exploring-cropland-gee.ipynb

{
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  {
   "cell_type": "raw",
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   "source": [
    "---\n",
    "title: Exploring Changes in Iowa's Cropland with Google Earth Engine and Python\n",
    "author: Elke Windschitl\n",
    "format: html\n",
    "editor: source\n",
    "date: '2023-12-04'\n",
    "toc: true\n",
    "output:\n",
    "  execute:\n",
    "    code: true\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Description: Here I use Python to access the Google Earth Engine API and explore how Iowa's cropland has changed between 2003 and 2022.\n",
    "\n",
    "![](images/iowa-crops.png)\n",
    "\n",
    "## Introduction: \n",
    "\n",
    "Iowa is an agriculture state. It is a major hub for corn and soy. In fact, my great-grandparents used to farm Iowa land. So how has Iowa's cropland changed over time? Here I begin exploring that question using remote sensing products accessed through Google Earth Engine.\n",
    "\n",
    "## The Data: \n",
    "\n",
    "I use the Cropland Data Layer created by the USDA, National Agricultural Statistics Service (NASS), Research and Development Division, Geospatial Information Branch, Spatial Analysis Research Section^1^. This dataset has been reported annually since 1997, but 2003 is the first year where coverage accross Iowa is complete. I use 2003 and 2022 in my explorations. I also use the FAO GAUL: Global Administrative Unit Layers 2015, First-Level Administrative Units to clip the cropland data to the state of Iowa^2^.\n",
    "\n",
    "## Methods & Results:\n",
    "\n",
    "I start by importing all necessary libraries/modules and reading in all data. I use *ee* for accessing the Google Earth Engine data, and *geemap* for mapping the rasters. *NumpPy* and *Pandas* are for handling tabular data. *Matplotlib* is used to visualize results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| warning: false\n",
    "# Import necessary packages\n",
    "import ee\n",
    "import geemap\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import ipyleaflet\n",
    "\n",
    "# Authenticate and initialize Google Earth Engine\n",
    "#ee.Authenticate()\n",
    "ee.Initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I read in the administrative boundary data and filter for the US and Iowa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| warning: false\n",
    "# Read in boundary gee data\n",
    "boundaries = ee.FeatureCollection(\"FAO/GAUL/2015/level1\") \n",
    "# Apply the filter to get Iowa\n",
    "iowa = boundaries.filter(ee.Filter.eq(\"ADM0_NAME\", \"United States of America\")) \\\n",
    "    .filter(ee.Filter.eq(\"ADM1_NAME\", \"Iowa\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I read in the crop data, filter for my years of interest and clip to the state of iowa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| warning: false\n",
    "# Read in crop data from gee\n",
    "crop_dat = ee.ImageCollection(\"USDA/NASS/CDL\") \n",
    "crop_vis = {'bands': ['cropland']}\n",
    "\n",
    "# Read in crop data from GEE for the year 2003\n",
    "cropland_03 = crop_dat \\\n",
    "    .filterDate('2003') \\\n",
    "    .first() \\\n",
    "    .clip(iowa)\n",
    "\n",
    "# Read in crop data from GEE for the year 2022\n",
    "cropland_22 = crop_dat \\\n",
    "    .filterDate('2022') \\\n",
    "    .first() \\\n",
    "    .clip(iowa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore the cropland use in Iowa in 2003."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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      "text/plain": [
       "Map(center=[42.075160654145094, -93.49593105615148], controls=(WidgetControl(options=['position', 'transparent…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#| warning: false\n",
    "# Create a map\n",
    "Map_03 = geemap.Map()\n",
    "\n",
    "# Change basemap\n",
    "Map_03.add_basemap('SATELLITE')\n",
    "\n",
    "# Add iowa and center/zoom\n",
    "Map_03.addLayer(iowa)\n",
    "Map_03.centerObject(iowa, 6)\n",
    "\n",
