{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://gishub.org/srm-workshop-colab)\n",
                "[![image](https://mybinder.org/badge_logo.svg)](https://gishub.org/srm-workshop-binder)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "**Interactive mapping and analysis of geospatial big data using geemap and Google Earth Engine**\n",
                "\n",
                "This notebook was developed for the geemap workshop at the [Society for Range Management (SRM) 2022 Annual Meeting](http://annualmeeting.rangelands.org).\n",
                "\n",
                "\n",
                "Authors: [Qiusheng Wu](https://github.com/giswqs)\n",
                "\n",
                "Link to this notebook: https://gishub.org/SRM\n",
                "\n",
                "\n",
                "## Introduction\n",
                "\n",
                "### Description\n",
                "\n",
                "Google Earth Engine (GEE) is a cloud computing platform with a multi-petabyte catalog of satellite imagery and geospatial datasets. It enables scientists, researchers, and developers to analyze and visualize changes on the Earth’s surface. The geemap Python package provides GEE users with an intuitive interface to manipulate, analyze, and visualize geospatial big data interactively in a Jupyter-based environment. The topics to be covered in this workshop include: \n",
                "\n",
                "1. Introducing geemap \n",
                "2. Creating interactive maps\n",
                "3. Searching GEE data catalog\n",
                "4. Data Visualization\n",
                "5. Data Analysis\n",
                "6. Data Export\n",
                "\n",
                "This workshop is intended for scientific programmers, data scientists, geospatial analysts, and concerned citizens of Earth. The attendees are expected to have a basic understanding of Python and the Jupyter ecosystem. Familiarity with Earth science and geospatial datasets is useful but not required.\n",
                "\n",
                "### Useful links\n",
                "- [Streamlit Geospatial](https://streamlit.gishub.org)\n",
                "- [Google Earth Engine](https://earthengine.google.com)\n",
                "- [geemap.org](https://geemap.org)\n",
                "- [Google Earth Engine and geemap Python Tutorials](https://www.youtube.com/playlist?list=PLAxJ4-o7ZoPccOFv1dCwvGI6TYnirRTg3) (55 videos with a total length of 15 hours)\n",
                "- [Spatial Data Management with Google Earth Engine](https://www.youtube.com/playlist?list=PLAxJ4-o7ZoPdz9LHIJIxHlZe3t-MRCn61) (19 videos with a total length of 9 hours)\n",
                "- [Ask geemap questions on GitHub](https://github.com/gee-community/geemap/discussions)\n",
                "\n",
                "### Prerequisite\n",
                "- A Google Earth Engine account. Sigh up [here](https://earthengine.google.com) if needed. \n",
                "- [Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/products/individual) has been installed on your computer.\n",
                "\n",
                "\n",
                "### Set up a conda environment\n",
                "\n",
                "```\n",
                "conda create -n gee python=3.9\n",
                "conda activate gee\n",
                "conda install geopandas\n",
                "conda install mamba -c conda-forge\n",
                "mamba install geemap localtileserver -c conda-forge\n",
                "\n",
                "```\n",
                "\n",
                "### Launch JupyterLab\n",
                "Open **Anaconda Prompt** or the **Terminal** and enter the following commands.\n",
                "\n",
                "```\n",
                "conda activate gee\n",
                "jupyter lab\n",
                "```"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "tags": []
            },
            "source": [
                "## Geemap basics\n",
                "\n",
                "### Install geemap"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "import subprocess\n",
                "\n",
                "try:\n",
                "    import geemap\n",
                "except ImportError:\n",
                "    print('Installing geemap ...')\n",
                "    subprocess.check_call([\"python\", '-m', 'pip', 'install', 'geemap'])"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Import libraries"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "import os\n",
                "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"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Customize the default map\n",
                "\n",
                "You can specify the center(lat, lon) and zoom for the default map. You can also set the visibility of the controls."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map(center=(40, -100), zoom=4, draw_ctrl=False, toolbar_ctrl=False)\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Add basemaps"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "Map.add_basemap('HYBRID')\n",
