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   "cell_type": "markdown",
   "id": "657431e4-bf40-4708-8639-0b014997aaea",
   "metadata": {},
   "source": [
    "## Accessing Esri 10m Land Use/Land Cover (9-class) data with the Planetary Computer STAC API\n",
    "\n",
    "This dataset contains global estimates of 10-class land use/land cover for 2017 - 2022, derived from ESA Sentinel-2 imagery at 10m resolution. In this notebook, we'll demonstrate how to access and work with this data through the Planetary Computer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc99c9cc",
   "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": "b87fbc90-35c8-42fd-92a8-b1a12e8676dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask.distributed\n",
    "from matplotlib.colors import ListedColormap\n",
    "import pystac_client\n",
    "\n",
    "import cartopy.crs as ccrs\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": "cd3350ab-2130-4064-b8c0-6304aa1ba746",
   "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": "8719fce6-dbd8-4daf-943b-6c358930e1c6",
   "metadata": {},
   "outputs": [],
   "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": "901a784a-842d-4aea-bd80-54d256e3fa4f",
   "metadata": {},
   "source": [
    "### Select a region and find data items\n",
    "\n",
    "We'll pick an area in Thailand and use the STAC API to find what data items are available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e81ac93-7c97-4126-a096-0334f5145c6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returned 6 Items\n"
     ]
    }
   ],
   "source": [
    "bbox_of_interest = [100.0868, 13.7554, 100.3354, 13.986]\n",
    "search = catalog.search(collections=[\"io-lulc-9-class\"], bbox=bbox_of_interest)\n",
    "\n",
    "items = search.item_collection()\n",
    "print(f\"Returned {len(items)} Items\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d800526-aebb-44cf-a0df-6e93041d7f2f",
   "metadata": {},
   "source": [
    "We found several items that intersect with our area of interest, one per year.\n",
    "\n",
    "We'll use the [stackstac](https://stackstac.readthedocs.io/en/latest/) library to read all of these into a single `xarray.DataArray`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ca1a7a9-0229-4b0d-bb2f-599f4bd4e3c3",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-ffe6a4c6af8232bd9842eba59c37bf4c&#x27; (time: 6,\n",
       "                                                                band: 1,\n",
       "                                                                y: 2565, x: 2701)&gt;\n",
       "dask.array&lt;getitem, shape=(6, 1, 2565, 2701), dtype=uint8, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray&gt;\n",
       "Coordinates: (12/15)\n",
       "  * time             (time) datetime64[ns] 2017-01-01 2018-01-01 ... 2022-01-01\n",
       "    id               (time) &lt;U8 &#x27;47P-2017&#x27; &#x27;47P-2018&#x27; ... &#x27;47P-2021&#x27; &#x27;47P-2022&#x27;\n",
       "  * band             (band) &lt;U4 &#x27;data&#x27;\n",
       "  * x                (x) float64 6.174e+05 6.174e+05 ... 6.444e+05 6.444e+05\n",
       "  * y                (y) float64 1.547e+06 1.547e+06 ... 1.521e+06 1.521e+06\n",
       "    end_datetime     (time) &lt;U20 &#x27;2018-01-01T00:00:00Z&#x27; ... &#x27;2023-01-01T00:00...\n",
       "    ...               ...\n",
       "    proj:bbox        object {1771251.1832111736, 830743.294771302, 884301.183...\n",
       "    start_datetime   (time) &lt;U20 &#x27;2017-01-01T00:00:00Z&#x27; ... &#x27;2022-01-01T00:00...\n",
       "    io:supercell_id  &lt;U3 &#x27;47P&#x27;\n",
       "    proj:transform   object {0.0, 10.0, 169263.29477130197, 1771251.183211173...\n",
       "    raster:bands     object {&#x27;nodata&#x27;: 0, &#x27;spatial_resolution&#x27;: 10}\n",
       "    epsg             int64 32647\n",
       "Attributes:\n",
       "    spec:        RasterSpec(epsg=32647, bounds=(617370.0, 1520940.0, 644380.0...\n",
       "    crs:         epsg:32647\n",
       "    transform:   | 10.00, 0.00, 617370.00|\\n| 0.00,-10.00, 1546590.00|\\n| 0.0...\n",