    "# Add your raster data to the map\n",
    "Map_03.addLayer(cropland_03, crop_vis, 'Landcover')\n",
    "Map_03.add_legend(title=\"USDA Land and Crop Classification\", builtin_legend='USDA/NASS/CDL')\n",
    "\n",
    "# Display the map\n",
    "display(Map_03)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::: {.callout-note}\n",
    "Quarto does not seem to consistently render GEE maps. This is something I am still trouble-shooting. Because of this, I have included pictures of the outputs, and a [link to the Jupyter Notebook.](https://github.com/elkewind/misc-code-examples/blob/main/exploring-cropland-gee.ipynb)\n",
    ":::\n",
    "\n",
    "![](images/crops_03.png)\n",
    "\n",
    "Let's explore the cropland use in Iowa in 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ec535aea1994a1ca99c0a2ae794c2f9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[42.075160654145094, -93.49593105615148], controls=(WidgetControl(options=['position', 'transparent…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a map\n",
    "Map_22 = geemap.Map()\n",
    "\n",
    "# Change basemap\n",
    "Map_22.add_basemap('SATELLITE')\n",
    "\n",
    "# Add iowa and center/zoom\n",
    "Map_22.addLayer(iowa)\n",
    "Map_22.centerObject(iowa, 6)\n",
    "\n",
    "# Add your raster data to the map\n",
    "Map_22.addLayer(cropland_22, crop_vis, 'Landcover')\n",
    "Map_22.add_legend(title=\"USDA Land and Crop Classification\", builtin_legend='USDA/NASS/CDL')\n",
    "\n",
    "# Display the map\n",
    "display(Map_22)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/crops_22.png)\n",
    "\n",
    "I used the image_area_by_group function from geepmap to calculate the area of each type of crop in each year. Now I have tabular data I can work with as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| warning: false\n",
    "# Reduce for image_area_by_group to work properly\n",
    "reduced_03 = crop_dat.select('cropland').filterDate('2003').reduce(ee.Reducer.mode()).clip(iowa)\n",
    "reduced_22 = crop_dat.select('cropland').filterDate('2022').reduce(ee.Reducer.mode()).clip(iowa)\n",
    "# Calculate square km of each group for each year and create dataframe\n",
    "areas_03 = geemap.image_area_by_group(reduced_03, scale=1000, denominator=1e6,decimal_places=4, verbose=False)\n",
    "areas_22 = geemap.image_area_by_group(reduced_22, scale=1000, denominator=1e6,decimal_places=4, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I also create a dataframe of the specified legend colors designated by the USDA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>num_crop</th>\n",
       "      <th>color</th>\n",
       "      <th>crop</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 Corn</td>\n",
       "      <td>#ffd300</td>\n",
       "      <td>Corn</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2 Cotton</td>\n",
       "      <td>#ff2626</td>\n",
       "      <td>Cotton</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3 Rice</td>\n",
       "      <td>#00a8e2</td>\n",
       "      <td>Rice</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4 Sorghum</td>\n",
       "      <td>#ff9e0a</td>\n",
       "      <td>Sorghum</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5 Soybeans</td>\n",
       "      <td>#267000</td>\n",
       "      <td>Soybeans</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>247 Turnips</td>\n",
       "      <td>#ff6666</td>\n",
       "      <td>Turnips</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>248 Eggplants</td>\n",
       "      <td>#ff6666</td>\n",
       "      <td>Eggplants</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>249 Gourds</td>\n",
       "      <td>#ff6666</td>\n",
       "      <td>Gourds</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>250 Cranberries</td>\n",
       "      <td>#ff6666</td>\n",
       "      <td>Cranberries</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>254 Dbl Crop Barley/Soybeans</td>\n",
       "      <td>#267000</td>\n",
       "      <td>Dbl Crop Barley/Soybeans</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         num_crop    color                      crop group\n",
