Data_preperation.ipynb

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  {
   "cell_type": "markdown",
   "id": "b77a793b",
   "metadata": {},
   "source": [
    "# Data preparation\n",
    "\n",
    "1) obtaining cloud-free composite for the AOI using GEE python API\n",
    "2) masking composite raster by label polygons with classes:\n",
    "\n",
    "1 - ``trees`` \\\n",
    "2 - ``flooded vegetation`` \\\n",
    "3 - ``open water`` \\\n",
    "4 - ``settlements`` \\\n",
    "5 - ``bare soil`` - a target class which dynamics is of the most interest along with ``trees`` \\\n",
    "6 - ``agriculture and grassland`` \\\n",
    "7 - ``shrubs`` \\"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9deb84b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#libraries\n",
    "import ee\n",
    "import pandas as pd\n",
    "#import altair as alt \n",
    "import numpy as np\n",
    "import geemap.foliumap as geemap\n",
    "import folium\n",
    "import geopandas as gpd\n",
    "import rasterio as rio\n",
    "from rasterio.mask import mask\n",
    "\n",
    "import requests\n",
    "import json\n",
    "from geojson import Point, Feature, FeatureCollection, dump\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d569ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0228009",
   "metadata": {},
   "source": [
    "## Sentinel-2 summer composite download using GEE\n",
    "\n",
    "Based on [Sentinel-2 Cloud Masking with s2cloudless](https://developers.google.com/earth-engine/tutorials/community/sentinel-2-s2cloudless) tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7c738292",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<p>To authorize access needed by Earth Engine, open the following\n",
       "        URL in a web browser and follow the instructions:</p>\n",
       "        <p><a href=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=t8MIkc3vF_GtvWJgMcIu3gI7wjkpc_EbiXesoCUBuZk&tc=-q4Cci-rC8dK0J41TysIpcwaPbexoOtyTgyqfJPjuHE&cc=XjUp-bNSUatOk-Z_uExFx59W3156OKJ8lsTR-4CeZrs>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=t8MIkc3vF_GtvWJgMcIu3gI7wjkpc_EbiXesoCUBuZk&tc=-q4Cci-rC8dK0J41TysIpcwaPbexoOtyTgyqfJPjuHE&cc=XjUp-bNSUatOk-Z_uExFx59W3156OKJ8lsTR-4CeZrs</a></p>\n",
       "        <p>The authorization workflow will generate a code, which you should paste in the box below.</p>\n",
       "        "
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     "text": [
      "\n",
      "Successfully saved authorization token.\n"
     ]
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   "source": [
    "#authorisation\n",
    "ee.Authenticate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8330edd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "ee.Initialize()"
   ]
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   "cell_type": "code",
   "execution_count": 21,
   "id": "5567f00d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#all helpers from GEE\n",
    "def get_s2_sr_cld_col(aoi, start_date, end_date):\n",
    "    # Import and filter S2 SR.\n",
    "    s2_sr_col = (ee.ImageCollection('COPERNICUS/S2_SR')\n",
    "        .filterBounds(aoi)\n",
    "        .filterDate(start_date, end_date)\n",
    "        .filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE', CLOUD_FILTER)))\n",
    "\n",
    "    # Import and filter s2cloudless.\n",
    "    s2_cloudless_col = (ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY')\n",
    "        .filterBounds(aoi)\n",
    "        .filterDate(start_date, end_date))\n",
    "\n",
    "    # Join the filtered s2cloudless collection to the SR collection by the 'system:index' property.\n",
    "    return ee.ImageCollection(ee.Join.saveFirst('s2cloudless').apply(**{\n",
    "        'primary': s2_sr_col,\n",
    "        'secondary': s2_cloudless_col,\n",
    "        'condition': ee.Filter.equals(**{\n",
