examples/notebooks/surface_water_mapping.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "<a href=\"https://githubtocolab.com/gee-community/geemap/blob/master/examples/notebooks/surface_water_mapping.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/></a>\n",
                "\n",
                "Uncomment the following line to install [geemap](https://geemap.org) if needed."
            ]
        },
        {
            "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/gee-community/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/gee-community/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": {},
            "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(\n",
                "    label=label,\n",
                "    start_year=1985,\n",
                "    bands=['NIR', 'Red', 'Green'],\n",
                "    font_color='white',\n",
                "    frames_per_second=10,\n",
                "    progress_bar_color='blue',\n",
                ")"
            ]
        },
        {
            "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(\n",
                "    label=label,\n",
                "    start_year=1985,\n",
                "    start_date='01-01',\n",
                "    end_date='12-31',\n",
                "    bands=['SWIR1', 'NIR', 'Red'],\n",
                "    font_color='white',\n",
                "    frames_per_second=10,\n",
                "    progress_bar_color='blue',\n",
                ")"
            ]
        },
        {
            "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 = (\n",
                "    ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')\n",
                "    .filterBounds(roi)\n",
                "    .filterDate('2015-01-01', '2019-12-31')\n",
                "    .sort('CLOUD_COVER')\n",
                ")"
            ]
        },
        {
            "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(\n",
                "    \"WRS_Path={}, WRS_ROW={}\".format(\n",
                "        best_image.get('WRS_PATH').getInfo(), best_image.get('WRS_ROW').getInfo()\n",
                "    )\n",
                ")"
            ]
        },
        {
            "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': [\n",
                "        '#ece7f2',\n",
                "        '#d0d1e6',\n",
                "        '#a6bddb',\n",
                "        '#74a9cf',\n",
                "        '#3690c0',\n",
                "        '#0570b0',\n",
                "        '#045a8d',\n",
                "        '#023858',\n",
                "    ],\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(\n",
                "    ee.Image().paint(lake_mead, 0, 2), {'palette': 'blue'}, 'Lake Mead Outline'\n",
                ")"
            ]
        },
        {
            "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 = (\n",
                "    ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')\n",
                "    .filterBounds(roi)\n",
                "    .filterDate(start_date, end_date)\n",
                "    .filterMetadata('CLOUD_COVER', 'less_than', cloud_threshold)\n",
                ")"
            ]
        },
        {
            "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(\n",
                "    lambda d: ee.Date(d).format('YYYY-MM-dd')\n",
                ")\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",
                "    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",
                "\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 = {'palette': 'blue'}\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(\n",
                "    left_ts=water_images,\n",
                "    right_ts=landsat_images,\n",
                "    left_names=water_layer_names,\n",
                "    right_names=landsat_layer_names,\n",
                "    left_vis=water_vis,\n",
                "    right_vis=landsat_vis,\n",
                ")\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(\n",
                "    occurrence.toInt(),\n",
                "    description='water_occurrence',\n",
                "    folder='export',\n",
                "    region=first_ndwi_image.geometry(),\n",
                "    scale=90,\n",
                ")"
            ]
        },
        {
            "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').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(\n",
                "    roi=roi, start_year=1984, end_year=2019, start_date='06-01', end_date='09-30'\n",
                ")"
            ]
        },
        {
            "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 = {'bands': ['NIR', 'Red', 'Green'], 'min': 0, 'max': 3500}\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(\n",
                "    left_ts=images,\n",
                "    right_ts=images,\n",
                "    left_names=layer_names,\n",
                "    right_names=layer_names,\n",
                "    left_vis=landsat_vis,\n",
                "    right_vis=landsat_vis,\n",
                ")\n",
                "Map.centerObject(roi, zoom=8)\n",
                "Map"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "ndwi_threshold = 0\n",
                "\n",
                "\n",
                "def extract_water(img):\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 = {'bands': ['NIR', 'Red', 'Green'], 'min': 0, 'max': 3500}\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(\n",
                "    water_images.toList(water_images.size()).get(layer_index)\n",
                ").selfMask()\n",
                "landsat_image = ee.Image(images.toList(images.size()).get(layer_index))\n",
                "\n",
                "water_vis = {'palette': 'blue'}\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 = {'min': 0, 'max': 100, 'palette': ['red', 'blue']}"
            ]
        },
        {
            "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 = {'palette': ['white', 'blue']}"
            ]
        },
        {
            "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 = {'min': -50, 'max': 50, 'palette': ['red', 'black', 'limegreen']}"
            ]
        },
        {
            "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(\n",
                "    label=label,\n",
                "    start_year=1985,\n",
                "    start_date='01-01',\n",
                "    end_date='12-31',\n",
                "    bands=['SWIR1', 'NIR', 'Red'],\n",
                "    font_color='white',\n",
                "    frames_per_second=10,\n",
                "    progress_bar_color='blue',\n",
                ")"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "language": "python",
            "name": "python3"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 5
}