Intro to Earth Engine with Python shown at EGU 2024. An example of calculating the difference NDVI for riparian and upland regions of a watershed. Operations are common GIS vector and raster tasks.

intro_to_earth_engine_with_python_ndvi.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/jdbcode/74c013f8e9909764210aa743351e7944/intro_to_earth_engine_with_python_ndvi.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setup"
      ],
      "metadata": {
        "id": "_86f47l8lFvy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QFVnLbu1LqIO"
      },
      "outputs": [],
      "source": [
        " import ee\n",
        " import geemap\n",
        " import geemap.colormaps as cm\n",
        " from google.colab import _frontend\n",
        " import pandas as pd\n",
        " import altair as alt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ee.Authenticate()\n",
        "ee.Initialize(project='MY-PROJECT')"
      ],
      "metadata": {
        "id": "LW8m6mX6Lt-6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Add a Map panel and define a print helper\n",
        "\n",
        "*BE SURE TO RUN THE SCRATCH CELL TO INSTANTIATE THE MAP*\n",
        "\n"
      ],
      "metadata": {
        "id": "leERuRU8kngf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "_frontend.create_scratch_cell(\"#@title Map\\nm = geemap.Map(draw_ctrl=False)\\nm\", False)"
      ],
      "metadata": {
        "id": "RL7sAW0gO2Hk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_table(col, name):\n",
        "    def get_ic_props(ic):\n",
        "        def to_props(i):\n",
        "            return ee.Feature(None, i.toDictionary())\n",
        "        return ic.map(to_props)\n",
        "    if str(type(col)) == \"<class 'ee.imagecollection.ImageCollection'>\":\n",
        "        col = get_ic_props(col)\n",
        "\n",
        "    df = ee.data.computeFeatures({\n",
        "        'expression': col, 'fileFormat': 'PANDAS_DATAFRAME'})\n",
        "    df = df.drop('geo', axis=1)\n",
        "\n",
        "    display(name, df)"
      ],
      "metadata": {
        "id": "0Q_MZCn_25pB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Vector analysis\n",
        "\n",
        "## Load data and display"
      ],
      "metadata": {
        "id": "txThCaSPHeqq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "basins = ee.FeatureCollection('WWF/HydroATLAS/v1/Basins/level06')\n",
        "rivers = ee.FeatureCollection('WWF/HydroSHEDS/v1/FreeFlowingRivers')\n",
        "\n",
        "\n",
        "m.add_layer(basins, {'color': 'grey'}, 'Level 6 basins')\n",
        "m.add_layer(rivers, {'color': 'blue'}, 'Rivers')\n",
        "m.center_object(ee.Geometry.Point([-118.3430, 46.0646]), 7)\n",
        "\n",
        "\n",
        "print_table(basins.limit(5), 'basins')\n",
        "print_table(rivers.limit(5), 'rivers')"
      ],
      "metadata": {
        "id": "6UHNS-_li-v2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Filtering"
      ],
      "metadata": {
        "id": "eG7i4S6aHiIN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### By attribute: Walla Walla basin by ID"
      ],
      "metadata": {
        "id": "EjBhOVwOS-aN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wawa_basin = basins.filter('HYBAS_ID == 7060382460')\n",
        "\n",
        "m.add_layer(wawa_basin, {'color': 'yellow'}, 'Walla Walla basin')"
      ],
      "metadata": {
        "id": "GugdnDc3IRLw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### By intersection: River segments in Walla Walla basin"
      ],
      "metadata": {
        "id": "FYRA8HC56JeW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "main_rivers = rivers.filterBounds(wawa_basin).filter('RIV_ORD <= 6')\n",
        "\n",
        "m.add_layer(main_rivers, {'color': 'blue'}, 'Rivers')"
      ],
      "metadata": {
        "id": "1k8BVJZ0IPIo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Buffer the river segments to define riparian region"
      ],
      "metadata": {
        "id": "liEmitY3FnN7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "riparian = main_rivers.geometry().buffer(100, 1)\n",
        "\n",
        "m.add_layer(riparian, {'color': 'black'}, 'Main rivers buffer')"
      ],
      "metadata": {
        "id": "4oKZoWdmWfJv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Clip the Walla Walla basin by the riparian region to define upland"
      ],
      "metadata": {
        "id": "SEkTjNZaXjTD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "upland = wawa_basin.geometry().difference(riparian)\n",
        "\n",
        "m.add_layer(upland, {'color': 'orange'}, 'Upland')"
      ],
      "metadata": {
        "id": "A652vH5OXhuJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Raster analysis"
      ],
      "metadata": {
        "id": "udAkTHv2xw5D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Collection filtering"
      ],
      "metadata": {
        "id": "gHKnOvA8yRdS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "toa_col = (\n",
        "    ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA')\n",
        "    .filterDate('2022-04-01', '2022-11-01')\n",
        "    .filterBounds(wawa_basin)\n",
        "    .filter('CLOUD_COVER < 1')\n",
        ")\n",
        "\n",
        "print_table(toa_col, 'Images')"
      ],
      "metadata": {
        "id": "_-HFyHaX6VGT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Image display"
      ],
      "metadata": {
        "id": "EfoQxWNoyV6k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "false_color_vis = {\n",
        "    'bands': ['B6', 'B5', 'B3'],\n",
        "    'min': 0,\n",
        "    'max': 0.4\n",
        "}\n",
        "m.add_layer(toa_col.first(), false_color_vis, 'TOA (first)')"
      ],
      "metadata": {
        "id": "MYOB3-iY7HGW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Image math\n",
        "\n",
        "Let's use band math to calculate NDVI and NBR. Define a function to perform the calculation so we can map the function over the image collection."
