examples/notebooks/surface_water_mapping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Mapping surface water dynamics using Earth Engine\n",
    "\n",
    "\n",
    "Author: Qiusheng Wu ([Website](https://wetlands.io) - [GitHub](https://github.com/giswqs))\n",
    "\n",
    "**Keyboard shortcuts for Jupyter notebook:**\n",
    "\n",
    "- **Shift-Enter**: run cell, select below\n",
    "- **Ctrl-Enter**: run selected cells\n",
    "- **Alt-Enter**: run cell and insert below\n",
    "- **Ctrl-/**: comment\n",
    "- **Tab**: code completion or indent\n",
    "- **Shift-Tab**: tooltip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "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=\"#Get-help\" data-toc-modified-id=\"Get-help-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Get help</a></span></li><li><span><a href=\"#Create-an-interactive-map\" data-toc-modified-id=\"Create-an-interactive-map-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Create an interactive map</a></span></li><li><span><a href=\"#Create-Landsat-timelapse\" data-toc-modified-id=\"Create-Landsat-timelapse-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Create Landsat timelapse</a></span></li><li><span><a href=\"#Select-the-best-cloud-free-image\" data-toc-modified-id=\"Select-the-best-cloud-free-image-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Select the best cloud-free image</a></span></li><li><span><a href=\"#Get-image-properties\" data-toc-modified-id=\"Get-image-properties-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Get image properties</a></span></li><li><span><a href=\"#Calculate-NDWI\" data-toc-modified-id=\"Calculate-NDWI-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Calculate NDWI</a></span></li><li><span><a href=\"#Extract-water\" data-toc-modified-id=\"Extract-water-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Extract water</a></span></li><li><span><a href=\"#Convert-raster-to-vector\" data-toc-modified-id=\"Convert-raster-to-vector-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>Convert raster to vector</a></span></li><li><span><a href=\"#Apply-the-algorithm-to-all-images\" data-toc-modified-id=\"Apply-the-algorithm-to-all-images-10\"><span class=\"toc-item-num\">10&nbsp;&nbsp;</span>Apply the algorithm to all images</a></span></li><li><span><a href=\"#Create-a-split-panel-map-to-visualize-results\" data-toc-modified-id=\"Create-a-split-panel-map-to-visualize-results-11\"><span class=\"toc-item-num\">11&nbsp;&nbsp;</span>Create a split-panel map to visualize results</a></span></li><li><span><a href=\"#Export-results-to-Google-Drive\" data-toc-modified-id=\"Export-results-to-Google-Drive-12\"><span class=\"toc-item-num\">12&nbsp;&nbsp;</span>Export results to Google Drive</a></span></li><li><span><a href=\"#Large-scale-surface-water-mapping\" data-toc-modified-id=\"Large-scale-surface-water-mapping-13\"><span class=\"toc-item-num\">13&nbsp;&nbsp;</span>Large-scale surface water mapping</a></span></li><li><span><a href=\"#Use-JRC-Global-Surface-Water-Product\" data-toc-modified-id=\"Use-JRC-Global-Surface-Water-Product-14\"><span class=\"toc-item-num\">14&nbsp;&nbsp;</span>Use JRC Global Surface Water Product</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install geemap\n",
    "\n",
    "To follow this tutorial, you need to install the [geemap](https://github.com/giswqs/geemap) Python package.Uncomment the following line to install and update the package to the latest version (v0.7.3). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install -U geemap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check package version. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ee\n",
    "import geemap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(ee.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(geemap.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get help\n",
    "\n",
    "- [Earth Engine API Documentation](https://developers.google.com/earth-engine/)\n",
    "- [geeamp API Documentation](https://geemap.readthedocs.io/en/latest/source/geemap.html#geemap-package)\n",
    "- [Report a geemap bug or submit a feature request](https://github.com/giswqs/geemap/issues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "geemap.api_docs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "geemap.open_youtube()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "geemap.ee_search()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an interactive map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map = geemap.Map(center=[40, -100], zoom=4)\n",
    "Map.add_basemap('HYBRID')\n",
    "Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Landsat timelapse\n",
    "\n",
    "Use the Drawing tool to draw any rectangle on the map.\n",
    "\n",
    "https://earthengine.google.com/timelapse/\n",
    "\n",
    "https://geemap.readthedocs.io/en/latest/source/geemap.html#geemap.geemap.Map.add_landsat_ts_gif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map.setCenter(-114.762293, 36.06462, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = 'Surface water dynamics in Lake Mead'\n",
    "Map.add_landsat_ts_gif(label=label, start_year=1985, bands=['NIR', 'Red', 'Green'], font_color='white', frames_per_second=10, progress_bar_color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map.setCenter(-74.4557, -8.4289, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = 'Surface Water Dynamics of Ucayali River, Peru'\n",
