D_Lab_Demo.ipynb

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        "<a href=\"https://colab.research.google.com/github/meiqingli/Geospatial-Software-Design/blob/master/D_Lab_Demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Extracting Time-Series Satellite Images with Google Earth Engine Python API\n",
        "\n",
        "by: Meiqing Li (meiqing@berkeley.edu), D-Lab Data Science Fellow"
      ],
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "This notebook demonstrates the process to filter, classify, resample, and visualiza time-series satellite images using Google Earth Engine Python API."
      ],
      "metadata": {
        "id": "B7DRqpDVnnbQ"
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      "source": [
        "### API Setup: import, authenticate and initialize\n",
        "\n",
        "Beofre running the code, we need to first import the Google Earth Enginer API. Since the Earth Engine API is installed by default in Google Colaboratory, I don't need to install it here."
      ],
      "metadata": {
        "id": "wlgRqNYJptdf"
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    {
      "cell_type": "code",
      "source": [
        "import ee"
      ],
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      "execution_count": 4,
      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "This will trigger the authentication flow of Google Earth Engine. We will follow the url, which will bring us to the authentication steps. Copy and paste the provided token. Once it is successfullly authorized, we can proceed with our analysis."
      ],
      "metadata": {
        "id": "5XM03xlKrzWk"
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      "cell_type": "code",
      "source": [
        "# Trigger the authentication flow.\n",
        "ee.Authenticate()\n",
        "\n",
        "# Initialize the library.\n",
        "ee.Initialize(project='ee-meiqingli')"
      ],
      "metadata": {
        "id": "SSUqL6yncr3R"
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      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Explore Earth Engine Data Catalog\n",
        "\n",
        "There are a lot of things we can explore wih Google Earth Engine. [The Earth Engine Data Catalog](https://developers.google.com/earth-engine/datasets/catalog) has a collection of public raster datasets which we can import directly to our projects by referring to the \"Earth Engine Snippet\". The two datasets we will use for this notebook are: 1)[CCNL: Consistent And Corrected Nighttime Light Dataset from DMSP-OLS (1992-2013)](https://developers.google.com/earth-engine/datasets/catalog/BNU_FGS_CCNL_v1); 2)[VIIRS Stray Light Corrected Nighttime Day/Night Band Composites Version 1](https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG#description)."
      ],
      "metadata": {
        "id": "VBo55HAZriPD"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "##### DMSP/OLS\n",
        "\n"
      ],
      "metadata": {
        "id": "9zu41KbJM_Rl"
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    {
      "cell_type": "code",
      "source": [
        "## import DMSP OLS Nighttime Lights (1992-01-01 - 2014-01-01)\n",
        "# select band and filter date\n",
        "DMSP = ee.ImageCollection('BNU/FGS/CCNL/v1').select('b1').filterDate('1992-01-01', '2014-01-01')"
      ],
      "metadata": {
        "id": "_I6mxDW3iwNM"
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      "execution_count": 9,
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    {
      "cell_type": "markdown",
      "source": [
        "##### VIIRS"
      ],
      "metadata": {
        "id": "kPJseGcpNAGX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## import VIIRS Nighttime Lights (2014-01-01 - 2022-11-01)\n",
        "# select band and filter date\n",
        "VIIRS = ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG').select('avg_rad').filterDate('2014-01-01', '2022-01-01')"
      ],
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      "execution_count": 10,
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      "source": [
        "### Import Features\n",
        "\n",
        "We can also [upload](https://developers.google.com/earth-engine/guides/table_upload) our own feature tables to Google Earth Engine, and import to our project. Here I import the California county and state boundaries which were uploaded from shapefiles.\n"
      ],
      "metadata": {
        "id": "iVP-u2tbd16t"
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    {
      "cell_type": "code",
      "source": [
        "# I have uploaded CA county and state boundaries through GEE console (add more description here)\n",
        "# import features\n",
        "CACounty = ee.FeatureCollection(\"projects/spherical-depth-278922/assets/California_County_Boundaries\")\n",
        "CAState = ee.FeatureCollection(\"projects/ee-meiqingli/assets/CAState\")"
      ],
      "metadata": {
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      "execution_count": 11,
      "outputs": []
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      "source": [
        "### Get time-series data for a location\n",
        "\n",
        "We can get the time-series data around a selected location. For example, the coordinate for UC Berkeley is [37.8719, -122.2585]. We can use `.getRegion()` to calculate the average nighttime light intensity across years around UC Berkeley from both DMSP/OLS and VIIRS datasets.\n",
