"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"To authorize access needed by Earth Engine, open the following\n",
" URL in a web browser and follow the instructions:
\n",
" https://code.earthengine.google.com/client-auth?scopes=https%3A//www.googleapis.com/auth/earthengine%20https%3A//www.googleapis.com/auth/devstorage.full_control&request_id=N2k51_3KlWyDMzkGlo643Wk_l0IPnklpaQQOSvYk2dQ&tc=crQOWG4pmg47yxmWUnwVu6HXophHiEjRwbnTQQ8fEgE&cc=MhT2NNOp4YX4THyajW6RiFrlrPJkepnEE-K22cmgVAc
\n",
" The authorization workflow will generate a code, which you should paste in the box below.
\n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Enter verification code: 4/1AfJohXm1Q2OIsWUh6yS1wa_hkd8jaAgs6fcbsbITq1gIO8I-MQlMQQxggm4\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Successfully saved authorization token.\n"
]
}
],
"source": [
"# To use geemap or any other Google Earth Engine related features in python, we need to authenticate it first.\n",
"ee.Authenticate()\n",
"ee.Initialize()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "5c0f2c43-70d8-4c75-bab9-832e2ed236ae",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing ... \n",
"Generating URL ...\n",
"Downloading data from https://earthengine.googleapis.com/v1/projects/earthengine-legacy/tables/f489c3d72f8b8dfc015d2f92cf761e13-32b9f58921f6c3423ee48e00c134c595:getFeatures\n",
"Please wait ...\n",
"Data downloaded to /home/jovyan/work/GGIS407_23Fall/Project/cdl_stats_2013.csv\n",
"Computing ... \n",
"Generating URL ...\n",
"Downloading data from https://earthengine.googleapis.com/v1/projects/earthengine-legacy/tables/2e8be02da7ace0babafc4ba8e401b948-024582f8415b405ee6f0f9fcff801e36:getFeatures\n",
"Please wait ...\n",
"Data downloaded to /home/jovyan/work/GGIS407_23Fall/Project/cdl_stats_2014.csv\n",
"Computing ... \n",
"Generating URL ...\n",
"Downloading data from https://earthengine.googleapis.com/v1/projects/earthengine-legacy/tables/697c714f320af33b69e0845ac86f3979-5670d67253ad2922f84e041647aa7d23:getFeatures\n",
"Please wait ...\n",
"Data downloaded to /home/jovyan/work/GGIS407_23Fall/Project/cdl_stats_2015.csv\n",
"Computing ... \n",
"Generating URL ...\n",
"Downloading data from https://earthengine.googleapis.com/v1/projects/earthengine-legacy/tables/49e98b868aea51e09ce733a10eeed15e-af5ce036a3f13b7ffd1b21789ce448c7:getFeatures\n",
"Please wait ...\n",
"Data downloaded to /home/jovyan/work/GGIS407_23Fall/Project/cdl_stats_2016.csv\n",
"Computing ... \n",
"Generating URL ...\n",
"Downloading data from https://earthengine.googleapis.com/v1/projects/earthengine-legacy/tables/f648eb54c0b08a6bdd28bd5de34acc96-f8234e08706bf6b632df7533fe9ff835:getFeatures\n",
"Please wait ...\n",
"Data downloaded to /home/jovyan/work/GGIS407_23Fall/Project/cdl_stats_2017.csv\n",
"Computing ... \n",
"Generating URL ...\n",
"Downloading data from https://earthengine.googleapis.com/v1/projects/earthengine-legacy/tables/250dd7414e06dfb8942eebaae02f2e1f-048196115b2dc375bc0fc93b4a98a1f1:getFeatures\n",
"Please wait ...\n",