       "0                          1 Corn  #ffd300                      Corn     1\n",
       "1                        2 Cotton  #ff2626                    Cotton     2\n",
       "2                          3 Rice  #00a8e2                      Rice     3\n",
       "3                       4 Sorghum  #ff9e0a                   Sorghum     4\n",
       "4                      5 Soybeans  #267000                  Soybeans     5\n",
       "..                            ...      ...                       ...   ...\n",
       "128                   247 Turnips  #ff6666                   Turnips   247\n",
       "129                 248 Eggplants  #ff6666                 Eggplants   248\n",
       "130                    249 Gourds  #ff6666                    Gourds   249\n",
       "131               250 Cranberries  #ff6666               Cranberries   250\n",
       "132  254 Dbl Crop Barley/Soybeans  #267000  Dbl Crop Barley/Soybeans   254\n",
       "\n",
       "[133 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "#| warning: false\n",
    "#| code-fold: true\n",
    "# Create dictionary of colors so we can use them in vis later (I don't think I can extract from built-in colors)\n",
    "legend_dict = {\n",
    "    '1 Corn': '#ffd300',\n",
    "    '2 Cotton': '#ff2626',\n",
    "    '3 Rice': '#00a8e2',\n",
    "    '4 Sorghum': '#ff9e0a',\n",
    "    '5 Soybeans': '#267000',\n",
    "    '6 Sunflower': '#ffff00',\n",
    "    '10 Peanuts': '#70a500',\n",
    "    '11 Tobacco': '#00af49',\n",
    "    '12 Sweet Corn': '#dda50a',\n",
    "    '13 Pop or Orn Corn': '#dda50a',\n",
    "    '14 Mint': '#7cd3ff',\n",
    "    '21 Barley': '#e2007c',\n",
    "    '22 Durum Wheat': '#896054',\n",
    "    '23 Spring Wheat': '#d8b56b',\n",
    "    '24 Winter Wheat': '#a57000',\n",
    "    '25 Other Small Grains': '#d69ebc',\n",
    "    '26 Dbl Crop WinWht/Soybeans': '#707000',\n",
    "    '27 Rye': '#aa007c',\n",
    "    '28 Oats': '#a05989',\n",
    "    '29 Millet': '#700049',\n",
    "    '30 Speltz': '#d69ebc',\n",
    "    '31 Canola': '#d1ff00',\n",
    "    '32 Flaxseed': '#7c99ff',\n",
    "    '33 Safflower': '#d6d600',\n",
    "    '34 Rape Seed': '#d1ff00',\n",
    "    '35 Mustard': '#00af49',\n",
    "    '36 Alfalfa': '#ffa5e2',\n",
    "    '37 Other Hay/Non Alfalfa': '#a5f28c',\n",
    "    '38 Camelina': '#00af49',\n",
    "    '39 Buckwheat': '#d69ebc',\n",
    "    '41 Sugarbeets': '#a800e2',\n",
    "    '42 Dry Beans': '#a50000',\n",
    "    '43 Potatoes': '#702600',\n",
    "    '44 Other Crops': '#00af49',\n",
    "    '45 Sugarcane': '#af7cff',\n",
    "    '46 Sweet Potatoes': '#702600',\n",
    "    '47 Misc Vegs & Fruits': '#ff6666',\n",
    "    '48 Watermelons': '#ff6666',\n",
    "    '49 Onions': '#ffcc66',\n",
    "    '50 Cucumbers': '#ff6666',\n",
    "    '51 Chick Peas': '#00af49',\n",
    "    '52 Lentils': '#00ddaf',\n",
    "    '53 Peas': '#54ff00',\n",
    "    '54 Tomatoes': '#f2a377',\n",
    "    '55 Caneberries': '#ff6666',\n",
    "    '56 Hops': '#00af49',\n",
    "    '57 Herbs': '#7cd3ff',\n",
    "    '58 Clover/Wildflowers': '#e8bfff',\n",
    "    '59 Sod/Grass Seed': '#afffdd',\n",
    "    '60 Switchgrass': '#00af49',\n",
    "    '61 Fallow/Idle Cropland': '#bfbf77',\n",
    "    '63 Forest': '#93cc93',\n",
    "    '64 Shrubland': '#c6d69e',\n",
    "    '65 Barren': '#ccbfa3',\n",
    "    '66 Cherries': '#ff00ff',\n",
    "    '67 Peaches': '#ff8eaa',\n",
    "    '68 Apples': '#ba004f',\n",
    "    '69 Grapes': '#704489',\n",
    "    '70 Christmas Trees': '#007777',\n",
    "    '71 Other Tree Crops': '#af9970',\n",
    "    '72 Citrus': '#ffff7c',\n",
    "    '74 Pecans': '#b5705b',\n",
    "    '75 Almonds': '#00a582',\n",
    "    '76 Walnuts': '#e8d6af',\n",
    "    '77 Pears': '#af9970',\n",
    "    '81 Clouds/No Data': '#f2f2f2',\n",
    "    '82 Developed': '#999999',\n",
    "    '83 Water': '#4970a3',\n",
    "    '87 Wetlands': '#7cafaf',\n",
    "    '88 Nonag/Undefined': '#e8ffbf',\n",
    "    '92 Aquaculture': '#00ffff',\n",
    "    '111 Open Water': '#4970a3',\n",
