2021 NLCD Pasture and Croplands vs Landsat 9 Slider - Unequal access to water.ipynb

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    "# Import the Earth Engine (ee) module, which allows interaction with Google Earth Engine.\n",
    "import ee\n",
    "\n",
    "# Import the geemap module, which provides a Python interface for interactive mapping with Google Earth Engine.\n",
    "import geemap"
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    "# Add baseamp\n",
    "# Note: Earth Engine will notify you to authorize access from a google account. More info here: https://developers.google.com/earth-engine/guides/access\n",
    "Map = geemap.Map(center=[40,-100], zoom=4)\n",
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    "# Import the NLCD collection\n",
    "dataset = ee.ImageCollection('USGS/NLCD_RELEASES/2021_REL/NLCD')\n",
    "\n",
    "# Filter collection\n",
    "nlcd2021 = dataset.filter(ee.Filter.eq('system:index', '2021')).first()\n",
    "\n",
    "# Select land cover band\n",
    "landcover = nlcd2021.select('landcover')\n",
    "\n",
    "# Load US states FeatureCollection\n",
    "states = ee.FeatureCollection('TIGER/2018/States')\n",
    "\n",
    "# Filter the states to include only the specified states, West of the Mississippi River \n",
    "state_list = ['Washington', 'Oregon', 'California', 'Idaho', 'Nevada', \n",
    "              'Utah', 'Arizona', 'Montana', 'Wyoming', 'Colorado', \n",
    "              'New Mexico', 'North Dakota', 'South Dakota', 'Nebraska', \n",
    "              'Kansas', 'Oklahoma', 'Texas', 'Minnesota', 'Iowa', \n",
    "              'Missouri', 'Arkansas', 'Louisiana']\n",
    "\n",
    "selected_states = states.filter(ee.Filter.inList('NAME', state_list))\n",
    "\n",
    "# Clip the land cover data to the selected states\n",
    "landcover_clipped = landcover.clipToCollection(selected_states)\n",
    "\n",
    "# Create a mask for Pasture/Hay (81) and Cultivated Crops (82)\n",
    "mask = landcover_clipped.eq(81).Or(landcover_clipped.eq(82))\n",
    "\n",
    "# Apply the mask to keep only the desired classes\n",
    "landcover_filtered = landcover_clipped.updateMask(mask)\n",
    "\n",
    "# Display the filtered land cover data - select \n",
    "Map.addLayer(landcover_filtered, {'min': 0, 'max': 82, 'palette': ['white', 'green', 'green']}, 'NLCD 2021 Pasture/Hay & Cultivated Crops')\n",
    "\n",
    "# Center the map over the filtered data\n",
    "Map.centerObject(landcover_filtered)\n"
   ]
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   "source": [
    "# Load the Landsat 9 ImageCollection (Level 2, Tier 1 data) from Google Earth Engine.\n",
    "collection = ee.ImageCollection(\"LANDSAT/LC09/C02/T1_L2\")\n",
    "\n",
    "# Compute the median value for each pixel across the collection. This helps to reduce noise like clouds.\n",
    "median = collection.median()\n",
    "\n",
    "# Function to apply scale factors to the Landsat image bands for optical and thermal corrections.\n",
    "def apply_scale_factors(image):\n",
    "    # Scale optical bands (surface reflectance) by multiplying by 0.0000275 and adding an offset of -0.2.\n",
    "    opticalBands = image.select(\"SR_B.\").multiply(0.0000275).add(-0.2)\n",
    "    \n",
    "    # Scale thermal bands (brightness temperature) by multiplying by 0.00341802 and adding an offset of 149.0.\n",
    "    thermalBands = image.select(\"ST_B.*\").multiply(0.00341802).add(149.0)\n",
    "    \n",
    "    # Add the scaled optical and thermal bands back to the image, replacing the original bands.\n",
    "    return image.addBands(opticalBands, None, True).addBands(thermalBands, None, True)\n",
    "\n",
    "# Apply the scale factors to the median Landsat image.\n",
    "landsat = apply_scale_factors(median)\n",
    "\n",
    "# Clip the Landsat image to a selected region (in this case, to the selected states).\n",
    "landsat_clipped = landsat.clipToCollection(selected_states)\n",
    "\n",
    "# Define visualization parameters for displaying the image in natural color (bands 4, 3, and 2 for red, green, blue).\n",
    "vis_natural = {\n",
    "    \"bands\": [\"SR_B4\", \"SR_B3\", \"SR_B2\"],  # Specify the bands for the true color image (red, green, blue).\n",
    "    \"min\": 0.0,  # Minimum reflectance value for visualization.\n",
    "    \"max\": 0.3,  # Maximum reflectance value for visualization.\n",
    "}\n",
    "\n",
    "# Add the clipped Landsat image to the map using the true color visualization.\n",
    "Map.addLayer(landsat_clipped, vis_natural, \"Landsat 9 True Color (432)\")\n",
    "\n",
    "# Display the map.\n",
    "Map"
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   "source": [
    "# Create split panel map with NLCD 2021 Pasture/Hay & Cultivated Crop on left and Landsat 2021 on right\n",
    "# Apply teal color pallete for NLCD that corresponds to Living Conditions \n",
    "left_layer = geemap.ee_tile_layer(landcover_filtered,{'min': 0, 'max': 82, 'palette': ['white', '#45a196', '#45a196']}, name= 'NLCD 2021 Pasture/Hay & Cultivated Crops')\n",
    "right_layer = geemap.ee_tile_layer(landsat_clipped, vis_natural, name=\"Landsat 2021\")\n",
    "\n",
    "m.split_map(left_layer, right_layer)\n",
    "m\n"
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