"Data downloaded to /home/jovyan/work/GGIS407_23Fall/Project/cdl_stats_2018.csv\n",
"Computing ... \n",
"Generating URL ...\n",
"Downloading data from https://earthengine.googleapis.com/v1/projects/earthengine-legacy/tables/2530255c343bca01e9428b3dc63540f6-e3b2549321a076a78e9192c0218ce230:getFeatures\n",
"Please wait ...\n",
"Data downloaded to /home/jovyan/work/GGIS407_23Fall/Project/cdl_stats_2019.csv\n",
"Computing ... \n",
"Generating URL ...\n",
"Downloading data from https://earthengine.googleapis.com/v1/projects/earthengine-legacy/tables/1afd37d2086bdd71529dd1f8cf9c69d5-9a5acf177d8273c3d694da4dbfd2dbc3:getFeatures\n",
"Please wait ...\n",
"Data downloaded to /home/jovyan/work/GGIS407_23Fall/Project/cdl_stats_2020.csv\n",
"Computing ... \n",
"Generating URL ...\n",
"Downloading data from https://earthengine.googleapis.com/v1/projects/earthengine-legacy/tables/16dc6d65b11ed8921f44bfa66bad2ed7-7694bfd845a38b8a00330fd01c7e107f:getFeatures\n",
"Please wait ...\n",
"Data downloaded to /home/jovyan/work/GGIS407_23Fall/Project/cdl_stats_2021.csv\n",
"Computing ... \n",
"Generating URL ...\n",
"Downloading data from https://earthengine.googleapis.com/v1/projects/earthengine-legacy/tables/509a30d7dff25f669c7c7e7137600adc-0c7b5d220d35ce5aeeea0e19f17a5114:getFeatures\n",
"Please wait ...\n",
"Data downloaded to /home/jovyan/work/GGIS407_23Fall/Project/cdl_stats_2022.csv\n",
"Computing ... \n",
"Generating URL ...\n",
"Downloading data from https://earthengine.googleapis.com/v1/projects/earthengine-legacy/tables/6ec96da8c6f72926f3f48298cceb938e-1d05331e4887ae790f8176420c51c8be:getFeatures\n",
"Please wait ...\n",
"Data downloaded to /home/jovyan/work/GGIS407_23Fall/Project/cdl_stats_2023.csv\n"
]
}
],
"source": [
"for YEAR in range(2013, 2024):\n",
" \n",
" # Specify the buffer storage path \n",
" output_buffer = os.path.normpath(f\"{buffer_dir}/sampled_buffer_{YEAR}.shp\") \n",
" \n",
" # Convert our shapefile to ee (Earth Engine) objects\n",
" buffer = geemap.shp_to_ee(output_buffer)\n",
" \n",
" # Note that since the 2023 CDL have not be published yet, we use the 2022 CDL as a proxy\n",
" if YEAR == 2023:\n",
" dataset = ee.Image(\"USDA/NASS/CDL/\" + str(2022))\n",
" else:\n",
" dataset = ee.Image(\"USDA/NASS/CDL/\" + str(YEAR))\n",
" \n",
" # Select the corresponding band in CDL dataset for crop type information\n",
" cropland = ee.Image(dataset.select('cropland'))\n",
" \n",
" # Set up the path for CDL statistics output\n",
" cdl_stats = os.path.normpath(f'{current_directory}/cdl_stats_{YEAR}.csv')\n",
" \n",
" # Use zonal_statistics_by_group to calculate the area ratio of each crop type within each buffer\n",
" # statistics_type can be either 'SUM' or 'PERCENTAGE'\n",
" # denominator can be used to convert square meters to other areal units, such as square kilometers\n",
" geemap.zonal_statistics_by_group(\n",
" cropland.clip(buffer),\n",
" buffer,\n",
" cdl_stats,\n",
" statistics_type='PERCENTAGE',\n",
" #denominator=1000000,\n",
" #decimal_places=2,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "897d7b4a-eb0f-4c00-a004-dd84daba41e4",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" \n",