    "    '112 Perennial Ice/Snow': '#d3e2f9',\n",
    "    '121 Developed/Open Space': '#999999',\n",
    "    '122 Developed/Low Intensity': '#999999',\n",
    "    '123 Developed/Med Intensity': '#999999',\n",
    "    '124 Developed/High Intensity': '#999999',\n",
    "    '131 Barren': '#ccbfa3',\n",
    "    '141 Deciduous Forest': '#93cc93',\n",
    "    '142 Evergreen Forest': '#93cc93',\n",
    "    '143 Mixed Forest': '#93cc93',\n",
    "    '152 Shrubland': '#c6d69e',\n",
    "    '176 Grassland/Pasture': '#e8ffbf',\n",
    "    '190 Woody Wetlands': '#7cafaf',\n",
    "    '195 Herbaceous Wetlands': '#7cafaf',\n",
    "    '204 Pistachios': '#00ff8c',\n",
    "    '205 Triticale': '#d69ebc',\n",
    "    '206 Carrots': '#ff6666',\n",
    "    '207 Asparagus': '#ff6666',\n",
    "    '208 Garlic': '#ff6666',\n",
    "    '209 Cantaloupes': '#ff6666',\n",
    "    '210 Prunes': '#ff8eaa',\n",
    "    '211 Olives': '#334933',\n",
    "    '212 Oranges': '#e27026',\n",
    "    '213 Honeydew Melons': '#ff6666',\n",
    "    '214 Broccoli': '#ff6666',\n",
    "    '215 Avocados': '#739755',\n",
    "    '216 Peppers': '#ff6666',\n",
    "    '217 Pomegranates': '#af9970',\n",
    "    '218 Nectarines': '#ff8eaa',\n",
    "    '219 Greens': '#ff6666',\n",
    "    '220 Plums': '#ff8eaa',\n",
    "    '221 Strawberries': '#ff6666',\n",
    "    '222 Squash': '#ff6666',\n",
    "    '223 Apricots': '#ff8eaa',\n",
    "    '224 Vetch': '#00af49',\n",
    "    '225 Dbl Crop WinWht/Corn': '#ffd300',\n",
    "    '226 Dbl Crop Oats/Corn': '#ffd300',\n",
    "    '227 Lettuce': '#ff6666',\n",
    "    '228 Dbl Crop Triticale/Corn': '#f8d248',\n",
    "    '229 Pumpkins': '#ff6666',\n",
    "    '230 Dbl Crop Lettuce/Durum Wht': '#896054',\n",
    "    '231 Dbl Crop Lettuce/Cantaloupe': '#ff6666',\n",
    "    '232 Dbl Crop Lettuce/Cotton': '#ff2626',\n",
    "    '233 Dbl Crop Lettuce/Barley': '#e2007c',\n",
    "    '234 Dbl Crop Durum Wht/Sorghum': '#ff9e0a',\n",
    "    '235 Dbl Crop Barley/Sorghum': '#ff9e0a',\n",
    "    '236 Dbl Crop WinWht/Sorghum': '#a57000',\n",
    "    '237 Dbl Crop Barley/Corn': '#ffd300',\n",
    "    '238 Dbl Crop WinWht/Cotton': '#a57000',\n",
    "    '239 Dbl Crop Soybeans/Cotton': '#267000',\n",
    "    '240 Dbl Crop Soybeans/Oats': '#267000',\n",
    "    '241 Dbl Crop Corn/Soybeans': '#ffd300',\n",
    "    '242 Blueberries': '#000099',\n",
    "    '243 Cabbage': '#ff6666',\n",
    "    '244 Cauliflower': '#ff6666',\n",
    "    '245 Celery': '#ff6666',\n",
    "    '246 Radishes': '#ff6666',\n",
    "    '247 Turnips': '#ff6666',\n",
    "    '248 Eggplants': '#ff6666',\n",
    "    '249 Gourds': '#ff6666',\n",
    "    '250 Cranberries': '#ff6666',\n",
    "    '254 Dbl Crop Barley/Soybeans': '#267000'\n",
    "}\n",
    "\n",
    "# Create a DataFrame from the legend dictionary\n",
    "legend_df = pd.DataFrame(list(legend_dict.items()), columns=['num_crop', 'color'])\n",
    "legend_df['crop'] = legend_df['num_crop'].apply(lambda x: x.split(' ', 1)[1])\n",
    "legend_df['group'] = legend_df['num_crop'].apply(lambda x: x.split(' ', 1)[0])\n",
    "legend_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I join the color data to the area data for consistent color use when plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| warning: false\n",
    "# Reset the index of the areas DataFrame\n",
    "areas_03.reset_index(inplace=True)\n",
    "\n",
    "# Merge the existing DataFrame with the legend DataFrame based on the 'group' column & sort\n",
    "crop_areas_03 = pd.merge(areas_03, legend_df, on='group', how='left')\n",
    "crop_areas_03['year'] = 2003\n",
    "area_sorted_03 = crop_areas_03.sort_values(by='area', ascending=False)\n",
    "\n",
    "# Reset the index of the areas DataFrame\n",
    "areas_22.reset_index(inplace=True)\n",
    "\n",
    "# Merge the existing DataFrame with the legend DataFrame based on the 'group' column & sort\n",
    "crop_areas_22 = pd.merge(areas_22, legend_df, on='group', how='left')\n",
    "crop_areas_22['year'] = 2022\n",