    "            'leftField': 'system:index',\n",
    "            'rightField': 'system:index'\n",
    "        })\n",
    "    }))\n",
    "\n",
    "def add_cloud_bands(img):\n",
    "    # Get s2cloudless image, subset the probability band.\n",
    "    cld_prb = ee.Image(img.get('s2cloudless')).select('probability')\n",
    "\n",
    "    # Condition s2cloudless by the probability threshold value.\n",
    "    is_cloud = cld_prb.gt(CLD_PRB_THRESH).rename('clouds')\n",
    "\n",
    "    # Add the cloud probability layer and cloud mask as image bands.\n",
    "    return img.addBands(ee.Image([cld_prb, is_cloud]))\n",
    "\n",
    "def add_shadow_bands(img):\n",
    "    # Identify water pixels from the SCL band.\n",
    "    not_water = img.select('SCL').neq(6)\n",
    "\n",
    "    # Identify dark NIR pixels that are not water (potential cloud shadow pixels).\n",
    "    SR_BAND_SCALE = 1e4\n",
    "    dark_pixels = img.select('B8').lt(NIR_DRK_THRESH*SR_BAND_SCALE).multiply(not_water).rename('dark_pixels')\n",
    "\n",
    "    # Determine the direction to project cloud shadow from clouds (assumes UTM projection).\n",
    "    shadow_azimuth = ee.Number(90).subtract(ee.Number(img.get('MEAN_SOLAR_AZIMUTH_ANGLE')));\n",
    "\n",
    "    # Project shadows from clouds for the distance specified by the CLD_PRJ_DIST input.\n",
    "    cld_proj = (img.select('clouds').directionalDistanceTransform(shadow_azimuth, CLD_PRJ_DIST*10)\n",
    "        .reproject(**{'crs': img.select(0).projection(), 'scale': 100})\n",
    "        .select('distance')\n",
    "        .mask()\n",
    "        .rename('cloud_transform'))\n",
    "\n",
    "    # Identify the intersection of dark pixels with cloud shadow projection.\n",
    "    shadows = cld_proj.multiply(dark_pixels).rename('shadows')\n",
    "\n",
    "    # Add dark pixels, cloud projection, and identified shadows as image bands.\n",
    "    return img.addBands(ee.Image([dark_pixels, cld_proj, shadows]))\n",
    "\n",
    "def add_cld_shdw_mask(img):\n",
    "    # Add cloud component bands.\n",
    "    img_cloud = add_cloud_bands(img)\n",
    "\n",
    "    # Add cloud shadow component bands.\n",
    "    img_cloud_shadow = add_shadow_bands(img_cloud)\n",
    "\n",
    "    # Combine cloud and shadow mask, set cloud and shadow as value 1, else 0.\n",
    "    is_cld_shdw = img_cloud_shadow.select('clouds').add(img_cloud_shadow.select('shadows')).gt(0)\n",
    "\n",
    "    # Remove small cloud-shadow patches and dilate remaining pixels by BUFFER input.\n",
    "    # 20 m scale is for speed, and assumes clouds don't require 10 m precision.\n",
    "    is_cld_shdw = (is_cld_shdw.focalMin(2).focalMax(BUFFER*2/20)\n",
    "        .reproject(**{'crs': img.select([0]).projection(), 'scale': 20})\n",
    "        .rename('cloudmask'))\n",
    "\n",
    "    # Add the final cloud-shadow mask to the image.\n",
    "    return img_cloud_shadow.addBands(is_cld_shdw)\n",
    "\n",
    "# Define a method for displaying Earth Engine image tiles to a folium map.\n",
    "def add_ee_layer(self, ee_image_object, vis_params, name, show=True, opacity=1, min_zoom=0):\n",
    "    map_id_dict = ee.Image(ee_image_object).getMapId(vis_params)\n",
    "    folium.raster_layers.TileLayer(\n",
    "        tiles=map_id_dict['tile_fetcher'].url_format,\n",
    "        attr='Map Data &copy; <a href=\"https://earthengine.google.com/\">Google Earth Engine</a>',\n",
    "        name=name,\n",
    "        show=show,\n",
    "        opacity=opacity,\n",
    "        min_zoom=min_zoom,\n",
    "        overlay=True,\n",
    "        control=True\n",
    "        ).add_to(self)\n",
    "\n",
    "# Add the Earth Engine layer method to folium.\n",
    "folium.Map.add_ee_layer = add_ee_layer\n",
    "\n",
    "def apply_cld_shdw_mask(img):\n",
    "    # Subset the cloudmask band and invert it so clouds/shadow are 0, else 1.\n",
