datasets/nrcan-landcover/nrcan-landcover-example.ipynb

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    "## Accessing NRCAN Landcover data with the Planetary Computer STAC API\n",
    "\n",
    "[National Resources Canada (NRCAN) Landcover](https://www.nrcan.gc.ca/maps-tools-publications/satellite-imagery-air-photos/application-development/land-cover/21755) is derived from cloud-free mosaics from thousands of satellite images collected in the short summer months when snow and ice cover is lowest. Advanced machine learning techniques and complex algorithms developed and verified through fieldwork are used to identify land cover types within the images. The result is a collection of continental, national scale and regional land cover products encompassing different time periods and frequencies, and at different spatial resolutions. \n",
    "\n",
    "In this notebook, we'll demonstrate how to access and work with this data through the Planetary Computer.\n",
    "\n",
    "Complete documentation for this dataset is available on the [Planetary Computer data catalog](https://planetarycomputer.microsoft.com/dataset/nrcan-landcover)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92aeae82",
   "metadata": {},
   "source": [
    "### Environment setup\n",
    "\n",
    "This notebook works with or without an API key, but you will be given more permissive access to the data with an API key.\n",
    "The [Planetary Computer Hub](https://planetarycomputer.microsoft.com/compute) is pre-configured to use your API key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8d41d189",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "import pystac_client\n",
    "from pystac.extensions.projection import ProjectionExtension as proj\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import planetary_computer\n",
    "import rasterio\n",
    "import rasterio.features\n",
    "import stackstac\n",
    "\n",
    "# Set the environment variable PC_SDK_SUBSCRIPTION_KEY, or set it here.\n",
    "# The Hub sets PC_SDK_SUBSCRIPTION_KEY automatically.\n",
    "# pc.settings.set_subscription_key(<YOUR API Key>)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b818930-29bf-49d3-8c42-8d0a1d135498",
   "metadata": {},
   "source": [
    "### Data access\n",
    "\n",
    "The datasets hosted by the Planetary Computer are available from [Azure Blob Storage](https://docs.microsoft.com/en-us/azure/storage/blobs/). We'll use [pystac-client](https://pystac-client.readthedocs.io/) to search the Planetary Computer's [STAC API](https://planetarycomputer.microsoft.com/api/stac/v1/docs) for the subset of the data that we care about, and then we'll load the data directly from Azure Blob Storage. We'll specify a `modifier` so that we can access the data stored in the Planetary Computer's private Blob Storage Containers. See [Reading from the STAC API](https://planetarycomputer.microsoft.com/docs/quickstarts/reading-stac/) and [Using tokens for data access](https://planetarycomputer.microsoft.com/docs/concepts/sas/) for more."
   ]
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   "cell_type": "code",
   "execution_count": 2,
   "id": "3c2aaa02-145a-4e31-92e6-887c89267525",
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   "source": [
    "catalog = pystac_client.Client.open(\n",
    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
    "    modifier=planetary_computer.sign_inplace,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b61bc6",
   "metadata": {},
   "source": [
    "### Select a region and find data items\n",
    "\n",
    "We'll pick an area surrounding Vancouver and use the STAC API to find what data items are available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d565f775",
   "metadata": {},
   "outputs": [],
   "source": [
    "area_of_interest = {\n",
    "    \"type\": \"Polygon\",\n",
    "    \"coordinates\": [\n",
    "        [\n",
    "            [-124.134521484375, 49.00724918431423],\n",
    "            [-121.75872802734375, 49.00724918431423],\n",
    "            [-121.75872802734375, 50.07300647938297],\n",
    "            [-124.134521484375, 50.07300647938297],\n",
    "            [-124.134521484375, 49.00724918431423],\n",
    "        ]\n",
    "    ],\n",
    "}\n",
    "\n",
    "bounds_latlon = rasterio.features.bounds(area_of_interest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "839dc1e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returned 2 Items\n"
     ]
    }
   ],
   "source": [
    "search = catalog.search(collections=[\"nrcan-landcover\"], intersects=area_of_interest)\n",
    "\n",
    "# Check how many items were returned\n",
    "items = search.item_collection()\n",
    "print(f\"Returned {len(items)} Items\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ffe064d",
   "metadata": {},
   "source": [
    "We found 2 items that intersect with our area of interest, which means the data we want to work with is spread out over 2 non-overlapping GeoTIFF files. In order to merge them together, we could open each item, clip to the subset of our area of interest, and merge them together manually with rasterio. We'd also have to reproject each item which may span multiple UTM projections. \n",
    "\n",
    "Instead, we'll use the [stackstac](https://stackstac.readthedocs.io/en/latest/) library to read, merge, and reproject in a single step - all without loading the rest of the file data we don't need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52cf095e",
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       "                                                                band: 1,\n",
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       "    proj:epsg          int64 3978\n",
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       "    crs:         epsg:3978\n",
       "    transform:   | 30.00, 0.00,-2057730.00|\\n| 0.00,-30.00, 586590.00|\\n| 0.0...\n",