        "\n"
      ],
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        "## Calculate the mean value around a location using the `getRegion()` method\n",
        "# DMSP/OLS\n",
        "poi = ee.Geometry.Point([-122.3790, 37.6213]) # note [lon, lat]\n",
        "scale = 30\n",
        "\n",
        "DMSP_poi = DMSP.getRegion(poi, scale).getInfo()\n",
        "\n",
        "# preview the result\n",
        "DMSP_poi[:5]"
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              "[['id', 'longitude', 'latitude', 'time', 'b1'],\n",
              " ['DMSP1992v1',\n",
              "  -122.37897337582122,\n",
              "  37.621309351632945,\n",
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              " ['DMSP1994v1',\n",
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              "  81.95031347136096],\n",
              " ['DMSP1995v1',\n",
              "  -122.37897337582122,\n",
              "  37.621309351632945,\n",
              "  788918400000,\n",
              "  91.53101235005826]]"
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          "execution_count": 12
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      "source": [
        "# VIIRS\n",
        "VIIRS_poi = VIIRS.getRegion(poi, scale).getInfo()\n",
        "\n",
        "# preview the result\n",
        "VIIRS_poi[:5]"
      ],
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      "execution_count": 13,
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              "[['id', 'longitude', 'latitude', 'time', 'avg_rad'],\n",
              " ['20140101',\n",
              "  -122.37897337582122,\n",
              "  37.621309351632945,\n",
              "  1388534400000,\n",
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              " ['20140401',\n",
              "  -122.37897337582122,\n",
              "  37.621309351632945,\n",
              "  1396310400000,\n",
              "  63.16453170776367]]"
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          "metadata": {},
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      "cell_type": "markdown",
      "source": [
        "To make it easier to access the data, we transform the time-series data into pandas dataframes and convert time to datetime format."
      ],
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      "source": [
        "import pandas as pd\n",
        "\n",
        "def ee_array_to_df(arr, list_of_bands):\n",
        "    \"\"\"Transforms client-side ee.Image.getRegion array to pandas.DataFrame.\"\"\"\n",
        "    df = pd.DataFrame(arr)\n",
        "\n",
        "    # Rearrange the header.\n",
        "    headers = df.iloc[0]\n",
        "    df = pd.DataFrame(df.values[1:], columns=headers)\n",
        "\n",
        "    # Remove rows without data inside.\n",
        "    df = df[['longitude', 'latitude', 'time', *list_of_bands]].dropna()\n",
        "\n",
        "    # Convert the data to numeric values.\n",
        "    for band in list_of_bands:\n",
        "        df[band] = pd.to_numeric(df[band], errors='coerce')\n",
        "\n",
        "    # Convert the time field into a datetime.\n",
        "    df['datetime'] = pd.to_datetime(df['time'], unit='ms')\n",
        "\n",
        "    # Keep the columns of interest.\n",
        "    df = df[['time','datetime',  *list_of_bands]]\n",
        "\n",
        "    return df"
      ],
      "metadata": {
        "id": "zCRdD5bqntS_"
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      "execution_count": 14,
      "outputs": []
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        "# select band of interest\n",
        "dmsp_poi = ee_array_to_df(DMSP_poi,['b1'])\n",
        "viirs_poi = ee_array_to_df(VIIRS_poi,['avg_rad'])\n",
        "\n",
        "viirs_poi.head()"
      ],
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              "0           time   datetime    avg_rad\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b20bb016-585d-41f4-b677-cb588d7fe6a5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "viirs_poi",
              "summary": "{\n  \"name\": \"viirs_poi\",\n  \"rows\": 96,\n  \"fields\": [\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1388534400000,\n        \"max\": 1638316800000,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          1598918400000,\n          1590969600000,\n          1580515200000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2014-01-01 00:00:00\",\n        \"max\": \"2021-12-01 00:00:00\",\n        \"num_unique_values\": 96,\n        \"samples\": [\n          \"2020-09-01 00:00:00\",\n          \"2020-06-01 00:00:00\",\n          \"2020-02-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_rad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.04932894835439,\n        \"min\": 24.819854736328125,\n        \"max\": 87.48999786376953,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          44.779998779296875,\n          81.16000366210938,\n          60.66999816894531\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once we have the time-series data, we can plot them on a scatter plot to examine the longitudinal change in nighttime light intensity around UC Berkeley, as well as the variation across two datasets."