    "    not_cld_shdw = img.select('cloudmask').Not()\n",
    "\n",
    "    # Subset reflectance bands and update their masks, return the result.\n",
    "    return img.select('B.*').updateMask(not_cld_shdw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "38f5b668",
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      "text/plain": [
       "<geemap.foliumap.Map at 0x175e52e50>"
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     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#bbox of the area of interest\n",
    "\n",
    "f_kola = open('data/monche_bbox.geojson')\n",
    "kola_bbox = json.load(f_kola)\n",
    "aoi_poly = ee.Geometry.MultiPolygon(kola_bbox['features'][0]['geometry']['coordinates'])\n",
    "\n",
    "Map = geemap.Map()\n",
    "Map.addLayer(aoi_poly,{},'AOI_Monchegorsk')\n",
    "Map.setCenter(32.8, 67.92,  11)\n",
    "Map"
   ]
  },
  {
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   "execution_count": 133,
   "id": "041c7897",
   "metadata": {},
   "outputs": [],
   "source": [
    "#settings of example\n",
    "AOI = aoi_poly\n",
    "START_DATE = '2021-06-15'\n",
    "END_DATE = '2021-08-25'\n",
    "CLOUD_FILTER = 60\n",
    "CLD_PRB_THRESH = 15\n",
    "NIR_DRK_THRESH = 0.15\n",
    "CLD_PRJ_DIST = 1\n",
    "BUFFER = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "a9bca99a",
   "metadata": {},
   "outputs": [
    {
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       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x177cfa910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#getting cloud-free median composite for AOI and period of time\n",
    "s2_sr_cld_col_eval = get_s2_sr_cld_col(AOI, START_DATE, END_DATE)\n",
    "s2_sr_cld_col = get_s2_sr_cld_col(AOI, START_DATE, END_DATE)\n",
    "s2_sr_median = (s2_sr_cld_col.map(add_cld_shdw_mask)\n",
    "                             .map(apply_cld_shdw_mask)\n",
    "                             .median())\n",
    "kola_2021 = s2_sr_median.clip(AOI)\n",
    "\n",
    "# Create a folium map object.\n",
    "center = AOI.centroid(10).coordinates().reverse().getInfo()\n",
    "m = folium.Map(location=center, zoom_start=12)\n",
    "\n",
    "# Add layers to the folium map.\n",
    "m.add_ee_layer(kola_2021,\n",
    "                {'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 2500, 'gamma': 1.1},\n",
    "                'S2 cloud-free mosaic', True, 1, 9)\n",
    "\n",
    "# Add a layer control panel to the map.\n",
    "m.add_child(folium.LayerControl())\n",
    "\n",
    "# Display the map.\n",
    "display(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "64d20a25",
   "metadata": {},
   "outputs": [
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       "\n",
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      ],
      "text/plain": [
       "<ee.image.Image at 0x177e636a0>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kola_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "24ac19e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'state': 'READY',\n",
       " 'description': '2018_kola_median_composite',\n",
       " 'creation_timestamp_ms': 1692556318737,\n",
       " 'update_timestamp_ms': 1692556318737,\n",
       " 'start_timestamp_ms': 0,\n",
       " 'task_type': 'EXPORT_IMAGE',\n",
       " 'id': 'OLXCRKALKEDU4OYSVAEIMHLV',\n",
       " 'name': 'projects/earthengine-legacy/operations/OLXCRKALKEDU4OYSVAEIMHLV'}"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#median composite writing to tiff file -- WARNING!! it will be written to the gdrive\n",
    "\n",
    "#task = ee.batch.Export.image.toDrive(image=kola_2021,\n",
    "#                                     description='2021_kola_median_composite',\n",
    "#                                     scale=30,\n",
    "#                                     #region=AOI,\n",