       "    resolution:  30.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-64cbbd58b96ccc13afda48dfe0727b26'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>band</span>: 1</li><li><span class='xr-has-index'>y</span>: 6002</li><li><span class='xr-has-index'>x</span>: 6855</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-349a3125-dbd7-4f0e-a43d-1d1101298826' class='xr-array-in' type='checkbox' checked><label for='section-349a3125-dbd7-4f0e-a43d-1d1101298826' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 1024, 1024), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 627.80 MiB </td>\n",
       "                        <td> 8.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2, 1, 6002, 6855) </td>\n",
       "                        <td> (1, 1, 1024, 1024) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 5 Graph Layers </td>\n",
       "                        <td> 84 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-9b49e98b-57a6-4743-81c0-be3ff03de8a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b49e98b-57a6-4743-81c0-be3ff03de8a8' class='xr-section-summary' >Coordinates: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-01 2015-01-01</div><input id='attrs-adee6f0a-9071-483f-a0a5-ef8bcc97450e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-adee6f0a-9071-483f-a0a5-ef8bcc97450e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc97725b-a2d7-4c71-8150-36c50a943af8' class='xr-var-data-in' type='checkbox'><label for='data-bc97725b-a2d7-4c71-8150-36c50a943af8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U21</div><div class='xr-var-preview xr-preview'>&#x27;CAN_LC_2015_CAL_12_03&#x27; &#x27;CAN_LC_...</div><input id='attrs-864cf222-a23f-4fd1-a206-e88793ef0b1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-864cf222-a23f-4fd1-a206-e88793ef0b1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cd5b74f-1106-4941-b5d1-3d84dd37da59' class='xr-var-data-in' type='checkbox'><label for='data-3cd5b74f-1106-4941-b5d1-3d84dd37da59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;CAN_LC_2015_CAL_12_03&#x27;, &#x27;CAN_LC_2015_CAL_12_02&#x27;], dtype=&#x27;&lt;U21&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;landcover&#x27;</div><input id='attrs-c7ced7d6-8405-40bd-acc4-16af4e4fb17c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7ced7d6-8405-40bd-acc4-16af4e4fb17c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-072661ba-568e-414c-bb90-36a3fd4549de' class='xr-var-data-in' type='checkbox'><label for='data-072661ba-568e-414c-bb90-36a3fd4549de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;landcover&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.058e+06 ... -1.852e+06</div><input id='attrs-9b1378ca-51a4-446e-9436-e8391edbc007' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b1378ca-51a4-446e-9436-e8391edbc007' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8186c3cd-abbb-45e2-89d1-77f7a23412d3' class='xr-var-data-in' type='checkbox'><label for='data-8186c3cd-abbb-45e2-89d1-77f7a23412d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-2057730., -2057700., -2057670., ..., -1852170., -1852140., -1852110.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.866e+05 5.866e+05 ... 4.066e+05</div><input id='attrs-99a9e5f8-0054-4d71-97a0-2985de67aad8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99a9e5f8-0054-4d71-97a0-2985de67aad8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebe24d38-3929-4ff8-a31b-984f5b42ab7f' class='xr-var-data-in' type='checkbox'><label for='data-ebe24d38-3929-4ff8-a31b-984f5b42ab7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([586590., 586560., 586530., ..., 406620., 406590., 406560.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3978</div><input id='attrs-70f937cd-0c25-47f4-8d4d-1a1ff8729ca9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70f937cd-0c25-47f4-8d4d-1a1ff8729ca9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0b000f0-ad37-4eba-8768-774677b1e0a8' class='xr-var-data-in' type='checkbox'><label for='data-e0b000f0-ad37-4eba-8768-774677b1e0a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3978)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>start_datetime</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>&#x27;2015-01-01T00:00:00Z&#x27;</div><input id='attrs-2a3e1517-42e5-4165-8d10-af129ae7bdbd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a3e1517-42e5-4165-8d10-af129ae7bdbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39be6adc-132c-4479-8c55-ad2b4ef8f114' class='xr-var-data-in' type='checkbox'><label for='data-39be6adc-132c-4479-8c55-ad2b4ef8f114' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;2015-01-01T00:00:00Z&#x27;, dtype=&#x27;&lt;U20&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>end_datetime</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>&#x27;2020-01-01T00:00:00Z&#x27;</div><input id='attrs-c9babc29-24a2-4b87-97ca-76be2e8ff110' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9babc29-24a2-4b87-97ca-76be2e8ff110' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0eaffb57-3e31-4237-b707-54b660726e29' class='xr-var-data-in' type='checkbox'><label for='data-0eaffb57-3e31-4237-b707-54b660726e29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;2020-01-01T00:00:00Z&#x27;, dtype=&#x27;&lt;U20&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>label:description</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;&#x27;</div><input id='attrs-c167feee-63c3-470f-84ed-79c30e1714c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c167feee-63c3-470f-84ed-79c30e1714c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38443e4c-ace7-45ac-a8f5-e8000201ac1d' class='xr-var-data-in' type='checkbox'><label for='data-38443e4c-ace7-45ac-a8f5-e8000201ac1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;&#x27;, dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>label:tasks</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U14</div><div class='xr-var-preview xr-preview'>&#x27;classification&#x27;</div><input