                "Map.add_basemap('OpenTopoMap')\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Change basemaps interactively"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "![](https://i.imgur.com/PXURCSP.png)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Use drawing tools"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "Map"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Map.user_roi.getInfo()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Map.user_rois.getInfo()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Convert GEE JavaScript to Python\n",
                "\n",
                "https://developers.google.com/earth-engine/guides/image_visualization"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "js_snippet = \"\"\"\n",
                "// Load an image.\n",
                "var image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318');\n",
                "\n",
                "// Define the visualization parameters.\n",
                "var vizParams = {\n",
                "  bands: ['B5', 'B4', 'B3'],\n",
                "  min: 0,\n",
                "  max: 0.5,\n",
                "  gamma: [0.95, 1.1, 1]\n",
                "};\n",
                "\n",
                "// Center the map and display the image.\n",
                "Map.setCenter(-122.1899, 37.5010, 10); // San Francisco Bay\n",
                "Map.addLayer(image, vizParams, 'false color composite');\n",
                "\n",
                "\"\"\""
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "geemap.js_snippet_to_py(\n",
                "    js_snippet, add_new_cell=True, import_ee=True, import_geemap=True, show_map=True\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "import ee\n",
                "import geemap\n",
                "\n",
                "Map = geemap.Map()\n",
                "\n",
                "# Load an image.\n",
                "image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')\n",
                "\n",
                "# Define the visualization parameters.\n",
                "vizParams = {'bands': ['B5', 'B4', 'B3'], 'min': 0, 'max': 0.5, 'gamma': [0.95, 1.1, 1]}\n",
                "\n",
                "# Center the map and display the image.\n",
                "Map.setCenter(-122.1899, 37.5010, 10)\n",
                "# San Francisco Bay\n",
                "Map.addLayer(image, vizParams, 'False color composite')\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "You can also convert GEE JavaScript to Python without coding.\n",
                "\n",
                "![](https://i.imgur.com/VnnrJwe.png)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Earth Engine datasets\n",
                "\n",
                "### Load Earth Engine datasets\n",
                "\n",
                "More GEE datasets can be found at https://developers.google.com/earth-engine/datasets/"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "\n",
                "# Add Earth Engine datasets\n",
                "dem = ee.Image('USGS/SRTMGL1_003')\n",
                "landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')\n",
                "states = ee.FeatureCollection(\"TIGER/2018/States\")\n",
                "\n",
                "# Set visualization parameters.\n",
                "vis_params = {\n",
                "    'min': 0,\n",
                "    'max': 4000,\n",
                "    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],\n",
                "}\n",
                "\n",
                "# Add Earth Engine layers to Map\n",
                "Map.addLayer(dem, vis_params, 'SRTM DEM', True, 0.5)\n",
                "Map.addLayer(\n",
                "    landsat7,\n",
                "    {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200, 'gamma': 1.5},\n",
                "    'Landsat 7',\n",
                ")\n",
                "Map.addLayer(states, {}, \"US States\")\n",
                "\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Search the Earth Engine Data Catalog"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "Map"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "dem = ee.Image('CGIAR/SRTM90_V4')\n",
                "Map.addLayer(dem, {}, \"CGIAR/SRTM90_V4\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "hillshade = ee.Terrain.hillshade(dem)\n",
                "Map.addLayer(hillshade, {}, 'Hillshade')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "vis_params = {\n",
                "    'min': 0,\n",
                "    'max': 4000,\n",
                "    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],\n",
                "}\n",
                "\n",
                "Map.addLayer(dem, vis_params, \"DEM\", True, 0.5)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Use the datasets module"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "from geemap.datasets import DATA"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "\n",
                "dem = ee.Image(DATA.USGS_SRTMGL1_003)\n",
                "\n",
                "vis_params = {\n",
                "    'min': 0,\n",
                "    'max': 4000,\n",