    "#                                     fileNamePrefix='2021_kola_median_composite',\n",
    "#                                     crs='EPSG:4326',\n",
    "#                                     fileFormat='GeoTIFF')\n",
    "#task.start()\n",
    "#task.status()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e9b45aa",
   "metadata": {},
   "source": [
    "## Getting dataset for predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b0f8e145",
   "metadata": {},
   "outputs": [],
   "source": [
    "#helper indices function\n",
    "\n",
    "def get_spectral_indices(df):\n",
    "    blue = df['B2']\n",
    "    green = df['B3']\n",
    "    red = df['B4']\n",
    "    nir = df['B8']\n",
    "    swir2 = df['B11']\n",
    "    swir22 = df['B12']\n",
    " \n",
    "    df['ndvi'] = (nir-red)/(nir+red)\n",
    "    df['evi'] = 2.5 * ((nir - red) / (nir + 6 * red - 7.5 * blue + 1))\n",
    "    df['savi'] = (nir - red) / (nir + red + 0.428) * (1.428)\n",
    "    df['msi'] = swir2/nir\n",
    "    df['bsi'] = ((swir2+red)-(nir+blue))/((swir2+red)+(nir+blue))\n",
    "    #https://doi.org/10.1016/j.envc.2022.100568\n",
    "    df['ndbi'] = ((swir2-nir)/(swir2+nir)) \n",
    "    df['nbi'] = (red*swir2)/nir\n",
    "\n",
    "    #https://awesome-ee-spectral-indices.readthedocs.io/en/latest/list.html#soil\n",
    "    df['bal'] = (red+swir2-nir)\n",
    "    df['mbi'] = ((swir2 - swir22 - nir)/(swir2+swir22+nir)) +0.5\n",
    "    df['ndsoil'] = (swir22-green)/(swir22+green)\n",
    "    df['blfei'] = (((green+red+swir22)/3.0)-swir2)/(((green+red+swir22)/3.0)+swir2)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a1f184e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_path = 'data/labels_satellite_osm.geojson'\n",
    "image_path = 'data/2021_kola_median_composite.tif'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c2380b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 'name': 'yet_another_osm_4',\n",
       " 'crs': {'type': 'name',\n",
       "  'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}},\n",
       " 'features': [{'type': 'Feature',\n",
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       "       [32.79538170622863, 67.9563941463257, 0.0],\n",
       "       [32.79289856178918, 67.9563941463257, 0.0]]]]},\n",
       "   'plot': 1},\n",
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       "       [32.80899000244736, 67.94912120494185, 0.0]]]]},\n",
       "   'plot': 2},\n",
       "  {'type': 'Feature',\n",
       "   'properties': {'id': 4},\n",
       "   'geometry': {'type': 'MultiPolygon',\n",
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       "       [32.8359983883114, 67.91700751530361, 0.0],\n",
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_osm = open(label_path) #new labels corrected with osm\n",
    "labels = json.load(label_osm)\n",
    "count = 0 # add unique number for each data label plot so further it would be possible to consider it and stratify train/test to avoid data overlap\n",
    "for item in labels['features']:\n",
    "    count += 1\n",
    "    item['plot'] = count\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "26388b24",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B1</th>\n",
       "      <th>B2</th>\n",
       "      <th>B3</th>\n",
       "      <th>B4</th>\n",
       "      <th>B5</th>\n",
       "      <th>B6</th>\n",
       "      <th>B7</th>\n",
       "      <th>B8</th>\n",
       "      <th>B8A</th>\n",
       "      <th>B9</th>\n",
       "      <th>...</th>\n",
       "      <th>evi</th>\n",
       "      <th>savi</th>\n",
       "      <th>msi</th>\n",
       "      <th>bsi</th>\n",
       "      <th>ndbi</th>\n",
       "      <th>nbi</th>\n",
       "      <th>bal</th>\n",
       "      <th>mbi</th>\n",
       "      <th>ndsoil</th>\n",
