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"cells": [
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"source": [
"# Complete ReadMe Example"
]
},
{
"cell_type": "markdown",
"id": "7db36512",
"metadata": {},
"source": [
"> **Note:** \n",
"> This notebook includes **interactive map visualizations** using the `geemap` package, organizes data using `pandas` for plotting, and plots data using the `matplotlib` package. \n",
"> Additionally, you will likely need to install `ipykernel` to utilize the interactive elements within a Jupyter Notebook.\n",
"> The `geemap`, `matplotlib`, and `ipykernel` packages are **not installed automatically** with `RadGEEToolbox` since they are optional dependencies focused on visualizations. \n",
"> If you have not already installed these packages, you can do so with:\n",
">\n",
"> ```bash\n",
"> pip install geemap ipykernel matplotlib\n",
"> ```\n",
">\n",
"> Alternatively, using conda:\n",
">\n",
"> ```bash\n",
"> conda install conda-forge::geemap anaconda::ipykernel conda-forge::matplotlib\n",
"> ```\n",
"> \n",
"> If you do not install them, the map-based portions of this notebook will not work."
]
},
{
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"id": "3c1a48ae",
"metadata": {},
"outputs": [],
"source": [
"# 1. Import necessary packages and modules\n",
"import ee\n",
"from RadGEEToolbox import LandsatCollection, VisParams\n",
"import geemap\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.dates import MonthLocator\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b4894ccc",
"metadata": {},
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" \n",
" "
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""
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"output_type": "display_data"
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"name": "stdout",
"output_type": "stream",
"text": [
"Earth Engine initialized successfully.\n"
]
}
],
"source": [
"# 2. Authenticate & Initialize GEE API\n",
"\n",
"# Store name of Google Cloud Project assosiated with Earth Engine - replace with your project ID/name\n",
"PROJECT_ID = 'your-cloud-project-id'\n",
"# Attempt to initialize Earth Engine\n",
"try:\n",
" ee.Initialize(project=PROJECT_ID)\n",
" print(\"Earth Engine initialized successfully.\")\n",
"except Exception as e:\n",
" print(\"Initialization failed, attempting authentication...\")\n",
" try:\n",
" ee.Authenticate()\n",
" ee.Initialize(project=PROJECT_ID)\n",
" print(\"Authentication and initialization successful.\")\n",
" except Exception as auth_error:\n",
" print(\"Authentication failed. Error details:\", auth_error)"
]
},
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"execution_count": 3,
"id": "7cbd4c86",
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"outputs": [
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"text/html": [
"\n",
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" "
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"text": [
"['2020-01-13', '2020-02-14', '2020-05-04', '2020-05-20', '2020-07-07', '2020-08-08', '2020-08-24', '2020-09-25', '2020-10-11', '2020-10-27', '2020-11-28', '2020-12-30', '2021-01-15', '2021-01-31', '2021-04-05', '2021-05-07', '2021-06-08', '2021-07-10', '2021-08-27', '2021-10-30', '2021-11-10', '2021-11-10', '2021-12-01', '2021-12-17', '2022-01-02', '2022-01-10', '2022-01-26', '2022-02-11', '2022-02-19', '2022-02-27', '2022-03-23', '2022-04-08', '2022-05-02', '2022-05-18', '2022-05-26', '2022-06-11', '2022-06-27', '2022-07-13', '2022-08-06', '2022-08-22', '2022-08-30', '2022-09-07', '2022-09-23', '2022-10-01', '2022-10-09', '2022-10-17', '2022-10-25', '2022-11-10', '2023-01-21', '2023-03-18', '2023-04-11', '2023-06-22', '2023-06-30', '2023-07-08', '2023-07-16', '2023-09-18', '2023-09-26', '2023-10-04', '2023-10-20', '2023-11-13', '2023-11-21', '2023-12-07', '2023-12-15', '2023-12-31', '2024-01-16', '2024-03-20', '2024-04-13', '2024-04-21', '2024-05-07', '2024-05-23', '2024-06-08', '2024-06-16', '2024-07-02', '2024-07-10', '2024-07-26', '2024-08-19', '2024-08-27', '2024-09-04', '2024-09-12', '2024-09-20', '2024-09-28', '2024-10-06', '2024-10-22', '2024-10-30', '2024-11-07', '2024-11-15', '2024-12-01']\n"