id='attrs-30d8e88a-0e67-4f9a-8fb2-3e9c179246ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30d8e88a-0e67-4f9a-8fb2-3e9c179246ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e74f715-8457-4534-993d-7f181353c228' class='xr-var-data-in' type='checkbox'><label for='data-5e74f715-8457-4534-993d-7f181353c228' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;classification&#x27;, dtype=&#x27;&lt;U14&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>label:properties</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-7793b7bd-1dce-4cf3-b7d5-a89eecaabde1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7793b7bd-1dce-4cf3-b7d5-a89eecaabde1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3cd3892-fc6f-4647-8b75-8a56d08b651e' class='xr-var-data-in' type='checkbox'><label for='data-f3cd3892-fc6f-4647-8b75-8a56d08b651e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(None, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>label:classes</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;name&#x27;: &#x27;&#x27;, &#x27;classes&#x27;: [&#x27;Temper...</div><input id='attrs-d746bb86-cb07-475a-983f-1d243ed37ce8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d746bb86-cb07-475a-983f-1d243ed37ce8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99b1da5b-fa0b-4fa3-aea8-525fe2ec5cb6' class='xr-var-data-in' type='checkbox'><label for='data-99b1da5b-fa0b-4fa3-aea8-525fe2ec5cb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;name&#x27;: &#x27;&#x27;, &#x27;classes&#x27;: [&#x27;Temperate or sub-polar needleleaf forest&#x27;, &#x27;Sub-polar taiga needleleaf forest&#x27;, &#x27;Temperate or sub-polar broadleaf deciduous forest&#x27;, &#x27;Mixed forest&#x27;, &#x27;Temperate or sub-polar shrubland&#x27;, &#x27;Temperate or sub-polar grassland&#x27;, &#x27;Sub-polar or polar shrubland-lichen-moss&#x27;, &#x27;Sub-polar or polar grassland-lichen-moss&#x27;, &#x27;Sub-polar or polar barren-lichen-moss&#x27;, &#x27;Wetland&#x27;, &#x27;Cropland&#x27;, &#x27;Barren lands&#x27;, &#x27;Urban&#x27;, &#x27;Water&#x27;, &#x27;Snow and Ice&#x27;]},\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U1265</div><div class='xr-var-preview xr-preview'>&#x27;Land cover information is neces...</div><input id='attrs-e02dd224-721e-4be9-a557-df5447addc54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e02dd224-721e-4be9-a557-df5447addc54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4453583-3b0b-46bb-b2a4-ea7ae7710b43' class='xr-var-data-in' type='checkbox'><label for='data-a4453583-3b0b-46bb-b2a4-ea7ae7710b43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. In Canada, a 2008 user survey indicated that the most practical land cover data is provided in a nationwide 30 m spatial resolution format, with an update frequency of five years. In response to this need, the Canada Centre for Remote Sensing (CCRS) has generated a 30 m land cover map of Canada for the base year 2010, as well as this 2015 land cover map. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution 2015 Land Cover Map of North America, which is produced by Mexican, American and Canadian government institutions under a collaboration called the North American Land Change Monitoring System (NALCMS). This land cover dataset for Canada is produced using observation from Operational Land Imager (OLI) Landsat sensor. An accuracy assessment based on 806 randomly distributed samples shows that land cover data produced with this new approach has achieved 79.90% accuracy with no marked spatial disparities.  \\\\n  \\\\n  - [Land Cover of Canada - Cartographic Product Collection](https://open.canada.ca/data/en/dataset/11990a35-912e-4002-b197-d57dd88836d7)&#x27;,\n",
       "      dtype=&#x27;&lt;U1265&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>label:type</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;raster&#x27;</div><input id='attrs-83f84707-56c9-4d2b-9cf1-c02f96e0c8cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83f84707-56c9-4d2b-9cf1-c02f96e0c8cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adf46f00-e081-4f8b-ad21-1ef3407c3475' class='xr-var-data-in' type='checkbox'><label for='data-adf46f00-e081-4f8b-ad21-1ef3407c3475' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;raster&#x27;, dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U24</div><div class='xr-var-preview xr-preview'>&#x27;Land cover of Canada COG&#x27;</div><input id='attrs-57b25d71-c1f8-4994-8ee1-955a47f1146b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57b25d71-c1f8-4994-8ee1-955a47f1146b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd765704-94ef-45df-8d89-b961a00a8c31' class='xr-var-data-in' type='checkbox'><label for='data-bd765704-94ef-45df-8d89-b961a00a8c31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;Land cover of Canada COG&#x27;, dtype=&#x27;&lt;U24&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{10001}</div><input id='attrs-4d724620-4b75-4812-855b-c4fad79551e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d724620-4b75-4812-855b-c4fad79551e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1d22ad4-7947-4596-bb7c-3c46feb2086b' class='xr-var-data-in' type='checkbox'><label for='data-e1d22ad4-7947-4596-bb7c-3c46feb2086b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({10001}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sci:citation</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U125</div><div class='xr-var-preview xr-preview'>&#x27;Latifovic, R., 2019. Canada’s l...</div><input id='attrs-d36c8aaa-3a74-4ea9-86cc-29b504328a50' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d36c8aaa-3a74-4ea9-86cc-29b504328a50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0afca006-3c1d-458d-8f33-1aa1d28f37d3' class='xr-var-data-in' type='checkbox'><label for='data-0afca006-3c1d-458d-8f33-1aa1d28f37d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;Latifovic, R., 2019. Canada’s land cover; Natural Resources Canada, General Information Product 119e, version 2015, 1 poster.&#x27;,\n",