       "    resolution:  10.0</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-ffe6a4c6af8232bd9842eba59c37bf4c'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 6</li><li><span class='xr-has-index'>band</span>: 1</li><li><span class='xr-has-index'>y</span>: 2565</li><li><span class='xr-has-index'>x</span>: 2701</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-df0fd7b6-0a5c-460b-b141-2a96aaab3fb8' class='xr-array-in' type='checkbox' checked><label for='section-df0fd7b6-0a5c-460b-b141-2a96aaab3fb8' 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 style=\"border-collapse: collapse;\">\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> 39.64 MiB </td>\n",
       "                        <td> 1.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (6, 1, 2565, 2701) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-82a57b8c-9c5d-4815-8f4f-57670d650480' class='xr-section-summary-in' type='checkbox'  checked><label for='section-82a57b8c-9c5d-4815-8f4f-57670d650480' class='xr-section-summary' >Coordinates: <span>(15)</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'>2017-01-01 ... 2022-01-01</div><input id='attrs-7dc5f303-5295-46cb-bf29-9d8e205a69ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7dc5f303-5295-46cb-bf29-9d8e205a69ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb6a1210-a5f0-468b-b5d6-1e4aaad94ed1' class='xr-var-data-in' type='checkbox'><label for='data-cb6a1210-a5f0-468b-b5d6-1e4aaad94ed1' 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;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-01T00:00:00.000000000&#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;U8</div><div class='xr-var-preview xr-preview'>&#x27;47P-2017&#x27; ... &#x27;47P-2022&#x27;</div><input id='attrs-603ad359-86c0-48d0-baf8-afd533765dbb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-603ad359-86c0-48d0-baf8-afd533765dbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-211136a5-c434-4bdd-b951-036a2ea30b7c' class='xr-var-data-in' type='checkbox'><label for='data-211136a5-c434-4bdd-b951-036a2ea30b7c' 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;47P-2017&#x27;, &#x27;47P-2018&#x27;, &#x27;47P-2019&#x27;, &#x27;47P-2020&#x27;, &#x27;47P-2021&#x27;,\n",
       "       &#x27;47P-2022&#x27;], dtype=&#x27;&lt;U8&#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;U4</div><div class='xr-var-preview xr-preview'>&#x27;data&#x27;</div><input id='attrs-ccbbfde8-2854-46d6-9f33-6585cfd64a04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ccbbfde8-2854-46d6-9f33-6585cfd64a04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a72fc21d-75a0-4f4a-8957-f589d643aa6c' class='xr-var-data-in' type='checkbox'><label for='data-a72fc21d-75a0-4f4a-8957-f589d643aa6c' 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;data&#x27;], dtype=&#x27;&lt;U4&#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'>6.174e+05 6.174e+05 ... 6.444e+05</div><input id='attrs-3fcfac48-a544-4176-9860-f41b51b945f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3fcfac48-a544-4176-9860-f41b51b945f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76038b71-f1fa-4199-b9de-94f6b8847ff5' class='xr-var-data-in' type='checkbox'><label for='data-76038b71-f1fa-4199-b9de-94f6b8847ff5' 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([617370., 617380., 617390., ..., 644350., 644360., 644370.])</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'>1.547e+06 1.547e+06 ... 1.521e+06</div><input id='attrs-abca2a6e-7ccc-4873-bce3-edff3dd3fd1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-abca2a6e-7ccc-4873-bce3-edff3dd3fd1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65f94720-e7c4-4ff4-8859-3158c9cefa16' class='xr-var-data-in' type='checkbox'><label for='data-65f94720-e7c4-4ff4-8859-3158c9cefa16' 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([1546590., 1546580., 1546570., ..., 1520970., 1520960., 1520950.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>end_datetime</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>&#x27;2018-01-01T00:00:00Z&#x27; ... &#x27;2023...</div><input id='attrs-689ae005-d0be-49dc-ad41-afc850325530' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-689ae005-d0be-49dc-ad41-afc850325530' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe9141f5-4c7d-46b5-b97b-73e1eb30f59c' class='xr-var-data-in' type='checkbox'><label for='data-fe9141f5-4c7d-46b5-b97b-73e1eb30f59c' 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;2018-01-01T00:00:00Z&#x27;, &#x27;2019-01-01T00:00:00Z&#x27;,\n",