]
}
],
"source": [
"# 3. Define study area boundary - in this case Lake Powell, Utah\n",
"study_area = ee.Geometry.Polygon(\n",
" [[[-111.35875055487008, 37.19999663127137],\n",
" [-111.35875055487008, 37.00119876939416],\n",
" [-111.12048456365915, 37.00119876939416],\n",
" [-111.12048456365915, 37.19999663127137]]])\n",
"\n",
"# 3. Create a Landsat image collection for a given time range and study area\n",
"# includes filtering imagery based on areal percentage of cloud cover\n",
"# WRS-2 tile(s) can also be used for filtering instead of an ROI geometry\n",
"collection = LandsatCollection(\n",
" start_date='2020-01-01', #date formats of 'YYYY-MM-DD'\n",
" end_date='2025-01-01',\n",
" cloud_percentage_threshold=10, #filtering to <10% cloud coverage\n",
" boundary=study_area #ee.Geometry() of your study area\n",
" )\n",
"\n",
"# 4. Check collection by printing the dates of all images in the collection\n",
"dates = collection.dates \n",
"print(dates)\n",
"\n"
]
},
{
"cell_type": "code",
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"source": [
"### 2. Apply a Cloud Mask and Compute NDWI\n",
"\n",
"# 1. Mask clouds \n",
"cloud_masked_collection = collection.masked_clouds_collection\n",
"# 2. Create a collection of singleband NDWI images with band names of 'ndwi'\n",
"cloud_masked_NDWI_collection = cloud_masked_collection.ndwi\n",
"# BONUS - 3. Create water classification maps using a user-set binary NDWI threshold\n",
"water_classification_maps = cloud_masked_collection.ndwi_collection(\n",
" threshold=0.01\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5eb20bf9",
"metadata": {},
"outputs": [
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"text/html": [
"\n",
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"name": "stdout",
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"text": [
"Date of displayed image: 2024-11-15\n"
]
},
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"Map(center=[37.1, -111.2], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=SearchDataGU…"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"### 2b. Visualize one of the water classification maps alongside true color imagery\n",
"Map = geemap.Map(center=(37.1, -111.2), zoom=11)\n",
"\n",
"image_collection_index = -2 # Change this index to visualize different images in the collection\n",
"\n",
"Map.addLayer(cloud_masked_collection.image_grab(image_collection_index), \n",
" vis_params=VisParams.get_visualization_params(satellite='landsat', index='TrueColor'), name='True Color')\n",
"\n",
"Map.addLayer(water_classification_maps.image_grab(image_collection_index), \n",
" vis_params={'band': 'ndwi', 'min': -1, 'max': 1, 'palette': ['cyan']}, \n",
" name='Water Classification', opacity=0.8)\n",
"\n",
"print('Date of displayed image:', dates[image_collection_index]) # Print date of image being visualized\n",
"Map #Display the map"
]
},
{
"cell_type": "code",
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"id": "629ad3e9",
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"text": [