       "      dtype=&#x27;&lt;U125&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:wkt2</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U1345</div><div class='xr-var-preview xr-preview'>&#x27;PROJCRS[&quot;NAD83 / Canada Atlas L...</div><input id='attrs-f4d6b6d3-774b-437b-8995-03e691b75d64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4d6b6d3-774b-437b-8995-03e691b75d64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75362a51-a597-45de-800b-2e776cf82b9c' class='xr-var-data-in' type='checkbox'><label for='data-75362a51-a597-45de-800b-2e776cf82b9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;PROJCRS[&quot;NAD83 / Canada Atlas Lambert&quot;,BASEGEOGCRS[&quot;NAD83&quot;,DATUM[&quot;North American Datum 1983&quot;,ELLIPSOID[&quot;GRS 1980&quot;,6378137,298.257222101,LENGTHUNIT[&quot;metre&quot;,1]]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4269]],CONVERSION[&quot;Canada Atlas Lambert&quot;,METHOD[&quot;Lambert Conic Conformal (2SP)&quot;,ID[&quot;EPSG&quot;,9802]],PARAMETER[&quot;Latitude of false origin&quot;,49,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8821]],PARAMETER[&quot;Longitude of false origin&quot;,-95,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8822]],PARAMETER[&quot;Latitude of 1st standard parallel&quot;,49,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8823]],PARAMETER[&quot;Latitude of 2nd standard parallel&quot;,77,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8824]],PARAMETER[&quot;Easting at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8826]],PARAMETER[&quot;Northing at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8827]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Transformation of coordinates at 5m level of accuracy.&quot;],AREA[&quot;Canada - onshore and offshore - Alberta; British Columbia; Manitoba; New Brunswick; Newfoundland and Labrador; Northwest Territories; Nova Scotia; Nunavut; Ontario; Prince Edward Island; Quebec; Saskatchewan; Yukon.&quot;],BBOX[38.21,-141.01,86.46,-40.73]],ID[&quot;EPSG&quot;,3978]]&#x27;,\n",
       "      dtype=&#x27;&lt;U1345&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sci:doi</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U14</div><div class='xr-var-preview xr-preview'>&#x27;10.4095/315659&#x27;</div><input id='attrs-c6451558-b91b-4863-bdd3-29959f34f676' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6451558-b91b-4863-bdd3-29959f34f676' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb627cd9-8e03-4ec7-a2dd-43261d3d37ed' class='xr-var-data-in' type='checkbox'><label for='data-cb627cd9-8e03-4ec7-a2dd-43261d3d37ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;10.4095/315659&#x27;, dtype=&#x27;&lt;U14&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>raster:bands</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;nodata&#x27;: 0, &#x27;sampling&#x27;: &#x27;area&#x27;...</div><input id='attrs-aaec6241-2c4f-4862-b305-0cc47851fb56' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aaec6241-2c4f-4862-b305-0cc47851fb56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cff1e4d6-12fa-4e22-a49a-dfe5318cfe77' class='xr-var-data-in' type='checkbox'><label for='data-cff1e4d6-12fa-4e22-a49a-dfe5318cfe77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;nodata&#x27;: 0, &#x27;sampling&#x27;: &#x27;area&#x27;, &#x27;data_type&#x27;: &#x27;uint8&#x27;, &#x27;spatial_resolution&#x27;: 30},\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3978</div><input id='attrs-320d433f-332c-4b4d-9aa3-48933a29a7e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-320d433f-332c-4b4d-9aa3-48933a29a7e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7abaa9df-a90d-4b3f-9181-cdce8a84adf9' class='xr-var-data-in' type='checkbox'><label for='data-7abaa9df-a90d-4b3f-9181-cdce8a84adf9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3978)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e4412206-fb51-4ac7-9144-edf59a55c73a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4412206-fb51-4ac7-9144-edf59a55c73a' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=3978, bounds=(-2057730.0, 406530.0, -1852080.0, 586590.0), resolutions_xy=(30.0, 30.0))</dd><dt><span>crs :</span></dt><dd>epsg:3978</dd><dt><span>transform :</span></dt><dd>| 30.00, 0.00,-2057730.00|\n",
       "| 0.00,-30.00, 586590.00|\n",
       "| 0.00, 0.00, 1.00|</dd><dt><span>resolution :</span></dt><dd>30.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'stackstac-64cbbd58b96ccc13afda48dfe0727b26' (time: 2,\n",
       "                                                                band: 1,\n",
       "                                                                y: 6002, x: 6855)>\n",
       "dask.array<where, shape=(2, 1, 6002, 6855), dtype=float64, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray>\n",
       "Coordinates: (12/21)\n",
       "  * time               (time) datetime64[ns] 2015-01-01 2015-01-01\n",
       "    id                 (time) <U21 'CAN_LC_2015_CAL_12_03' 'CAN_LC_2015_CAL_1...\n",
       "  * band               (band) <U9 'landcover'\n",
       "  * x                  (x) float64 -2.058e+06 -2.058e+06 ... -1.852e+06\n",
       "  * y                  (y) float64 5.866e+05 5.866e+05 ... 4.066e+05 4.066e+05\n",
       "    proj:epsg          int64 3978\n",
       "    ...                 ...\n",
       "    proj:shape         object {10001}\n",
       "    sci:citation       <U125 'Latifovic, R., 2019. Canada’s land cover; Natur...\n",
       "    proj:wkt2          <U1345 'PROJCRS[\"NAD83 / Canada Atlas Lambert\",BASEGEO...\n",
       "    sci:doi            <U14 '10.4095/315659'\n",
       "    raster:bands       object {'nodata': 0, 'sampling': 'area', 'data_type': ...\n",
       "    epsg               int64 3978\n",
       "Attributes:\n",
       "    spec:        RasterSpec(epsg=3978, bounds=(-2057730.0, 406530.0, -1852080...\n",
       "    crs:         epsg:3978\n",
       "    transform:   | 30.00, 0.00,-2057730.00|\\n| 0.00,-30.00, 586590.00|\\n| 0.0...\n",
       "    resolution:  30.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The STAC metadata contains some information we'll want to use when creating\n",
