datasets/mtbs/mtbs-example.ipynb

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   "source": [
    "## Accessing MTBS data with the Planetary Computer STAC API\n",
    "\n",
    "This notebook provides an example of accessing Monitoring Trends in Burn Severity (MTBS) Mosiacs for CONUS and Alaska using the Planetary Computer API. The data is stored in annual cloud optimized GeoTIFF files.\n",
    "\n",
    "Complete documentation for this dataset is available on the [Planetary Computer data catalog](https://planetarycomputer.microsoft.com/dataset/mtbs)."
   ]
  },
  {
   "cell_type": "markdown",
   "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. The Planetary Computer Hub is pre-configured to use your API key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pystac_client\n",
    "from pystac.extensions.projection import ProjectionExtension as proj\n",
    "from shapely.geometry import shape\n",
    "from shapely.ops import transform\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "import planetary_computer\n",
    "import pyproj\n",
    "import rioxarray"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "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",
   "metadata": {},
   "source": [
    "The MTBS data on Azure is available for the CONUS or Alaska regions on an annual basis from 1984 to 2018. Let's query the Planetary Computer to find data items within the `mbts` collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "carr_fire_aoi = {\n",
    "    \"type\": \"Polygon\",\n",
    "    \"coordinates\": [\n",
    "        [\n",
    "            [-122.73925781250001, 40.50857873259441],\n",
    "            [-122.33139038085936, 40.50857873259441],\n",
    "            [-122.33139038085936, 40.891715295571046],\n",
    "            [-122.73925781250001, 40.891715295571046],\n",
    "            [-122.73925781250001, 40.50857873259441],\n",
    "        ]\n",
    "    ],\n",
    "}"
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  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returned 35 Items\n"
     ]
    }
   ],
   "source": [
    "search = catalog.search(collections=[\"mtbs\"], intersects=carr_fire_aoi)\n",
    "items = search.item_collection()\n",
    "print(f\"Returned {len(items)} Items\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While only reading metadata from the STAC API response we can check the footprint shape of this data, and what assets it contains. Based on the size, we'll want to only read a subsection of the underlying file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Footprint shape: [70357, 69612]\n",
      "Assets: metadata, burn-severity\n"
     ]
    }
   ],
   "source": [
    "item = items[0]\n",
    "print(\"Footprint shape:\", proj.ext(item).shape)\n",
    "print(\"Assets:\", \", \".join(item.assets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read item asset with xarray\n",
    "\n",
    "Open the asset file on blob storage using `rioxarray`. We'll select the data asset `burn-severity` which has the URL for the underlying GeoTIFF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "    add_offset:    0.0</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'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 70357</li><li><span class='xr-has-index'>x</span>: 69612</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e9c8ce94-2888-4683-b70a-50ef5a17b77b' class='xr-array-in' type='checkbox' checked><label for='section-e9c8ce94-2888-4683-b70a-50ef5a17b77b' 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=(2560, 2560), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-41892d06-8fb4-4cc9-b9a8-dd70ee6a2098' class='xr-section-summary-in' type='checkbox'  checked><label for='section-41892d06-8fb4-4cc9-b9a8-dd70ee6a2098' class='xr-section-summary' >Coordinates: <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-var-name'><span>band</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-66dd61b9-b806-4fa2-866f-bba996ff4b46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66dd61b9-b806-4fa2-866f-bba996ff4b46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8162237-ecfc-48b1-ac79-f1d64347a947' class='xr-var-data-in' type='checkbox'><label for='data-b8162237-ecfc-48b1-ac79-f1d64347a947' 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(1)</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'>-2.298e+06 ... -2.101e+05</div><input id='attrs-de421a07-e5ac-474d-81f0-f11c7143c222' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de421a07-e5ac-474d-81f0-f11c7143c222' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fd41a9f-41c1-4c70-bd29-9c2f2ce7e552' class='xr-var-data-in' type='checkbox'><label for='data-9fd41a9f-41c1-4c70-bd29-9c2f2ce7e552' 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([-2298450., -2298420., -2298390., ...,  -210180.,  -210150.,  -210120.])</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'>3.096e+06 3.096e+06 ... 