                "    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],\n",
                "}\n",
                "\n",
                "Map.addLayer(dem, vis_params, 'SRTM DEM')\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Use the Inspector tool"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "![](https://i.imgur.com/drnfJ6N.png)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "\n",
                "# Add Earth Engine datasets\n",
                "dem = ee.Image('USGS/SRTMGL1_003')\n",
                "landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003').select(\n",
                "    ['B1', 'B2', 'B3', 'B4', 'B5', 'B7']\n",
                ")\n",
                "states = ee.FeatureCollection(\"TIGER/2018/States\")\n",
                "\n",
                "# Set visualization parameters.\n",
                "vis_params = {\n",
                "    'min': 0,\n",
                "    'max': 4000,\n",
                "    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],\n",
                "}\n",
                "\n",
                "# Add Earth Engine layers to Map\n",
                "Map.addLayer(dem, vis_params, 'SRTM DEM', True, 0.5)\n",
                "Map.addLayer(\n",
                "    landsat7,\n",
                "    {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 180, 'gamma': 1.5},\n",
                "    'Landsat 7',\n",
                ")\n",
                "Map.addLayer(states, {}, \"US States\")\n",
                "\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Data visualization \n",
                "\n",
                "### Use the Plotting tool"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "![](https://i.imgur.com/t4jKsNo.png)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "\n",
                "landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003').select(\n",
                "    ['B1', 'B2', 'B3', 'B4', 'B5', 'B7']\n",
                ")\n",
                "\n",
                "landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4}\n",
                "Map.addLayer(landsat7, landsat_vis, \"Landsat\")\n",
                "\n",
                "hyperion = ee.ImageCollection('EO1/HYPERION').filter(\n",
                "    ee.Filter.date('2016-01-01', '2017-03-01')\n",
                ")\n",
                "\n",
                "hyperion_vis = {\n",
                "    'min': 1000.0,\n",
                "    'max': 14000.0,\n",
                "    'gamma': 2.5,\n",
                "}\n",
                "Map.addLayer(hyperion, hyperion_vis, 'Hyperion')\n",
                "\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Change layer opacity"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map(center=(40, -100), zoom=4)\n",
                "\n",
                "dem = ee.Image('USGS/SRTMGL1_003')\n",
                "states = ee.FeatureCollection(\"TIGER/2018/States\")\n",
                "\n",
                "vis_params = {\n",
                "    'min': 0,\n",
                "    'max': 4000,\n",
                "    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],\n",
                "}\n",
                "\n",
                "Map.addLayer(dem, vis_params, 'SRTM DEM', True, 1)\n",
                "Map.addLayer(states, {}, \"US States\", True)\n",
                "\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Visualize raster data"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map(center=(40, -100), zoom=4)\n",
                "\n",
                "# Add Earth Engine dataset\n",
                "dem = ee.Image('USGS/SRTMGL1_003')\n",
                "landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003').select(\n",
                "    ['B1', 'B2', 'B3', 'B4', 'B5', 'B7']\n",
                ")\n",
                "\n",
                "vis_params = {\n",
                "    'min': 0,\n",
                "    'max': 4000,\n",
                "    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],\n",
                "}\n",
                "\n",
                "Map.addLayer(dem, vis_params, 'SRTM DEM', True, 1)\n",
                "Map.addLayer(\n",
                "    landsat7,\n",
                "    {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200, 'gamma': 2},\n",
                "    'Landsat 7',\n",
                ")\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Visualize vector data"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "\n",
                "states = ee.FeatureCollection(\"TIGER/2018/States\")\n",
                "Map.addLayer(states, {}, \"US States\")\n",
                "Map"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "vis_params = {\n",
                "    'color': '000000',\n",
                "    'colorOpacity': 1,\n",
                "    'pointSize': 3,\n",
                "    'pointShape': 'circle',\n",
                "    'width': 2,\n",
                "    'lineType': 'solid',\n",
                "    'fillColorOpacity': 0.66,\n",
                "}\n",
                "\n",
                "palette = ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5']\n",