       "       &#x27;2020-01-01T00:00:00Z&#x27;, &#x27;2021-01-01T00:00:00Z&#x27;,\n",
       "       &#x27;2022-01-01T00:00:00Z&#x27;, &#x27;2023-01-01T00:00:00Z&#x27;], dtype=&#x27;&lt;U20&#x27;)</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'>32647</div><input id='attrs-0f513bf6-34a3-4343-9ad4-629a99ef055d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f513bf6-34a3-4343-9ad4-629a99ef055d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52b410ee-f384-45a9-b421-c59ae9340a22' class='xr-var-data-in' type='checkbox'><label for='data-52b410ee-f384-45a9-b421-c59ae9340a22' 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(32647)</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'>{66148, 88695}</div><input id='attrs-4f8f94c7-b52c-4bbf-9aec-126e14c9de0b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f8f94c7-b52c-4bbf-9aec-126e14c9de0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01b9dc5a-977b-413c-b445-adea1b570901' class='xr-var-data-in' type='checkbox'><label for='data-01b9dc5a-977b-413c-b445-adea1b570901' 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({66148, 88695}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>io:tile_id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;47P&#x27;</div><input id='attrs-870c2410-b182-417d-8298-49058aadcc1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-870c2410-b182-417d-8298-49058aadcc1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8f4fce3-d48c-4842-a171-69c3c6f8cdda' class='xr-var-data-in' type='checkbox'><label for='data-c8f4fce3-d48c-4842-a171-69c3c6f8cdda' 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;47P&#x27;, dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{1771251.1832111736, 830743.2947...</div><input id='attrs-77628504-92fa-44fa-bd62-9e2cc2f51e7f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77628504-92fa-44fa-bd62-9e2cc2f51e7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7e93c74-b783-4a97-b0de-c03947a4c8e6' class='xr-var-data-in' type='checkbox'><label for='data-a7e93c74-b783-4a97-b0de-c03947a4c8e6' 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({1771251.1832111736, 830743.294771302, 884301.1832111736, 169263.29477130197},\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>start_datetime</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>&#x27;2017-01-01T00:00:00Z&#x27; ... &#x27;2022...</div><input id='attrs-ec56894a-753a-40cd-9bb3-fd2bd33a85b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ec56894a-753a-40cd-9bb3-fd2bd33a85b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60ac8325-c49e-4b20-9d39-6998eebb0b31' class='xr-var-data-in' type='checkbox'><label for='data-60ac8325-c49e-4b20-9d39-6998eebb0b31' 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;2017-01-01T00:00:00Z&#x27;, &#x27;2018-01-01T00:00:00Z&#x27;,\n",
       "       &#x27;2019-01-01T00:00:00Z&#x27;, &#x27;2020-01-01T00:00:00Z&#x27;,\n",
       "       &#x27;2021-01-01T00:00:00Z&#x27;, &#x27;2022-01-01T00:00:00Z&#x27;], dtype=&#x27;&lt;U20&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>io:supercell_id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;47P&#x27;</div><input id='attrs-1ec2d668-7f56-4762-be54-c17372f1d761' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ec2d668-7f56-4762-be54-c17372f1d761' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0196e68b-c289-48fb-8211-bff37e05361e' class='xr-var-data-in' type='checkbox'><label for='data-0196e68b-c289-48fb-8211-bff37e05361e' 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;47P&#x27;, dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 10.0, 169263.29477130197, ...</div><input id='attrs-2ceaf714-9bd5-46e0-bab8-29d90469a020' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ceaf714-9bd5-46e0-bab8-29d90469a020' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6417af6-9176-41df-9c43-a07311371836' class='xr-var-data-in' type='checkbox'><label for='data-b6417af6-9176-41df-9c43-a07311371836' 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({0.0, 10.0, 169263.29477130197, 1771251.1832111736, -10.0},\n",