9.854e+05</div><input id='attrs-015ddb2b-7624-4d71-836c-f963286f4b28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-015ddb2b-7624-4d71-836c-f963286f4b28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee0f1bbb-b054-45a6-b980-4c1234e4d6d4' class='xr-var-data-in' type='checkbox'><label for='data-ee0f1bbb-b054-45a6-b980-4c1234e4d6d4' 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([3096030., 3096000., 3095970., ...,  985410.,  985380.,  985350.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-d3325ceb-b329-43f5-b647-5baa5fa02896' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3325ceb-b329-43f5-b647-5baa5fa02896' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6ffb038-b04e-4f16-9c12-c03a9abfc008' class='xr-var-data-in' type='checkbox'><label for='data-c6ffb038-b04e-4f16-9c12-c03a9abfc008' 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'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;USA_Contiguous_Albers_Equal_Area_Conic_USGS_version&quot;,GEOGCS[&quot;NAD83&quot;,DATUM[&quot;North_American_Datum_1983&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101004,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6269&quot;]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4269&quot;]],PROJECTION[&quot;Albers_Conic_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,23],PARAMETER[&quot;longitude_of_center&quot;,-96],PARAMETER[&quot;standard_parallel_1&quot;,29.5],PARAMETER[&quot;standard_parallel_2&quot;,45.5],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101004</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>NAD83</dd><dt><span>horizontal_datum_name :</span></dt><dd>North American Datum 1983</dd><dt><span>projected_crs_name :</span></dt><dd>USA_Contiguous_Albers_Equal_Area_Conic_USGS_version</dd><dt><span>grid_mapping_name :</span></dt><dd>albers_conical_equal_area</dd><dt><span>standard_parallel :</span></dt><dd>(29.5, 45.5)</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>23.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-96.0</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;USA_Contiguous_Albers_Equal_Area_Conic_USGS_version&quot;,GEOGCS[&quot;NAD83&quot;,DATUM[&quot;North_American_Datum_1983&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101004,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6269&quot;]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4269&quot;]],PROJECTION[&quot;Albers_Conic_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,23],PARAMETER[&quot;longitude_of_center&quot;,-96],PARAMETER[&quot;standard_parallel_1&quot;,29.5],PARAMETER[&quot;standard_parallel_2&quot;,45.5],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>-2298465.0 30.0 0.0 3096045.0 0.0 -30.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2941963e-2d95-4f0e-b2f6-467175d3b323' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2941963e-2d95-4f0e-b2f6-467175d3b323' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>_FillValue :</span></dt><dd>0.0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (y: 70357, x: 69612)>\n",
       "dask.array<getitem, shape=(70357, 69612), dtype=uint8, chunksize=(2560, 2560), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    band         int64 1\n",
       "  * x            (x) float64 -2.298e+06 -2.298e+06 ... -2.102e+05 -2.101e+05\n",
       "  * y            (y) float64 3.096e+06 3.096e+06 ... 9.854e+05 9.854e+05\n",
       "    spatial_ref  int64 0\n",
       "Attributes:\n",
       "    _FillValue:    0.0\n",
       "    scale_factor:  1.0\n",
       "    add_offset:    0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da = rioxarray.open_rasterio(\n",
    "    item.assets[\"burn-severity\"].href, chunks={\"x\": 2560, \"y\": 2560}\n",
    ").squeeze()\n",
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot one fire event\n",
    "\n",
    "We can select a subset of the data to make a plot over our single fire event. To select from the larger DataArray, we'll transform our original GeoJSON area of iterest to the CRS of the dataset and use the bounds to slice the array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 54.6 ms, sys: 12.6 ms, total: 67.2 ms\n",
      "Wall time: 65.4 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "aoi_poly = shape(carr_fire_aoi)\n",
    "from_crs = pyproj.CRS(\"EPSG:4326\")\n",
    "\n",
    "project = pyproj.Transformer.from_crs(from_crs, da.rio.crs, always_xy=True).transform\n",
    "utm_poly = transform(project, aoi_poly)\n",
    "minx, miny, maxx, maxy = utm_poly.bounds\n",
    "\n",
    "fire_da = da.sel(y=slice(maxy, miny), x=slice(minx, maxx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Up to this point, we haven't actually read any data yet, but by plotting we'll trigger the read and see out results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-mtbs-mtbs-example.ipynb/8.png\"/>"
      ],
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize=(8, 6),\n",
    "    dpi=150,\n",
    "    subplot_kw=dict(projection=ccrs.PlateCarree()),\n",
    "    frameon=False,\n",
    ")\n",
    "ax.patch.set_facecolor(\"black\")\n",
    "fire_da.plot(\n",
    "    ax=ax, transform=ccrs.AlbersEqualArea(), cbar_kwargs={\"aspect\": 50}, cmap=\"afmhot\"\n",
    ")\n",
    "plt.title(\"Burn severity, 2018\")\n",
    "plt.show()"
   ]
  }
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