                "\n",
                "Map.add_styled_vector(\n",
                "    states, column=\"NAME\", palette=palette, layer_name=\"Styled vector\", **vis_params\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Add a legend"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "legends = geemap.builtin_legends\n",
                "for legend in legends:\n",
                "    print(legend)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "Map.add_basemap('HYBRID')\n",
                "\n",
                "dataset = ee.ImageCollection('USGS/NLCD_RELEASES/2016_REL')\n",
                "nlcd2016 = dataset.filter(ee.Filter.eq('system:index', '2016')).first()\n",
                "landcover = nlcd2016.select('landcover')\n",
                "\n",
                "Map.addLayer(landcover, {}, 'NLCD Land Cover 2016')\n",
                "Map.add_legend(builtin_legend='NLCD')\n",
                "Map"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "\n",
                "legend_dict = {\n",
                "    '11 Open Water': '466b9f',\n",
                "    '12 Perennial Ice/Snow': 'd1def8',\n",
                "    '21 Developed, Open Space': 'dec5c5',\n",
                "    '22 Developed, Low Intensity': 'd99282',\n",
                "    '23 Developed, Medium Intensity': 'eb0000',\n",
                "    '24 Developed High Intensity': 'ab0000',\n",
                "    '31 Barren Land (Rock/Sand/Clay)': 'b3ac9f',\n",
                "    '41 Deciduous Forest': '68ab5f',\n",
                "    '42 Evergreen Forest': '1c5f2c',\n",
                "    '43 Mixed Forest': 'b5c58f',\n",
                "    '51 Dwarf Scrub': 'af963c',\n",
                "    '52 Shrub/Scrub': 'ccb879',\n",
                "    '71 Grassland/Herbaceous': 'dfdfc2',\n",
                "    '72 Sedge/Herbaceous': 'd1d182',\n",
                "    '73 Lichens': 'a3cc51',\n",
                "    '74 Moss': '82ba9e',\n",
                "    '81 Pasture/Hay': 'dcd939',\n",
                "    '82 Cultivated Crops': 'ab6c28',\n",
                "    '90 Woody Wetlands': 'b8d9eb',\n",
                "    '95 Emergent Herbaceous Wetlands': '6c9fb8',\n",
                "}\n",
                "\n",
                "dataset = ee.ImageCollection('USGS/NLCD_RELEASES/2016_REL')\n",
                "nlcd2016 = dataset.filter(ee.Filter.eq('system:index', '2016')).first()\n",
                "landcover = nlcd2016.select('landcover')\n",
                "\n",
                "Map.addLayer(landcover, {}, 'NLCD Land Cover 2016')\n",
                "Map.add_legend(legend_title=\"NLCD Land Cover Classification\", legend_dict=legend_dict)\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Add a colorbar"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "\n",
                "dem = ee.Image('USGS/SRTMGL1_003')\n",
                "vis_params = {\n",
                "    'min': 0,\n",
                "    'max': 4000,\n",
                "    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],\n",
                "}\n",
                "\n",
                "Map.addLayer(dem, vis_params, 'SRTM DEM')\n",
                "\n",
                "colors = vis_params['palette']\n",
                "vmin = vis_params['min']\n",
                "vmax = vis_params['max']\n",
                "\n",
                "Map.add_colorbar(vis_params, label=\"Elevation (m)\", layer_name=\"SRTM DEM\")\n",
                "Map"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map.add_colorbar(\n",
                "    vis_params, label=\"Elevation (m)\", layer_name=\"SRTM DEM\", orientation=\"vertical\"\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map.add_colorbar(\n",
                "    vis_params,\n",
                "    label=\"Elevation (m)\",\n",
                "    layer_name=\"SRTM DEM\",\n",
                "    orientation=\"vertical\",\n",
                "    transparent_bg=True,\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map.add_colorbar(\n",
                "    vis_params,\n",
                "    label=\"Elevation (m)\",\n",
                "    layer_name=\"SRTM DEM\",\n",
                "    orientation=\"vertical\",\n",
                "    transparent_bg=True,\n",
                "    discrete=True,\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Create a split-panel map"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "Map.split_map(left_layer='HYBRID', right_layer='TERRAIN')\n",
                "Map"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map(center=(40, -100), zoom=4)\n",
                "Map.split_map(\n",
                "    left_layer='NLCD 2016 CONUS Land Cover', right_layer='NLCD 2001 CONUS Land Cover'\n",
                ")\n",
                "Map"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map(center=(40, -100), zoom=4)\n",
                "\n",
                "dataset = ee.ImageCollection('USGS/NLCD_RELEASES/2016_REL')\n",
                "nlcd_2001 = (\n",
                "    dataset.filter(ee.Filter.eq('system:index', '2001')).first().select('landcover')\n",