    "area_sorted_22 = crop_areas_22.sort_values(by='area', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check out the top 10 crops each year by area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>group</th>\n",
       "      <th>area</th>\n",
       "      <th>percentage</th>\n",
       "      <th>num_crop</th>\n",
       "      <th>color</th>\n",
       "      <th>crop</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>52415.6902</td>\n",
       "      <td>0.3599</td>\n",
       "      <td>1 Corn</td>\n",
       "      <td>#ffd300</td>\n",
       "      <td>Corn</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>176</td>\n",
       "      <td>42853.8696</td>\n",
       "      <td>0.2942</td>\n",
       "      <td>176 Grassland/Pasture</td>\n",
       "      <td>#e8ffbf</td>\n",
       "      <td>Grassland/Pasture</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>41927.8154</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>5 Soybeans</td>\n",
       "      <td>#267000</td>\n",
       "      <td>Soybeans</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>61</td>\n",
       "      <td>2933.1871</td>\n",
       "      <td>0.0201</td>\n",
       "      <td>61 Fallow/Idle Cropland</td>\n",
       "      <td>#bfbf77</td>\n",
       "      <td>Fallow/Idle Cropland</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>63</td>\n",
       "      <td>1673.6551</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>63 Forest</td>\n",
       "      <td>#93cc93</td>\n",
       "      <td>Forest</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>1472.0163</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>36 Alfalfa</td>\n",
       "      <td>#ffa5e2</td>\n",
       "      <td>Alfalfa</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>82</td>\n",
       "      <td>1370.3573</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>82 Developed</td>\n",
       "      <td>#999999</td>\n",
       "      <td>Developed</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>83</td>\n",
       "      <td>478.8680</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>83 Water</td>\n",
       "      <td>#4970a3</td>\n",
       "      <td>Water</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>218.6646</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>25 Other Small Grains</td>\n",
       "      <td>#d69ebc</td>\n",
       "      <td>Other Small Grains</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>81</td>\n",
       "      <td>186.8087</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>81 Clouds/No Data</td>\n",
       "      <td>#f2f2f2</td>\n",
       "      <td>Clouds/No Data</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group        area  percentage                 num_crop    color  \\\n",
       "0      1  52415.6902      0.3599                   1 Corn  #ffd300   \n",
       "13   176  42853.8696      0.2942    176 Grassland/Pasture  #e8ffbf   \n",
       "1      5  41927.8154      0.2879               5 Soybeans  #267000   \n",
       "7     61   2933.1871      0.0201  61 Fallow/Idle Cropland  #bfbf77   \n",
       "8     63   1673.6551      0.0115                63 Forest  #93cc93   \n",
       "4     36   1472.0163      0.0101               36 Alfalfa  #ffa5e2   \n",
       "10    82   1370.3573      0.0094             82 Developed  #999999   \n",
       "11    83    478.8680      0.0033                 83 Water  #4970a3   \n",
       "2     25    218.6646      0.0015    25 Other Small Grains  #d69ebc   \n",
       "9     81    186.8087      0.0013        81 Clouds/No Data  #f2f2f2   \n",
       "\n",
       "                    crop  year  \n",
       "0                   Corn  2003  \n",
       "13     Grassland/Pasture  2003  \n",
       "1               Soybeans  2003  \n",
       "7   Fallow/Idle Cropland  2003  \n",
       "8                 Forest  2003  \n",
       "4                Alfalfa  2003  \n",
       "10             Developed  2003  \n",
       "11                 Water  2003  \n",
       "2     Other Small Grains  2003  \n",
       "9         Clouds/No Data  2003  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#| warning: false\n",