       "      <th>blfei</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>446.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>652.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>1645.0</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>1932.0</td>\n",
       "      <td>2096.0</td>\n",
       "      <td>2058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.356153</td>\n",
       "      <td>0.684366</td>\n",
       "      <td>1.108696</td>\n",
       "      <td>0.075457</td>\n",
       "      <td>0.051546</td>\n",
       "      <td>753.913043</td>\n",
       "      <td>890.0</td>\n",
       "      <td>0.260383</td>\n",
       "      <td>0.410488</td>\n",
       "      <td>-0.379266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>482.5</td>\n",
       "      <td>445.0</td>\n",
       "      <td>596.5</td>\n",
       "      <td>595.5</td>\n",
       "      <td>993.5</td>\n",
       "      <td>1577.0</td>\n",
       "      <td>1776.5</td>\n",
       "      <td>1873.5</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.514218</td>\n",
       "      <td>0.739031</td>\n",
       "      <td>1.161996</td>\n",
       "      <td>0.089177</td>\n",
       "      <td>0.074929</td>\n",
       "      <td>691.968775</td>\n",
       "      <td>899.0</td>\n",
       "      <td>0.277084</td>\n",
       "      <td>0.444858</td>\n",
       "      <td>-0.408226</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>482.5</td>\n",
       "      <td>445.0</td>\n",
       "      <td>596.5</td>\n",
       "      <td>595.5</td>\n",
       "      <td>993.5</td>\n",
       "      <td>1577.0</td>\n",
       "      <td>1776.5</td>\n",
       "      <td>1873.5</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.514218</td>\n",
       "      <td>0.739031</td>\n",
       "      <td>1.161996</td>\n",
       "      <td>0.089177</td>\n",
       "      <td>0.074929</td>\n",
       "      <td>691.968775</td>\n",
       "      <td>899.0</td>\n",
       "      <td>0.277084</td>\n",
       "      <td>0.444858</td>\n",
       "      <td>-0.408226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>482.5</td>\n",
       "      <td>704.5</td>\n",
       "      <td>888.5</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>1206.5</td>\n",
       "      <td>1521.5</td>\n",
       "      <td>1677.5</td>\n",
       "      <td>1711.0</td>\n",
       "      <td>1838.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.701614</td>\n",
       "      <td>0.367172</td>\n",
       "      <td>1.405319</td>\n",
       "      <td>0.171497</td>\n",
       "      <td>0.168509</td>\n",
       "      <td>1420.777031</td>\n",
       "      <td>1704.5</td>\n",
       "      <td>0.294876</td>\n",
       "      <td>0.370528</td>\n",
       "      <td>-0.305906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>482.5</td>\n",
       "      <td>704.5</td>\n",
       "      <td>888.5</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>1206.5</td>\n",
       "      <td>1521.5</td>\n",
       "      <td>1677.5</td>\n",
       "      <td>1711.0</td>\n",
       "      <td>1838.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.701614</td>\n",
       "      <td>0.367172</td>\n",
       "      <td>1.405319</td>\n",
       "      <td>0.171497</td>\n",
       "      <td>0.168509</td>\n",
       "      <td>1420.777031</td>\n",
       "      <td>1704.5</td>\n",
       "      <td>0.294876</td>\n",
       "      <td>0.370528</td>\n",
       "      <td>-0.305906</td>\n",
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       "      <th>...</th>\n",
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       "      <th>3933</th>\n",
       "      <td>742.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>872.0</td>\n",
       "      <td>930.0</td>\n",
       "      <td>1084.0</td>\n",
       "      <td>1169.0</td>\n",
       "      <td>1221.0</td>\n",