                ")\n",
                "nlcd_2016 = (\n",
                "    dataset.filter(ee.Filter.eq('system:index', '2016')).first().select('landcover')\n",
                ")\n",
                "\n",
                "left_layer = geemap.ee_tile_layer(nlcd_2001, {}, 'NLCD 2001')\n",
                "right_layer = geemap.ee_tile_layer(nlcd_2016, {}, 'NLCD 2016')\n",
                "\n",
                "Map.split_map(left_layer, right_layer)\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Create linked maps"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "image = (\n",
                "    ee.ImageCollection('COPERNICUS/S2')\n",
                "    .filterDate('2018-09-01', '2018-09-30')\n",
                "    .map(lambda img: img.divide(10000))\n",
                "    .median()\n",
                ")\n",
                "\n",
                "vis_params = [\n",
                "    {'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 0.3, 'gamma': 1.3},\n",
                "    {'bands': ['B8', 'B11', 'B4'], 'min': 0, 'max': 0.3, 'gamma': 1.3},\n",
                "    {'bands': ['B8', 'B4', 'B3'], 'min': 0, 'max': 0.3, 'gamma': 1.3},\n",
                "    {'bands': ['B12', 'B12', 'B4'], 'min': 0, 'max': 0.3, 'gamma': 1.3},\n",
                "]\n",
                "\n",
                "labels = [\n",
                "    'Natural Color (B4/B3/B2)',\n",
                "    'Land/Water (B8/B11/B4)',\n",
                "    'Color Infrared (B8/B4/B3)',\n",
                "    'Vegetation (B12/B11/B4)',\n",
                "]\n",
                "\n",
                "geemap.linked_maps(\n",
                "    rows=2,\n",
                "    cols=2,\n",
                "    height=\"400px\",\n",
                "    center=[38.4151, 21.2712],\n",
                "    zoom=12,\n",
                "    ee_objects=[image],\n",
                "    vis_params=vis_params,\n",
                "    labels=labels,\n",
                "    label_position=\"topright\",\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Use timeseries inspector\n",
                "\n",
                "Using data from the [Rangeland Analysis Platform](https://support.rangelands.app/article/46-new-to-rap-start-here-landing).\n",
                "- Vegetation cover dataset: `ee.ImageCollection('projects/rangeland-analysis-platform/vegetation-cover-v3')`\n",
                "- Rangeland production dataset: `ee.ImageCollection('projects/rangeland-analysis-platform/npp-partitioned-v3')`"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "import geemap.colormaps as cm"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "collection = ee.ImageCollection(\n",
                "    'projects/rangeland-analysis-platform/vegetation-cover-v3'\n",
                ").select('TRE')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "years = collection.aggregate_array('year').getInfo()\n",
                "years"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "names = [str(i) for i in years]\n",
                "names"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "palette = cm.palettes.ndvi\n",
                "palette"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "vis_params = {'min': 0, 'max': 50, 'palette': palette}"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Map.addLayer(collection.first(), {}, \"First image\")\n",
                "Map.ts_inspector(\n",
                "    left_ts=collection,\n",
                "    right_ts=collection,\n",
                "    left_names=names,\n",
                "    right_names=names,\n",
                "    left_vis=vis_params,\n",
                "    right_vis=vis_params,\n",
                ")\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Data analysis"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Descriptive statistics"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "\n",
                "centroid = ee.Geometry.Point([-122.4439, 37.7538])\n",
                "\n",
                "image = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR').filterBounds(centroid).first()\n",
                "\n",
                "vis = {'min': 0, 'max': 3000, 'bands': ['B5', 'B4', 'B3']}\n",
                "\n",
                "Map.centerObject(centroid, 8)\n",
                "Map.addLayer(image, vis, \"Landsat-8\")\n",
                "Map"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "image.propertyNames().getInfo()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "image.get('CLOUD_COVER').getInfo()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "props = geemap.image_props(image)\n",
                "props.getInfo()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "stats = geemap.image_stats(image, scale=90)\n",
                "stats.getInfo()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Zonal statistics"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "\n",
                "# Add Earth Engine dataset\n",
                "dem = ee.Image('USGS/SRTMGL1_003')\n",
                "\n",
                "# Set visualization parameters.\n",