      ],
      "metadata": {
        "id": "ftXSRrwYrGq_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "\n",
        "# Fitting curves.\n",
        "## First, extract x values (times) from the dfs.\n",
        "x_data_u = np.asanyarray(dmsp_poi['time'].apply(float))\n",
        "x_data_r = np.asanyarray(viirs_poi['time'].apply(float))\n",
        "\n",
        "## Secondly, extract y values (LST) from the dfs.\n",
        "y_data_u = np.asanyarray(dmsp_poi['b1'].apply(float))\n",
        "y_data_r = np.asanyarray(viirs_poi['avg_rad'].apply(float))\n",
        "\n",
        "# Subplots.\n",
        "fig, ax = plt.subplots(figsize=(14, 6))\n",
        "\n",
        "# Add scatter plots.\n",
        "ax.scatter(dmsp_poi['datetime'], dmsp_poi['b1'],\n",
        "           c='blue', alpha=0.2, label='DMSP/OLS')\n",
        "ax.scatter(viirs_poi['datetime'], viirs_poi['avg_rad'],\n",
        "           c='green', alpha=0.35, label='VIIRS')\n",
        "\n",
        "\n",
        "# Add some parameters.\n",
        "ax.set_title('Nighttime Light Intensity Near UC Berkeley', fontsize=16)\n",
        "ax.set_xlabel('Date', fontsize=14)\n",
        "ax.set_ylabel('Index', fontsize=14)\n",
        "ax.set_ylim(0, 100)\n",
        "ax.grid(lw=0.2)\n",
        "ax.legend(fontsize=14, loc='lower right')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "8iT2MnJLntYI",
        "outputId": "a477effa-0038-42b3-e9d0-a682650fb688"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Static image\n",
        "\n",
        "We can also extract the average values from a certain region. In this example, I filter the data by Alameda County, and visualize them in a customized color palette. Comparing the two thumbnails using DMSP/OLS and VIIRS, the latter has a higher resolution and less overcast."
      ],
      "metadata": {
        "id": "ZjRTvF4Tfh3t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# filter county boundary by name, get geometry\n",
        "alameda = CACounty.filterMetadata(\"COUNTY_NAM\",\"equals\", \"Alameda\").first().geometry()"
      ],
      "metadata": {
        "id": "4Lu1g4nGzqht"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the Image function from the IPython.display module.\n",
        "from IPython.display import Image\n",
        "\n",
        "# Display a thumbnail of global nighttime light.\n",
        "Image(url = DMSP.mean()\n",
        "  .getThumbURL({'min': 0, 'max': 63, 'region': alameda, 'dimensions': 512,\n",
        "                'palette': ['000044','ffff00','ffffff']}))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "r8MJAfZUSeVH",
        "outputId": "d859e6f1-cc6b-4a6e-fa93-24a4bf13830f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://earthengine.googleapis.com/v1/projects/ee-meiqingli/thumbnails/22671c2b14aa5c73ffec0782fb8fdc1c-afb68dee9490ebb6139133a588525860:getPixels\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display a thumbnail of global nighttime light.\n",
        "Image(url = VIIRS.mean()\n",
        "  .getThumbURL({'min': 0, 'max': 63, 'region': alameda, 'dimensions': 512,\n",
        "                'palette': ['000044','ffff00','ffffff']}))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "4AdwsKs3ozOv",
        "outputId": "bf845e3b-25d1-4a20-a13d-d58cb5062c77"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://earthengine.googleapis.com/v1/projects/ee-meiqingli/thumbnails/d6a106fad17807a9a24908f29c8fa94d-cb0bd28e0cdf644af4c6d369c2cf6ccb:getPixels\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Export a GEOTIFF file\n",
        "\n",
        "Once we have the static image, we can save it as a GEOTIFF file to Google Drive."
      ],
      "metadata": {
        "id": "9WzNDjysSuog"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "VIIRS_img = VIIRS.mean()\n",
        "DMSP_img = DMSP.mean()\n",
        "\n",
        "task = ee.batch.Export.image.toDrive(image=VIIRS_img,\n",
        "                                     description='night light intensity in Aladema',\n",
        "                                     scale=30,\n",
        "                                     region=alameda,\n",
        "                                     fileNamePrefix='VIIRS_alameda',\n",
        "                                     crs='EPSG:4326',\n",
        "                                     fileFormat='GeoTIFF')\n",
        "task.start()"
      ],
      "metadata": {
        "id": "hhmJmb0_St6T"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "task.status()"
      ],
      "metadata": {
        "id": "P4rP_5-h61ko",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3df28090-ddb4-435b-a1b8-4372fbf82b0a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'state': 'READY',\n",
              " 'description': 'night light intensity in Aladema',\n",
              " 'priority': 100,\n",
              " 'creation_timestamp_ms': 1712550091297,\n",
              " 'update_timestamp_ms': 1712550091297,\n",
              " 'start_timestamp_ms': 0,\n",
              " 'task_type': 'EXPORT_IMAGE',\n",
              " 'id': '4Q3YE6OXLBOT5HH5HXZESAVU',\n",
              " 'name': 'projects/ee-meiqingli/operations/4Q3YE6OXLBOT5HH5HXZESAVU'}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Or, we can get a link to download the GEOTIFF file."