    "Map.add_landsat_ts_gif(label=label, start_year=1985, start_date='01-01', end_date='12-31', bands=['SWIR1', 'NIR', 'Red'], font_color='white', frames_per_second=10, progress_bar_color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select the best cloud-free image\n",
    "\n",
    "In this case study, we can going to use [USGS Landsat 8 Surface Reflectance Tier 1](https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_SR).\n",
    "\n",
    "- `ee.ImageCollection(\"LANDSAT/LC08/C01/T1_SR\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map = geemap.Map()\n",
    "Map.add_basemap('HYBRID')\n",
    "Map.setCenter(-114.762293, 36.06462, 9)\n",
    "Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pan and zoom the map to Lake Mead near Las Vegas, NV. Use the Drawing Tools to place a marker inside Lake Mead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "roi = Map.user_roi\n",
    "print(roi.getInfo())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can define an ee.Geometry() as an ROI. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "roi = ee.Geometry.Point([-114.762293, 36.06462])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's filter the ImageCollection by roi and date, and then sort by cloud cover."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR') \\\n",
    "    .filterBounds(roi) \\\n",
    "    .filterDate('2015-01-01', '2019-12-31') \\\n",
    "    .sort('CLOUD_COVER')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many Landsat 8 images (2015-2019) are available for the study area?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(images.size().getInfo())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the returned images are already sorted by CLOUD_COVER in ascending order, the first image would be the best cloud-free image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# best_image = images.toList(images.size()).get(0)\n",
    "best_image = images.first().select(['B1', 'B2', 'B3', 'B4',  'B5', 'B6', 'B7'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add the best image to the Map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vis_params = {\n",
    "  'bands': ['B5', 'B4', 'B3'],\n",
    "  'min': 0,\n",
    "  'max': 6000,\n",
    "  'gamma': 1.4,\n",
    "}\n",
    "Map.addLayer(best_image, vis_params, 'Best image')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the Inspector and Plotting to check pixel values and spectral signature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get image properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(best_image.getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(best_image.propertyNames().getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(best_image.get('system:id').getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_039035_20150724')\n",
    "vis_params = {\n",
    "  'bands': ['B6', 'B5', 'B4'],\n",
    "  'min': 0,\n",
    "  'max': 6000,\n",
    "  'gamma': 1.4,\n",
    "}\n",
    "Map.addLayer(image, vis_params, 'image')\n",
    "Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(best_image.get('CLOUD_COVER').getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(best_image.get('system:band_names').getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(best_image.get('system:time_start').getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(ee.Date(best_image.get('system:time_start')).format('YYYY-MM-dd').getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"WRS_Path={}, WRS_ROW={}\".format(best_image.get('WRS_PATH').getInfo(), best_image.get('WRS_ROW').getInfo()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate NDWI\n",
    "\n",
    "The Normalized Difference Water Index (NDWI) is used to monitor changes related to water content in water bodies, using green and NIR wavelengths, defined by McFeeters (1996):\n",
    "\n",
    "![](https://wikimedia.org/api/rest_v1/media/math/render/svg/fd21ea077007b78b7bf753498d4953991837cb26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map = geemap.Map()\n",
    "Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_039035_20150724')\n",
    "vis_params = {\n",
    "  'bands': ['B5', 'B4', 'B3'],\n",
    "  'min': 0,\n",
    "  'max': 6000,\n",
    "  'gamma': 1.4,\n",
    "}\n",
    "Map.addLayer(image, vis_params, 'image')\n",
    "Map.centerObject(image, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndwi_image = image.normalizedDifference(['B3', 'B5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vis_ndwi = {\n",
    "    'min': -1, \n",
    "    'max': 1,\n",
    "    'palette': ['#ece7f2', '#d0d1e6', '#a6bddb', '#74a9cf', '#3690c0', '#0570b0', '#045a8d', '#023858']\n",
    "}\n",
    "Map.addLayer(ndwi_image, vis_ndwi, 'NDWI image')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndwi_threshold = 0\n",
    "water_image = ndwi_image.gt(ndwi_threshold).selfMask()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map.addLayer(water_image, {'palette': 'blue'}, 'Water image')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "water_layer = Map.layers[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "water_layer.interact(opacity=(0.0,1.0,0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert raster to vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "water_vector = water_image.reduceToVectors(scale=30, maxPixels=60000000)\n",
    "Map.addLayer(water_vector, {}, 'Water vector')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "roi = ee.Geometry.Point([-114.762293, 36.06462])\n",
