notebooks/Data_Challenge/LandCover.ipynb

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    "## 2022 EY Challenge - Land Cover\n",
    "This notebook can be used to create a land cover dataset. This land cover information can be used as a \"predictor variable\" to relate to species samples. For example, certain land cover classifications (e.g. water, grass, trees) may be conducive to species habitats. This dataset contains global estimates of 10-class land use/land cover for the year 2020, derived from ESA Sentinel-2 imagery at 10-meter spatial resolution. The data can be found in the MS Planetary Computer catalog: https://planetarycomputer.microsoft.com/dataset/io-lulc#overview"
   ]
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   "source": [
    "# Supress Warnings \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Import common GIS tools\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import rioxarray as rio\n",
    "import rasterio.features\n",
    "import folium\n",
    "import math\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# Import Planetary Computer tools\n",
    "import stackstac\n",
    "import pystac_client\n",
    "import planetary_computer as pc\n",
    "from pystac.extensions.raster import RasterExtension as raster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the analysis region and view on a map\n",
    "\n",
    "First, we define our area of interest using latitude and longitude coordinates. Our test region is near Richmond, NSW, Australia. The first line defines the lower-left corner of the bounding box and the second line defines the upper-right corner of the bounding box. GeoJSON format uses a specific order: (longitude, latitude), so be careful when entering the coordinates."
   ]
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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   "source": [
    "# Define the bounding box using corners\n",
    "min_lon, min_lat = (150.62, -33.69)  # Lower-left corner (longitude, latitude)\n",
    "max_lon, max_lat = (150.83, -33.48)  # Upper-right corner (longitude, latitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bbox = (min_lon, min_lat, max_lon, max_lat)\n",
    "latitude = (min_lat, max_lat)\n",
    "longitude = (min_lon, max_lon)"
   ]
  },
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    "def _degree_to_zoom_level(l1, l2, margin = 0.0):\n",
    "    \n",
    "    degree = abs(l1 - l2) * (1 + margin)\n",
    "    zoom_level_int = 0\n",
    "    if degree != 0:\n",
    "        zoom_level_float = math.log(360/degree)/math.log(2)\n",
    "        zoom_level_int = int(zoom_level_float)\n",
    "    else:\n",
    "        zoom_level_int = 18\n",
    "    return zoom_level_int\n",
    "\n",
    "def display_map(latitude = None, longitude = None):\n",
    "\n",
    "    margin = -0.5\n",
    "    zoom_bias = 0\n",
    "    lat_zoom_level = _degree_to_zoom_level(margin = margin, *latitude ) + zoom_bias\n",
    "    lon_zoom_level = _degree_to_zoom_level(margin = margin, *longitude) + zoom_bias\n",
    "    zoom_level = min(lat_zoom_level, lon_zoom_level) \n",
    "    center = [np.mean(latitude), np.mean(longitude)]\n",
    "    \n",
    "    map_hybrid = folium.Map(location=center,zoom_start=zoom_level, \n",
    "        tiles=\" http://mt1.google.com/vt/lyrs=y&z={z}&x={x}&y={y}\",attr=\"Google\")\n",
    "    \n",
    "    line_segments = [(latitude[0],longitude[0]),(latitude[0],longitude[1]),\n",
    "                     (latitude[1],longitude[1]),(latitude[1],longitude[0]),\n",
    "                     (latitude[0],longitude[0])]\n",
    "    \n",
    "    map_hybrid.add_child(folium.features.PolyLine(locations=line_segments,color='red',opacity=0.8))\n",
    "    map_hybrid.add_child(folium.features.LatLngPopup())        \n",
    "\n",
    "    return map_hybrid"
   ]
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot bounding box on a map\n",
    "f = folium.Figure(width=600, height=600)\n",
    "m = display_map(latitude,longitude)\n",
    "f.add_child(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discover and load the data for analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `pystac_client` we can search the Planetary Computer's STAC endpoint for items matching our query parameters. We will look for data tiles (1-degree square) that intersect our bounding box. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "stac = pystac_client.Client.open(\"https://planetarycomputer.microsoft.com/api/stac/v1\")\n",
    "search = stac.search(bbox=bbox,collections=[\"io-lulc\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data tiles intersecting our bounding box: 4\n"
     ]
    }
   ],
   "source": [
    "items = list(search.get_items())\n",
    "print('Number of data tiles intersecting our bounding box:',len(items))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll load the data into an [xarray](https://xarray.pydata.org/en/stable/) DataArray using [stackstac](https://stackstac.readthedocs.io/) and then \"clip\" the data to only the pixels within our region (bounding box). There are also several other <b>important settings for the data</b>: We have changed the projection to EPSG=4326 which is standard latitude-longitude in degrees. We have specified the spatial resolution of each pixel to be 10-meters, which is the baseline accuracy for this data. After creating the DataArray, we will need to mosaic the raster chunks across the time dimension (remember, they're all from a single synthesized \"time\" from 2020) and drop the single band dimension. Finally, we will read the actual data by calling .compute(). In the end, the dataset will include land cover classifications (10 total) at 10-meters spatial resolution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "item = next(search.get_items())\n",