       "      dtype=object)</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;spatial_resolutio...</div><input id='attrs-7b281347-a671-4fef-b475-533e5b5bb1bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b281347-a671-4fef-b475-533e5b5bb1bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8c17f16-b651-4847-834b-cf214db89bfa' class='xr-var-data-in' type='checkbox'><label for='data-a8c17f16-b651-4847-834b-cf214db89bfa' 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;spatial_resolution&#x27;: 10}, 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'>32647</div><input id='attrs-5858f08c-0075-4255-b266-3472cb24df76' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5858f08c-0075-4255-b266-3472cb24df76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a9b7f2a-916e-4dea-89e6-8f92c6053d92' class='xr-var-data-in' type='checkbox'><label for='data-4a9b7f2a-916e-4dea-89e6-8f92c6053d92' 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(32647)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fdf35666-573c-4b85-8cb1-559f658eb0eb' class='xr-section-summary-in' type='checkbox'  ><label for='section-fdf35666-573c-4b85-8cb1-559f658eb0eb' class='xr-section-summary' >Indexes: <span>(4)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f7418b56-5e77-4ce1-9128-ff65baacffdc' class='xr-index-data-in' type='checkbox'/><label for='index-f7418b56-5e77-4ce1-9128-ff65baacffdc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;,\n",
       "               &#x27;2021-01-01&#x27;, &#x27;2022-01-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-19f48358-0410-43cd-b736-39678ffaaf59' class='xr-index-data-in' type='checkbox'/><label for='index-19f48358-0410-43cd-b736-39678ffaaf59' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;data&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9f1db56e-3361-4acc-8f15-0dfe343e8850' class='xr-index-data-in' type='checkbox'/><label for='index-9f1db56e-3361-4acc-8f15-0dfe343e8850' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([617370.0, 617380.0, 617390.0, 617400.0, 617410.0, 617420.0, 617430.0,\n",
       "       617440.0, 617450.0, 617460.0,\n",
       "       ...\n",
       "       644280.0, 644290.0, 644300.0, 644310.0, 644320.0, 644330.0, 644340.0,\n",
       "       644350.0, 644360.0, 644370.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=2701))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0bd7b634-bf65-45d9-9db2-da54dc702a64' class='xr-index-data-in' type='checkbox'/><label for='index-0bd7b634-bf65-45d9-9db2-da54dc702a64' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1546590.0, 1546580.0, 1546570.0, 1546560.0, 1546550.0, 1546540.0,\n",
       "       1546530.0, 1546520.0, 1546510.0, 1546500.0,\n",
       "       ...\n",
       "       1521040.0, 1521030.0, 1521020.0, 1521010.0, 1521000.0, 1520990.0,\n",
       "       1520980.0, 1520970.0, 1520960.0, 1520950.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=2565))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b872c04-ab0b-4c42-8da9-9c7d9c97cbf7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6b872c04-ab0b-4c42-8da9-9c7d9c97cbf7' 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=32647, bounds=(617370.0, 1520940.0, 644380.0, 1546590.0), resolutions_xy=(10.0, 10.0))</dd><dt><span>crs :</span></dt><dd>epsg:32647</dd><dt><span>transform :</span></dt><dd>| 10.00, 0.00, 617370.00|\n",
       "| 0.00,-10.00, 1546590.00|\n",
       "| 0.00, 0.00, 1.00|</dd><dt><span>resolution :</span></dt><dd>10.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'stackstac-ffe6a4c6af8232bd9842eba59c37bf4c' (time: 6,\n",
       "                                                                band: 1,\n",
       "                                                                y: 2565, x: 2701)>\n",
       "dask.array<getitem, shape=(6, 1, 2565, 2701), dtype=uint8, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray>\n",
       "Coordinates: (12/15)\n",
       "  * time             (time) datetime64[ns] 2017-01-01 2018-01-01 ... 2022-01-01\n",
       "    id               (time) <U8 '47P-2017' '47P-2018' ... '47P-2021' '47P-2022'\n",
       "  * band             (band) <U4 'data'\n",
       "  * x                (x) float64 6.174e+05 6.174e+05 ... 6.444e+05 6.444e+05\n",
       "  * y                (y) float64 1.547e+06 1.547e+06 ... 1.521e+06 1.521e+06\n",
       "    end_datetime     (time) <U20 '2018-01-01T00:00:00Z' ... '2023-01-01T00:00...\n",
       "    ...               ...\n",