                "dem_vis = {\n",
                "    'min': 0,\n",
                "    'max': 4000,\n",
                "    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],\n",
                "}\n",
                "\n",
                "# Add Earth Engine DEM to map\n",
                "Map.addLayer(dem, dem_vis, 'SRTM DEM')\n",
                "\n",
                "# Add Landsat data to map\n",
                "landsat = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')\n",
                "\n",
                "landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4}\n",
                "Map.addLayer(landsat, landsat_vis, \"LE7_TOA_5YEAR/1999_2003\")\n",
                "\n",
                "states = ee.FeatureCollection(\"TIGER/2018/States\")\n",
                "Map.addLayer(states, {}, 'US States')\n",
                "Map"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "out_dem_stats = 'dem_stats.csv'\n",
                "\n",
                "# Allowed output formats: csv, shp, json, kml, kmz\n",
                "# Allowed statistics type: MEAN, MAXIMUM, MINIMUM, MEDIAN, STD, MIN_MAX, VARIANCE, SUM\n",
                "geemap.zonal_stats(dem, states, out_dem_stats, stat_type='MEAN', scale=1000)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "out_landsat_stats = 'landsat_stats.csv'\n",
                "geemap.zonal_stats(\n",
                "    landsat, states, out_landsat_stats, stat_type='SUM', scale=1000\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Zonal statistics by group"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "\n",
                "dataset = ee.ImageCollection('USGS/NLCD_RELEASES/2016_REL')\n",
                "nlcd2016 = dataset.filter(ee.Filter.eq('system:index', '2016')).first()\n",
                "landcover = nlcd2016.select('landcover')\n",
                "Map.addLayer(landcover, {}, 'NLCD 2016')\n",
                "\n",
                "states = ee.FeatureCollection(\"TIGER/2018/States\")\n",
                "Map.addLayer(states, {}, 'US States')\n",
                "Map.add_legend(builtin_legend='NLCD')\n",
                "Map"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "nlcd_stats = 'nlcd_stats.csv'\n",
                "\n",
                "# statistics_type can be either 'SUM' or 'PERCENTAGE'\n",
                "# denominator can be used to convert square meters to other areal units, such as square kilimeters\n",
                "geemap.zonal_stats_by_group(\n",
                "    landcover,\n",
                "    states,\n",
                "    nlcd_stats,\n",
                "    stat_type='SUM',\n",
                "    denominator=1000000,\n",
                "    decimal_places=2,\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### WhiteboxTools\n",
                "\n",
                "https://www.whiteboxgeo.com/manual/wbt_book/intro.html"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "import whiteboxgui"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "whiteboxgui.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "whiteboxgui.show(tree=True)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "![](https://i.imgur.com/aNRfUIf.png)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Data export"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Export ee.Image"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "\n",
                "image = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')\n",
                "\n",
                "landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4}\n",
                "Map.addLayer(image, landsat_vis, \"LE7_TOA_5YEAR/1999_2003\", True, 1)\n",
                "\n",
                "Map"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Draw any shapes on the map using the Drawing tools before executing this code block\n",
                "roi = Map.user_roi\n",
                "\n",
                "if roi is None:\n",
                "    roi = ee.Geometry.Polygon(\n",
                "        [\n",
                "            [\n",
                "                [-115.413031, 35.889467],\n",
                "                [-115.413031, 36.543157],\n",
                "                [-114.034328, 36.543157],\n",
                "                [-114.034328, 35.889467],\n",
                "                [-115.413031, 35.889467],\n",
                "            ]\n",
                "        ]\n",
                "    )"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "filename = 'landsat.tif'"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Exporting all bands as one single image"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "image = image.clip(roi).unmask()\n",
                "geemap.ee_export_image(\n",
                "    image, filename=filename, scale=90, region=roi, file_per_band=False\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Exporting each band as one image"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "geemap.ee_export_image(\n",