      ],
      "metadata": {
        "id": "KsNXOQD986BV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "link = VIIRS_img.getDownloadURL({\n",
        "    'scale': 30,\n",
        "    'crs': 'EPSG:4326',\n",
        "    'fileFormat': 'GeoTIFF',\n",
        "    'region': alameda})\n",
        "print(link)"
      ],
      "metadata": {
        "id": "TGrvCjSR84RG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e365991-2098-402c-b8a9-7b52392f54fe"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://earthengine.googleapis.com/v1/projects/ee-meiqingli/thumbnails/022c413565f3aefe4897931e417c48db-b97bd23dd6a76c7a2978064139f91f10:getPixels\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interactive map\n",
        "\n",
        "Our final step is to map the global nighttime light data onto an interactive map. Toggle between DMSP and VIIRS layers by clicking checkboxes on the top right."
      ],
      "metadata": {
        "id": "s807kb8RflVT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the Folium library.\n",
        "import folium\n",
        "\n",
        "# Define a method for displaying Earth Engine image tiles to folium map.\n",
        "def add_ee_layer(self, ee_image_object, vis_params, name):\n",
        "  map_id_dict = ee.Image(ee_image_object).getMapId(vis_params)\n",
        "  folium.raster_layers.TileLayer(\n",
        "    tiles = map_id_dict['tile_fetcher'].url_format,\n",
        "    attr = 'Map Data &copy; <a href=\"https://earthengine.google.com/\">Google Earth Engine</a>',\n",
        "    name = name,\n",
        "    overlay = True,\n",
        "    control = True\n",
        "  ).add_to(self)\n",
        "\n",
        "# Add EE drawing method to folium.\n",
        "folium.Map.add_ee_layer = add_ee_layer\n",
        "\n",
        "# Set visualization parameters.\n",
        "vis_params = {\n",
        "  'min': 0,\n",
        "  'max': 63,\n",
        "  'palette': ['000044','ffff00','ffffff']}\n",
        "\n",
        "# Create a folium map object. Zoom to California\n",
        "my_map = folium.Map(location=[38, -121], zoom_start=5.5)\n",
        "\n",
        "# Arrange layers inside a list\n",
        "ee_tiles = [DMSP_img, VIIRS_img]\n",
        "\n",
        "# Arrange visualization parameters inside a list.\n",
        "ee_vis_params = [vis_params, vis_params]\n",
        "\n",
        "# Arrange layer names inside a list.\n",
        "ee_tiles_names = ['DMSP', 'VIIRS']\n",
        "\n",
        "# Add layers to the map using a loop.\n",
        "for tile, vis_param, name in zip(ee_tiles, ee_vis_params, ee_tiles_names):\n",
        "    my_map.add_ee_layer(tile, vis_param, name)\n",
        "\n",
        "folium.LayerControl(collapsed = False).add_to(my_map)\n",
        "\n",
        "\n",
        "# Display the map.\n",
        "display(my_map)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "_Eq6ggPVx8un",
        "outputId": "64261200-b54a-4101-b442-1f4905ccdcbe"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<folium.folium.Map at 0x783f0d5a2d40>"
            ],
            "text/html": [
              "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
              "&lt;html&gt;\n",
              "&lt;head&gt;\n",
              "    \n",
              "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
              "    \n",
              "        &lt;script&gt;\n",
              "            L_NO_TOUCH = false;\n",
              "            L_DISABLE_3D = false;\n",
              "        &lt;/script&gt;\n",
              "    \n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
              "    \n",
              "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "            &lt;style&gt;\n",
              "                #map_f19b9030b8f8555ad00010c932d24017 {\n",
              "                    position: relative;\n",
              "                    width: 100.0%;\n",
              "                    height: 100.0%;\n",
              "                    left: 0.0%;\n",
              "                    top: 0.0%;\n",
              "                }\n",
              "                .leaflet-container { font-size: 1rem; }\n",
              "            &lt;/style&gt;\n",
              "        \n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;\n",
              "    \n",
              "    \n",