" "
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]
},
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],
"source": [
"# Use the glob module to get a list of CSV file paths in a directory.\n",
"file_paths = glob.glob('cdl_stats_*')\n",
"\n",
"# Create an empty list to store DataFrames, one for each CSV file\n",
"dataframes = []\n",
"\n",
"# Read each CSV file into a DataFrame and append it to the list of DataFrames\n",
"for file_path in file_paths:\n",
" df = pd.read_csv(file_path)\n",
" dataframes.append(df)\n",
"\n",
"# Concatenate the DataFrames vertically to merge them while retaining all columns\n",
"merged_df = pd.concat(dataframes, ignore_index=True)\n",
"\n",
"# Fill missing values (empty entries) with 0\n",
"merged_df.fillna(0, inplace=True)\n",
"\n",
"# Replace 'column_to_remove' with the name of the column you want to remove\n",
"column_to_remove = 'system:index'\n",
"if column_to_remove in merged_df.columns:\n",
" merged_df.drop(columns=[column_to_remove], inplace=True)\n",
"column_to_remove = 'Class_sum'\n",
"if column_to_remove in merged_df.columns:\n",
" merged_df.drop(columns=[column_to_remove], inplace=True)\n",
"\n",
"# Save the merged DataFrame to a new CSV file if needed\n",
"merged_df.to_csv('merged_file.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "023a6639-a503-46d8-ac95-fd585cbb338e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
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{
"data": {
"text/plain": [
"Index(['Class_61', 'Class_190', 'Class_195', 'Class_141', 'Class_152',\n",
" 'Class_21', 'Class_123', 'Class_36', 'Class_131', 'Class_142',\n",
" 'Class_121', 'Class_176', 'Class_124', 'Class_1', 'Class_3', 'Class_5',\n",
" 'id', 'year', 'Class_111', 'Class_122', 'Class_24', 'Class_37',\n",
" 'Class_212', 'Class_2', 'Class_4', 'Class_76', 'Class_33', 'Class_23',\n",
" 'Class_112', 'Class_143', 'Class_6', 'Class_41', 'Class_42', 'Class_69',\n",
" 'Class_75', 'Class_205', 'Class_28', 'Class_216', 'Class_59',\n",
" 'Class_217'],\n",
" dtype='object')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_df.columns"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "12742314-3ec3-4aee-b368-26cc62676d5a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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],
"source": [
"# Load the CSV file containing the crop mapping\n",
"codebook_df = pd.read_csv('CDL_crop_codebook.csv')\n",
"\n",
"# Create a custom mapping dictionary from the codebook DataFrame\n",
"custom_mapping = dict(zip(codebook_df['Codes'], codebook_df['Class_Names']))\n",
"\n",
"# Iterate through the DataFrame's column names\n",
"for col_name in merged_df.columns:\n",
" if (col_name != 'id') & (col_name != 'year'):\n",
"\n",
" # Extract the numerical part from the column name\n",
" num = int(col_name.split('_')[1])\n",
"\n",
" # Look up the corresponding crop name from the custom mapping\n",
" if num in custom_mapping:\n",
" crop_name = custom_mapping[num]\n",