    "# our merged dataset. Get the EPSG code of the first item and the nodata value.\n",
    "item = items[0]\n",
    "epsg = proj.ext(item).epsg\n",
    "nodata = item.assets[\"landcover\"].extra_fields[\"raster:bands\"][0][\"nodata\"]\n",
    "\n",
    "# Create a single DataArray from out multiple resutls with the corresponding\n",
    "# rasters projected to a single CRS. Note that we set the dtype to ubyte, which\n",
    "# matches our data, since stackstac will use float64 by default.\n",
    "stack = stackstac.stack(items, epsg=epsg, bounds_latlon=bounds_latlon)\n",
    "\n",
    "# Mask no data values\n",
    "stack = stack.where(stack != nodata)\n",
    "\n",
    "stack"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b213ccfc",
   "metadata": {},
   "source": [
    "### Mosaic and clip the raster\n",
    "\n",
    "So far, we haven't read in any data. Stackstac has used the STAC metadata to construct a DataArray that will contain our Item data. Let's mosaic the rasters across the `time` dimension (remember, they're all from a single synthesized \"time\" from 2020) and drop the single `band` dimension. Finally, we ask Dask to read the actual data by calling `.compute()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9e823a18",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
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       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-64cbbd58b96ccc13afda48dfe0727b26&#x27; (y: 6002, x: 6855)&gt;\n",
       "array([[18., 18., 18., ...,  1.,  1.,  1.],\n",
       "       [18., 18., 18., ...,  1.,  1.,  1.],\n",
       "       [18., 18., 18., ...,  1.,  1.,  1.],\n",
       "       ...,\n",
       "       [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "       [ 6.,  6.,  6., ...,  1.,  1.,  1.],\n",
       "       [ 6.,  6.,  6., ...,  1.,  1.,  1.]])\n",
       "Coordinates: (12/19)\n",
       "    band               &lt;U9 &#x27;landcover&#x27;\n",
       "  * x                  (x) float64 -2.058e+06 -2.058e+06 ... -1.852e+06\n",
       "  * y                  (y) float64 5.866e+05 5.866e+05 ... 4.066e+05 4.066e+05\n",
       "    proj:epsg          int64 3978\n",
       "    start_datetime     &lt;U20 &#x27;2015-01-01T00:00:00Z&#x27;\n",
       "    end_datetime       &lt;U20 &#x27;2020-01-01T00:00:00Z&#x27;\n",
       "    ...                 ...\n",
       "    proj:shape         object {10001}\n",
       "    sci:citation       &lt;U125 &#x27;Latifovic, R., 2019. Canada’s land cover; Natur...\n",
       "    proj:wkt2          &lt;U1345 &#x27;PROJCRS[&quot;NAD83 / Canada Atlas Lambert&quot;,BASEGEO...\n",
       "    sci:doi            &lt;U14 &#x27;10.4095/315659&#x27;\n",
       "    raster:bands       object {&#x27;nodata&#x27;: 0, &#x27;sampling&#x27;: &#x27;area&#x27;, &#x27;data_type&#x27;: ...\n",
       "    epsg               int64 3978\n",
       "Attributes:\n",
       "    spec:        RasterSpec(epsg=3978, bounds=(-2057730.0, 406530.0, -1852080...\n",
       "    crs:         epsg:3978\n",
       "    transform:   | 30.00, 0.00,-2057730.00|\\n| 0.00,-30.00, 586590.00|\\n| 0.0...\n",
       "    resolution:  30.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-64cbbd58b96ccc13afda48dfe0727b26'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 6002</li><li><span class='xr-has-index'>x</span>: 6855</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-206d38f9-24d5-49f9-a5f3-bc595302f0af' class='xr-array-in' type='checkbox' checked><label for='section-206d38f9-24d5-49f9-a5f3-bc595302f0af' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>18.0 18.0 18.0 18.0 18.0 18.0 18.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0</span></div><div class='xr-array-data'><pre>array([[18., 18., 18., ...,  1.,  1.,  1.],\n",
       "       [18., 18., 18., ...,  1.,  1.,  1.],\n",
       "       [18., 18., 18., ...,  1.,  1.,  1.],\n",
       "       ...,\n",
       "       [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "       [ 6.,  6.,  6., ...,  1.,  1.,  1.],\n",
       "       [ 6.,  6.,  6., ...,  1.,  1.,  1.]])</pre></div></div></li><li class='xr-section-item'><input id='section-17806f89-8ca2-49c7-9237-092471ee0506' class='xr-section-summary-in' type='checkbox'  checked><label for='section-17806f89-8ca2-49c7-9237-092471ee0506' class='xr-section-summary' >Coordinates: <span>(19)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>band</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;landcover&#x27;</div><input id='attrs-ee7074fc-5494-4369-b84d-ef1eca5f3027' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee7074fc-5494-4369-b84d-ef1eca5f3027' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d3bab31-3f75-4ff9-8703-966c1b49775b' class='xr-var-data-in' type='checkbox'><label for='data-0d3bab31-3f75-4ff9-8703-966c1b49775b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;landcover&#x27;, dtype=&#x27;&lt;U9&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.058e+06 ... -1.852e+06</div><input id='attrs-04858e7f-7c78-4c63-a6a9-eecc7c4cecbe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-04858e7f-7c78-4c63-a6a9-eecc7c4cecbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c349dae4-0280-47b5-a3a4-28322f181f8c' class='xr-var-data-in' type='checkbox'><label for='data-c349dae4-0280-47b5-a3a4-28322f181f8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-2057730., -2057700., -2057670., ..., -1852170., -1852140., -1852110.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.866e+05 5.866e+05 ... 4.066e+05</div><input id='attrs-15941c60-2908-47e0-9d38-082e5e904bdc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-15941c60-2908-47e0-9d38-082e5e904bdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48354b16-0b4d-4e61-ad8d-b1cd5b7029d2' class='xr-var-data-in' type='checkbox'><label for='data-48354b16-0b4d-4e61-ad8d-b1cd5b7029d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([586590., 586560., 586530., ..., 406620., 406590., 406560.