       "      <td>1165.0</td>\n",
       "      <td>1226.0</td>\n",
       "      <td>1616.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.473409</td>\n",
       "      <td>0.160149</td>\n",
       "      <td>1.460944</td>\n",
       "      <td>0.161774</td>\n",
       "      <td>0.187304</td>\n",
       "      <td>1358.678112</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>0.272057</td>\n",
       "      <td>0.277548</td>\n",
       "      <td>-0.208521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3934</th>\n",
       "      <td>742.0</td>\n",
       "      <td>888.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>1209.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>1393.0</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>1313.0</td>\n",
       "      <td>1616.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.193095</td>\n",
       "      <td>0.083921</td>\n",
       "      <td>1.388971</td>\n",
       "      <td>0.158997</td>\n",
       "      <td>0.162819</td>\n",
       "      <td>1679.265441</td>\n",
       "      <td>1738.0</td>\n",
       "      <td>0.253641</td>\n",
       "      <td>0.233998</td>\n",
       "      <td>-0.164253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3935</th>\n",
       "      <td>742.0</td>\n",
       "      <td>888.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>1209.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>1393.0</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>1313.0</td>\n",
       "      <td>1616.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.193095</td>\n",
       "      <td>0.083921</td>\n",
       "      <td>1.388971</td>\n",
       "      <td>0.158997</td>\n",
       "      <td>0.162819</td>\n",
       "      <td>1679.265441</td>\n",
       "      <td>1738.0</td>\n",
       "      <td>0.253641</td>\n",
       "      <td>0.233998</td>\n",
       "      <td>-0.164253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3936</th>\n",
       "      <td>592.0</td>\n",
       "      <td>821.5</td>\n",
       "      <td>1021.5</td>\n",
       "      <td>1195.5</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>1424.0</td>\n",
       "      <td>1514.0</td>\n",
       "      <td>1592.0</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>1581.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.380555</td>\n",
       "      <td>0.203091</td>\n",
       "      <td>1.534862</td>\n",
       "      <td>0.202478</td>\n",
       "      <td>0.211002</td>\n",
       "      <td>1834.927293</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>0.293409</td>\n",
       "      <td>0.350501</td>\n",
       "      <td>-0.256137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3937</th>\n",
       "      <td>592.0</td>\n",
       "      <td>821.5</td>\n",
       "      <td>1021.5</td>\n",
       "      <td>1195.5</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>1424.0</td>\n",
       "      <td>1514.0</td>\n",
       "      <td>1592.0</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>1581.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.380555</td>\n",
       "      <td>0.203091</td>\n",
       "      <td>1.534862</td>\n",
       "      <td>0.202478</td>\n",
       "      <td>0.211002</td>\n",
       "      <td>1834.927293</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>0.293409</td>\n",
       "      <td>0.350501</td>\n",
       "      <td>-0.256137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3938 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         B1     B2      B3      B4      B5      B6      B7      B8     B8A  \\\n",
       "0     446.0  494.0   652.0   680.0  1021.0  1645.0  1888.0  1932.0  2096.0   \n",
       "1     482.5  445.0   596.5   595.5   993.5  1577.0  1776.5  1873.5  1991.0   \n",
       "2     482.5  445.0   596.5   595.5   993.5  1577.0  1776.5  1873.5  1991.0   \n",