              "            &lt;div class=&quot;folium-map&quot; id=&quot;map_f19b9030b8f8555ad00010c932d24017&quot; &gt;&lt;/div&gt;\n",
              "        \n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;\n",
              "    \n",
              "    \n",
              "            var map_f19b9030b8f8555ad00010c932d24017 = L.map(\n",
              "                &quot;map_f19b9030b8f8555ad00010c932d24017&quot;,\n",
              "                {\n",
              "                    center: [38.0, -121.0],\n",
              "                    crs: L.CRS.EPSG3857,\n",
              "                    zoom: 5.5,\n",
              "                    zoomControl: true,\n",
              "                    preferCanvas: false,\n",
              "                }\n",
              "            );\n",
              "\n",
              "            \n",
              "\n",
              "        \n",
              "    \n",
              "            var tile_layer_6dc02e353a24dcbf811d2322320b420c = L.tileLayer(\n",
              "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
              "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
              "            ).addTo(map_f19b9030b8f8555ad00010c932d24017);\n",
              "        \n",
              "    \n",
              "            var tile_layer_a8ac93e4e6069537849dd9ee3be900b3 = L.tileLayer(\n",
              "                &quot;https://earthengine.googleapis.com/v1/projects/ee-meiqingli/maps/43917683766d1fda53d15c76907096e7-cac534c7c382da7bfa86fc4a14255119/tiles/{z}/{x}/{y}&quot;,\n",
              "                {&quot;attribution&quot;: &quot;Map Data \\u0026copy; \\u003ca href=\\&quot;https://earthengine.google.com/\\&quot;\\u003eGoogle Earth Engine\\u003c/a\\u003e&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
              "            ).addTo(map_f19b9030b8f8555ad00010c932d24017);\n",
              "        \n",
              "    \n",
              "            var tile_layer_c84dfc28340f81c08bcba8531940ba96 = L.tileLayer(\n",
              "                &quot;https://earthengine.googleapis.com/v1/projects/ee-meiqingli/maps/bc0da70a4053ec04fc7baf42cf70a4ce-aefb37aa720fd430a6383d7c71609630/tiles/{z}/{x}/{y}&quot;,\n",
              "                {&quot;attribution&quot;: &quot;Map Data \\u0026copy; \\u003ca href=\\&quot;https://earthengine.google.com/\\&quot;\\u003eGoogle Earth Engine\\u003c/a\\u003e&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
              "            ).addTo(map_f19b9030b8f8555ad00010c932d24017);\n",
              "        \n",
              "    \n",
              "            var layer_control_db209b75fb1c0e03e3aa90588ef88623 = {\n",
              "                base_layers : {\n",
              "                    &quot;openstreetmap&quot; : tile_layer_6dc02e353a24dcbf811d2322320b420c,\n",
              "                },\n",
              "                overlays :  {\n",
              "                    &quot;DMSP&quot; : tile_layer_a8ac93e4e6069537849dd9ee3be900b3,\n",
              "                    &quot;VIIRS&quot; : tile_layer_c84dfc28340f81c08bcba8531940ba96,\n",
              "                },\n",
              "            };\n",
              "            L.control.layers(\n",
              "                layer_control_db209b75fb1c0e03e3aa90588ef88623.base_layers,\n",
              "                layer_control_db209b75fb1c0e03e3aa90588ef88623.overlays,\n",
              "                {&quot;autoZIndex&quot;: true, &quot;collapsed&quot;: false, &quot;position&quot;: &quot;topright&quot;}\n",
              "            ).addTo(map_f19b9030b8f8555ad00010c932d24017);\n",
              "        \n",
              "&lt;/script&gt;\n",
              "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## References\n",
        "\n",
        "*  [EE API Colab Setup](https://colab.research.google.com/github/google/earthengine-api/blob/master/python/examples/ipynb/ee-api-colab-setup.ipynb)\n",
        "*   [EE Python API Tutorial](https://colab.research.google.com/github/google/earthengine-community/blob/master/tutorials/intro-to-python-api/index.ipynb)\n",
        "\n",
        "* More tutorials: [Geemap package for interative mapping with GEE](https://github.com/gee-community/geemap)\n"
      ],
      "metadata": {
        "id": "xL1rOH1H_Ip8"
      }
    }
  ]
}