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3978</div><input id='attrs-f5a43d7d-eae3-4e0a-801f-dcd9051781d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5a43d7d-eae3-4e0a-801f-dcd9051781d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2864ed6a-5b66-4773-84a7-99feec6d8771' class='xr-var-data-in' type='checkbox'><label for='data-2864ed6a-5b66-4773-84a7-99feec6d8771' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3978)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>start_datetime</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>&#x27;2015-01-01T00:00:00Z&#x27;</div><input id='attrs-39f285c9-5d52-4ea2-aa1e-309d6a3305e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39f285c9-5d52-4ea2-aa1e-309d6a3305e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48ee273b-8306-4a00-ba3a-27590baa342c' class='xr-var-data-in' type='checkbox'><label for='data-48ee273b-8306-4a00-ba3a-27590baa342c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;2015-01-01T00:00:00Z&#x27;, dtype=&#x27;&lt;U20&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>end_datetime</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>&#x27;2020-01-01T00:00:00Z&#x27;</div><input id='attrs-ba87ee9d-8a91-4488-b594-cbecd5f6e753' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba87ee9d-8a91-4488-b594-cbecd5f6e753' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0874cb8-7b0e-471d-a6f5-562f8f808707' class='xr-var-data-in' type='checkbox'><label for='data-d0874cb8-7b0e-471d-a6f5-562f8f808707' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;2020-01-01T00:00:00Z&#x27;, dtype=&#x27;&lt;U20&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>label:description</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;&#x27;</div><input id='attrs-91a450ba-5f1d-4ac5-a4c9-18e61607eb37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91a450ba-5f1d-4ac5-a4c9-18e61607eb37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71ae22b4-e38f-4ca5-9082-f2b1ce0885a6' class='xr-var-data-in' type='checkbox'><label for='data-71ae22b4-e38f-4ca5-9082-f2b1ce0885a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;&#x27;, dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>label:tasks</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U14</div><div class='xr-var-preview xr-preview'>&#x27;classification&#x27;</div><input id='attrs-1cba8511-25ad-48f9-86e4-3dbbe428ec68' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1cba8511-25ad-48f9-86e4-3dbbe428ec68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38139e28-7691-4dca-9374-85fbaa0ece5f' class='xr-var-data-in' type='checkbox'><label for='data-38139e28-7691-4dca-9374-85fbaa0ece5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;classification&#x27;, dtype=&#x27;&lt;U14&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>label:properties</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-7fe0b68a-c80b-4ec6-bac1-484a575e7e10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7fe0b68a-c80b-4ec6-bac1-484a575e7e10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00fcb2d8-3de4-4ae0-8337-960cd625757e' class='xr-var-data-in' type='checkbox'><label for='data-00fcb2d8-3de4-4ae0-8337-960cd625757e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(None, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>label:classes</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;name&#x27;: &#x27;&#x27;, &#x27;classes&#x27;: [&#x27;Temper...</div><input id='attrs-bc2cdaeb-2e04-4089-9a77-649a5d4fd8d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc2cdaeb-2e04-4089-9a77-649a5d4fd8d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47a867bb-87c8-4717-95fc-56d3dc4f54a1' class='xr-var-data-in' type='checkbox'><label for='data-47a867bb-87c8-4717-95fc-56d3dc4f54a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;name&#x27;: &#x27;&#x27;, &#x27;classes&#x27;: [&#x27;Temperate or sub-polar needleleaf forest&#x27;, &#x27;Sub-polar taiga needleleaf forest&#x27;, &#x27;Temperate or sub-polar broadleaf deciduous forest&#x27;, &#x27;Mixed forest&#x27;, &#x27;Temperate or sub-polar shrubland&#x27;, &#x27;Temperate or sub-polar grassland&#x27;, &#x27;Sub-polar or polar shrubland-lichen-moss&#x27;, &#x27;Sub-polar or polar grassland-lichen-moss&#x27;, &#x27;Sub-polar or polar barren-lichen-moss&#x27;, &#x27;Wetland&#x27;, &#x27;Cropland&#x27;, &#x27;Barren lands&#x27;, &#x27;Urban&#x27;, &#x27;Water&#x27;, &#x27;Snow and Ice&#x27;]},\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U1265</div><div class='xr-var-preview xr-preview'>&#x27;Land cover information is neces...</div><input id='attrs-b0b90451-a168-4305-8111-f79fd31198f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0b90451-a168-4305-8111-f79fd31198f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e11b43f9-c337-45df-97b2-cb113d931d6d' class='xr-var-data-in' type='checkbox'><label for='data-e11b43f9-c337-45df-97b2-cb113d931d6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. In Canada, a 2008 user survey indicated that the most practical land cover data is provided in a nationwide 30 m spatial resolution format, with an update frequency of five years. In response to this need, the Canada Centre for Remote Sensing (CCRS) has generated a 30 m land cover map of Canada for the base year 2010, as well as this 2015 land cover map. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution 2015 Land Cover Map of North America, which is produced by Mexican, American and Canadian government institutions under a collaboration called the North American Land Change Monitoring System (NALCMS). This land cover dataset for Canada is produced using observation from Operational Land Imager (OLI) Landsat sensor. An accuracy assessment based on 806 randomly distributed samples shows that land cover data produced with this new approach has achieved 79.90% accuracy with no marked spatial disparities.  \\\\n  \\\\n  - [Land Cover of Canada - Cartographic Product Collection](https://open.canada.ca/data/en/dataset/11990a35-912e-4002-b197-d57dd88836d7)&#x27;,\n",