    "items = [pc.sign(item).to_dict() for item in search.get_items()]\n",
    "nodata = raster.ext(item.assets[\"data\"]).bands[0].nodata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the pixel resolution for the final product\n",
    "# Define the scale according to our selected crs, so we will use degrees\n",
    "resolution = 10  # meters per pixel \n",
    "scale = resolution / 111320.0 # degrees per pixel for crs=4326 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = stackstac.stack(\n",
    "    items, # use only the data from our search results\n",
    "    epsg=4326, # use common lat-lon coordinates\n",
    "    resolution=scale, # Use degrees for crs=4326\n",
    "    dtype=np.ubyte, # matches the data versus default float64\n",
    "    fill_value=nodata, # fills voids with no data\n",
    "    bounds_latlon=bbox # clips to our bounding box\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "land_cover = stackstac.mosaic(data, dim=\"time\", axis=None).squeeze().drop(\"band\").compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Land Cover Map\n",
    "Now we will create a land cover classification map. The source GeoTIFFs contain a colormap and the STAC metadata contains the class names. We'll open one of the source files just to read this metadata and construct the right colors and names for our plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a custom colormap using the file metadata\n",
    "class_names = land_cover.coords[\"label:classes\"].item()[\"classes\"]\n",
    "class_count = len(class_names)\n",
    "\n",
    "with rasterio.open(pc.sign(item.assets[\"data\"].href)) as src:\n",
    "    colormap_def = src.colormap(1)  # get metadata colormap for band 1\n",
    "    colormap = [np.array(colormap_def[i]) / 255 for i in range(class_count)\n",
    "               ]  # transform to matplotlib color format\n",
    "\n",
    "cmap = ListedColormap(colormap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = land_cover.plot(size=8,cmap=cmap,add_colorbar=False,vmin=0,vmax=class_count)\n",
    "cbar = plt.colorbar(image)\n",
    "cbar.set_ticks(range(class_count))\n",
    "cbar.set_ticklabels(class_names)\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.title('Land Cover Classification')\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the output data in a GeoTIFF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "filename = \"Land_Cover_sample2.tiff\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the dimensions of file in pixels\n",
    "height = land_cover.shape[0]\n",
    "width = land_cover.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Coordinate Reference System (CRS) to be common Lat-Lon coordinates\n",
    "# Define the tranformation using our bounding box so the Lat-Lon information is written to the GeoTIFF\n",
    "gt = rasterio.transform.from_bounds(min_lon,min_lat,max_lon,max_lat,width,height)\n",
    "land_cover.rio.write_crs(\"epsg:4326\", inplace=True)\n",
    "land_cover.rio.write_transform(transform=gt, inplace=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the GeoTIFF output file using the defined parameters\n",
    "with rasterio.open(filename,'w',driver='GTiff',width=width,height=height,\n",
    "                   crs='epsg:4326',transform=gt,count=1,compress='lzw',dtype=np.ubyte) as dst:\n",
    "    dst.write(land_cover,1)\n",
    "    dst.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-rw-r-- 1 jovyan users 122M Jan 19 17:49 DEM_sample.tiff\n",
      "-rw-r--r-- 1 jovyan users 5.2M Jan 21 18:25 DEM_sample8.tiff\n",
      "-rw-rw-r-- 1 jovyan users 273K Jan  7 17:21 Land_Cover_sample.tiff\n",
      "-rw-r--r-- 1 jovyan users 327K Jan 21 19:33 Land_Cover_sample2.tiff\n",
      "-rw-rw-r-- 1 jovyan users  49M Jan  5 20:26 S2_mosaic_sample.tiff\n",
      "-rw-r--r-- 1 jovyan users  49M Jan 21 18:40 S2_mosaic_sample2.tiff\n",
      "-rw-rw-r-- 1 jovyan users  305 Jan  6 01:03 Weather_sample.tiff\n"
     ]
    }
   ],
   "source": [
    "# Show the location and size of the new output file\n",
    "!ls *.tiff -lah"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How will the participants use this data?\n",
    "The GeoTIFF file will contain the Lat-Lon coordinates of each pixel and will also contain the land class for each pixel. Since the FrogID data is also Lat-Lon position, it is possible to find the closest pixel using code similar to what is demonstrated below. Once this pixel is found, then the corresponding land class can be used for modeling species distribution. In addition, participants may want to consider proximity to specific land classes. For example, there may be a positive correlation with land classes such as trees, grass or water and there may be a negative correlation with land classes such as built-up area or bare soil."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the possible <b>land classifications</b>, reported below:<br>\n",
    "1 = water, 2 = trees, 3 = grass, 4 = flooded vegetation, 5 = crops<br>\n",
    "6 = scrub, 7 = built-up (urban), 8 = bare soil, 9 = snow/ice, 10=clouds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the land classification for the closest pixel:  2\n"
     ]
    }
   ],
   "source": [
    "# This is an example for a specific Lon-Lat location randomly selected within our sample region.\n",
    "values = land_cover.sel(x=150.71, y=-33.51, method=\"nearest\").values    \n",
    "print(\"This is the land classification for the closest pixel: \",values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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    "name": "ipython",
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   "file_extension": ".py",
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