" merged_df.rename(columns={col_name: crop_name}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "8bb5c1db-2f7c-4299-bf18-c7fa4cda29bb",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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{
"data": {
"text/plain": [
"Index(['Fallow/Idle Cropland', 'Woody Wetlands', 'Herbaceous Wetlands',\n",
" 'Deciduous Forest', 'Shrubland', 'Barley', 'Developed/Med Intensity',\n",
" 'Alfalfa', 'Barren', 'Evergreen Forest', 'Developed/Open Space',\n",
" 'Grass/Pasture', 'Developed/High Intensity', 'Corn', 'Rice', 'Soybeans',\n",
" 'id', 'year', 'Open Water', 'Developed/Low Intensity', 'Winter Wheat',\n",
" 'Other Hay/Non Alfalfa', 'Oranges', 'Cotton', 'Sorghum', 'Walnuts',\n",
" 'Safflower', 'Spring Wheat', 'Perennial Ice/Snow ', 'Mixed Forest',\n",
" 'Sunflower', 'Sugarbeets', 'Dry Beans', 'Grapes', 'Almonds',\n",
" 'Triticale', 'Oats', 'Peppers', 'Sod/Grass Seed', 'Pomegranates'],\n",
" dtype='object')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_df.columns"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "c3c5b738-9fab-4f94-b3f3-657865910678",
"metadata": {},
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" | 5 | \n",
" 151970324 | \n",
" 2023-03-21 | \n",
" 43.036608 | \n",
" -112.481733 | \n",
"
\n",
" \n",
" | 6 | \n",
" 180432061 | \n",
" 2023-08-27 | \n",
" 38.891274 | \n",
" -87.134136 | \n",
"
\n",
" \n",
" | 7 | \n",
" 144993828 | \n",
" 2022-12-23 | \n",
" 38.197551 | \n",
" -121.501237 | \n",
"
\n",
" \n",
" | 8 | \n",
" 142121284 | \n",
" 2022-11-13 | \n",
" 38.160591 | \n",
" -121.476371 | \n",
"
\n",
" \n",
" | 9 | \n",
" 119643906 | \n",
" 2022-05-30 | \n",
" 40.221247 | \n",
" -111.707807 | \n",
"
\n",
" \n",
" | 10 | \n",
" 109229187 | \n",
" 2022-03-15 | \n",
" 44.688756 | \n",
" -92.962979 | \n",
"
\n",
" \n",
" | 11 | \n",
" 113250058 | \n",
" 2022-04-28 | \n",
" 44.688790 | \n",
" -92.965724 | \n",
"
\n",
" \n",
" | 12 | \n",
" 141789316 | \n",
" 2022-11-11 | \n",
" 34.937956 | \n",
" -106.056497 | \n",
"
\n",
" \n",
" | 13 | \n",
" 21407838 | \n",
" 2019-03-19 | \n",
" 42.457908 | \n",
" -89.118133 | \n",
"
\n",
" \n",
" | 14 | \n",
" 8919418 | \n",
" 2017-05-08 | \n",
" 33.705039 | \n",
" -80.501501 | \n",
"
\n",
" \n",
" | 15 | \n",
" 69743841 | \n",
" 2021-02-17 | \n",
" 38.212669 | \n",
" -121.501287 | \n",
"
\n",
" \n",
" | 16 | \n",
" 103853866 | \n",
" 2021-12-28 | \n",
" 38.171917 | \n",
" -121.512593 | \n",
"
\n",
" \n",
" | 17 | \n",
" 68025771 | \n",
" 2021-01-13 | \n",
" 38.164362 | \n",
" -121.516341 | \n",
"
\n",
" \n",
" | 18 | \n",
" 103937493 | \n",
" 2021-11-25 | \n",
" 38.157489 | \n",
" -121.465637 | \n",
"
\n",
" \n",
" | 19 | \n",
" 82995323 | \n",
" 2021-06-12 | \n",
" 45.280931 | \n",
" -92.814049 | \n",
"
\n",
" \n",
" | 20 | \n",
" 73540094 | \n",
" 2021-04-10 | \n",
" 45.544775 | \n",
" -93.860925 | \n",
"
\n",
" \n",
" | 21 | \n",
" 41257012 | \n",
" 2020-03-29 | \n",
" 43.756469 | \n",
" -116.987699 | \n",
"
\n",
" \n",
" | 22 | \n",
" 43245038 | \n",
" 2020-04-16 | \n",