       "3     482.5  704.5   888.5  1011.0  1206.5  1521.5  1677.5  1711.0  1838.0   \n",
       "4     482.5  704.5   888.5  1011.0  1206.5  1521.5  1677.5  1711.0  1838.0   \n",
       "...     ...    ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "3933  742.0  734.0   872.0   930.0  1084.0  1169.0  1221.0  1165.0  1226.0   \n",
       "3934  742.0  888.0  1095.0  1209.0  1307.0  1344.0  1393.0  1360.0  1313.0   \n",
       "3935  742.0  888.0  1095.0  1209.0  1307.0  1344.0  1393.0  1360.0  1313.0   \n",
       "3936  592.0  821.5  1021.5  1195.5  1324.0  1424.0  1514.0  1592.0  1649.0   \n",
       "3937  592.0  821.5  1021.5  1195.5  1324.0  1424.0  1514.0  1592.0  1649.0   \n",
       "\n",
       "          B9  ...       evi      savi       msi       bsi      ndbi  \\\n",
       "0     2058.0  ...  1.356153  0.684366  1.108696  0.075457  0.051546   \n",
       "1     1910.0  ...  1.514218  0.739031  1.161996  0.089177  0.074929   \n",
       "2     1910.0  ...  1.514218  0.739031  1.161996  0.089177  0.074929   \n",
       "3     1910.0  ...  0.701614  0.367172  1.405319  0.171497  0.168509   \n",
       "4     1910.0  ...  0.701614  0.367172  1.405319  0.171497  0.168509   \n",
       "...      ...  ...       ...       ...       ...       ...       ...   \n",
       "3933  1616.0  ...  0.473409  0.160149  1.460944  0.161774  0.187304   \n",
       "3934  1616.0  ...  0.193095  0.083921  1.388971  0.158997  0.162819   \n",
       "3935  1616.0  ...  0.193095  0.083921  1.388971  0.158997  0.162819   \n",
       "3936  1581.5  ...  0.380555  0.203091  1.534862  0.202478  0.211002   \n",
       "3937  1581.5  ...  0.380555  0.203091  1.534862  0.202478  0.211002   \n",
       "\n",
       "              nbi     bal       mbi    ndsoil     blfei  \n",
       "0      753.913043   890.0  0.260383  0.410488 -0.379266  \n",
       "1      691.968775   899.0  0.277084  0.444858 -0.408226  \n",
       "2      691.968775   899.0  0.277084  0.444858 -0.408226  \n",
       "3     1420.777031  1704.5  0.294876  0.370528 -0.305906  \n",
       "4     1420.777031  1704.5  0.294876  0.370528 -0.305906  \n",
       "...           ...     ...       ...       ...       ...  \n",
       "3933  1358.678112  1467.0  0.272057  0.277548 -0.208521  \n",
       "3934  1679.265441  1738.0  0.253641  0.233998 -0.164253  \n",
       "3935  1679.265441  1738.0  0.253641  0.233998 -0.164253  \n",
       "3936  1834.927293  2047.0  0.293409  0.350501 -0.256137  \n",
       "3937  1834.927293  2047.0  0.293409  0.350501 -0.256137  \n",
       "\n",
       "[3938 rows x 25 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting dataset of band values in pixels of polygons \n",
    "general = pd.DataFrame()\n",
    "with rio.open(image_path, 'r+') as dataset:\n",
    "    for row in labels['features']:\n",
    "        df = pd.DataFrame(rio.mask.mask(dataset, \n",
    "                                    [row['geometry']], \n",
    "                                    all_touched=False,\n",
    "                                    crop=True)[0].reshape([12,-1]).T, \n",
    "                      columns=list(dataset.descriptions))\n",
    "        df['id'] = row['properties']['id']\n",
    "        df['plot'] = row['plot']\n",
    "        general = pd.concat([general, df])\n",
    "        general = general[(general[(list(dataset.descriptions))] > 0).any(1)]\n",
    "        general = general.reset_index().iloc[:,1:]\n",
    "        get_spectral_indices(general)\n",
    "\n",
    "general    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "133ce4ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "general.to_csv('data/general_osm_2021.csv', index=False)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fb0b3f8",
   "metadata": {},
   "source": [
    "🙌"
   ]
  }
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