"List of square meters of water in images: [119468493.65587278, 110921772.78125958, 91528045.8450157, 108648883.17436236, 116851935.02397366, 113213717.13579006, 108491055.85271907, 107088565.6888672, 107073791.12062846, 108535637.83579773, 108686518.55878142, 109656793.29963809, 98943045.00580575, 24051340.084162455, 90535573.4586263, 90127733.94363703, 92272644.77111673, 90564523.54212815, 86327193.58412033, 85903129.37486789, 34560333.030642994, 16123285.508434819, 87760479.04216835, 91194868.25898245, 94780826.90225184, 89799707.77026847, 86869098.41571692, 80467932.22284581, 78249124.53695235, 77506955.47923368, 73278758.19566867, 72434238.5125517, 72021990.64313151, 72737006.41659582, 74251084.16470972, 64642984.42667165, 80107985.3697438, 68265061.07168543, 78558158.02590762, 77236215.5065736, 76442892.05442517, 76340111.75266735, 75334836.30380285, 75625722.7914388, 76824643.96483418, 76799581.57267731, 78229114.55117953, 75895636.42466682, 94658499.6336531, 69641590.48180148, 70589562.66440716, 96056836.12859221, 100069788.89661077, 101385843.26526311, 100598990.40198186, 94718120.7702225, 95175308.32971047, 94704702.746785, 96155902.8663469, 84848207.11699985, 100016113.12202054, 76903691.99774817, 96389553.23861252, 103439266.74470168, 100525579.30571193, 88778602.02301815, 86821988.3655465, 87393122.2987745, 86960903.93820274, 85546232.87625422, 708129.810680913, 97079820.33867188, 100515449.56841874, 102914469.5050092, 102443170.06164983, 99792390.22034124, 99328192.98786382, 98973024.88489202, 98902129.23254827, 97535594.91783854, 97612111.05719975, 97568119.4721201, 99217888.19225259, 100540361.08978055, 99617817.1615847, 100302350.68244676, 50740286.40523514]\n"
]
}
],
"source": [
"### 3. Calculate Water Area Time Series\n",
"calculate_water_area = cloud_masked_NDWI_collection.pixelAreaSumCollection(\n",
" band_name='ndwi', #specify band to use from collection\n",
" geometry=study_area, #ee.Geometry() of your study area\n",
" threshold=0, #binary classification threshold for unclassified rasters,\n",
" scale=90 #pixel size for zonal statistics\n",
" )\n",
"water_area_time_series = calculate_water_area.exportProperties('ndwi')\n",
"print('List of square meters of water in images:', water_area_time_series)"
]
},
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"cell_type": "code",
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"id": "c8cfe048",
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"text": [
" Date water_area_m2\n",
"0 2020-01-13 1.194685e+08\n",
"1 2020-02-14 1.109218e+08\n",
"2 2020-05-04 9.152805e+07\n",
"3 2020-05-20 1.086489e+08\n",
"4 2020-07-07 1.168519e+08\n",
".. ... ...\n",
"81 2024-10-06 9.756812e+07\n",
"82 2024-10-22 9.921789e+07\n",
"83 2024-10-30 1.005404e+08\n",
"84 2024-11-07 9.961782e+07\n",
"85 2024-11-15 1.003024e+08\n",
"\n",
"[82 rows x 2 columns]\n"
]
}
],
"source": [
"### 4. Organize Water Area Data into a Pandas DataFrame for Plotting\n",
"#Filter outlier values due to cloud cover\n",
"water_area_df = water_area_time_series[water_area_time_series['ndwi'] > 0.6e8] \n",
"water_area_df['water_area_m2'] = water_area_df['ndwi']\n",
"print(water_area_df)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9d686172",
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"outputs": [
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 6)) # Set figure size\n",
"plt.plot(water_area_df['Date'], water_area_df['water_area_m2'], marker='s', markersize=5, linewidth=0.9, color='darkcyan') # Plot water area time series\n",
"plt.fill_between(water_area_df['Date'], water_area_df['water_area_m2'], color='darkcyan', alpha=0.1) # Fill area under the curve\n",
"plt.title('Lake Powell Water Area Time Series', fontsize=16) # Set plot title\n",
"plt.xlabel('Date', fontsize=14) # Set x-axis label\n",
"plt.ylabel('Water Area (1E8 m²)', fontsize=14) # Set y-axis label\n",
"plt.xticks(rotation=45) # Rotate x-axis labels for better readability\n",
"plt.gca().xaxis.set_major_locator(MonthLocator(interval=6)) # Limit major x-ticks to every 6 months\n",
"plt.gca().xaxis.set_minor_locator(MonthLocator(interval=1)) # Limit minor x-ticks to every month\n",
"plt.xlim(water_area_df['Date'].min(), water_area_df['Date'].max()) # Set x-axis limits\n",
"plt.ylim(0, water_area_df['water_area_m2'].max() * 1.1) # Set y-axis limits\n",
"plt.tight_layout()\n",
"plt.show() # Display the plot"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}