    "area_sorted_03.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>group</th>\n",
       "      <th>area</th>\n",
       "      <th>percentage</th>\n",
       "      <th>num_crop</th>\n",
       "      <th>color</th>\n",
       "      <th>crop</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>57108.8402</td>\n",
       "      <td>0.3918</td>\n",
       "      <td>1 Corn</td>\n",
       "      <td>#ffd300</td>\n",
       "      <td>Corn</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>43917.2096</td>\n",
       "      <td>0.3013</td>\n",
       "      <td>5 Soybeans</td>\n",
       "      <td>#267000</td>\n",
       "      <td>Soybeans</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>176</td>\n",
       "      <td>25775.1745</td>\n",
       "      <td>0.1769</td>\n",
       "      <td>176 Grassland/Pasture</td>\n",
       "      <td>#e8ffbf</td>\n",
       "      <td>Grassland/Pasture</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>141</td>\n",
       "      <td>10638.4229</td>\n",
       "      <td>0.0730</td>\n",
       "      <td>141 Deciduous Forest</td>\n",
       "      <td>#93cc93</td>\n",
       "      <td>Deciduous Forest</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>122</td>\n",
       "      <td>1590.3012</td>\n",
       "      <td>0.0109</td>\n",
       "      <td>122 Developed/Low Intensity</td>\n",
       "      <td>#999999</td>\n",
       "      <td>Developed/Low Intensity</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>190</td>\n",
       "      <td>1503.0166</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>190 Woody Wetlands</td>\n",
       "      <td>#7cafaf</td>\n",
       "      <td>Woody Wetlands</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>111</td>\n",
       "      <td>1291.7266</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>111 Open Water</td>\n",
       "      <td>#4970a3</td>\n",
       "      <td>Open Water</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>121</td>\n",
       "      <td>999.4524</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>121 Developed/Open Space</td>\n",
       "      <td>#999999</td>\n",
       "      <td>Developed/Open Space</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>123</td>\n",
       "      <td>901.4150</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>123 Developed/Med Intensity</td>\n",
       "      <td>#999999</td>\n",
       "      <td>Developed/Med Intensity</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>195</td>\n",
       "      <td>759.0405</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>195 Herbaceous Wetlands</td>\n",
       "      <td>#7cafaf</td>\n",
       "      <td>Herbaceous Wetlands</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group        area  percentage                     num_crop    color  \\\n",
       "0      1  57108.8402      0.3918                       1 Corn  #ffd300   \n",
       "1      5  43917.2096      0.3013                   5 Soybeans  #267000   \n",
       "25   176  25775.1745      0.1769        176 Grassland/Pasture  #e8ffbf   \n",
       "22   141  10638.4229      0.0730         141 Deciduous Forest  #93cc93   \n",
       "18   122   1590.3012      0.0109  122 Developed/Low Intensity  #999999   \n",
       "26   190   1503.0166      0.0103           190 Woody Wetlands  #7cafaf   \n",
       "16   111   1291.7266      0.0089               111 Open Water  #4970a3   \n",
       "17   121    999.4524      0.0069     121 Developed/Open Space  #999999   \n",
       "19   123    901.4150      0.0062  123 Developed/Med Intensity  #999999   \n",
       "27   195    759.0405      0.0052      195 Herbaceous Wetlands  #7cafaf   \n",
       "\n",
       "                       crop  year  \n",
       "0                      Corn  2022  \n",
       "1                  Soybeans  2022  \n",
       "25        Grassland/Pasture  2022  \n",
       "22         Deciduous Forest  2022  \n",
       "18  Developed/Low Intensity  2022  \n",
       "26           Woody Wetlands  2022  \n",
       "16               Open Water  2022  \n",
       "17     Developed/Open Space  2022  \n",