       "      dtype=&#x27;&lt;U1265&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>label:type</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;raster&#x27;</div><input id='attrs-abb72104-4d98-4724-be10-19190d86bb86' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-abb72104-4d98-4724-be10-19190d86bb86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af6f14d7-7fb3-4858-a563-d30ae1d4e796' class='xr-var-data-in' type='checkbox'><label for='data-af6f14d7-7fb3-4858-a563-d30ae1d4e796' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;raster&#x27;, dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U24</div><div class='xr-var-preview xr-preview'>&#x27;Land cover of Canada COG&#x27;</div><input id='attrs-1e166880-5427-4ddc-ab62-78335c0c63dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e166880-5427-4ddc-ab62-78335c0c63dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0be9d336-fbe1-4221-9c1c-cd22643e8824' class='xr-var-data-in' type='checkbox'><label for='data-0be9d336-fbe1-4221-9c1c-cd22643e8824' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;Land cover of Canada COG&#x27;, dtype=&#x27;&lt;U24&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{10001}</div><input id='attrs-e1c0cfbe-96a3-4546-a480-199c75459eca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1c0cfbe-96a3-4546-a480-199c75459eca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a31b275-49e2-4cbc-bd39-63adbe65182b' class='xr-var-data-in' type='checkbox'><label for='data-1a31b275-49e2-4cbc-bd39-63adbe65182b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({10001}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sci:citation</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U125</div><div class='xr-var-preview xr-preview'>&#x27;Latifovic, R., 2019. Canada’s l...</div><input id='attrs-c430899c-31c4-4606-acf5-a1aa7efd04b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c430899c-31c4-4606-acf5-a1aa7efd04b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c81df28-4ddd-47e4-ab36-4f2be755a622' class='xr-var-data-in' type='checkbox'><label for='data-5c81df28-4ddd-47e4-ab36-4f2be755a622' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;Latifovic, R., 2019. Canada’s land cover; Natural Resources Canada, General Information Product 119e, version 2015, 1 poster.&#x27;,\n",
       "      dtype=&#x27;&lt;U125&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:wkt2</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U1345</div><div class='xr-var-preview xr-preview'>&#x27;PROJCRS[&quot;NAD83 / Canada Atlas L...</div><input id='attrs-896b0151-189d-4f08-ab80-90c0755a4db5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-896b0151-189d-4f08-ab80-90c0755a4db5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d092c4f2-da5f-4c64-a46b-65e5bbe3dd97' class='xr-var-data-in' type='checkbox'><label for='data-d092c4f2-da5f-4c64-a46b-65e5bbe3dd97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;PROJCRS[&quot;NAD83 / Canada Atlas Lambert&quot;,BASEGEOGCRS[&quot;NAD83&quot;,DATUM[&quot;North American Datum 1983&quot;,ELLIPSOID[&quot;GRS 1980&quot;,6378137,298.257222101,LENGTHUNIT[&quot;metre&quot;,1]]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4269]],CONVERSION[&quot;Canada Atlas Lambert&quot;,METHOD[&quot;Lambert Conic Conformal (2SP)&quot;,ID[&quot;EPSG&quot;,9802]],PARAMETER[&quot;Latitude of false origin&quot;,49,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8821]],PARAMETER[&quot;Longitude of false origin&quot;,-95,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8822]],PARAMETER[&quot;Latitude of 1st standard parallel&quot;,49,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8823]],PARAMETER[&quot;Latitude of 2nd standard parallel&quot;,77,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8824]],PARAMETER[&quot;Easting at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8826]],PARAMETER[&quot;Northing at false origin&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8827]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Transformation of coordinates at 5m level of accuracy.&quot;],AREA[&quot;Canada - onshore and offshore - Alberta; British Columbia; Manitoba; New Brunswick; Newfoundland and Labrador; Northwest Territories; Nova Scotia; Nunavut; Ontario; Prince Edward Island; Quebec; Saskatchewan; Yukon.&quot;],BBOX[38.21,-141.01,86.46,-40.73]],ID[&quot;EPSG&quot;,3978]]&#x27;,\n",
       "      dtype=&#x27;&lt;U1345&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sci:doi</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U14</div><div class='xr-var-preview xr-preview'>&#x27;10.4095/315659&#x27;</div><input id='attrs-d8c901db-ea59-4931-8a28-c4667ffc76b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8c901db-ea59-4931-8a28-c4667ffc76b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed2c25ac-9247-4ba0-aeb7-fa51f3f7f466' class='xr-var-data-in' type='checkbox'><label for='data-ed2c25ac-9247-4ba0-aeb7-fa51f3f7f466' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;10.4095/315659&#x27;, dtype=&#x27;&lt;U14&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>raster:bands</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;nodata&#x27;: 0, &#x27;sampling&#x27;: &#x27;area&#x27;...</div><input id='attrs-150ab9ce-1edc-4adc-80b5-f2adf809af3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-150ab9ce-1edc-4adc-80b5-f2adf809af3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21ebc982-b48d-4168-bd3b-0c1ecccd97eb' class='xr-var-data-in' type='checkbox'><label for='data-21ebc982-b48d-4168-bd3b-0c1ecccd97eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;nodata&#x27;: 0, &#x27;sampling&#x27;: &#x27;area&#x27;, &#x27;data_type&#x27;: &#x27;uint8&#x27;, &#x27;spatial_resolution&#x27;: 30},\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3978</div><input id='attrs-1a7430c7-0bdb-44ab-84d5-325abd16f295' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a7430c7-0bdb-44ab-84d5-325abd16f295' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ad178ed-ed2b-40eb-9d0e-f481839a5f40' class='xr-var-data-in' type='checkbox'><label for='data-7ad178ed-ed2b-40eb-9d0e-f481839a5f40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3978)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-50d2f5d3-3d52-4987-8737-9290b9380cef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-50d2f5d3-3d52-4987-8737-9290b9380cef' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=3978, bounds=(-2057730.0, 406530.0, -1852080.0, 586590.0), resolutions_xy=(30.0, 30.0))</dd><dt><span>crs :</span></dt><dd>epsg:3978</dd><dt><span>transform :</span></dt><dd>| 30.00, 0.00,-2057730.00|\n",