" 44.041486 | \n",
" -92.563167 | \n",
"
\n",
" \n",
" | 23 | \n",
" 44267370 | \n",
" 2020-04-29 | \n",
" 41.083733 | \n",
" -96.245114 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" ID | \n",
" Panoid | \n",
" Lat | \n",
" Lon | \n",
" Heading | \n",
" Pitch | \n",
" Roll | \n",
" Year | \n",
" Month | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" ID | \n",
" Panoid | \n",
" Lat | \n",
" Lon | \n",
" Heading | \n",
" Pitch | \n",
" Roll | \n",
" Year | \n",
" Month | \n",
"
\n",
" \n",
" | 1 | \n",
" 1 | \n",
" YdyDug5KipDP88_g3LlzcA | \n",
" 38.2613558375869 | \n",
" -121.4406002341907 | \n",
" 169.7516479492188 | \n",
" 93.66971588134766 | \n",
" 0.8723912239074707 | \n",
" 2007 | \n",
" 6 | \n",
"
\n",
" \n",
" | 2 | \n",
" 2 | \n",
" zjOWG3903_sas4CstIlUWg | \n",
" 38.26135944130262 | \n",
" -121.440610609495 | \n",
" 169.9563446044922 | \n",
" 88.99956512451172 | \n",
" 1.432158350944519 | \n",
" 2015 | \n",
" 8 | \n",
"
\n",
" \n",
" | 3 | \n",
" 3 | \n",
" 3kA8skjnYcwFXRB4WofR8w | \n",
" 38.26133969218689 | \n",
" -121.4406068604355 | \n",
" 169.4710083007812 | \n",
" 89.78001403808594 | \n",
" 1.364865064620972 | \n",
" 2016 | \n",
" 11 | \n",
"
\n",
" \n",
" | 4 | \n",
" 4 | \n",
" 5lZ2CQ6r13vrIyEE1fB9CA | \n",
" 30.62476293717697 | \n",
" -97.39541297004071 | \n",
" 341.0656433105469 | \n",
" 94.39850616455078 | \n",
" 1.765115857124329 | \n",
" 2008 | \n",
" 2 | \n",
"
\n",
" \n",
" | 5 | \n",
" 5 | \n",
" 4v3QNsEpA7dGAcmNV77-tg | \n",
" 30.62476069881489 | \n",
" -97.39543697001042 | \n",
" 156.5848693847656 | \n",
" 89.1475830078125 | \n",
" 0.5970472097396851 | \n",
" 2009 | \n",
" 5 | \n",
"
\n",
" \n",
" | 6 | \n",
" 6 | \n",
" 62v-z86n9AVfzwO_a25BNg | \n",
" 30.62474089233968 | \n",
" -97.39541231024785 | \n",
" 339.2624206542969 | \n",
" 89.32353973388672 | \n",
" 4.39376974105835 | \n",
" 2011 | \n",
" 5 | \n",
"
\n",
" \n",
" | 7 | \n",
" 7 | \n",
" FjH1CP_ZEqDyOVnTeVPKSg | \n",
" 30.62474800067029 | \n",
" -97.39540703190518 | \n",
" 339.759521484375 | \n",
" 89.33876037597656 | \n",
" 1.649017810821533 | \n",
" 2019 | \n",
" 2 | \n",
"
\n",
" \n",
" | 8 | \n",
" 8 | \n",
" 72C1xZJJiczquY1mNCcz4w | \n",
" 30.62477748232022 | \n",
" -97.39543499063116 | \n",
" 339.3891906738281 | \n",
" 90.19628143310547 | \n",
" 2.001705646514893 | \n",
" 2021 | \n",
" 12 | \n",
"
\n",
" \n",
" | 9 | \n",
" 9 | \n",
" Tg0ssRBHpm9nbY6Qz-pCSQ | \n",
" 46.33407233763008 | \n",
" -119.3732652217733 | \n",
" 340.61767578125 | \n",
" 89.03545379638672 | \n",
" 2.730541944503784 | \n",
" 2012 | \n",
" 4 | \n",
"
\n",
" \n",
" | 10 | \n",
" 10 | \n",
" eFWOzRvfUvGS5fqcHXGVeQ | \n",
" 45.54476323631403 | \n",
" -93.86085421527041 | \n",
" 89.4974594116211 | \n",
" 95.06910705566406 | \n",
" 1.029643774032593 | \n",
" 2009 | \n",
" 6 | \n",
"
\n",
" \n",
" | 34 | \n",
" 1 | \n",
" eFWOzRvfUvGS5fqcHXGVeQ | \n",
" 45.54476323631403 | \n",
" -93.86085421527041 | \n",
" 89.4974594116211 | \n",
" 95.06910705566406 | \n",
" 1.029643774032593 | \n",
" 2009 | \n",
" 6 | \n",
"
\n",
" \n",
"
\n",
"