       "19  Developed/Med Intensity  2022  \n",
       "27      Herbaceous Wetlands  2022  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#| warning: false\n",
    "area_sorted_22.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot corn, soybean, and grassland/pasture to see how their area's have changed over the 20 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 390,
       "width": 575
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#| warning: false\n",
    "# Create a grouped bar plot\n",
    "plt.figure(figsize=(9, 6))\n",
    "\n",
    "# Define colors\n",
    "lightgray = '#f5f5f5'\n",
    "darkgray = '#2b2b2b'\n",
    "\n",
    "# Define crops/landcover of interest\n",
    "selected_crops = ['Corn', 'Soybeans', 'Grassland/Pasture']\n",
    "\n",
    "# Set bar choices\n",
    "bar_width = 0.4\n",
    "bar_positions_2003 = np.arange(len(selected_crops))\n",
    "bar_positions_2022 = bar_positions_2003 + bar_width + 0.05\n",
    "\n",
    "# Plot bars for 2003\n",
    "for i, crop in enumerate(selected_crops):\n",
    "    crop_data_2003 = area_sorted_03[(area_sorted_03['crop'] == crop) & (area_sorted_03['year'] == 2003)]\n",
    "    plt.bar(bar_positions_2003[i], crop_data_2003['area'].values[0], width=bar_width, color=crop_data_2003['color'].values[0], label=crop)\n",
    "\n",
    "# Plot bars for 2022\n",
    "for i, crop in enumerate(selected_crops):\n",
    "    crop_data_2022 = area_sorted_22[(area_sorted_22['crop'] == crop) & (area_sorted_22['year'] == 2022)]\n",
    "    plt.bar(bar_positions_2022[i], crop_data_2022['area'].values[0], width=bar_width, color=crop_data_2022['color'].values[0])\n",
    "\n",
    "# Set background color\n",
    "plt.gca().set_facecolor(darkgray)\n",
    "fig = plt.gcf()\n",
    "fig.patch.set_facecolor(darkgray)\n",
    "\n",
    "# Set labels and their aesthetics\n",
    "plt.xlabel('Year', fontname='Arial', color=lightgray, size = 12)\n",
    "plt.ylabel('Area (km$^2$)', fontname='Arial', color=lightgray, size = 12)\n",
    "plt.title('Area of Selected Landcover in 2003 and 2022 by Type', fontname='Arial', color=lightgray, size=16)\n",
    "\n",
    "# Adjust x-axis ticks to include both years\n",
    "plt.xticks(np.concatenate([bar_positions_2003, bar_positions_2022]), [2003, 2003, 2003, 2022, 2022, 2022], fontname='Arial', color=lightgray)\n",
    "plt.yticks(color=lightgray)\n",
    "\n",
    "# Set border color\n",
    "plt.gca().spines['bottom'].set_color(lightgray)\n",
    "plt.gca().spines['top'].set_color(lightgray)\n",
    "plt.gca().spines['right'].set_color(lightgray)\n",
    "plt.gca().spines['left'].set_color(lightgray)\n",
    "\n",
    "# Set tick marks color\n",
    "plt.tick_params(axis='both', colors='lightgray')\n",
    "\n",
    "# Set legend text color\n",
    "legend = plt.legend(title='Crop/Type', bbox_to_anchor=(.72, .97), loc='upper left', prop={'family': 'Arial'})\n",
    "plt.setp(legend.get_title(), color=darkgray)\n",
    "for text in legend.get_texts():\n",
    "    text.set_color(darkgray)\n",
    "\n",
    "plt.show()"
   ]
  },
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   "metadata": {},
   "source": [
    "# Conclusions\n",
    "Unsurprisingly, Iowa's land cover has changed a bit over the years, but corn remains the predominant crop. Google Earth Engine allowed me to smoothly access, use, and visualize a remote sensing data product. Further analyses could be completed to identify changes in crops through all years or what landcover type has had the largest % change over time.\n",
    "\n",
    "## References:\n",
    "\n",
    "^1^USDA National Agricultural Statistics Service Cropland Data Layer. 2022. Published crop-specific data layer [Online]. Available at https://nassgeodata.gmu.edu/CropScape/ (accessed 12/03/2023). USDA-NASS, Washington, DC.\n",
    "\n",
    "^2^FAO GAUL: Global Administrative Unit Layers 2015, First-Level Administrative Units. 2015. Available at https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level1#terms-of-use (accessed 12/03/2023). FAO UN, Rome, Italy."
   ]
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