                "    image, filename=filename, scale=90, region=roi, file_per_band=True\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Export an image to Google Drive¶"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# geemap.ee_export_image_to_drive(image, description='landsat', folder='export', region=roi, scale=30)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Export ee.ImageCollection"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "loc = ee.Geometry.Point(-99.2222, 46.7816)\n",
                "collection = (\n",
                "    ee.ImageCollection('USDA/NAIP/DOQQ')\n",
                "    .filterBounds(loc)\n",
                "    .filterDate('2008-01-01', '2020-01-01')\n",
                "    .filter(ee.Filter.listContains(\"system:band_names\", \"N\"))\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "collection.aggregate_array('system:index').getInfo()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "out_dir = os.getcwd()\n",
                "geemap.ee_export_image_collection(collection, out_dir=out_dir)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# geemap.ee_export_image_collection_to_drive(collection, folder='export', scale=10)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Extract pixels as a numpy array"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "import matplotlib.pyplot as plt\n",
                "\n",
                "img = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_038029_20180810').select(['B4', 'B5', 'B6'])\n",
                "\n",
                "aoi = ee.Geometry.Polygon(\n",
                "    [[[-110.8, 44.7], [-110.8, 44.6], [-110.6, 44.6], [-110.6, 44.7]]], None, False\n",
                ")\n",
                "\n",
                "rgb_img = geemap.ee_to_numpy(img, region=aoi)\n",
                "print(rgb_img.shape)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "rgb_img_test = (255 * ((rgb_img[:, :, 0:3] - 100) / 3500)).astype('uint8')\n",
                "plt.imshow(rgb_img_test)\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Export pixel values to points"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "\n",
                "# Add Earth Engine dataset\n",
                "dem = ee.Image('USGS/SRTMGL1_003')\n",
                "landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')\n",
                "\n",
                "# Set visualization parameters.\n",
                "vis_params = {\n",
                "    'min': 0,\n",
                "    'max': 4000,\n",
                "    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],\n",
                "}\n",
                "\n",
                "# Add Earth Engine layers to Map\n",
                "Map.addLayer(\n",
                "    landsat7, {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200}, 'Landsat 7'\n",
                ")\n",
                "Map.addLayer(dem, vis_params, 'SRTM DEM', True, 1)\n",
                "Map"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "**Download sample data**"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "work_dir = os.getcwd()\n",
                "in_shp = os.path.join(work_dir, 'us_cities.shp')\n",
                "if not os.path.exists(in_shp):\n",
                "    data_url = 'https://github.com/giswqs/data/raw/main/us/us_cities.zip'\n",
                "    geemap.download_from_url(data_url, out_dir=work_dir)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "in_fc = geemap.shp_to_ee(in_shp)\n",
                "Map.addLayer(in_fc, {}, 'Cities')"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "**Export pixel values as a shapefile**"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "out_shp = os.path.join(work_dir, 'dem.shp')\n",
                "geemap.extract_values_to_points(in_fc, dem, out_shp)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "**Export pixel values as a csv**"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "out_csv = os.path.join(work_dir, 'landsat.csv')\n",
                "geemap.extract_values_to_points(in_fc, landsat7, out_csv)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Export ee.FeatureCollection"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "Map = geemap.Map()\n",
                "\n",
                "fc = ee.FeatureCollection('users/giswqs/public/countries')\n",
                "Map.addLayer(fc, {}, \"Countries\")\n",
                "Map"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "out_shp = 'countries.shp'"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "geemap.ee_to_shp(fc, filename=out_shp)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "out_csv = os.path.join(out_dir, 'countries.csv')\n",
                "geemap.ee_export_vector(fc, filename=out_csv)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "out_kml = os.path.join(out_dir, 'countries.kml')\n",
                "geemap.ee_export_vector(fc, filename=out_kml)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# geemap.ee_export_vector_to_drive(fc, description=\"countries\", folder=\"export\", file_format=\"shp\")"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "language": "python",
            "name": "python3"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 5
}