",
"id": "17",
"metadata": {},
"source": [
"## Calculate surface water areas"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "18",
"metadata": {},
"outputs": [],
"source": [
"def cal_area(img):\n",
" pixel_area = img.multiply(ee.Image.pixelArea()).divide(1e6)\n",
" img_area = pixel_area.reduceRegion(\n",
" **{\n",
" \"geometry\": roi.geometry(),\n",
" \"reducer\": ee.Reducer.sum(),\n",
" \"scale\": 1000,\n",
" \"maxPixels\": 1e12,\n",
" }\n",
" )\n",
" return img.set({\"water_area\": img_area})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "19",
"metadata": {},
"outputs": [],
"source": [
"water_areas = water_images.map(cal_area)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "20",
"metadata": {},
"outputs": [],
"source": [
"water_stats = water_areas.aggregate_array(\"water_area\").getInfo()\n",
"water_stats"
]
},
{
"cell_type": "markdown",
"id": "21",
"metadata": {},
"source": [
"## Plot temporal trend"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "22",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = list(range(1984, 2021))\n",
"y = [item.get(\"ndwi\") for item in water_stats]\n",
"\n",
"plt.bar(x, y, align=\"center\", alpha=0.5)\n",
"# plt.xticks(y_pos, objects)\n",
"plt.ylabel(\"Area (km2)\")\n",
"plt.title(\"Surface water dynamics in Tennessee\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23",
"metadata": {},
"outputs": [],
"source": [
"Map.addLayerControl()\n",
"Map"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"nbformat": 4,
"nbformat_minor": 5
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