       "| 0.00,-30.00, 586590.00|\n",
       "| 0.00, 0.00, 1.00|</dd><dt><span>resolution :</span></dt><dd>30.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'stackstac-64cbbd58b96ccc13afda48dfe0727b26' (y: 6002, x: 6855)>\n",
       "array([[18., 18., 18., ...,  1.,  1.,  1.],\n",
       "       [18., 18., 18., ...,  1.,  1.,  1.],\n",
       "       [18., 18., 18., ...,  1.,  1.,  1.],\n",
       "       ...,\n",
       "       [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
       "       [ 6.,  6.,  6., ...,  1.,  1.,  1.],\n",
       "       [ 6.,  6.,  6., ...,  1.,  1.,  1.]])\n",
       "Coordinates: (12/19)\n",
       "    band               <U9 'landcover'\n",
       "  * x                  (x) float64 -2.058e+06 -2.058e+06 ... -1.852e+06\n",
       "  * y                  (y) float64 5.866e+05 5.866e+05 ... 4.066e+05 4.066e+05\n",
       "    proj:epsg          int64 3978\n",
       "    start_datetime     <U20 '2015-01-01T00:00:00Z'\n",
       "    end_datetime       <U20 '2020-01-01T00:00:00Z'\n",
       "    ...                 ...\n",
       "    proj:shape         object {10001}\n",
       "    sci:citation       <U125 'Latifovic, R., 2019. Canada’s land cover; Natur...\n",
       "    proj:wkt2          <U1345 'PROJCRS[\"NAD83 / Canada Atlas Lambert\",BASEGEO...\n",
       "    sci:doi            <U14 '10.4095/315659'\n",
       "    raster:bands       object {'nodata': 0, 'sampling': 'area', 'data_type': ...\n",
       "    epsg               int64 3978\n",
       "Attributes:\n",
       "    spec:        RasterSpec(epsg=3978, bounds=(-2057730.0, 406530.0, -1852080...\n",
       "    crs:         epsg:3978\n",
       "    transform:   | 30.00, 0.00,-2057730.00|\\n| 0.00,-30.00, 586590.00|\\n| 0.0...\n",
       "    resolution:  30.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = stackstac.mosaic(stack, dim=\"time\", axis=None).squeeze().compute()\n",
    "merged"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "097e4d97",
   "metadata": {},
   "source": [
    "Now a quick plot to check that we've got the data we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee24338b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-nrcan-landcover-nrcan-landcover-example.ipynb/7.png\"/>"
      ],
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b105bbe2",
   "metadata": {},
   "source": [
    "It looks good, but it doesn't look like a land cover 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": 8,
   "id": "03fb3c5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of class names indexed according to the landcover values\n",
    "file_values = {\n",
    "    d[\"values\"][0]: d[\"summary\"]\n",
    "    for d in items[0].assets[\"landcover\"].extra_fields[\"file:values\"]\n",
    "}\n",
    "min_value = int(min(file_values.keys()))\n",
    "max_value = int(max(file_values.keys()))\n",
    "class_names = [\n",
    "    f\"{i}: {file_values.get(value, 'Empty class')}\"\n",
    "    for i, value in enumerate(range(min_value, max_value + 1), start=min_value)\n",
    "]\n",
    "\n",
    "# Create color map for plotting\n",
    "with rasterio.open(item.assets[\"landcover\"].href) as src:\n",
    "    colormap = src.colormap(1)  # get metadata colormap for band 1\n",
    "    colors_rgb = [\n",
    "        c for i, c in colormap.items() if i >= min_value and i <= max_value\n",
    "    ]  # keep only colors within the range of the named classes\n",
    "    colors_mpl = [\n",
    "        np.array(colors_rgb[i]) / 255 for i in range(len(colors_rgb))\n",
    "    ]  # transform to matplotlib color format\n",
    "cmap = ListedColormap(colors_mpl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e2835dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-nrcan-landcover-nrcan-landcover-example.ipynb/9.png\"/>"
      ],
      "text/plain": [
       "<Figure size 2250x1125 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize=(18, 9), dpi=125, subplot_kw=dict(projection=ccrs.epsg(epsg)), frameon=False\n",
    ")\n",
    "p = merged.plot(\n",
    "    ax=ax,\n",
    "    transform=ccrs.epsg(epsg),\n",
    "    cmap=cmap,\n",
    "    add_colorbar=False,\n",
    "    vmin=min_value,\n",
    "    vmax=max_value + 1,\n",
    ")\n",
    "ax.set_title(\"Land Cover Around Vancouver\")\n",
    "\n",
    "cbar = plt.colorbar(p)\n",
    "cbar.set_ticks(range(1, max_value + 1))\n",
    "cbar.set_ticklabels(class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a4163cb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-nrcan-landcover-nrcan-landcover-example.ipynb/10.png\"/>"
      ],
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure(figsize=(8, 6), dpi=100).gca()\n",
    "\n",
    "colors = list(cmap.colors)\n",
    "\n",
    "df = (\n",
    "    pd.value_counts(merged.data.ravel(), sort=False)\n",
    "    .divide(merged.shape[0] * merged.shape[1] / 100)\n",
    "    .sort_index()\n",
    "    .reindex(range(min_value, max_value + 1), fill_value=0)\n",
    "    .rename(dict(enumerate(class_names, start=min_value)))\n",
    ")\n",
    "df.plot.barh(ax=ax, color=colors, rot=0, width=0.9, linewidth=1, edgecolor=\"black\")\n",
    "\n",
    "ax.set(\n",
    "    title=\"Distribution of Land Cover Classes\",\n",
    "    ylabel=\"Landcover Class\",\n",
    "    xlabel=\"Percentage Cover\",\n",
    "    facecolor=\"white\",\n",
    ");"
   ]
  }
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