python/0.3.gee_landsat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b3793557",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1.360\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.chdir(\"../\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import ee\n",
    "ee.Initialize()\n",
    "import geemap\n",
    "print(ee.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c34ded8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dc = ee.FeatureCollection('TIGER/2018/States').filterMetadata('NAME', 'equals', 'District of Columbia').geometry()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dab5b203",
   "metadata": {},
   "source": [
    "## LANDSAT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "28cc3881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 29 images in this collection.\n"
     ]
    }
   ],
   "source": [
    "dc_ls = (ee.ImageCollection(\"LANDSAT/LC08/C01/T1_SR\")\n",
    "           .filterDate('2010-01-01', '2022-12-31')\n",
    "           .filterBounds(dc)\n",
    "           .filter('CLOUD_COVER < 5'))\n",
    "\n",
    "print(f\"there are {dc_ls.size().getInfo()} images in this collection.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1f618f83",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ndvi(img):\n",
    "    ndvi = img.normalizedDifference(['B5', 'B4']).rename('NDVI')\n",
    "    return img.addBands(ndvi)\n",
    "\n",
    "dc_ndvi = dc_ls.map(get_ndvi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21eb9ea1",
   "metadata": {},
   "source": [
    "### Get Each Year's NDVI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0572c21a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_year_avg(img):\n",
    "    mean = img.reduceRegion(ee.Reducer.mean(), dc, 30).get('NDVI')\n",
    "    return img.set('date', img.date().format()).set('mean', mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1cecf636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NDVI</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.255553</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.415431</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.425041</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.329087</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.348490</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.326505</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.429704</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.492855</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.388530</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       NDVI  year\n",
       "0  0.255553  2013\n",
       "1  0.415431  2014\n",
       "2  0.425041  2015\n",
       "3  0.329087  2016\n",
       "4  0.348490  2017\n",
       "5  0.326505  2018\n",
       "6  0.429704  2019\n",
       "7  0.492855  2020\n",
       "8  0.388530  2021"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yr_range = ee.List.sequence(2013,2021)\n",
    "def getYearlyNDVI(y):\n",
    "    yr_range = ee.Filter.calendarRange(y, y, 'year')\n",
    "    mean = (dc_ndvi.filter(yr_range)\n",
    "                   .select('NDVI')\n",
    "                   .mean()\n",
    "                   .reduceRegion(ee.Reducer.mean(), dc, 30))\n",
    "    return mean.set('year', y)\n",
    "\n",
    "DCYearlyNDVI = yr_range.map(getYearlyNDVI).getInfo()\n",
    "dc_yr_ndvi = pd.DataFrame(DCYearlyNDVI)\n",
    "display(dc_yr_ndvi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a561a744",
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_ls_ts = (dc_ndvi.select(\"NDVI\")\n",
    "                   .map(get_year_avg)\n",
    "                   .reduceColumns(ee.Reducer.toList(2), ['date', 'mean'])\n",
    "                   .values().get(0))\n",
    "dc_ls_ts_dict = ee.Dictionary(ee.List(dc_ls_ts).flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9903465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>ndvi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-12-31</td>\n",
       "      <td>0.255476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>0.415412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>0.425042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>0.329101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>0.348467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>0.326514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.429633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>0.492992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>0.388516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    datetime      ndvi\n",
       "0 2013-12-31  0.255476\n",
       "1 2014-12-31  0.415412\n",
       "2 2015-12-31  0.425042\n",
       "3 2016-12-31  0.329101\n",
       "4 2017-12-31  0.348467\n",
       "5 2018-12-31  0.326514\n",
       "6 2019-12-31  0.429633\n",
       "7 2020-12-31  0.492992\n",
       "8 2021-12-31  0.388516"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc_ndvi_df = pd.DataFrame(dc_ls_ts_dict.getInfo(), index=[0]).T.reset_index()\n",
    "dc_ndvi_df = dc_ndvi_df.rename({\"index\": \"datetime\", 0: \"ndvi\"}, axis=1)\n",
    "dc_ndvi_df[\"datetime\"] = pd.to_datetime(dc_ndvi_df[\"datetime\"])\n",
    "dc_ndvi_by_year = (dc_ndvi_df.set_index(\"datetime\")\n",
    "                   .groupby(pd.Grouper(freq=\"Y\"))\n",
    "                   .mean()\n",
    "                   .reset_index())\n",
    "display(dc_ndvi_by_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b9c9d354",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 3))\n",
    "sns.lineplot(x=\"year\", y=\"NDVI\", data=dc_yr_ndvi, ax=ax)\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"NDVI\")\n",
    "plt.title(\"DC's NDVI from 2013 to 2021\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "124a13e1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14648d68517243cdb95e01f55240e45d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[38.897957, -77.03656], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=HBox…"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Map = geemap.Map(center=(38.897957, -77.036560), zoom=8)\n",
    "Map.add_basemap('CartoDB.DarkMatter')\n",
    "vis_params = {'min': -1, 'max': 1, 'palette': ['blue', 'white', 'green'], 'bands': [\"NDVI\"]}\n",
    "Map.addLayer(dc_ndvi.first(), vis_params, \"Landsat-8\")\n",
    "Map"
   ]
  }
 ],
 "metadata": {
  "gist": {
   "data": {
    "description": "python/0_0_3_gee.ipynb",
    "public": false
   },
   "id": ""
  },
  "kernelspec": {
   "display_name": "ox",
   "language": "python",
   "name": "ox"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}