       "    proj:bbox        object {1771251.1832111736, 830743.294771302, 884301.183...\n",
       "    start_datetime   (time) <U20 '2017-01-01T00:00:00Z' ... '2022-01-01T00:00...\n",
       "    io:supercell_id  <U3 '47P'\n",
       "    proj:transform   object {0.0, 10.0, 169263.29477130197, 1771251.183211173...\n",
       "    raster:bands     object {'nodata': 0, 'spatial_resolution': 10}\n",
       "    epsg             int64 32647\n",
       "Attributes:\n",
       "    spec:        RasterSpec(epsg=32647, bounds=(617370.0, 1520940.0, 644380.0...\n",
       "    crs:         epsg:32647\n",
       "    transform:   | 10.00, 0.00, 617370.00|\\n| 0.00,-10.00, 1546590.00|\\n| 0.0...\n",
       "    resolution:  10.0"
      ]
     },
     "execution_count": 4,
     "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",
    "\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 = (\n",
    "    stackstac.stack(\n",
    "        items,\n",
    "        dtype=np.ubyte,\n",
    "        fill_value=255,\n",
    "        bounds_latlon=bbox_of_interest,\n",
    "        sortby_date=False,\n",
    "    )\n",
    "    .assign_coords(\n",
    "        time=pd.to_datetime([item.properties[\"start_datetime\"] for item in items])\n",
    "        .tz_convert(None)\n",
    "        .to_numpy()\n",
    "    )\n",
    "    .sortby(\"time\")\n",
    ")\n",
    "\n",
    "stack"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c8b22c9-9260-48af-8dc9-bb46fb531673",
   "metadata": {},
   "source": [
    "Start up a local Dask cluster to allow us to do parallel reads. Use the following URL to open a dashboard in the Hub's Dask Extension. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2161565e-163c-4996-a5cd-0c6b14390eee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/proxy/8787/status\n"
     ]
    }
   ],
   "source": [
    "client = dask.distributed.Client(processes=False)\n",
    "print(f\"/proxy/{client.scheduler_info()['services']['dashboard']}/status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0e8e4e2-c477-4a43-885e-59fc1f5a86fb",
   "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\") 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": "5108587f-10d1-4b52-9861-3aeb161bce46",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = stack.squeeze().compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f31c78-fe4d-4b0d-bdcb-0289abc7e860",
   "metadata": {},
   "source": [
    "Now a quick plot to check that we've got the data we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "534e9b80-3c86-4029-a236-14653df64333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-io-lulc-io-lulc-9-class-example.ipynb/7.png\"/>"
      ],
      "text/plain": [
       "<Figure size 1600x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = merged.plot(col=\"time\")\n",
    "for ax in g.axes.flat:\n",
    "    ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc3c5100-12f2-4488-a19a-cc174efeee6e",
   "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": "94b19cb8-bfe1-47fb-bdcf-676c62057ab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'No Data': 0,\n",
       " 'Water': 1,\n",
       " 'Trees': 2,\n",
       " 'Flooded vegetation': 4,\n",
       " 'Crops': 5,\n",
       " 'Built area': 7,\n",
       " 'Bare ground': 8,\n",
       " 'Snow/ice': 9,\n",
       " 'Clouds': 10,\n",
       " 'Rangeland': 11}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pystac.extensions.item_assets import ItemAssetsExtension\n",
    "\n",
    "collection = catalog.get_collection(\"io-lulc-9-class\")\n",
    "ia = ItemAssetsExtension.ext(collection)\n",
    "\n",
    "x = ia.item_assets[\"data\"]\n",
    "class_names = {x[\"summary\"]: x[\"values\"][0] for x in x.properties[\"file:values\"]}\n",
    "values_to_classes = {v: k for k, v in class_names.items()}\n",
    "class_count = len(class_names)\n",
    "class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dc1de1fd-ecd8-48d0-b378-d33abb81b154",
   "metadata": {},
   "outputs": [],
   "source": [
    "with rasterio.open(item.assets[\"data\"].href) as src:\n",
    "    colormap_def = src.colormap(1)  # get metadata colormap for band 1\n",
    "    colormap = [\n",
    "        np.array(colormap_def[i]) / 255 for i in range(max(class_names.values()) + 1)\n",
    "    ]  # transform to matplotlib color format\n",