      ],
      "metadata": {
        "id": "GUwf80sy2gdj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def calc_ndvi(image):\n",
        "    ndvi = ee.Image().expression(\n",
        "       'ndvi = (nir-red)/(nir+red)',\n",
        "       {\n",
        "          'nir': image.select('B5'),\n",
        "          'red': image.select('B4')\n",
        "       }\n",
        "    )\n",
        "\n",
        "    return image.addBands([ndvi])"
      ],
      "metadata": {
        "id": "NlUu69Sv8qTV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Function mapping"
      ],
      "metadata": {
        "id": "66j49Tkw9s9F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "toa_col = toa_col.map(calc_ndvi)\n",
        "toa_col.first()"
      ],
      "metadata": {
        "id": "ioY13FoJ9woh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Image collection compositing"
      ],
      "metadata": {
        "id": "MYQOxalayc7x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "toa_max = toa_col.max()\n",
        "\n",
        "m.add_layer(toa_max, false_color_vis, 'TOA max')"
      ],
      "metadata": {
        "id": "OuWsqjf5An1N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Clip image to Walla Walla basin"
      ],
      "metadata": {
        "id": "JUEdqLa8yjgH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "toa_max_basin = toa_max.clip(wawa_basin)\n",
        "\n",
        "ndvi_vis = {\n",
        "    'bands': ['ndvi'],\n",
        "    'min': 0,\n",
        "    'max': 0.7,\n",
        "    'palette': cm.get_palette('Greens')\n",
        "}\n",
        "m.add_layer(toa_max_basin, ndvi_vis, 'NDVI max basin')\n",
        "m.add_layer(toa_max_basin, ndvi_vis, 'TOA (first)')\n",
        "m.remove_ee_layer('TOA max')"
      ],
      "metadata": {
        "id": "4WEvGGShBTdB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Zonal statistics: median max NDVI riparian vs upland"
      ],
      "metadata": {
        "id": "nl3dNcUQ1fOv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "toa_riparian = toa_max_basin.reduceRegion(\n",
        "    reducer=ee.Reducer.median(),\n",
        "    geometry=riparian,\n",
        "    scale=50\n",
        ")\n",
        "\n",
        "toa_upland = toa_max_basin.reduceRegion(\n",
        "    reducer=ee.Reducer.median(),\n",
        "    geometry=upland,\n",
        "    scale=200\n",
        ")\n",
        "\n",
        "display('Median riparian NDVI', toa_riparian.getNumber('ndvi'))\n",
        "display('Median upland NDVI', toa_upland.getNumber('ndvi'))"
      ],
      "metadata": {
        "id": "EeBOheg8E9Sg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sampling"
      ],
      "metadata": {
        "id": "Kg1m-6HF15XO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "toa_max_sample = toa_max_basin.sample(\n",
        "    region=wawa_basin,\n",
        "    scale=30,\n",
        "    numPixels=1000\n",
        ")\n",
        "\n",
        "print_table(toa_max_sample, 'Sample')"
      ],
      "metadata": {
        "id": "vCRtN_16NTGW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = ee.data.computeFeatures({\n",
        "    'expression': toa_max_sample,\n",
        "    'fileFormat': 'PANDAS_DATAFRAME'\n",
        "})\n",
        "\n",
        "alt.Chart(df).mark_bar().encode(\n",
        "    alt.X(\"ndvi:Q\", bin=True),\n",
        "    y='count()',\n",
        ")"
      ],
      "metadata": {
        "id": "kp8DC7W34611"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}