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    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/giswqs/geemap/blob/master/examples/notebooks/tn_surface_water.ipynb)"
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   "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))"
   ]
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   "source": [
    "<h1>Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Install-geemap\" data-toc-modified-id=\"Install-geemap-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Install geemap</a></span></li><li><span><a href=\"#Create-an-interactive-map\" data-toc-modified-id=\"Create-an-interactive-map-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Create an interactive map</a></span></li><li><span><a href=\"#Define-region-of-interest-(ROI)\" data-toc-modified-id=\"Define-region-of-interest-(ROI)-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Define region of interest (ROI)</a></span></li><li><span><a href=\"#Create-Landsat-timeseries\" data-toc-modified-id=\"Create-Landsat-timeseries-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Create Landsat timeseries</a></span></li><li><span><a href=\"#Calculate-Normalized-Difference-Water-Index-(NDWI)\" data-toc-modified-id=\"Calculate-Normalized-Difference-Water-Index-(NDWI)-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Calculate Normalized Difference Water Index (NDWI)</a></span></li><li><span><a href=\"#Extract-surface-water-extent\" data-toc-modified-id=\"Extract-surface-water-extent-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Extract surface water extent</a></span></li><li><span><a href=\"#Calculate-surface-water-areas\" data-toc-modified-id=\"Calculate-surface-water-areas-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Calculate surface water areas</a></span></li><li><span><a href=\"#Plot-temporal-trend\" data-toc-modified-id=\"Plot-temporal-trend-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Plot temporal trend</a></span></li></ul></div>"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install geemap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "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,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ee\n",
    "import geemap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an interactive map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map = geemap.Map()\n",
    "Map"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define region of interest (ROI)"
   ]
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "roi = ee.FeatureCollection('TIGER/2018/States') \\\n",
    "    .filter(ee.Filter.eq('NAME', 'Tennessee'))\n",
    "Map.addLayer(roi, {}, \"TN\")\n",
    "Map.centerObject(roi, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Landsat timeseries"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = geemap.landsat_timeseries(roi=roi, start_year=1984, end_year=2020, start_date='01-01', end_date='12-31')"
   ]
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    "first = images.first()\n",
    "\n",
    "vis_params = {\n",
    "    'bands': ['NIR', 'Red', 'Green'],\n",
    "    'min': 0,\n",
    "    'max': 3000\n",
    "}\n",
    "\n",
    "Map.addLayer(first, vis_params, 'First image')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Normalized Difference Water Index (NDWI) "
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "ndwi_images = images.map(lambda img: img.normalizedDifference(['Green', 'SWIR1']).rename('ndwi'))\n",
    "\n",
    "ndwi_palette = ['#ece7f2', '#d0d1e6', '#a6bddb', '#74a9cf', '#3690c0', '#0570b0', '#045a8d', '#023858']\n",
    "\n",
    "first_ndwi = ndwi_images.first()\n",
    "\n",
    "Map.addLayer(first_ndwi, {'palette': ndwi_palette}, 'First NDWI')"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract surface water extent"
   ]
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   "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')"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate surface water areas"
   ]
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    "def cal_area(img):\n",
    "    pixel_area = img.multiply(ee.Image.pixelArea()).divide(1e6)\n",
    "    img_area = pixel_area.reduceRegion(**{\n",
    "        'geometry': roi.geometry(),\n",
    "        'reducer': ee.Reducer.sum(),\n",
    "        'scale': 1000,\n",
    "        'maxPixels': 1e12\n",
    "    })\n",
    "    return img.set({'water_area': img_area})"
   ]
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    "water_areas = water_images.map(cal_area)"
   ]
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    "water_stats = water_areas.aggregate_array('water_area').getInfo()\n",
    "water_stats"
   ]
  },
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   "metadata": {},
   "source": [
    "## Plot temporal trend"
   ]
  },
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   "execution_count": null,
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   "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()"
   ]
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    "Map.addLayerControl() \n",
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