    "\n",
    "cmap = ListedColormap(colormap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b9d365ca-a063-4640-a00f-731f340f40d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmin = 0\n",
    "vmax = max(class_names.values()) + 1\n",
    "epsg = merged.epsg.item()\n",
    "\n",
    "p = merged.plot(\n",
    "    subplot_kws=dict(projection=ccrs.epsg(epsg)),\n",
    "    col=\"time\",\n",
    "    transform=ccrs.epsg(epsg),\n",
    "    cmap=cmap,\n",
    "    vmin=vmin,\n",
    "    vmax=vmax,\n",
    "    figsize=(16, 6),\n",
    ")\n",
    "ticks = np.linspace(0.5, vmax - 0.5, vmax - vmin)\n",
    "labels = [values_to_classes.get(i, \"\") for i in range(cmap.N)]\n",
    "p.cbar.set_ticks(ticks, labels=labels)\n",
    "p.cbar.set_label(\"Class\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e742d47e-dda6-4bdf-a441-854f23b3f752",
   "metadata": {},
   "source": [
    "That looks better. Let's also plot a histogram of the pixel values to see the distribution of land cover types within our area of interest. We can reuse the colormap we generated to help tie the two visualizations together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d777c3f6-f380-40d7-b7b9-3067b8e360a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/cb/fj2byfwd3pv960xpfrz42yph0000gn/T/ipykernel_63630/4112736583.py:4: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  pd.value_counts(merged.data.ravel(), sort=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = list(cmap.colors)\n",
    "\n",
    "ax = (\n",
    "    pd.value_counts(merged.data.ravel(), sort=False)\n",
    "    .sort_index()\n",
    "    .reindex(range(cmap.N), fill_value=0)\n",
    "    .rename(values_to_classes)\n",
    "    .plot.barh(color=colors, rot=0, width=0.9)\n",
    ")\n",
    "ax.set(\n",
    "    title=\"Distribution of Land Cover classes\",\n",
    "    ylabel=\"Landcover class\",\n",
    "    xlabel=\"Class count\",\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00280f61-678f-4b7c-b8d0-7729bfce409c",
   "metadata": {},
   "source": [
    "We can make a similar plot per year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "772505af-f2de-4929-b08a-d2d0c9bf3396",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = merged.stack(pixel=(\"y\", \"x\")).T.to_pandas()\n",
    "\n",
    "counts = (\n",
    "    df.stack()\n",
    "    .rename(\"class\")\n",
    "    .reset_index()\n",
    "    .groupby(\"time\")[\"class\"]\n",
    "    .value_counts()\n",
    "    .nlargest(9)\n",
    "    .rename(\"count\")\n",
    "    .swaplevel()\n",
    "    .sort_index(ascending=False)\n",
    "    .rename(lambda x: x.year, level=\"time\")\n",
    ")\n",
    "\n",
    "colors = cmap(counts.index.get_level_values(\"class\") / cmap.N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1ee17228-67cd-494b-870b-54e7a3e26706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-io-lulc-io-lulc-9-class-example.ipynb/13.png\"/>"
      ],
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = counts.rename(values_to_classes, level=\"class\").plot.barh(color=colors, width=0.9)\n",
    "ax.set(xlabel=\"Class count\", title=\"Land Cover distribution per year\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf1bd0df-e2f3-404d-9c7a-107026bb3f78",
   "metadata": {},
   "source": [
    "Finally, we can plot the transitions between years, to show which classes are transitioning to which other classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "73125b84-3784-4237-8620-6cf6af74d94c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-io-lulc-io-lulc-9-class-example.ipynb/14.png\"/>"
      ],
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "by_year = df.groupby(\"2017-01-01\")[\"2018-01-01\"].value_counts()\n",
    "\n",
    "years = by_year.index.levels[0]\n",
    "classes = by_year.index.levels[1]\n",
    "\n",
    "idx = pd.MultiIndex.from_product([years, classes])\n",
    "transitions = by_year.reindex(idx, fill_value=0)\n",
    "by_year[by_year.index.get_level_values(0) == by_year.index.get_level_values(1)] = np.nan\n",
    "\n",
    "transitions = by_year.unstack().rename(\n",
    "    columns=values_to_classes, index=values_to_classes\n",
    ")\n",
    "\n",
    "sns.heatmap(transitions, cmap=\"OrRd\");"
   ]
  }
 ],
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