    "lake_mead = water_vector.filterBounds(roi)\n",
    "Map.addLayer(lake_mead, {}, 'Lake Mead')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map.addLayer(ee.Image().paint(lake_mead, 0, 2), {'palette': 'blue'}, 'Lake Mead Outline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "area = lake_mead.geometry().area(1).divide(1e6).round().getInfo()\n",
    "print(\"Area = {} km2\".format(area))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply the algorithm to all images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map = geemap.Map()\n",
    "Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define input parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "roi = ee.Geometry.Point([-114.762293, 36.06462])\n",
    "start_date = '2015-01-01'\n",
    "end_date = '2019-12-31'\n",
    "cloud_threshold = 0.05\n",
    "ndwi_threshold = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR') \\\n",
    "    .filterBounds(roi) \\\n",
    "    .filterDate(start_date, end_date) \\\n",
    "    .filterMetadata('CLOUD_COVER', 'less_than', cloud_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(images.size().getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(images.aggregate_array('system:id').getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = images.aggregate_array('system:time_start').map(lambda d: ee.Date(d).format('YYYY-MM-dd'))\n",
    "print(dates.getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_image = ee.Image(images.toList(images.size()).get(3))\n",
    "vis_params = {\n",
    "  'bands': ['B5', 'B4', 'B3'],\n",
    "  'min': 0,\n",
    "  'max': 6000,\n",
    "  'gamma': 1.4,\n",
    "}\n",
    "Map.addLayer(first_image, vis_params, 'First image')\n",
    "Map.centerObject(roi, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_water(img):\n",
    "    \n",
    "    ndwi_image = img.normalizedDifference(['B3', 'B5'])\n",
    "    water_image = ndwi_image.gt(ndwi_threshold)\n",
    "    return water_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndwi_images = images.map(extract_water)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_ndwi_image = ee.Image(ndwi_images.toList(ndwi_images.size()).get(0)).selfMask()\n",
    "Map.addLayer(first_ndwi_image, {'palette': 'blue'}, 'First NDWI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "occurrence = ndwi_images.sum().selfMask()\n",
    "Map.addLayer(occurrence.randomVisualizer(), {}, 'Water occurrence')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ras_to_vec(img):\n",
    "    vec = img.selfMask().reduceToVectors(scale=30, maxPixels=60000000)\n",
    "    vec = vec.filterBounds(roi)\n",
    "    return vec.set({'area': vec.geometry().area(1).divide(1e6).round()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndwi_vectors = ndwi_images.map(ras_to_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "areas = ndwi_vectors.aggregate_array('area')\n",
    "print(areas.getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "# %matplotlib qt \n",
    "\n",
    "fig= plt.figure(figsize=(12,6))\n",
    "\n",
    "x = dates.getInfo()\n",
    "y = areas.getInfo()\n",
    "\n",
    "plt.plot(x, y, marker='o')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Lake Mead Area (km2)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a split-panel map to visualize results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "water_images = ndwi_images.map(lambda img: img.selfMask())\n",
    "landsat_images = images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "water_layer_names = ['Water ' + str(date) for date in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "landsat_layer_names = ['Landsat ' + str(date) for date in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "water_vis = {\n",
    "    'palette': 'blue'\n",
    "}\n",
    "\n",
    "landsat_vis = {\n",
    "  'bands': ['B5', 'B4', 'B3'],\n",
    "  'min': 0,\n",
    "  'max': 6000,\n",
    "  'gamma': 1.4,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map = geemap.Map()\n",
    "Map.ts_inspector(left_ts=water_images, right_ts=landsat_images, left_names=water_layer_names, right_names=landsat_layer_names, left_vis=water_vis, right_vis=landsat_vis)\n",
    "Map.centerObject(roi, zoom=8)\n",
    "Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_index = 9  # Change this index to your desired date\n",
    "print(\"Image date: {}\".format(x[layer_index]))\n",
    "\n",
    "water_image = ee.Image(water_images.toList(water_images.size()).get(layer_index))\n",
    "landsat_image = ee.Image(landsat_images.toList(landsat_images.size()).get(layer_index))\n",
    "\n",
    "\n",
    "left_layer = geemap.ee_tile_layer(water_image, water_vis, 'Water')\n",
    "right_layer = geemap.ee_tile_layer(landsat_image, landsat_vis, 'Landsat')\n",
    "\n",
    "Map = geemap.Map()\n",
    "Map.addLayer(landsat_image, landsat_vis, 'Landsat')\n",
    "Map.split_map(left_layer, right_layer)\n",
    "Map.centerObject(roi, zoom=12)\n",
    "Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export results to Google Drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "geemap.ee_export_image_collection_to_drive(water_images, folder='export', scale=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "geemap.ee_export_image_to_drive(occurrence.toInt(), description='water_occurrence', folder='export', region=first_ndwi_image\n",
    ".geometry(), scale=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Large-scale surface water mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ee\n",
    "import geemap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map = geemap.Map()\n",
    "Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_shp = geemap.shp_to_ee('../data/us-states.shp')\n",
    "Map.addLayer(states_shp, {}, 'US States SHP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_name = 'Nevada'\n",
    "roi = ee.FeatureCollection('TIGER/2018/States') \\\n",
    "    .filter(ee.Filter.eq('NAME', state_name))\n",
    "# roi = states_shp.filterBounds(Map.user_roi)\n",
    "Map.addLayer(ee.Image().paint(roi, 0, 2), {'palette': 'red'}, state_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = geemap.landsat_timeseries(roi=roi, start_year=1984, end_year=2019, start_date='06-01', end_date='09-30')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_image = ee.Image(images.toList(images.size()).get(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "landsat_vis = {\n",
    "    'bands': ['NIR', 'Red', 'Green'],\n",
    "    'min': 0, \n",
    "    'max': 3500\n",
    "}\n",
    "Map.addLayer(first_image, landsat_vis, 'First image')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_names = ['Landsat ' + str(year) for year in range(1984, 2020)]\n",
    "Map = geemap.Map()\n",
    "Map.ts_inspector(left_ts=images, right_ts=images, left_names=layer_names, right_names=layer_names, left_vis=landsat_vis, right_vis=landsat_vis)\n",
    "Map.centerObject(roi, zoom=8)\n",
    "Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndwi_threshold = 0\n",
    "def extract_water(img):\n",
    "    \n",
    "    ndwi_image = img.normalizedDifference(['Green', 'NIR'])\n",
    "    water_image = ndwi_image.gt(ndwi_threshold)\n",
    "    return water_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "water_images = images.map(extract_water)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map = geemap.Map()\n",
    "\n",
    "first_image = ee.Image(images.toList(images.size()).get(0))\n",
    "landsat_vis = {\n",
    "    'bands': ['NIR', 'Red', 'Green'],\n",
    "    'min': 0, \n",
    "    'max': 3500\n",
    "}\n",
    "Map.addLayer(first_image, landsat_vis, 'First image')\n",
    "\n",
    "first_water_image = ee.Image(water_images.toList(water_images.size()).get(0)).selfMask()\n",
    "Map.addLayer(first_water_image, {'palette': 'blue'}, 'First NDWI')\n",
    "\n",
    "Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_index = 0  # Change this index to your desired date\n",
    "print(layer_names[layer_index])\n",
    "\n",
    "water_image = ee.Image(water_images.toList(water_images.size()).get(layer_index)).selfMask()\n",
    "landsat_image = ee.Image(images.toList(images.size()).get(layer_index))\n",
    "\n",
    "water_vis = {\n",
    "    'palette': 'blue'\n",
    "}\n",
    "\n",
    "landsat_vis = {\n",
    "  'bands': ['NIR', 'Red', 'Green'],\n",
    "  'min': 0,\n",
    "  'max': 4000,\n",
    "  'gamma': 1.4,\n",
    "}\n",
    "\n",
    "left_layer = geemap.ee_tile_layer(water_image, water_vis, 'Water')\n",
    "right_layer = geemap.ee_tile_layer(landsat_image, landsat_vis, 'Landsat')\n",
    "\n",
    "Map = geemap.Map()\n",
    "Map.addLayer(landsat_image, landsat_vis, 'Landsat')\n",
    "Map.split_map(left_layer, right_layer)\n",
    "Map.centerObject(roi, zoom=12)\n",
    "Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use JRC Global Surface Water Product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ee\n",
    "import geemap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map = geemap.Map()\n",
    "Map.add_basemap('HYBRID')\n",
    "Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gsw = ee.Image('JRC/GSW1_1/GlobalSurfaceWater')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(gsw.bandNames().getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "occurrence = gsw.select('occurrence')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vis_occurrence = {\n",
    "  'min':0,\n",
    "  'max':100,\n",
    "  'palette': ['red', 'blue']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map.addLayer(occurrence, vis_occurrence, 'Occurrence')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "water_mask = occurrence.gt(90).selfMask()\n",
    "vis_water_mask = {\n",
    "  'palette': ['white', 'blue']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map.addLayer(water_mask, vis_water_mask, 'Water Mask (>90%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map.setCenter(-74.4557, -8.4289, 11)\n",
    "Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "change = gsw.select(\"change_abs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vis_change = {\n",
    "    'min':-50,\n",
    "    'max':50,\n",
    "    'palette': ['red', 'black', 'limegreen']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map.addLayer(change, vis_change, 'Occurrence change intensity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "transition = gsw.select('transition')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Map.addLayer(transition, {}, 'Transition classes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = 'Surface Water Dynamics'\n",
    "Map.add_landsat_ts_gif(label=label, start_year=1985, start_date='01-01', end_date='12-31', bands=['SWIR1', 'NIR', 'Red'], font_color='white', frames_per_second=10, progress_bar_color='blue')"
   ]
  }
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